Customized Quantum Annealing Schedules

Khezri, Mostafa; Dai, Xianzhe; Yang, Rui; Albash, Tameem; Lupaşcu, Adrian; Lidar, Daniel A.

doi:10.1103/physrevapplied.17.044005

Cited by 7 publications

(4 citation statements)

References 46 publications

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“…One of the main routes to tackle this problem, in the unitary limit, is to engineer the annealing schedules as opposed to using the simple linear schedules. There have been many approaches to construct optimal schedules for the given problem in the recent past [58][59][60][61][62][63], and some of them [64] are based on genetic algorithms, that have been also fruitfully applied to the embedding problem [65][66][67][68][69] for QA.…”

Section: Introductionmentioning

confidence: 99%

Deep learning optimal quantum annealing schedules for random Ising models

Hegde

Passarelli²,

Cantele³

et al. 2023

New J. Phys.

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A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training. 

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Section: Introductionmentioning

confidence: 99%

Deep learning optimal quantum annealing schedules for random Ising models

Hegde

Passarelli²,

Cantele³

et al. 2023

New J. Phys.

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“…The paradigmatic example of the quantum Ising chain, very studied today by its applicability in adiabatic quantum computing or quantum annealing [3][4][5][6][7][8][9][10][11][12], has such characteristics. Even though, its phenomenology has been largely elucidated over the years with the formulation of the Kibble-Zurek mechanism [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Quantum Ising chain with time-averaged work in linear response theory

Nazé

2023

J. Stat. Mech.

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For systems performing a weakly isothermal process, the decorrelation time dictates how fast the relaxation function decorrelates. However, like many other thermally isolated systems, the transverse-field quantum Ising chain presents an ill-defined decorrelation time. On the other hand, the Kibble–Zurek mechanism uses a heuristic relaxation time to achieve its famous scaling. The problem however of having a well-defined decorrelation time, derived from first principles, agreeing with the Kibble–Zurek mechanism is still open. Such a solution is proposed here by measuring the work using the time-averaged relaxation function of the system, which offers a new and well-defined decorrelation time for thermally isolated systems. I recover with this the Kibble–Zurek mechanism in the finite-time and weak driving regime, and new features in the slowly-varying one. The gain in control over the system in such distinction is desirable for potential applications.

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“…Such truncations are common in numerical simulations of experimental systems of the type that our results are designed to be applied to, e.g. [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Quantum adiabatic theorem for unbounded Hamiltonians with a cutoff and its application to superconducting circuits

Mozgunov

Lidar

2022

Phil. Trans. R. Soc. A.

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We present a new quantum adiabatic theorem that allows one to rigorously bound the adiabatic timescale for a variety of systems, including those described by originally unbounded Hamiltonians that are made finite-dimensional by a cutoff. Our bound is geared towards the qubit approximation of superconducting circuits and presents a sufficient condition for remaining within the 2 n -dimensional qubit subspace of a circuit model of n qubits. The novelty of this adiabatic theorem is that, unlike previous rigorous results, it does not contain 2 n as a factor in the adiabatic timescale, and it allows one to obtain an expression for the adiabatic timescale independent of the cutoff of the infinite-dimensional Hilbert space of the circuit Hamiltonian. As an application, we present an explicit dependence of this timescale on circuit parameters for a superconducting flux qubit and demonstrate that leakage out of the qubit subspace is inevitable as the tunnelling barrier is raised towards the end of a quantum anneal. We also discuss a method of obtaining a 2 n × 2 n effective Hamiltonian that best approximates the true dynamics induced by slowly changing circuit control parameters. This article is part of the theme issue ‘Quantum annealing and computation: challenges and perspectives’.

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Customized Quantum Annealing Schedules

Cited by 7 publications

References 46 publications

Deep learning optimal quantum annealing schedules for random Ising models

Deep learning optimal quantum annealing schedules for random Ising models

Quantum Ising chain with time-averaged work in linear response theory

Quantum adiabatic theorem for unbounded Hamiltonians with a cutoff and its application to superconducting circuits

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