Quantum Optimization for Optimal Power Flow: CVQLS-Augmented Interior Point Method

• URL: http://arxiv.org/abs/2412.14095v2
• Date: Wed, 27 Aug 2025 21:43:13 GMT
• Title: Quantum Optimization for Optimal Power Flow: CVQLS-Augmented Interior Point Method
• Authors: Farshad Amani, Amin Kargarian,
• Abstract summary: This paper presents a quantum-enhanced optimization approach for solving optimal power flow (OPF)<n>We integrate the interior point method (IPM) with a coherent variational quantum linear solver (CVQLS)<n>We evaluate our approaches on multiple systems to show their effectiveness in providing reliable OPF solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a quantum-enhanced optimization approach for solving optimal power flow (OPF) by integrating the interior point method (IPM) with a coherent variational quantum linear solver (CVQLS). The objective is to explore the applicability of quantum computing to power systems optimization and address the associated challenges. A comparative analysis of state-of-the-art quantum linear solvers - Harrow-Hassidim-Lloyd (HHL), variational quantum linear solver (VQLS), and CVQLS - revealed that CVQLS is most suitable for OPF due to its stability with ill-conditioned matrices, such as the Hessian in IPM. To ensure high-quality solutions, prevent suboptimal convergence, and avoid the barren plateau problem, we propose a quantum circuit parameter initialization technique along with a method to guide the IPM along the central path. Moreover, we design an ansatz tailored for OPF, optimizing the expressibility and trainability of the quantum circuit to ensure efficient convergence and robustness in solving quantum OPF. Various optimizers are also tested for quantum circuit parameter optimization to select the best one. We evaluate our approaches on multiple systems to show their effectiveness in providing reliable OPF solutions. Simulations for the 2-bus system are conducted on a commercial IBMQ quantum device, while simulations for the other larger cases are performed using the IBM quantum simulator. While promising, CVQLS is limited by current quantum hardware, especially for larger systems. We use a quantum noise simulator to test scalability.

Related papers

• 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)
• Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization [0.0]
  Quantum circuits (PQCs) are key components of variational quantum algorithms (VQAs)<n>PQCs enable flexible encoding of quantum information through quantum gates and have been successfully applied across domains such as quantum chemistry, optimization, and quantum machine learning.<n>Despite their potential, PQC performance on NISQ hardware is hindered by noise, decoherence, and the presence of barren plateaus, which can impede gradient-based optimization.
  arXiv  Detail & Related papers  (2025-07-10T13:22:31Z)
• RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
  Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
  arXiv  Detail & Related papers  (2025-06-04T08:30:35Z)
• A Comparative Study of Quantum Optimization Techniques for Solving Combinatorial Optimization Benchmark Problems [4.266376725904727]
  We introduce a comprehensive benchmarking framework designed to evaluate quantum optimization techniques against well-established NP-hard problems. Our framework focuses on key problem classes, including the Multi-Dimensional Knapsack Problem (MDKP), Maximum Independent Set (MIS), Quadratic Assignment Problem (QAP), and Market Share Problem (MSP)
  arXiv  Detail & Related papers  (2025-03-15T13:02:22Z)
• ML-QLS: Multilevel Quantum Layout Synthesis [6.706813469929441]
  We present ML-QLS, the first multilevel quantum layout tool with a scalable refinement operation integrated with novel cost functions and clustering strategies.<n>Our experimental results demonstrate that ML-QLS can scale up to problems involving hundreds of qubits and achieve a remarkable 52% performance improvement over leading QLS tools for large circuits.
  arXiv  Detail & Related papers  (2024-05-28T17:10:20Z)
• Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms [0.0]
  We adapt quantum algorithms to tackle robust optimization problems. We present two innovative methods for obtaining robust optimal solutions. Theses are applied on two use cases within the energy sector.
  arXiv  Detail & Related papers  (2024-04-25T17:32:55Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Surrogate optimization of variational quantum circuits [1.0546736060336612]
  Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for VQE.
  arXiv  Detail & Related papers  (2024-04-03T18:00:00Z)
• A joint optimization approach of parameterized quantum circuits with a tensor network [0.0]
  Current intermediate-scale quantum (NISQ) devices remain limited in their capabilities. We propose the use of parameterized Networks (TNs) to attempt an improved performance of the Variational Quantum Eigensolver (VQE) algorithm.
  arXiv  Detail & Related papers  (2024-02-19T12:53:52Z)
• Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems [65.268245109828]
  We compare the performance of classicals across a series of partially-randomized tasks. We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
  arXiv  Detail & Related papers  (2023-10-14T02:13:26Z)
• Quantum approximate optimization via learning-based adaptive optimization [5.399532145408153]
  Quantum approximate optimization algorithm (QAOA) is designed to solve objective optimization problems. Our results demonstrate that the algorithm greatly outperforms conventional approximations in terms of speed, accuracy, efficiency and stability. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.
  arXiv  Detail & Related papers  (2023-03-27T02:14:56Z)
• Multiobjective variational quantum optimization for constrained problems: an application to Cash Management [45.82374977939355]
  We introduce a new method for solving optimization problems with challenging constraints using variational quantum algorithms. We test our proposal on a real-world problem with great relevance in finance: the Cash Management problem. Our empirical results show a significant improvement in terms of the cost of the achieved solutions, but especially in the avoidance of local minima.
  arXiv  Detail & Related papers  (2023-02-08T17:09:20Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Constrained Optimization via Quantum Zeno Dynamics [23.391640416533455]
  We introduce a technique that uses quantum Zeno dynamics to solve optimization problems with multiple arbitrary constraints, including inequalities. We show that the dynamics of quantum optimization can be efficiently restricted to the in-constraint subspace on a fault-tolerant quantum computer.
  arXiv  Detail & Related papers  (2022-09-29T18:00:40Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Benchmarking of Different Optimizers in the Variational Quantum Algorithms for Applications in Quantum Chemistry [0.0]
  Classical yardsticks play a crucial role in determining the accuracy and convergence of variational quantum algorithms. We consider a few popular yardsticks and assess their performance in variational quantum algorithms for applications in quantum chemistry.
  arXiv  Detail & Related papers  (2022-08-22T13:02:00Z)
• Efficient Use of Quantum Linear System Algorithms in Interior Point Methods for Linear Optimization [0.0]
  We develop an Inexact Infeasible Quantum Interior Point Method to solve linear optimization problems. We also discuss how can we get an exact solution by Iterative Refinement without excessive time of quantum solvers.
  arXiv  Detail & Related papers  (2022-05-02T21:30:56Z)
• Identification of topological phases using classically-optimized variational quantum eigensolver [0.6181093777643575]
  Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for quantum computers. We propose classically-optimized VQE (co-VQE), where the whole process of the optimization is efficiently conducted on a classical computer. In co-VQE, we only use quantum computers to measure nonlocal quantities after the parameters are optimized.
  arXiv  Detail & Related papers  (2022-02-07T02:26:58Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• 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.