Benchmarking Hybrid Quantum-Classical Algorithms for Power Grid Optimization Problems

• URL: http://arxiv.org/abs/2607.15543v1
• Date: Fri, 17 Jul 2026 01:25:12 GMT
• Title: Benchmarking Hybrid Quantum-Classical Algorithms for Power Grid Optimization Problems
• Abstract summary: Alternating Current Power Flow Commitment (AC-OPFUC) is a difficult mixed-integer nonlinear optimization problem.<n>We consider whether hybrid quantum-classical algorithms can improve the solution-period AC-OPF-UC.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alternating Current Optimal Power Flow Unit Commitment (AC-OPF-UC) is a difficult mixed-integer nonlinear optimization problem that combines binary generator commitment decisions with nonconvex continuous AC power-flow constraints. In this work, we investigate whether hybrid quantum-classical variational algorithms can improve the solution of single-period AC-OPF-UC relative to classical approaches. To the best of our knowledge, this is the first study to directly evaluate quantum or hybrid quantum-classical algorithms for the full AC-OPF-UC problem. We consider two candidate algorithms for improving AC-OPF-UC solution quality relative to purely classical methods on ideal quantum hardware. The first applies QAOA directly to a fully discretized formulation of the problem, with equality and inequality constraints incorporated through penalty terms and slack variables. Although conceptually straightforward, this approach requires a prohibitively large number of qubits even for small instances. The second, qubit-efficient approach encodes only the binary generator status variables on a quantum computer, while optimizing the continuous power-flow variables classically for each sampled bitstring. We benchmark this method on randomly generated AC-OPF-UC instances with 5 to 13 generators and compare it against SCIP, SMAC, and uniform random sampling. Our simulations show that the qubit-efficient hybrid method does not outperform uniform sampling. These results suggest that in order to establish potential advantage of the variational hybrid strategy considered here over the best classical algorithms, if any, much larger system sizes (25+ generators) need to be tested, which is beyond our computational capacity. Alternatively, different approaches, such as quantum versions of branch-and-bound methods, may be more promising.

Related papers

• Enhancing Hybrid Methods in Parameterized Quantum Circuit Optimization [0.0]
  Quantum circuits (PQCs) play an essential role in the application of variational quantum algorithms (VQAs) in noisy quantum devices.<n>We introduce two new hybrid algorithms that are more robust and scalable.<n>We find that they are feasible for NISQ devices with different noise profiles.
  arXiv  Detail & Related papers  (2025-10-09T12:24:57Z)
• Quantum Approximate Optimization Algorithm for MIMO with Quantized b-bit Beamforming [47.98440449939344]
  Multiple-input multiple-output (MIMO) is critical for 6G communication, offering improved spectral efficiency and reliability.<n>This paper explores the use of the Quantum Approximate Optimization Algorithm (QAOA) and alternating optimization to address the problem of b-bit quantized phase shifters both at the transmitter and the receiver.<n>We demonstrate that the structure of this quantized beamforming problem aligns naturally with hybrid-classical methods like QAOA, as the phase shifts used in beamforming can be directly mapped to rotation gates in a quantum circuit.
  arXiv  Detail & Related papers  (2025-10-07T17:53:02Z)
• Learning Feasible Quantum States for Quadratic Constrained Binary Optimization Problems [41.23247424467223]
  We develop a variational approach that creates an equal superposition of quantum states that satisfy constraints in a QCBO.<n>The resulting equal superposition can be used as an initial state for quantum algorithms that solve QUBOs/QCBOs.
  arXiv  Detail & Related papers  (2025-08-04T16:44:53Z)
• Improving Variational Quantum Circuit Optimization via Hybrid Algorithms and Random Axis Initialization [0.0]
  Variational quantum circuits (VQCs) are an essential tool in applying noisy intermediate-scale quantum computers to practical problems.<n>We enhance the performance of the Rotosolve method, a gradient-free optimization algorithm specifically designed for VQCs.<n>We construct hybrid algorithms that benefit from the rapid initial convergence of Rotosolve while leveraging the superior expressivity of FQS.
  arXiv  Detail & Related papers  (2025-03-26T17:10:19Z)
• A hybrid classical-quantum approach to highly constrained Unit Commitment problems [0.0]
  The unit commitment (UC) problem stands as a critical optimization challenge in the electrical power industry.<n>This paper introduces a novel hybrid quantum-classical algorithm designed to efficiently solve the UC problem in time.
  arXiv  Detail & Related papers  (2024-12-15T21:21:36Z)
• Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization [41.94295877935867]
  We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2023-08-29T17:53:08Z)
• Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes [57.70237375696411]
  The paper proposes a fast data-driven setup that uses the sparse and hybrid Gaussian processes (GP) framework to model the power flow equations with input uncertainty. We advocate the efficiency of the proposed approach by a numerical study over multiple IEEE test cases showing up to two times faster and more accurate solutions.
  arXiv  Detail & Related papers  (2022-08-30T09:27:59Z)
• Twisted hybrid algorithms for combinatorial optimization [68.8204255655161]
  Proposed hybrid algorithms encode a cost function into a problem Hamiltonian and optimize its energy by varying over a set of states with low circuit complexity. We show that for levels $p=2,ldots, 6$, the level $p$ can be reduced by one while roughly maintaining the expected approximation ratio.
  arXiv  Detail & Related papers  (2022-03-01T19:47:16Z)
• Robust resource-efficient quantum variational ansatz through evolutionary algorithm [0.46180371154032895]
  Vari quantum algorithms (VQAsational) are promising methods to demonstrate quantum advantage on near-term devices. We show that a fixed VQA circuit design, such as the widely-used hardware efficient ansatz, is not necessarily robust against imperfections. We propose a genome-length-adjustable evolutionary algorithm to design a robust VQA circuit that is optimized over variations of both circuit ansatz and gate parameters.
  arXiv  Detail & Related papers  (2022-02-28T12:14:11Z)
• Adapting Quantum Approximation Optimization Algorithm (QAOA) for Unit Commitment [2.8060379263058794]
  We formulate and apply a hybrid quantum-classical algorithm to a power system optimization problem called Unit Commitment. Our algorithm extends the Quantum Approximation Optimization Algorithm (QAOA) with a classical minimizer in order to support mixed binary optimization. Our results indicate that classical solvers are effective for our simulated Unit Commitment instances with fewer than 400 power generation units.
  arXiv  Detail & Related papers  (2021-10-25T03:37:34Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.