Discriminating nonisomorphic graphs with an experimental quantum annealer

Izquierdo, Zoe Gonzalez; Zhou, Ruilin; Markström, Klas; Hen, Itay

doi:10.1103/physreva.102.032622

Cited by 4 publications

(2 citation statements)

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“…[16]. The role of quantum fluctuations in causing uneven sampling is also important to graph isomorphism applications which have been proposed and implemented on quantum annealers [25][26][27] In this paper, we explore a different advantage of this preferential search, the fact that it tends to find states which are flexible in the sense that some variables can be changed at little to no energy cost.…”

Section: Introductionmentioning

confidence: 99%

Fluctuation-guided search in quantum annealing

Chancellor

2020

Phys. Rev. A

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Section: Introductionmentioning

confidence: 99%

Fluctuation-guided search in quantum annealing

Chancellor

2020

Phys. Rev. A

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“…Furthermore, the tendency of the annealer to seek out states with more free spins plays a fundamental role in the effect observed in [15]. The role of quantum fluctuations in causing uneven sampling is also important to graph isomorphism applications which have been proposed and implemented on quantum annealers [24][25][26] In this paper, I explore a different advantage of this preferential search, the fact that it tends to find states which are flexible in the sense that some variables can be changed at little to no energy cost.…”

mentioning

confidence: 99%

Fluctuation guided search in quantum annealing

Chancellor

2020

Preprint

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Quantum annealing has great promise in leveraging quantum mechanics to solve combinatorial optimisation problems. However, to realize this promise to it's fullest extent we must appropriately leverage the underlying physics. In this spirit, I examine how the well known tendency of quantum annealers to seek solutions where higher levels of quantum fluctuations are present can be used to trade off optimality of the solution to a synthetic problem for the ability to have a more flexible solution, where some variables can be changed at little or no cost. I demonstrate this tradeoff experimentally using the reverse annealing feature a D-Wave Systems QPU for both problems composed of all binary variables, and those containing some higher-than-binary discrete variables. I further demonstrate how local controls on the qubits can be used to control the levels of fluctuations and guide the search. I discuss places where leveraging this tradeoff could be practically important, namely in hybrid algorithms where some penalties cannot be directly implemented on the annealer and provide some proof-of-concept evidence of how these algorithms could work. ContentsAppendix: Review of the domain wall variable encoding strategy 12References 12

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