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Chip-Based Photon Source Is 100X More Efficient than Previous, Bringing Quantum Integration Within Reach – HPCwire

Posted: December 24, 2020 at 10:57 am


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Dec. 18, 2020 Super-fast quantum computers and communication devices could revolutionize countless aspects of our livesbut first, researchers need a fast, efficient source of the entangled pairs of photons such systems use to transmit and manipulate information. Researchers at Stevens Institute of Technology have done just that, not only creating a chip-based photon source 100 times more efficient that previously possible, but bringing massive quantum device integration within reach.

Its long been suspected that this was possible in theory, but were the first to show it in practice, said Yuping Huang, Gallagher associate professor of physics and director of the Center for Quantum Science and Engineering.

To createphoton pairs, researchers trap light in carefully sculpted nanoscale microcavities; as light circulates in the cavity, its photons resonate and split into entangled pairs. But theres a catch: at present, such systems are extremely inefficient, requiring a torrent of incoming laser light comprising hundreds of millions of photons before a single entangled photon pair will grudgingly drip out at the other end.

Huang and colleagues at Stevens have now developed a new chip-based photon source thats 100 times more efficient than any previous device, allowing the creation of tens of millions of entangled photon pairs per second from a single microwatt-powered laser beam.

This is a huge milestone for quantum communications, said Huang, whose work will appear in the Dec. 17 issue ofPhysical Review Letters.

Working with Stevens graduate students Zhaohui Ma and Jiayang Chen, Huang built on his laboratorys previous research to carve extremely high-quality microcavities into flakes of lithium niobate crystal. The racetrack-shaped cavities internally reflect photons with very little loss of energy, enabling light to circulate longer and interact with greater efficiency.

By fine-tuning additional factors such as temperature, the team was able to create an unprecedentedly bright source of entangled photon pairs. In practice, that allows photon pairs to be produced in far greater quantities for a given amount of incoming light, dramatically reducing the energy needed to power quantum components.

The team is already working on ways to further refine their process, and say they expect to soon attain the true Holy Grail of quantum optics: a system with that can turn a single incoming photon into an entangled pair of outgoing photons, with virtually no waste energy along the way. Its definitely achievable, said Chen. At this point we just need incremental improvements.

Until then, the team plans to continue refining their technology, and seeking ways to use theirphotonsource to drive logic gates and other quantum computing or communication components. Because this technology is already chip-based, were ready to start scaling up by integrating other passive or active optical components, explained Huang.

The ultimate goal, Huang said, is to make quantum devices so efficient and cheap to operate that they can be integrated into mainstream electronic devices. We want to bring quantum technology out of the lab, so that it can benefit every single one of us, he explained. Someday soon we want kids to have quantum laptops in their backpacks, and were pushing hard to make that a reality.

More information:Ultrabright quantum photon sources on chip,Physical Review Letters(2020).arxiv.org/abs/2010.04242,journals.aps.org/prl/accepted/ da6c4d64a454565839ae

Source: Stevens Institute of Technology

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Chip-Based Photon Source Is 100X More Efficient than Previous, Bringing Quantum Integration Within Reach - HPCwire

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December 24th, 2020 at 10:57 am

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Quantum computing – Wikipedia

Posted: December 17, 2020 at 3:52 am


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Study of a model of computation

Quantum computing is the use of quantum phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.[1]:I-5 Quantum computers are believed to be able to solve certain computational problems, such as integer factorization (which underlies RSA encryption), substantially faster than classical computers. The study of quantum computing is a subfield of quantum information science.

Quantum computing began in the early 1980s, when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine.[2]Richard FeynmanandYuri Maninlater suggested that a quantum computer had the potential to simulate things that a classical computer could not.[3][4] In 1994, Peter Shor developed a quantum algorithm for factoring integers that had the potential to decrypt RSA-encrypted communications.[5] Despite ongoing experimental progress since the late 1990s, most researchers believe that "fault-tolerant quantum computing [is] still a rather distant dream."[6] In recent years, investment into quantum computing research has increased in both the public and private sector.[7][8] On 23 October 2019, Google AI, in partnership with the U.S. National Aeronautics and Space Administration (NASA), claimed to have performed a quantum computation that is infeasible on any classical computer.[9]

There are several models of quantum computers (or rather, quantum computing systems), including the quantum circuit model, quantum Turing machine, adiabatic quantum computer, one-way quantum computer, and various quantum cellular automata. The most widely used model is the quantum circuit. Quantum circuits are based on the quantum bit, or "qubit", which is somewhat analogous to the bit in classical computation. Qubits can be in a 1 or 0 quantum state, or they can be in a superposition of the 1 and 0 states. However, when qubits are measured the result of the measurement is always either a 0 or a 1; the probabilities of these two outcomes depend on the quantum state that the qubits were in immediately prior to the measurement.

Progress towards building a physical quantum computer focuses on technologies such as transmons, ion traps and topological quantum computers, which aim to create high-quality qubits.[1]:213 These qubits may be designed differently, depending on the full quantum computer's computing model, whether quantum logic gates, quantum annealing, or adiabatic quantum computation. There are currently a number of significant obstacles in the way of constructing useful quantum computers. In particular, it is difficult to maintain the quantum states of qubits as they suffer from quantum decoherence and state fidelity. Quantum computers therefore require error correction.[10][11]

Any computational problem that can be solved by a classical computer can also be solved by a quantum computer.[12] Conversely, any problem that can be solved by a quantum computer can also be solved by a classical computer, at least in principle given enough time. In other words, quantum computers obey the ChurchTuring thesis. While this means that quantum computers provide no additional advantages over classical computers in terms of computability, quantum algorithms for certain problems have significantly lower time complexities than corresponding known classical algorithms. Notably, quantum computers are believed to be able to quickly solve certain problems that no classical computer could solve in any feasible amount of timea feat known as "quantum supremacy." The study of the computational complexity of problems with respect to quantum computers is known as quantum complexity theory.

The prevailing model of quantum computation describes the computation in terms of a network of quantum logic gates.[13] This model can be thought of as an abstract linear-algebraic generalization of a classical circuit. Since this circuit model obeys quantum mechanics, a quantum computer capable of efficiently running these circuits is believed to be physically realizable.

A memory consisting of n {textstyle n} bits of information has 2 n {textstyle 2^{n}} possible states. A vector representing all memory states thus has 2 n {textstyle 2^{n}} entries (one for each state). This vector is viewed as a probability vector and represents the fact that the memory is to be found in a particular state.

In the classical view, one entry would have a value of 1 (i.e. a 100% probability of being in this state) and all other entries would be zero. In quantum mechanics, probability vectors are generalized to density operators. This is the technically rigorous mathematical foundation for quantum logic gates, but the intermediate quantum state vector formalism is usually introduced first because it is conceptually simpler. This article focuses on the quantum state vector formalism for simplicity.

We begin by considering a simple memory consisting of only one bit. This memory may be found in one of two states: the zero state or the one state. We may represent the state of this memory using Dirac notation so that

| 0 := ( 1 0 ) ; | 1 := ( 0 1 ) {displaystyle |0rangle :={begin{pmatrix}1\0end{pmatrix}};quad |1rangle :={begin{pmatrix}0\1end{pmatrix}}}

| := | 0 + | 1 = ( ) ; | | 2 + | | 2 = 1. {displaystyle |psi rangle :=alpha ,|0rangle +beta ,|1rangle ={begin{pmatrix}alpha \beta end{pmatrix}};quad |alpha |^{2}+|beta |^{2}=1.}

The state of this one-qubit quantum memory can be manipulated by applying quantum logic gates, analogous to how classical memory can be manipulated with classical logic gates. One important gate for both classical and quantum computation is the NOT gate, which can be represented by a matrix

X := ( 0 1 1 0 ) . {displaystyle X:={begin{pmatrix}0&1\1&0end{pmatrix}}.}

The mathematics of single qubit gates can be extended to operate on multiqubit quantum memories in two important ways. One way is simply to select a qubit and apply that gate to the target qubit whilst leaving the remainder of the memory unaffected. Another way is to apply the gate to its target only if another part of the memory is in a desired state. These two choices can be illustrated using another example. The possible states of a two-qubit quantum memory are

| 00 := ( 1 0 0 0 ) ; | 01 := ( 0 1 0 0 ) ; | 10 := ( 0 0 1 0 ) ; | 11 := ( 0 0 0 1 ) . {displaystyle |00rangle :={begin{pmatrix}1\0\0\0end{pmatrix}};quad |01rangle :={begin{pmatrix}0\1\0\0end{pmatrix}};quad |10rangle :={begin{pmatrix}0\0\1\0end{pmatrix}};quad |11rangle :={begin{pmatrix}0\0\0\1end{pmatrix}}.}

C N O T := ( 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 ) . {displaystyle CNOT:={begin{pmatrix}1&0&0&0\0&1&0&0\0&0&0&1\0&0&1&0end{pmatrix}}.}

In summary, a quantum computation can be described as a network of quantum logic gates and measurements. However, any measurement can be deferred to the end of a quantum computation, though this deferment may come at a computational cost, so most quantum circuits depict a network consisting only of quantum logic gates and no measurements.

