Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy

• URL: http://arxiv.org/abs/2510.23171v1
• Date: Mon, 27 Oct 2025 09:57:26 GMT
• Title: Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy
• Authors: Zakaria Boutakka, Nouhaila Innan, Muhammed Shafique, Mohamed Bennai, Z. Sakhi,
• Abstract summary: This work investigates the performance of the Variational Quantumsolver (VQE) in estimating the ground-state energy of the silicon atom.<n>Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD.<n>The main contribution of this work lies in a systematic exploration of how these configuration choices interact to influence VQE performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the performance of the Variational Quantum Eigensolver (VQE) in estimating the ground-state energy of the silicon atom, a relatively heavy element that poses significant computational complexity. Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD, combined with various optimizers such as gradient descent, SPSA, and ADAM. The main contribution of this work lies in a systematic methodological exploration of how these configuration choices interact to influence VQE performance, establishing a structured benchmark for selecting optimal settings in quantum chemical simulations. Key findings show that parameter initialization plays a decisive role in the algorithm's stability, and that the combination of a chemically inspired ansatz with adaptive optimization yields superior convergence and precision compared to conventional approaches.

Related papers

• Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion [8.808367903406316]
  Param-ADAPT-VQE is a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric.<n>We show that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs.
  arXiv  Detail & Related papers  (2026-02-04T06:26:58Z)
• Numerical Optimization Methods in the environment with Quantum Noise [0.0]
  This thesis focuses on the State-Averaged Orbital-d Variational Quantumsolver (SAOOVQE)<n>This hybrid quantum-classical algorithm provides a balanced description of multiple electronic states.<n>A comparative study against classical algorithms like the Broyden-Fletcher-Goldfarb-Shanno (BFGS) and Sequential Squares Programming (SLSQP)<n>Results show that orbital optimization is essential for correctly capturing the potential energy surface.
  arXiv  Detail & Related papers  (2025-09-15T19:00:27Z)
• TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing [60.996803677584424]
  TensoMeta-VQC is a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly.<n>Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• BenchQC: A Benchmarking Toolkit for Quantum Computation [0.4037357056611557]
  Variational Quantum Eigensolver (VQE) is a promising algorithm for quantum computing applications in chemistry and materials science.<n>This study benchmarks the performance of the VQE for calculating ground-state energies of aluminum clusters.
  arXiv  Detail & Related papers  (2025-02-13T18:51:08Z)
• 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)
• Benchmarking hybrid digitized-counterdiabatic quantum optimization [2.983864486954652]
  Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of near-term quantum computers. In this study, we analyze the convergence behavior and solution quality of various classicals when used in conjunction with the digitized-counterdiabatic approach.
  arXiv  Detail & Related papers  (2024-01-18T10:05:07Z)
• Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
  We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
  arXiv  Detail & Related papers  (2023-11-05T12:03:58Z)
• Benchmarking Metaheuristic-Integrated QAOA against Quantum Annealing [0.0]
  The study provides insights into the strengths and limitations of both Quantum Annealing and metaheuristic-integrated QAOA across different problem domains. The findings suggest that the hybrid approach can leverage classical optimization strategies to enhance the solution quality and convergence speed of QAOA.
  arXiv  Detail & Related papers  (2023-09-28T18:55:22Z)
• 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)
• Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
  We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves. We find both methods provide good estimates of the energy and ground state. gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
  arXiv  Detail & Related papers  (2020-11-02T19:52:04Z)
• 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.