Experimental evaluation of an adiabiatic quantum system for combinatorial optimization

McGeoch, Catherine C.; Wang, Cong

doi:10.1145/2482767.2482797

Cited by 122 publications

(128 citation statements)

References 24 publications

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“…In short, floppy qubits lead to wide, degenerate valleys in the state space that are favored in the time-dependent quantum potential early in the anneal. We now illustrate the effect of floppy qubits on degeneracy in the random Ising instances often used to study D-Wave processors [1,2,11,12,14].…”

Section: B Floppy Qubits and Degeneracy In J = ±1mentioning

confidence: 99%

“…The recent development of quantum annealing (QA) processors has spurred research into how to construct input instances-Ising spin instances-that might confirm or refute any purported computational advantage conferred by such hardware [1][2][3][4][5][6][7][8][9][10]. These recent works have proposed various important considerations such as error sensitivity, thermal hardness [10], ground state degeneracy, consistency of energy scale across a random ensemble [6,7], potential barrier shape [4,5], and the existence of classical phase transitions [8].…”

Section: Introductionmentioning

confidence: 99%

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Degeneracy, degree, and heavy tails in quantum annealing

et al. 2016

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Both simulated quantum annealing and physical quantum annealing have shown the emergence of "heavy tails" in their performance as optimizers: The total time needed to solve a set of random input instances is dominated by a small number of very hard instances. Classical simulated annealing, in contrast, does not show such heavy tails. Here we explore the origin of these heavy tails, which appear for inputs with high local degeneracy-large isoenergetic clusters of states in Hamming space. This category includes the low-precision Chimera-structured problems studied in recent benchmarking work comparing the D-Wave Two quantum annealing processor with simulated annealing. On similar inputs designed to suppress local degeneracy, performance of a quantum annealing processor on hard instances improves by orders of magnitude at the 512-qubit scale, while classical performance remains relatively unchanged. Simulations indicate that perturbative crossings are the primary factor contributing to these heavy tails, while sensitivity to Hamiltonian misspecification error plays a less significant role in this particular setting.

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Section: B Floppy Qubits and Degeneracy In J = ±1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Degeneracy, degree, and heavy tails in quantum annealing

et al. 2016

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“…For example, one could straightforwardly construct an adiabatic version of Shor's integer factorization algorithm [1] using only two-local and three-local Hamiltonians for its adiabatic gates. The theoretical and practical implications of an implementable Shor's algorithm, on a manyqubit quantum annealer that will become available in the near future [4][5][6], may be tremendous, both in the field of Quantum Computing and well beyond it.…”

Section: Discussionmentioning

confidence: 99%

“…Recent promising experimental research findings [4][5][6] in the field of Adiabatic Quantum Computing (AQC) suggest that a leading candidate to be the first device to solve practical classically-hard problems using quantum principles is the so called 'quantum annealer', which implements the simple yet potentially-powerful quantum-adiabatic algorithmic approach proposed by Farhi et al [7] about a decade ago.…”

Section: Introductionmentioning

confidence: 99%

Quantum gates with controlled adiabatic evolutions

Hen

2015

Phys. Rev. A

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We introduce a class of quantum adiabatic evolutions that we claim may be interpreted as the equivalents of the unitary gates of the quantum gate model. We argue that these gates form a universal set and may therefore be used as building blocks in the construction of arbitrary 'adiabatic circuits', analogously to the manner in which gates are used in the circuit model. One implication of the above construction is that arbitrary classical boolean circuits as well as gate model circuits may be directly translated to adiabatic algorithms with no additional resources or complexities. We show that while these adiabatic algorithms fail to exhibit certain aspects of the inherent fault tolerance of traditional quantum adiabatic algorithms, they may have certain other experimental advantages acting as quantum gates.

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“…As a result of recent progress in nanotechnology, the implementation of a quantum computer has become a more realistic prospect. In fact, Dwave Systems Inc. has developed a quantum processor composed of 512 qubits [1], [2], and this achievement could be a breakthrough for the development of quantum computing architectures. Hence, quantum annealing (QA) [3], which is the quantum version of simulated annealing [4] and is realized via adiabatic Hamiltonian evolution, has been demonstrated, although the range of application was restricted [5].…”

Section: Introductionmentioning

confidence: 99%

Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution

Osakabe

Akima

Sakuraba

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARY There is increasing interest in quantum computing, because of its enormous computing potential. A small number of powerful quantum algorithms have been proposed to date; however, the development of new quantum algorithms for practical use remains essential. Parallel computing with a neural network has successfully realized certain unique functions such as learning and recognition; therefore, the introduction of certain neural computing techniques into quantum computing to enlarge the quantum computing application field is worthwhile. In this paper, a novel quantum associative memory (QuAM) is proposed, which is achieved with a quantum neural network by employing adiabatic Hamiltonian evolution. The memorization and retrieval procedures are inspired by the concept of associative memory realized with an artificial neural network. To study the detailed dynamics of our QuAM, we examine two types of Hamiltonians for pattern memorization. The first is a Hamiltonian having diagonal elements, which is known as an Ising Hamiltonian and which is similar to the cost function of a Hopfield network. The second is a Hamiltonian having non-diagonal elements, which is known as a neuro-inspired Hamiltonian and which is based on interactions between qubits. Numerical simulations indicate that the proposed methods for pattern memorization and retrieval work well with both types of Hamiltonians. Further, both Hamiltonians yield almost identical performance, although their retrieval properties differ. The QuAM exhibits new and unique features, such as a large memory capacity, which differs from a conventional neural associative memory. key words: associative memory, quantum computing, adiabatic Hamiltonian evolution, neural network

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Experimental evaluation of an adiabiatic quantum system for combinatorial optimization

Cited by 122 publications

References 24 publications

Degeneracy, degree, and heavy tails in quantum annealing

Degeneracy, degree, and heavy tails in quantum annealing

Quantum gates with controlled adiabatic evolutions

Quantum Associative Memory with Quantum Neural Network via Adiabatic Hamiltonian Evolution

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