Any quantum computation (which is, in the above formalism, any unitary matrix over n {displaystyle n} qubits) can be represented as a network of quantum logic gates from a fairly small family of gates. A choice of gate family that enables this construction is known as a universal gate set, since a computer that can run such circuits is a universal quantum computer. One common such set includes all single-qubit gates as well as the CNOT gate from above. This means any quantum computation can be performed by executing a sequence of single-qubit gates together with CNOT gates. Though this gate set is infinite, it can be replaced with a finite gate set by appealing to the Solovay-Kitaev theorem. The representation of multiple qubits can be shown as Qsphere.

Progress in finding quantum algorithms typically focuses on this quantum circuit model, though exceptions like the quantum adiabatic algorithm exist. Quantum algorithms can be roughly categorized by the type of speedup achieved over corresponding classical algorithms.[14]

Quantum algorithms that offer more than a polynomial speedup over the best known classical algorithm include Shor's algorithm for factoring and the related quantum algorithms for computing discrete logarithms, solving Pell's equation, and more generally solving the hidden subgroup problem for abelian finite groups.[14] These algorithms depend on the primitive of the quantum Fourier transform. No mathematical proof has been found that shows that an equally fast classical algorithm cannot be discovered, although this is considered unlikely.[15] Certain oracle problems like Simon's problem and the BernsteinVazirani problem do give provable speedups, though this is in the quantum query model, which is a restricted model where lower bounds are much easier to prove, and doesn't necessarily translate to speedups for practical problems.

Other problems, including the simulation of quantum physical processes from chemistry and solid state physics, the approximation of certain Jones polynomials, and the quantum algorithm for linear systems of equations have quantum algorithms appearing to give super-polynomial speedups and are BQP-complete. Because these problems are BQP-complete, an equally fast classical algorithm for them would imply that no quantum algorithm gives a super-polynomial speedup, which is believed to be unlikely.[16]

Some quantum algorithms, like Grover's algorithm and amplitude amplification, give polynomial speedups over corresponding classical algorithms.[14] Though these algorithms give comparably modest quadratic speedup, they are widely applicable and thus give speedups for a wide range of problems.[17] Many examples of provable quantum speedups for query problems are related to Grover's algorithm, including Brassard, Hyer, and Tapp's algorithm for finding collisions in two-to-one functions,[18] which uses Grover's algorithm, and Farhi, Goldstone, and Gutmann's algorithm for evaluating NAND trees,[19] which is a variant of the search problem.

A notable application of quantum computation is for attacks on cryptographic systems that are currently in use. Integer factorization, which underpins the security of public key cryptographic systems, is believed to be computationally infeasible with an ordinary computer for large integers if they are the product of few prime numbers (e.g., products of two 300-digit primes).[20] By comparison, a quantum computer could efficiently solve this problem using Shor's algorithm to find its factors. This ability would allow a quantum computer to break many of the cryptographic systems in use today, in the sense that there would be a polynomial time (in the number of digits of the integer) algorithm for solving the problem. In particular, most of the popular public key ciphers are based on the difficulty of factoring integers or the discrete logarithm problem, both of which can be solved by Shor's algorithm. In particular, the RSA, DiffieHellman, and elliptic curve DiffieHellman algorithms could be broken. These are used to protect secure Web pages, encrypted email, and many other types of data. Breaking these would have significant ramifications for electronic privacy and security.

Identifying cryptographic systems that may be secure against quantum algorithms is an actively researched topic under the field of post-quantum cryptography. [21][22] Some public-key algorithms are based on problems other than the integer factorization and discrete logarithm problems to which Shor's algorithm applies, like the McEliece cryptosystem based on a problem in coding theory.[21][23]Lattice-based cryptosystems are also not known to be broken by quantum computers, and finding a polynomial time algorithm for solving the dihedral hidden subgroup problem, which would break many lattice based cryptosystems, is a well-studied open problem.[24] It has been proven that applying Grover's algorithm to break a symmetric (secret key) algorithm by brute force requires time equal to roughly 2n/2 invocations of the underlying cryptographic algorithm, compared with roughly 2n in the classical case,[25] meaning that symmetric key lengths are effectively halved: AES-256 would have the same security against an attack using Grover's algorithm that AES-128 has against classical brute-force search (see Key size).

Quantum cryptography could potentially fulfill some of the functions of public key cryptography. Quantum-based cryptographic systems could, therefore, be more secure than traditional systems against quantum hacking.[26]

The most well-known example of a problem admitting a polynomial quantum speedup is unstructured search, finding a marked item out of a list of n {displaystyle n} items in a database. This can be solved by Grover's algorithm using O ( n ) {displaystyle O({sqrt {n}})} queries to the database, quadratically fewer than than the ( n ) {displaystyle Omega (n)} queries required for classical algorithms. In this case, the advantage is not only provable but also optimal: it has been shown that Grover's algorithm gives the maximal possible probability of finding the desired element for any number of oracle lookups.

Problems that can be addressed with Grover's algorithm have the following properties:[citation needed]

For problems with all these properties, the running time of Grover's algorithm on a quantum computer will scale as the square root of the number of inputs (or elements in the database), as opposed to the linear scaling of classical algorithms. A general class of problems to which Grover's algorithm can be applied[27] is Boolean satisfiability problem. In this instance, the database through which the algorithm is iterating is that of all possible answers. An example (and possible) application of this is a password cracker that attempts to guess the password or secret key for an encrypted file or system. Symmetric ciphers such as Triple DES and AES are particularly vulnerable to this kind of attack.[citation needed] This application of quantum computing is a major interest of government agencies.[28]

Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate in an efficient manner classically, many believe quantum simulation will be one of the most important applications of quantum computing.[29] Quantum simulation could also be used to simulate the behavior of atoms and particles at unusual conditions such as the reactions inside a collider.[30] Quantum simulations might be used to predict future paths of particles and protons under superposition in the double-slit experiment.[citation needed] About 2% of the annual global energy output is used for Nitrogen fixation to produce ammonia for the Haber process in the agricultural fertilizer industry while naturally occurring organisms also produce ammonia. Quantum simulations might be used to understand this process increasing production.[31]

Quantum annealing or Adiabatic quantum computation relies on the adiabatic theorem to undertake calculations. A system is placed in the ground state for a simple Hamiltonian, which is slowly evolved to a more complicated Hamiltonian whose ground state represents the solution to the problem in question. The adiabatic theorem states that if the evolution is slow enough the system will stay in its ground state at all times through the process.

