Adiabatic quantum computing for finding low-peak-sidelobe codes

Coxson, Gregory E.; Hill, Connie R.; Russo, Jon C.

doi:10.1109/hpec.2014.7040953

Cited by 23 publications

(21 citation statements)

References 19 publications

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“…Quantum annealing is an approach to quantum computing where optimization and other problems are mapped such that their solutions correspond to the ground and low energy states of a Hamiltonian, and quantum fluctuations are used to solve the problem. Small proof-of-concept experiments have been performed based on a variety of real problems, with applications as diverse as cryptography [1], design of radar waveforms [2], protein folding [3], air traffic control [4], scheduling [5][6][7], and hydrology [8]. To the best of our knowledge, no scaling advantage has been observed on these devices for optimization, although recent work suggests one may be present for quantum simulation [9].…”

Section: Introductionmentioning

confidence: 99%

Experimental test of search range in quantum annealing

Chancellor

Kendon

2021

Phys. Rev. A

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Section: Introductionmentioning

confidence: 99%

Experimental test of search range in quantum annealing

Chancellor

Kendon

2021

Phys. Rev. A

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“…A complete list of all potential applications would be too long to give here. However applications have been studied in such diverse fields as finance [4], computer science [5], machine learning [6][7][8][9], communications [10][11][12][13], graph theory [14], and aeronautics [15], illustrating the importance of such algorithms to real world problems. While some of these applications rely on the ability of the QAA to perform optimization by finding the lowest energy state of a classical problem Hamiltonian, others such as [6][7][8][9][10]13], instead rely on the fact that open quantum systems effects allow for sampling of an approximate Boltzmann distribution.…”

Section: Introductionmentioning

confidence: 99%

Modernizing quantum annealing using local searches

2017

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I describe how real quantum annealers may be used to perform local (in state space) searches around specified states, rather than the global searches traditionally implemented in the quantum annealing algorithm (QAA). Such protocols will have numerous advantages over simple quantum annealing. By using such searches the effect of problem mis-specification can be reduced, as only energy differences between the searched states will be relevant. The QAA is an analogue of simulated annealing, a classical numerical technique which has now been superseded. Hence, I explore two strategies to use an annealer in a way which takes advantage of modern classical optimization algorithms. Specifically, I show how sequential calls to quantum annealers can be used to construct analogues of population annealing and parallel tempering which use quantum searches as subroutines. The techniques given here can be applied not only to optimization, but also to sampling. I examine the feasibility of these protocols on real devices and note that implementing such protocols should require minimal if any change to the current design of the flux qubit-based annealers by D-Wave Systems Inc. I further provide proof-of-principle numerical experiments based on quantum Monte Carlo that demonstrate simple examples of the discussed techniques. c Î

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“…Experimentally, this field is exciting because it allows for large-scale experiments on superconducting hardware designed to solve difficult optimization problems. Proof-of-concept studies have taken place on a diverse range of topics, including aerospace problems [13], [14], hydrology [15], radar waveform design [16], scheduling [17]- [19], and traffic flow optimization [20], [21]. While, to our knowledge, a scaling advantage for optimization has yet to be seen, signs of a potential advantage have been observed in recent quantum simulation experiments [22].…”

Section: Introductionmentioning

confidence: 99%

Performance of Domain-Wall Encoding for Quantum Annealing

Chen

Stollenwerk

Chancellor

2021

IEEE Trans. Quantum Eng.

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In this article, we experimentally test the performance of the recently proposed domain-wall encoding of discrete variables Chancellor, 2019, on Ising model flux qubit quantum annealers. We compare this encoding with the traditional one-hot methods and find that they outperform the one-hot encoding for three different problems at different sizes of both the problem and the variables. From these results, we conclude that the domain-wall encoding yields superior performance against a variety of metrics furthermore; we do not find a single metric by which one hot performs better. We even find that a 2000Q quantum annealer with a drastically less connected hardware graph but using the domain-wall encoding can outperform the next-generation Advantage processor if that processor uses one-hot encoding.

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Adiabatic quantum computing for finding low-peak-sidelobe codes

Cited by 23 publications

References 19 publications

Experimental test of search range in quantum annealing

Experimental test of search range in quantum annealing

Modernizing quantum annealing using local searches

Performance of Domain-Wall Encoding for Quantum Annealing

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