Energy Storage Scheduling: A QUBO Formulation for Quantum Computing

Phillipson, Frank; Bontekoe, Tariq; Chiscop, Irina

doi:10.1007/978-3-030-75004-6_17

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…makes the two problems equivalent. There exists Ising or QUBO formulations for many well-known optimisation problems [17,28] and applications of all kinds can be found in recent literature, from financial [36], to machine learning [32], logistics [31,39] and network optimisation [37,38].…”

Section: Supporting Hardwarementioning

confidence: 99%

Searching for Optimisation

Phillipson¹

2022

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Section: Supporting Hardwarementioning

confidence: 99%

Searching for Optimisation

Phillipson¹

2022

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“…Recently, Abel et al (2021) show that this device is very likely to be more successful in finding a global optimum of an objective function than many classical methods. Because QA QPUs specialize in solving optimization problems, these machines have so far shown a much wider range of use cases (Laumann et al, 2015;Neukart et al, 2017;Crosson and Lidar, 2021;Mato et al, 2021;Phillipson and Chiscop, 2021c;Phillipson et al, 2021b). Quantum annealers also are seen as possible enablers in training any ML/artificial intelligence applications (Delilbasic et al, 2021;Phillipson et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

et al. 2023

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Much of recent progress in geophysics can be attributed to the adaptation of heterogeneous high-performance computing architectures. It is projected that the next major leap in many areas of science, and hence hopefully in geophysics too, will be due to the emergence of quantum computers. Finding a right combination of hardware, algorithms and a use-case, however, proves to be a very challenging task especially when looking for a relevant application that scales efficiently on a quantum computer and is difficult to solve using classical means. We show that maximizing stack-power for residual statics correction, an NP-hard combinatorial optimization problem, appears to naturally fit a particular type of quantum computing known as quantum annealing. We express the underlying objective function as a quadratic unconstrained binary optimization, which is a quantum-native formulation of the problem. We choose some solution space, and define a proper encoding to translate the problem-variables into qubit states. We show that these choices can have a significant impact on the maximum problem size that can fit on the quantum annealer and on the fidelity of the final result. To improve the latter, we embed the quantum optimization step in a hybrid classical-quantum workflow, which aims to increase the frequency of finding the global, rather than some local, optimum of the objective function. Lastly, we show that a generic, black-box, hybrid classical-quantum solver could also be used to solve stack-power maximization problems proximal to industrial relevance, and capable to surpassing deterministic solvers prone to cycle-skipping. A custom-build workflow capable of solving larger problems with an even higher robustness and greater control of the user appears to be within reach in the very near future.

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A Quantum Approach for Tactical Capacity Management of Distributed Electricity Generation

Phillipson

Chiscop

2022

Innovations for Community Services

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Energy Storage Scheduling: A QUBO Formulation for Quantum Computing

Cited by 5 publications

References 30 publications

Searching for Optimisation

Searching for Optimisation

Quantum computer-assisted global optimization in geophysics illustrated with stack-power maximization for refraction residual statics estimation

A Quantum Approach for Tactical Capacity Management of Distributed Electricity Generation

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