Since quantum computers can produce outputs that classical computers cannot produce efficiently, and since quantum computation is fundamentally linear algebraic, some express hope in developing quantum algorithms that can speed up machine learning tasks.[32][33] For example, the quantum algorithm for linear systems of equations, or "HHL Algorithm", named after its discoverers Harrow, Hassidim, and Lloyd, is believed to provide speedup over classical counterparts.[34][33]

John Preskill has introduced the term quantum supremacy to refer to the hypothetical speedup advantage that a quantum computer would have over a classical computer in a certain field.[35]Google announced in 2017 that it expected to achieve quantum supremacy by the end of the year though that did not happen. IBM said in 2018 that the best classical computers will be beaten on some practical task within about five years and views the quantum supremacy test only as a potential future benchmark.[36] Although skeptics like Gil Kalai doubt that quantum supremacy will ever be achieved,[37][38] in October 2019, a Sycamore processor created in conjunction with Google AI Quantum was reported to have achieved quantum supremacy,[39] with calculations more than 3,000,000 times as fast as those of Summit, generally considered the world's fastest computer.[40]Bill Unruh doubted the practicality of quantum computers in a paper published back in 1994.[41]Paul Davies argued that a 400-qubit computer would even come into conflict with the cosmological information bound implied by the holographic principle.[42]

There are a number of technical challenges in building a large-scale quantum computer.[43] Physicist David DiVincenzo has listed the following requirements for a practical quantum computer:[44]

Sourcing parts for quantum computers is also very difficult. Many quantum computers, like those constructed by Google and IBM, need Helium-3, a nuclear research byproduct, and special superconducting cables that are only made by the Japanese company Coax Co.[45]

The control of multi qubit systems requires the generation and coordination of a large number of electrical signals with tight and deterministic timing resolution. This had led to the development of quantum controllers which enable interfacing the qubit. Scaling these systems to support a growing number of qubits is an additional challenge in the scaling of quantum computers.[citation needed]

One of the greatest challenges involved with constructing quantum computers is controlling or removing quantum decoherence. This usually means isolating the system from its environment as interactions with the external world cause the system to decohere. However, other sources of decoherence also exist. Examples include the quantum gates, and the lattice vibrations and background thermonuclear spin of the physical system used to implement the qubits. Decoherence is irreversible, as it is effectively non-unitary, and is usually something that should be highly controlled, if not avoided. Decoherence times for candidate systems in particular, the transverse relaxation time T2 (for NMR and MRI technology, also called the dephasing time), typically range between nanoseconds and seconds at low temperature.[46] Currently, some quantum computers require their qubits to be cooled to 20 millikelvins in order to prevent significant decoherence.[47] A 2020 study argues that ionizing radiation such as cosmic rays can nevertheless cause certain systems to decohere within milliseconds.[48]

As a result, time-consuming tasks may render some quantum algorithms inoperable, as maintaining the state of qubits for a long enough duration will eventually corrupt the superpositions.[49]

These issues are more difficult for optical approaches as the timescales are orders of magnitude shorter and an often-cited approach to overcoming them is optical pulse shaping. Error rates are typically proportional to the ratio of operating time to decoherence time, hence any operation must be completed much more quickly than the decoherence time.

As described in the Quantum threshold theorem, if the error rate is small enough, it is thought to be possible to use quantum error correction to suppress errors and decoherence. This allows the total calculation time to be longer than the decoherence time if the error correction scheme can correct errors faster than decoherence introduces them. An often cited figure for the required error rate in each gate for fault-tolerant computation is 103, assuming the noise is depolarizing.

Meeting this scalability condition is possible for a wide range of systems. However, the use of error correction brings with it the cost of a greatly increased number of required qubits. The number required to factor integers using Shor's algorithm is still polynomial, and thought to be between L and L2, where L is the number of qubits in the number to be factored; error correction algorithms would inflate this figure by an additional factor of L. For a 1000-bit number, this implies a need for about 104 bits without error correction.[50] With error correction, the figure would rise to about 107 bits. Computation time is about L2 or about 107 steps and at 1MHz, about 10 seconds.

A very different approach to the stability-decoherence problem is to create a topological quantum computer with anyons, quasi-particles used as threads and relying on braid theory to form stable logic gates.[51][52]

Physicist Mikhail Dyakonov has expressed skepticism of quantum computing as follows:

There are a number of quantum computing models, distinguished by the basic elements in which the computation is decomposed. The four main models of practical importance are:

The quantum Turing machine is theoretically important but the physical implementation of this model is not feasible. All four models of computation have been shown to be equivalent; each can simulate the other with no more than polynomial overhead.

For physically implementing a quantum computer, many different candidates are being pursued, among them (distinguished by the physical system used to realize the qubits):

A large number of candidates demonstrates that quantum computing, despite rapid progress, is still in its infancy.[citation needed]

Any computational problem solvable by a classical computer is also solvable by a quantum computer.[80] Intuitively, this is because it is believed that all physical phenomena, including the operation of classical computers, can be described using quantum mechanics, which underlies the operation of quantum computers.

Conversely, any problem solvable by a quantum computer is also solvable by a classical computer; or more formally, any quantum computer can be simulated by a Turing machine. In other words, quantum computers provide no additional power over classical computers in terms of computability. This means that quantum computers cannot solve undecidable problems like the halting problem and the existence of quantum computers does not disprove the ChurchTuring thesis.[81]

As of yet, quantum computers do not satisfy the strong Church thesis. While hypothetical machines have been realized, a universal quantum computer has yet to be physically constructed. The strong version of Church's thesis requires a physical computer, and therefore there is no quantum computer that yet satisfies the strong Church thesis.

While quantum computers cannot solve any problems that classical computers cannot already solve, it is suspected that they can solve many problems faster than classical computers. For instance, it is known that quantum computers can efficiently factor integers, while this is not believed to be the case for classical computers. However, the capacity of quantum computers to accelerate classical algorithms has rigid upper bounds, and the overwhelming majority of classical calculations cannot be accelerated by the use of quantum computers.[82]

The class of problems that can be efficiently solved by a quantum computer with bounded error is called BQP, for "bounded error, quantum, polynomial time". More formally, BQP is the class of problems that can be solved by a polynomial-time quantum Turing machine with error probability of at most 1/3. As a class of probabilistic problems, BQP is the quantum counterpart to BPP ("bounded error, probabilistic, polynomial time"), the class of problems that can be solved by polynomial-time probabilistic Turing machines with bounded error.[83] It is known that BPP {displaystyle subseteq } BQP and is widely suspected that BQP {displaystyle nsubseteq } BPP, which intuitively would mean that quantum computers are more powerful than classical computers in terms of time complexity.[84]

The exact relationship of BQP to P, NP, and PSPACE is not known. However, it is known that P {displaystyle subseteq } BQP {displaystyle subseteq } PSPACE; that is, all problems that can be efficiently solved by a deterministic classical computer can also be efficiently solved by a quantum computer, and all problems that can be efficiently solved by a quantum computer can also be solved by a deterministic classical computer with polynomial space resources. It is further suspected that BQP is a strict superset of P, meaning there are problems that are efficiently solvable by quantum computers that are not efficiently solvable by deterministic classical computers. For instance, integer factorization and the discrete logarithm problem are known to be in BQP and are suspected to be outside of P. On the relationship of BQP to NP, little is known beyond the fact that some NP problems that are believed not to be in P are also in BQP (integer factorization and the discrete logarithm problem are both in NP, for example). It is suspected that NP {displaystyle nsubseteq } BQP; that is, it is believed that there are efficiently checkable problems that are not efficiently solvable by a quantum computer. As a direct consequence of this belief, it is also suspected that BQP is disjoint from the class of NP-complete problems (if an NP-complete problem were in BQP, then it would follow from NP-hardness that all problems in NP are in BQP).[86]

The relationship of BQP to the basic classical complexity classes can be summarized as follows:

It is also known that BQP is contained in the complexity class #P (or more precisely in the associated class of decision problems P#P),[86] which is a subclass of PSPACE.

It has been speculated that further advances in physics could lead to even faster computers. For instance, it has been shown that a non-local hidden variable quantum computer based on Bohmian Mechanics could implement a search of an N {displaystyle N} -item database in at most O ( N 3 ) {displaystyle O({sqrt[{3}]{N}})} steps, a slight speedup over Grover's algorithm, which runs in O ( N ) {displaystyle O({sqrt {N}})} steps. Note, however, that neither search method would allow quantum computers to solve NP-complete problems in polynomial time.[87] Theories of quantum gravity, such as M-theory and loop quantum gravity, may allow even faster computers to be built. However, defining computation in these theories is an open problem due to the problem of time; that is, within these physical theories there is currently no obvious way to describe what it means for an observer to submit input to a computer at one point in time and then receive output at a later point in time.[88][89]

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Quantum computing is an area of study focused on the development of computer based technologies centered around the principles ofquantum theory. Quantum theory explains the nature and behavior of energy and matter on thequantum(atomic and subatomic) level. Quantum computing uses a combination ofbitsto perform specific computational tasks. All at a much higher efficiency than their classical counterparts. Development ofquantum computersmark a leap forward in computing capability, with massive performance gains for specific use cases. For example quantum computing excels at like simulations.

