A Quantum Approach for Tactical Capacity Management of Distributed Electricity Generation

Phillipson, Frank; Chiscop, Irina

doi:10.1007/978-3-031-06668-9_23

Cited by 4 publications

(2 citation statements)

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“…To assess performance, a synthetic dataset was created. The use of a synthetic dataset is necessary in quantum computing benchmarking to ensure the validity of the algorithms used [40], [41], [42], [43], [44].…”

Section: B Quantum Annealing On a Synthetic Datasetmentioning

confidence: 99%

Power Network Optimization: A Quantum Approach

Colucci,

Linde,

Phillipson

2023

IEEE Access

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Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. In this paper, we show how to optimize such network costs using a quantum annealing approach. First, we define the Quadratic Unconstrained Binary Optimization (QUBO) problem for network partitioning. To achieve this, we introduce a method to translate inequality constraints with real-valued coefficients into approximate penalty functions. Next, we test the implementation on purely quantum and quantum-classical hybrid architectures. We then solve the problem using the D-Wave hybrid Constrained Quadratic Model (CQM) solver, the D-Wave hybrid Binary Quadratic Model (BQM) solver, as well as classical solvers available on Azure Quantum Cloud. Finally, we find that the value of the objective function obtained with the quantum-classical hybrid solvers is always lower compared to the classical approaches across a range of different problem sizes. This demonstrates that the quantum-classical hybrid methods outperform the classical methods in terms of solution quality.

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Section: B Quantum Annealing On a Synthetic Datasetmentioning

confidence: 99%

Power Network Optimization: A Quantum Approach

Colucci,

Linde,

Phillipson

2023

IEEE Access

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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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