The quantum computer gains much of its processing power through the ability for bits to be in multiple states at one time. They can perform tasks using a combination of 1s, 0s and both a 1 and 0 simultaneously. Current research centers in quantum computing include MIT, IBM, Oxford University, and the Los Alamos National Laboratory. In addition, developers have begun gaining access toquantum computers through cloud services.

Quantum computing began with finding its essential elements. In 1981, Paul Benioff at Argonne National Labs came up with the idea of a computer that operated with quantum mechanical principles. It is generally accepted that David Deutsch of Oxford University provided the critical idea behind quantum computing research. In 1984, he began to wonder about the possibility of designing a computer that was based exclusively on quantum rules, publishing a breakthrough paper a few months later.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

Quantum Theory

Quantum theory's development began in 1900 with a presentation by Max Planck. The presentation was to the German Physical Society, in which Planck introduced the idea that energy and matter exists in individual units. Further developments by a number of scientists over the following thirty years led to the modern understanding of quantum theory.

The Essential Elements of Quantum Theory:

Further Developments of Quantum Theory

Niels Bohr proposed the Copenhagen interpretation of quantum theory. This theory asserts that a particle is whatever it is measured to be, but that it cannot be assumed to have specific properties, or even to exist, until it is measured. This relates to a principle called superposition. Superposition claims when we do not know what the state of a given object is, it is actually in all possible states simultaneously -- as long as we don't look to check.

To illustrate this theory, we can use the famous analogy of Schrodinger's Cat. First, we have a living cat and place it in a lead box. At this stage, there is no question that the cat is alive. Then throw in a vial of cyanide and seal the box. We do not know if the cat is alive or if it has broken the cyanide capsule and died. Since we do not know, the cat is both alive and dead, according to quantum law -- in a superposition of states. It is only when we break open the box and see what condition the cat is in that the superposition is lost, and the cat must be either alive or dead.

The principle that, in some way, one particle can exist in numerous states opens up profound implications for computing.

A Comparison of Classical and Quantum Computing

Classical computing relies on principles expressed by Boolean algebra; usually Operating with a 3 or 7-modelogic gateprinciple. Data must be processed in an exclusive binary state at any point in time; either 0 (off / false) or 1 (on / true). These values are binary digits, or bits. The millions of transistors and capacitors at the heart of computers can only be in one state at any point. In addition, there is still a limit as to how quickly these devices can be made to switch states. As we progress to smaller and faster circuits, we begin to reach the physical limits of materials and the threshold for classical laws of physics to apply.

The quantum computer operates with a two-mode logic gate:XORand a mode called QO1 (the ability to change 0 into a superposition of 0 and 1). In a quantum computer, a number of elemental particles such as electrons or photons can be used. Each particle is given a charge, or polarization, acting as a representation of 0 and/or 1. Each particle is called a quantum bit, or qubit. The nature and behavior of these particles form the basis of quantum computing and quantum supremacy. The two most relevant aspects of quantum physics are the principles of superposition andentanglement.

Superposition

Think of a qubit as an electron in a magnetic field. The electron's spin may be either in alignment with the field, which is known as aspin-upstate, or opposite to the field, which is known as aspin-downstate. Changing the electron's spin from one state to another is achieved by using a pulse of energy, such as from alaser. If only half a unit of laser energy is used, and the particle is isolated the particle from all external influences, the particle then enters a superposition of states. Behaving as if it were in both states simultaneously.

Each qubit utilized could take a superposition of both 0 and 1. Meaning, the number of computations a quantum computer could take is 2^n, where n is the number of qubits used. A quantum computer comprised of 500 qubits would have a potential to do 2^500 calculations in a single step. For reference, 2^500 is infinitely more atoms than there are in the known universe. These particles all interact with each other via quantum entanglement.

In comparison to classical, quantum computing counts as trueparallel processing. Classical computers today still only truly do one thing at a time. In classical computing, there are just two or more processors to constitute parallel processing. EntanglementParticles (like qubits) that have interacted at some point retain a type can be entangled with each other in pairs, in a process known ascorrelation. Knowing the spin state of one entangled particle - up or down -- gives away the spin of the other in the opposite direction. In addition, due to the superposition, the measured particle has no single spin direction before being measured. The spin state of the particle being measured is determined at the time of measurement and communicated to the correlated particle, which simultaneously assumes the opposite spin direction. The reason behind why is not yet explained.

Quantum entanglement allows qubits that are separated by large distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated.

Taken together, quantum superposition and entanglement create an enormously enhanced computing power. Where a 2-bit register in an ordinary computer can store only one of four binary configurations (00, 01, 10, or 11) at any given time, a 2-qubit register in a quantum computer can store all four numbers simultaneously. This is because each qubit represents two values. If more qubits are added, the increased capacity is expanded exponentially.

Quantum Programming

Quantum computing offers an ability to write programs in a completely new way. For example, a quantum computer could incorporate a programming sequence that would be along the lines of "take all the superpositions of all the prior computations." This would permit extremely fast ways of solving certain mathematical problems, such as factorization of large numbers.

The first quantum computing program appeared in 1994 by Peter Shor, who developed a quantum algorithm that could efficiently factorize large numbers.

The Problems - And Some Solutions

The benefits of quantum computing are promising, but there are huge obstacles to overcome still. Some problems with quantum computing are:

There are many problems to overcome, such as how to handle security and quantum cryptography. Long time quantum information storage has been a problem in the past too. However, breakthroughs in the last 15 years and in the recent past have made some form of quantum computing practical. There is still much debate as to whether this is less than a decade away or a hundred years into the future. However, the potential that this technology offers is attracting tremendous interest from both the government and the private sector. Military applications include the ability to break encryptions keys via brute force searches, while civilian applications range from DNA modeling to complex material science analysis.

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This is the first in a series of explainers on quantum technology. The other two are on quantum communication and post-quantum cryptography.

A quantum computer harnesses some of the almost-mystical phenomena of quantum mechanics to deliver huge leaps forward in processing power. Quantum machines promise to outstrip even the most capable of todaysand tomorrowssupercomputers.

They wont wipe out conventional computers, though. Using a classical machine will still be the easiest and most economical solution for tackling most problems. But quantum computers promise to power exciting advances in various fields, from materials science to pharmaceuticals research. Companies are already experimenting with them to develop things like lighter and more powerful batteries for electric cars, and to help create novel drugs.

The secret to a quantum computers power lies in its ability to generate and manipulate quantum bits, or qubits.

Today's computers use bitsa stream of electrical or optical pulses representing1s or0s. Everything from your tweets and e-mails to your iTunes songs and YouTube videos are essentially long strings of these binary digits.

Quantum computers, on the other hand, usequbits, whichare typically subatomic particles such as electrons or photons. Generating and managing qubits is a scientific and engineering challenge. Some companies, such as IBM, Google, and Rigetti Computing, use superconducting circuits cooled to temperatures colder than deep space. Others, like IonQ, trap individual atoms in electromagnetic fields on a silicon chip in ultra-high-vacuum chambers. In both cases, the goal is to isolate the qubits in a controlled quantum state.

Qubits have some quirky quantum properties that mean a connected group of them can provide way more processing power than the same number of binary bits. One of those properties is known as superposition and another is called entanglement.

Qubits can represent numerous possible combinations of 1and 0 at the same time. This ability to simultaneously be in multiple states is called superposition. To put qubits into superposition, researchers manipulate them using precision lasers or microwave beams.

Thanks to this counterintuitive phenomenon, a quantum computer with several qubits in superposition can crunch through a vast number of potential outcomes simultaneously. The final result of a calculation emerges only once the qubits are measured, which immediately causes their quantum state to collapse to either 1or 0.

Researchers can generate pairs of qubits that are entangled, which means the two members of a pair exist in a single quantum state. Changing the state of one of the qubits will instantaneously change the state of the other one in a predictable way. This happens even if they are separated by very long distances.

Nobody really knows quite how or why entanglement works. It even baffled Einstein, who famously described it as spooky action at a distance. But its key to the power of quantum computers. In a conventional computer, doubling the number of bits doubles its processing power. But thanks to entanglement, adding extra qubits to a quantum machine produces an exponential increase in its number-crunching ability.

Quantum computers harness entangled qubits in a kind of quantum daisy chain to work their magic. The machines ability to speed up calculations using specially designed quantum algorithms is why theres so much buzz about their potential.

Thats the good news. The bad news is that quantum machines are way more error-prone than classical computers because of decoherence.

The interaction of qubits with their environment in ways that cause their quantum behavior to decay and ultimately disappear is called decoherence. Their quantum state is extremely fragile. The slightest vibration or change in temperaturedisturbances known as noise in quantum-speakcan cause them to tumble out of superposition before their job has been properly done. Thats why researchers do their best to protect qubits from the outside world in those supercooled fridges and vacuum chambers.

But despite their efforts, noise still causes lots of errors to creep into calculations. Smart quantum algorithmscan compensate for some of these, and adding more qubits also helps. However, it will likely take thousands of standard qubits to create a single, highly reliable one, known as a logical qubit. This will sap a lot of a quantum computers computational capacity.

And theres the rub: so far, researchers havent been able to generate more than 128 standard qubits (see our qubit counter here). So were still many years away from getting quantum computers that will be broadly useful.

That hasnt dented pioneers hopes of being the first to demonstrate quantum supremacy.

Its the point at which a quantum computer can complete a mathematical calculation that is demonstrably beyond the reach of even the most powerful supercomputer.

Its still unclear exactly how many qubits will be needed to achieve this because researchers keep finding new algorithms to boost the performance of classical machines, and supercomputing hardware keeps getting better. But researchers and companies are working hard to claim the title, running testsagainst some of the worlds most powerful supercomputers.

Theres plenty of debate in the research world about just how significant achieving this milestone will be. Rather than wait for supremacy to be declared, companies are already starting to experiment with quantum computers made by companies like IBM, Rigetti, and D-Wave, a Canadian firm. Chinese firms like Alibaba are also offering access to quantum machines. Some businesses are buying quantum computers, while others are using ones made available through cloud computing services.

One of the most promising applications of quantum computers is for simulating the behavior of matterdown to the molecular level. Auto manufacturers like Volkswagen and Daimler are using quantum computers to simulate the chemical composition of electrical-vehicle batteries to help find new ways to improve their performance. And pharmaceutical companies are leveraging them to analyze and compare compounds that could lead to the creation of new drugs.

The machines are also great for optimization problems because they can crunch through vast numbers of potential solutions extremely fast. Airbus, for instance, is using them to help calculate the most fuel-efficient ascent and descent paths for aircraft. And Volkswagen has unveiled a service that calculates the optimal routes for buses and taxis in cities in order to minimize congestion. Some researchers also think the machines could be used to accelerate artificial intelligence.

It could take quite a few years for quantum computers to achieve their full potential. Universities and businesses working on them are facing a shortage of skilled researchersin the fieldand a lack of suppliersof some key components. But if these exotic new computing machines live up to their promise, they could transform entire industries and turbocharge global innovation.

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Explainer: What is a quantum computer? | MIT Technology Review

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The use of quantum computers has grown over the past several months as researchers have relied on these systems to make sense of the massive amounts of data related to the COVID-19 virus.

Quantum computers are based on qubits, a unit that can hold more data than classic binary bits, said Heather West, a senior research analyst at IDC.

Besides better understanding of the virus, manufacturers have been using quantum systems to determine supply and demand on certain products -- toilet paper, for example -- so they can make estimates based on trends, such as how much is being sold in particular geographic areas, she said.

"Quantum computers can help better determine demand and supply, and it allows manufacturers to better push out supplies in a more scientific way,'' West said. "If there is that push in demand it can also help optimize the manufacturing process and accelerate it and actually modernize it by identifying breakdowns and bottlenecks."

Quantum has gained momentum this year because it has moved from the academic realm to "more commercially evolving ecosystems,'' West said.

In late 2019, Google claimed that it had reached quantum supremacy, observed Carmen Fontana, an IEEE member and a cloud and emerging tech practice lead at Centric Consulting. "While there was pushback on this announcement by other leaders in tech, one thing was certain -- it garnered many headlines."

Echoing West, Fontana said that until then, "quantum computing had felt to many as largely an academic exercise with far-off implications. After the announcement, sentiment seemed to shift to 'Quantum computing is real and happening sooner than later'."

In 2020, there have been more tangible timelines and applications for quantum computing, indicating that the space is rapidly advancing and maturing, Fontana said.

"For instance, IBM announced plans to go from their present 65-qubit computer to a 1,000-qubit computer over the next three years," he said. "Google conducted a large-scale chemical simulation on a quantum computer, demonstrating the practicality of the technology in solving real-world problems."

Improved artificial intelligence (AI) capabilities, accelerated business intelligence, and increased productivity and efficiency were the top expectations cited by organizations currently investing in cloud-based quantum computing technologies, according to an IDC surveyearlier this year.

"Initial survey findings indicate that while cloud-based quantum computing is a young market, and allocated funds for quantum computing initiatives are limited (0-2% of IT budgets), end users are optimistic that early investment will result in a competitive advantage,'' IDC said.

Manufacturing, financial services, and security industries are currently leading the way by experimenting with more potential use cases, developing advanced prototypes, and being further along in their implementation status, according to IDC.

Quantum is not without its challenges, though. The biggest one West sees is decoherence, which happens when qubits are exposed to "environmental factors" or too many try to work together at once. Because they are "very, very sensitive," they can lose their power and ability to function, and as result, cause errors in a calculation, she said.

"Right now, that is what many of the vendors are looking to solve with their qubit solutions,'' West said.

Another issue preventing quantum from becoming more of a mainstream technology right now is the ability to manage the quantum systems. "In order to keep qubits stable, they have to be kept at very cold, subzero temps, and that makes it really difficult for a lot of people to work with them,'' West said.

Nevertheless, With the time horizon of accessible quantum computing now shrinking to a decade or less, Fontana believes we can expect to see "an explosion of start-ups looking to be first movers in the quantum applications space. These companies will seek to apply quantum's powerful compute power to solve existing problems in novel ways."

Here are eight companies that are already focused on quantum computing.

Atom Computing is a quantum computing hardware company specializing in neutral atom quantum computers. While it is currently prototyping its first offerings, Atom Computing said it will provide cloud access "to large numbers of very coherent qubits by optically trapping and addressing individual atoms," said Ben Bloom, founder and CEO.

The company also builds and creates "complicated hardware control systems for use in the academic community,'' Bloom said.

Xanadu is a Canadian quantum technology company with the mission to build quantum computers that are useful and available to people everywhere. Founded in 2016, Xanadu is building toward a universal quantum computer using silicon photonic hardware, according to Sepehr Taghavi, corporate development manager.

The company also provides users access to near-term quantum devices through its Xanadu Quantum Cloud (XQC) service. The company also leads the development of PennyLane, an open-source software library for quantum machine learning and application development, Taghavi said.

In 2016, IBM was the first company to put a quantum computer on the cloud. The company has since built up an active community of more than 260,000 registered users, who run more than one billion every day on real hardware and simulators.

In 2017, IBM was the first company to offer universal quantum computing systems via theIBM Q Network. The network now includes more than 125 organizations, including Fortune 500s, startups, research labs, and education institutions. Partners include Daimler AG,JPMorgan Chase, andExxonMobil. All use IBM's most advanced quantum computers to simulate new materials for batteries, model portfolios and financial risk, and simulate chemistry for new energy technologies, the company said.

By2023, IBM scientists will deliver a quantum computer with a 1,121-qubit processor, inside a 10-foot tall "super-fridge" that will be online and capable of delivering a Quantum Advantage-- the point where certain information processing tasks can be performed more efficiently or cost effectively on a quantum computer, versus a classical one, according to the company.

ColdQuanta commercializes quantum atomics, which it said is "the next wave of the information age." The company's Quantum Core technology is based on ultra-cold atoms cooled to a temperature of nearly absolute zero; lasers manipulate and control the atoms with extreme precision.

The company manufactures components, instruments, and turnkey systems that address a broad spectrum of applications: quantum computing, timekeeping, navigation, radiofrequency sensors, and quantum communications. It also develops interface software.

ColdQuanta's global customers include major commercial and defense companies; all branches of the US Department of Defense; national labs operated by the Department of Energy; NASA; NIST; and major universities, the company said.

In April 2020, ColdQuanta was selected by the Defense Advanced Research Projects Agency (DARPA) to develop a scalable, cold-atom-based quantum computing hardware and software platform that can demonstrate quantum advantage on real-world problems.

Zapata Computing empowers enterprise teams to accelerate quantum solutions and capabilities. It introduced Orquestra, an end-to-end, workflow-based toolset for quantum computing. In addition to previously available backends that include a full range of simulators and classical resources, Orquestra now integrates with Qiskit and IBM Quantum's open quantum systems, Honeywell's System Model H, and Amazon Braket, the company said.

The Orquestra workflow platform provides access to Honeywell's H, and was designed to enable teams to compose, run, and analyze complex, quantum-enabled workflows and challenging computational solutions at scale, Zapata said. Orquestra is purpose-built for quantum machine learning, optimization, and simulation problems across industries.

Recently introduced Azure Quantum provides a "one-stop-shop" to create a path to scalable quantum computing, Microsoft said. It is available in preview to select customers and partners through Azure.

For developers, Azure Quantum offers:

Founded in 1999, D-Wave claims to be the first company to sell a commercial quantum computer, in 2011, and the first to give developers real-time cloud access to quantum processors with Leap, its quantum cloud service.

D-Wave's approach to quantum computing, known as quantum annealing, is best suited to optimization tasks in fields such as AI, logistics, cybersecurity, financial modeling, fault detection, materials sciences, and more. More than 250 early quantum applications have been built to-date using D-Wave's technology, the company said.

The company has seen a lot of momentum in 2020. In February, D-Wave announced the launch of Leap 2, which introduced new tools and features designed to make it easier for developers to build bigger applications. In July, the company expanded access to Leap to India and Australia. In March, D-Wave opened free access to Leap for researchers working on responses to the COVID-19 pandemic. In September, the company launched Advantage, a quantum system designed for business. Advantage has more than 5,000 qubits, 15-way qubit connectivity, and an expanded hybrid solver service to run problems with up to one million variables, D-Wave said. Advantage is accessible through Leap.

Strangeworks, a startup based in Austin, Texas, claims to be lowering the barrier to entry into quantum computing by providing tools for development on all quantum hardware and software platforms. Strangeworks launched in March 2018, and one year later, deployed a beta version of its software platform to users from more than 140 different organizations. Strangeworks will open its initial offering of the platform in Q1 2021, and the enterprise edition is coming in late 2021, according to Steve Gibson, chief strategy officer.

The Strangeworks Quantum Computing platform provides tools to access and program quantum computing devices. The Strangeworks IDE is platform-agnostic, and integrates all hardware, software frameworks, and supporting languages, the company said. To facilitate this goal, Strangeworks manages assembly, integrations, and product updates. Users can share their work privately with collaborators, or publicly. Users' work belongs to them and open sourcing is not required to utilize the Strangeworks platform.

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Wall Streets latest shiny new thing: quantum computing – The Economist

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Dec 19th 2020

THE FINANCE industry has had a long and profitable relationship with computing. It was an early adopter of everything from mainframe computers to artificial intelligence (see timeline). For most of the past decade more trades have been done at high frequency by complex algorithms than by humans. Now big banks have their eyes on quantum computing, another cutting-edge technology.

This is the idea, developed by physicists in the 1980s, that the counter-intuitive properties of quantum mechanics might allow for the construction of computers that could perform mathematical feats that no non-quantum machine would ever be capable of. The promise is now starting to be realised. Computing giants like Google and IBM, as well as a flock of smaller competitors, are building and refining quantum hardware.

Quantum computers will not beat their classical counterparts at everything. But much of the maths at which they will excel is of interest to bankers. At a conference on December 10th William Zeng, head of quantum research at Goldman Sachs told the audience that quantum computing could have a revolutionary impact on the bank, and on finance more broadly.

Many financial calculations boil down to optimisation problems, a known strength of quantum computers, says Marco Pistoia, the head of a research unit at JPMorgan Chase, who spent many years at IBM before that. Quantum quants hope their machines will boost profits by speeding up asset pricing, digging up better-performing portfolios and making machine-learning algorithms more accurate. A study by BBVA, a Spanish bank, concluded in July that quantum computers could boost credit-scoring, spot arbitrage opportunities and accelerate so-called Monte Carlo simulations, which are commonly used in finance to try to model the likely behaviour of markets.

Finance is not the only industry looking for a way to profit from even the small, unstable quantum computers that mark the current state of the art; sectors from aerospace to pharmaceuticals are also hunting for a quantum advantage. But there are reasons to think finance may be among the first to find it. Mike Biercuk of Q-CTRL, a startup that makes control software for quantum computers, points out that a new financial algorithm can be deployed faster than a new industrial process. The size of financial markets means that even a small advance would be worth a lot of money.

Banks are also buying in expertise. Firms including BBVA, Citigroup, JPMorgan and Standard Chartered have set up research teams and signed deals with computing firms. The Boston Consulting Group reckons that, as of June, banks and insurers in America and Europe had hired more than 115 expertsa big number for what remains, even in academia, a small specialism. We have more physics and maths PhDs than some big universities, jokes Alexei Kondratyev, head of data analytics at Standard Chartered.

Startups are exploring possibilities too. Enrique Lizaso of Multiverse Computing reckons his firms quantum-enhanced algorithms can spot fraud more effectively, and around a hundred times faster, than existing ones. The firm has also experimented with portfolio optimisation, in which analysts seek well-performing investment strategies. Multiverse re-ran decisions made by real traders at a bank. The job was to choose, over the course of a year, the most profitable mix from a group of 50 assets, subject to restrictions, such as how often trades could be made.

The result was a problem with around 101,300 possible solutions, a number that far outstrips the number of atoms in the visible universe. In reality, the banks traders, assisted by models running on classical computers, managed an annual return of 19%. Depending on the amount of volatility investors were prepared to put up with, Multiverses algorithm generated returns of 20-80%though it stops short of claiming a definitive quantum advantage.

Not all potential uses are so glamorous. Monte Carlo simulations are often used in regulatory stress tests. Christopher Savoie of Zapata, a quantum-computing firm based in Boston, recalls one bank executive telling him: Dont bring me trading algorithms, bring me a solution to CCAR [an American stress-test regulation]. That stuff eats up half my computing budget.

All this is promising. But quantum financiers acknowledge that, for now, hardware is a limitation. Were not yet able to perform these calculations at a scale where a quantum machine offers a real-world advantage over a classical one, says Mr Biercuk. One rough way to measure a quantum computers capability is its number of qubits, the analogue of classical computings 1-or-0 bits. For many problems a quantum computer with thousands of stable qubits is provably far faster than any non-quantum machine that could ever be builtit just does not exist yet.

For now, the field must make do with small, unstable devices, which can perform calculations for only tiny fractions of a second before their delicate quantum states break down. John Preskill of the California Institute of Technology has dubbed these NISQsNoisy, Intermediate-Scale Quantum computers.

Bankers are working on ways to conduct computations on such machines. Mr Zeng of Goldman pointed out that the computational resources needed to run quantum algorithms have fallen as programmers have tweaked their methods. Mr Pistoia points to papers his team has written exploring ways to scale useful financial calculations into even small machines.

And at some point those programmers will meet hardware-makers coming the other way. In 2019 Google was the first to demonstrate quantum supremacy, using a 53-qubit NISQ machine to perform in minutes a calculation that would have taken the worlds fastest supercomputer more than 10,000 years. IBM, which has invested heavily in quantum computing, reckons it can build a 1,000-qubit machine by 2023. Both it and Google have talked of a million qubits by the end of the decade.

When might the financial revolution come? Mr Savoie thinks simple algorithms could be in use within 18 months, with credit-scoring a plausible early application. Mr Kondratyev says three to five years is more realistic. But the crucial point, says one observer, is that no one wants to be late to the party. One common worry is that whoever makes a breakthrough first may choose to reap the rewards in obscurity, rather than broadcast the fact to the world. After all, says Mr Biercuk, that is how high-frequency trading got started.

This article appeared in the Finance & economics section of the print edition under the headline "Quantum for quants"

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Quantum computing: Strings of ultracold atoms reveal the surprising behavior of quantum particles – ZDNet

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Spinning atoms in a magnetic field notoriously behave in ways that scientists are yet to understand entirely. New research from MIT has now shed some light on the obscure laws that govern the smallest of particles, which could pave the way for further developments in the design of quantum devices that rely on atomic spin.

The team exposed spinning lithium atoms to magnetic forces of different strengths to observe how the quantum particles reacted both individually and as a group. They were faced in each scenario with a surprising choreography of atoms, revealing unexpected diversity of behavior in a well-known and studied magnetic material.

Spin, like mass or charge, is an intrinsic property of atoms: the particles rotate around an axis in either a clockwise manner (often described as "down") or anticlockwise ("up"). Based on their spin, atoms can react to magnetic fields in different ways, for example by aligning themselves with other atoms in a specific pattern.

The spin of many atoms together in a magnetic material that is exposed to a magnetic field can reach an equilibrium state, where all the atom spins are aligned; or the atoms can adopt dynamic behavior, where the spins across many atoms create a wave-like pattern.

MIT's research team focused on the way that atoms evolve from dynamic behavior back into an equilibrium state and found that the magnetic force that the atoms are exposed to plays a key part in determining the particles' behavior. Some magnets triggered a so-called "ballistic" behavior, where the atomic spins shot quickly back into an equilibrium state, while others revealed "diffusive behavior", with the particles spinning back to equilibrium in a much slower fashion.

"Studying one of the simplest magnetic materials, we have advanced the understanding of magnetism," said Wolfgang Ketterle, professor of physics at MIT and the leader of the research team. "When you find new phenomena in one of the simplest models in physics for magnetism, then you have a chance to fully describe and understand it. This is what gets me out of bed in the morning, and gets me excited."

To study the phenomenon, Ketterle's team brought the lithium atoms down to temperatures more than ten times colder than interstellar space, which freezes the particles to a near standstill and enables easier observation. Using lasers as a type of tweezer, the scientists then grabbed the atoms and arranged them into strings of beads. With 1,000 strings, each comprising 40 atoms, the team created an ultra-cold 40,000-strong atom lattice.

Pulsed magnetic forces of different strengths were then applied to the lattice, causing each atom along the string to tilt its spin in a wavelike manner. The researchers were able to image those wave patterns on a detector, and watched how the atoms gradually evolved from dynamic behavior to equilibrium, depending on the nature of the magnetic field that they were exposed to.

The process, explained Ketterle, is similar to plucking a guitar's strings: playing the strings brings them out of their equilibrium condition, and allows the scientists to watch what happens before they return to their original state.

"What we're doing here is, we're kind of plucking the string of spins. We're putting in this helix pattern, and then observing how this pattern behaves as a function of time," Ketterle said. "This allows us to see the effect of different magnetic forces between the spins."

Although some of this behavior had been theoretically predicted in the past, detailed observation of patterns of atomic spins had never been observed in detail until now. These patterns, however, were found to fit an existing mathematical model called the Heisenberg model, which is commonly used to predict magnetic behavior.

Together with a team of scientists at Harvard, MIT's researchers were able to calculate the spin's dynamics. The results, therefore, aren't only useful to advance the knowledge of magnetism at a fundamental level; but they could also be used as a blueprint for a device that could predict the properties and behaviors of new materials at the quantum level.

"With all of the current excitement about the promise of quantum information science to solve practical problems in the future, it is great to see work like this actually coming to fruition today," said John Gillaspy, program officer in the Division of Physics at the National Science Foundation, and a funder of the research.

A higher-level understanding of quantum particles could also lead to the design of new technologies, such as spintronic devices, according to the researchers. Unlike electronics, which leverage the flow of electrons, spintronics tap the spin of quantum particles to transmit, process and store information. They hold promise, therefore, for quantum computing, where the spin of particles would constitute a bit of quantum information.

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Quantum computing: Strings of ultracold atoms reveal the surprising behavior of quantum particles - ZDNet

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Anyon Systems to Deliver a Quantum Computer to the Canadian Department of National Defense – GlobeNewswire

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Anyon Systems's Quantum Computer

Anyon System's superconducting quantum processor.

MONTREAL, Dec. 15, 2020 (GLOBE NEWSWIRE) -- Anyon Systems Inc. (Anyon), a quantum computing company based in Montreal, Canada, announced today that it is to deliver Canadas first gate-based quantum computer for the Department of National Defenses Defence Research and Development Canada (DRDC). The quantum computer will feature Anyons Yukon generation superconducting quantum processor. Named after Canadas westernmost territory, the quantum computer will enable DRDC researchers to explore quantum computing to solve problems of interest to their mission.

Quantum computing is expected to be a disruptive technology and is of strategic importance to many industries and government agencies. Anyon is focused on delivering large-scale, fault-tolerant quantum computers to a wide group of early adopters including government agencies, high performance computing centers and universities in the near term, said Dr. Alireza Yazdi, founder and CEO of Anyon.

Founded in 2014, Anyon Systems is the first Canadian company manufacturing gate-based quantum computing platform for universal quantum computation. Anyon Systems delivers turnkey gate-based quantum computers. The company is headquartered in Montreal, Quebec.

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Anyon Systems to Deliver a Quantum Computer to the Canadian Department of National Defense - GlobeNewswire

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Chinese quantum computer may be the most powerful ever seen – Siliconrepublic.com

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This week in future tech, a Chinese quantum computer can reportedly solve a problem in 200 seconds, compared to the 2.5bn years a supercomputer needs.

A quantum computer developed at the University of Science and Technology in Hefei, China, has caught the worlds attention due to what appears to be a performance vastly exceeding others that exist today.

According to findings in published in Scienceand reported by Nature, researchers claim they demonstrated a quantum advantage, using laser beams to perform a computation that is not mathematically possible using traditional binary computers.

We have shown that we can use photons, the fundamental unit of light, to demonstrate quantum computational power well beyond the classical counterpart, said researcher Jian-Wei Pan.

Tasked with solving the so-called boson sampling problem, the researchers found solutions in as little as 200 seconds. By comparison, it could take Chinas TaihuLight supercomputer about 2.5bn years to do the same.

However, Christian Weedbrook, chief executive of quantum-computing start-up Xanadu, said that unlike Googles Sycamore quantum computer announced last year, the Chinese quantum computer is not programmable. This means that, so far, it cannot be used for solving practical problems.

Scientists from the University of Washington have unveiled a drone that smells, using the power of a moth. Writing in IOP Bioinspiration and Biomimetics, they revealed their Smellicopter design.

The autonomous drone uses a live antenna from a moth to navigate toward smells, while also having the ability to sense and avoid obstacles. A moth uses its antennae to sense chemicals in its environment and navigate toward sources of food or potential mates.

In this case, the researchers used antennae from the Manduca sexta hawkmoth for Smellicopter. The moths were placed in a fridge to anaesthetise them before removing their antennae. Once separated, the live moth antennae could stay chemically active for four hours.

By adding tiny wires into either end of the antenna, the researchers were able to connect it to an electrical circuit and measure the average signal from all of the cells in the antenna. They said Smellicopter could be used to detect things such as gas leaks, explosives and disaster survivors.

From a robotics perspective, this is genius, said Sawyer Fuller of the University of Washington. The classic approach in robotics is to add more sensors, and maybe build a fancy algorithm or use machine learning to estimate wind direction. It turns out, all you need is to add a fin.

German air taxi firm Volocopter said it plans to make regular services a reality in Singapore within the next three years. In October 2019, Volocopter completed the its first air taxi demonstration flight over the Marina Bay area of Singapore and the company is now looking to obtain the necessary regulatory approvals for regular service, including those from Civil Aviation Authority of Singapore and the European Union Aviation Safety.

The first route is expected to be a touristic route over the southern waters, offering views of the Marina Bay skyline, and future routes may include cross-border flights. The company is expected to hire over 200 full-time employees in Singapore to manage a network of routes by 2026.

The citys research institutes conducting R&D play an integral part in this, said Florian Reuter, CEO of Volocopter. Topics like route validation for autonomous operations, material science and research regarding battery technology are very important for our long-term business success.

The Global Mobile Suppliers Association (GSA) has reported that the number of announced 5G devices has surpassed 500 for the first time. By the end of November this year, there were 519 announced 5G devices, of which 303 were commercially available.

In the last three months, the number of announced 5G devices has grown by 29.4pc, while there has been a 59.5pc increase in the number of commercially available 5G devices over the same period.

This year weve seen more and more symbolically important milestones being passed over 500 announced 5G devices, more than 100 vendors, over 250 different phones, and 100 fixed wireless access CPE devices, said Joe Barrett, president of the GSA.

And it doesnt stop there; we expect more 5G devices to become commercially available, surpassing the 330 mark before the year is out. The device vendor community has stepped up and delivered in the face of unprecedented challenges. As an industry, we can be excited about the opportunities 2021 will bring.

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‘Magic’ angle graphene and the creation of unexpected topological quantum states – Princeton University

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Electrons inhabit a strange and topsy-turvy world. These infinitesimally small particles have never ceased to amaze and mystify despite the more than a century that scientists have studied them. Now, in an even more amazing twist, physicists have discovered that, under certain conditions, interacting electrons can create what are called topological quantum states. This finding, which was recently published in the journal Nature,holds great potential for revolutionizing electrical engineering, materials science and especially computer science.

Topological states of matter are particularly intriguing classes of quantum phenomena. Their study combines quantum physics with topology, which is the branch of theoretical mathematics that studies geometric properties that can be deformed but not intrinsically changed. Topological quantum states first came to the publics attention in 2016 when three scientists Princetons Duncan Haldane, who is Princetons Thomas D. Jones Professor of Mathematical Physics and Sherman Fairchild University Professor of Physics, together with David Thouless and Michael Kosterlitz were awarded the Nobel Prize for their work in uncovering the role of topology in electronic materials.

A Princeton-led team of physicists have discovered that, under certain conditions, interacting electrons can create what are called topological quantum states, which,has implications for many technological fields of study, especially information technology. To get the desired quantum effect, the researchersplaced two sheets of graphene on top of each other with the top layer twisted at the "magic" angle of 1.1 degrees, whichcreates a moir pattern. This diagram shows a scanning tunneling microscopeimaging the magic-angle twisted bilayer graphene.

Image courtesy of Kevin Nuckolls

The last decade has seen quite a lot of excitement about new topological quantum states of electrons, said Ali Yazdani, the Class of 1909 Professor of Physics at Princeton and the senior author of the study. Most of what we have uncovered in the last decade has been focused on how electrons get these topological properties, without thinking about them interacting with one another.

But by using a material known as magic-angle twisted bilayer graphene, Yazdani and his team were able to explore how interacting electrons can give rise to surprising phases of matter.

The remarkable properties of graphene were discovered two years ago when Pablo Jarillo-Herrero and his team at the Massachusetts Institute of Technology (MIT) used it to induce superconductivity a state in which electrons flow freely without any resistance. The discovery was immediately recognized as a new material platform for exploring unusual quantum phenomena.

Yazdani and his fellow researchers were intrigued by this discovery and set out to further explore the intricacies of superconductivity.

But what they discovered led them down a different and untrodden path.

This was a wonderful detour that came out of nowhere, said Kevin Nuckolls, the lead author of the paper and a graduate student in physics. It was totally unexpected, and something we noticed that was going to be important.

Following the example of Jarillo-Herrero and his team, Yazdani, Nuckolls and the other researchers focused their investigation on twisted bilayer graphene.

Its really a miracle material, Nuckolls said. Its a two-dimensional lattice of carbon atoms thats a great electrical conductor and is one of the strongest crystals known.

Graphene is produced in a deceptively simple but painstaking manner: a bulk crystal of graphite, the same pure graphite in pencils, is exfoliated using sticky tape to remove the top layers until finally reaching a single-atom-thin layer of carbon, with atoms arranged in a flat honeycomb lattice pattern.

To get the desired quantum effect, the Princeton researchers, following the work of Jarillo-Herrero, placed two sheets of graphene on top of each other with the top layer angled slightly. This twisting creates a moir pattern, which resembles and is named after a common French textile design. The important point, however, is the angle at which the top layer of graphene is positioned: precisely 1.1 degrees, the magic angle that produces the quantum effect.

Its such a weird glitch in nature, Nuckolls said, that it is exactly this one angle that needs to be achieved. Angling the top layer of graphene at 1.2 degrees, for example, produces no effect.

The researchers generated extremely low temperatures and created a slight magnetic field. They then used a machine called a scanning tunneling microscope, which relies on a technique called quantum tunneling rather than light to view the atomic and subatomic world. They directed the microscopes conductive metal tip on the surface of the magic-angle twisted graphene and were able to detect the energy levels of the electrons.

They found that the magic-angle graphene changed how electrons moved on the graphene sheet. It creates a condition which forces the electrons to be at the same energy, said Yazdani. We call this a flat band.

When electrons have the same energy are in a flat band material they interact with each other very strongly. This interplay can make electrons do many exotic things, Yazdani said.

One of these exotic things, the researchers discovered, was the creation of unexpected and spontaneous topological states.

This twisting of the graphene creates the right conditions to create a very strong interaction between electrons, Yazdani explained. And this interaction unexpectedly favors electrons to organize themselves into a series of topological quantum states.

The researchers discovered that the interaction between electrons creates topological insulators:unique devices that whose interiors do not conduct electricity but whose edges allow the continuous and unimpeded movement ofelectrons. This diagram depicts thedifferent insulating states of the magic-angle graphene, each characterized by an integer called its Chern number, which distinguishes between different topological phases.

Image courtesy of Kevin Nuckolls

Specifically, they discovered that the interaction between electrons creates what are called topological insulators. These are unique devices that act as insulators in their interiors, which means that the electrons inside are not free to move around and therefore do not conduct electricity. However, the electrons on the edges are free to move around, meaning they are conductive. Moreover, because of the special properties of topology, the electrons flowing along the edges are not hampered by any defects or deformations. They flow continuously and effectively circumvent the constraints such as minute imperfections in a materials surface that typically impede the movement of electrons.

During the course of the work, Yazdanis experimental group teamed up two other Princetonians Andrei Bernevig, professor of physics, and Biao Lian, assistant professor of physics to understand the underlying physical mechanism for their findings.

Our theory shows that two important ingredients interactions and topology which in nature mostly appear decoupled from each other, combine in this system, Bernevig said. This coupling creates the topological insulator states that were observed experimentally.

Although the field of quantum topology is relatively new, itcouldtransform computer science. People talk a lot about its relevance to quantum computing, where you can use these topological quantum states to make better types of quantum bits, Yazdani said. The motivation for what were trying to do is to understand how quantum information can be encoded inside a topological phase. Research in this area is producing exciting new science and can have potential impact in advancing quantum information technologies.

Yazdani and his team will continue their research into understanding how the interactions of electrons give rise to different topological states.

The interplay between the topology and superconductivity in this material system is quite fascinating and is something we will try to understand next, Yazdani said.

In addition to Yazdani, Nuckolls, Bernevig and Lian, contributors to the study included co-first authors Myungchul Oh and Dillon Wong, postdoctoral research associates, as well as Kenji Watanabe and Takashi Taniguchi of the National Institute for Material Science in Japan.

Strongly Correlated Chern Insulators in Magic-Angle Twisted Bilayer Graphene, by Kevin P. Nuckolls, Myungchul Oh, Dillon Wong, Biao Lian, Kenji Watanabe, Takashi Taniguchi, B. Andrei Bernevig and Ali Yazdani, was published Dec. 14 in the journal Nature (DOI:10.1038/s41586-020-3028-8). This work was primarily supported by the Gordon and Betty Moore Foundations EPiQS initiative (GBMF4530, GBMF9469) and the Department of Energy (DE-FG02-07ER46419 and DE-SC0016239). Other support for the experimental work was provided by the National Science Foundation (Materials Research Science and Engineering Centers through the Princeton Center for Complex Materials (NSF-DMR-1420541, NSF-DMR-1904442) and EAGER DMR-1643312), ExxonMobil through the Andlinger Center for Energy and the Environment at Princeton, the Princeton Catalysis Initiative, the Elemental Strategy Initiative conducted by Japans Ministry of Education, Culture, Sports, Science and Technology (JPMXP0112101001, JSPS KAKENHI grant JP20H0035, and CREST JPMJCR15F3), the Princeton Center for Theoretical Science at Princeton University, the Simons Foundation, the Packard Foundation, the Schmidt Fund for Innovative Research, BSF Israel US foundation (2018226), the Office of Naval Research (N00014-20-1-2303) and the Princeton Global Network Funds.

Read more here:

'Magic' angle graphene and the creation of unexpected topological quantum states - Princeton University

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December 17th, 2020 at 3:51 am

Posted in Quantum Computing


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