Velocity Verlet-based optimization for variational quantum eigensolvers

• URL: http://arxiv.org/abs/2603.09862v1
• Date: Tue, 10 Mar 2026 16:24:34 GMT
• Title: Velocity Verlet-based optimization for variational quantum eigensolvers
• Abstract summary: Variational Quantum Eigensolver (VQE) is a key algorithm for near-term quantum computers.<n>We propose using the velocity Verlet algorithm, inspired by classical molecular dynamics, to address this challenge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Variational Quantum Eigensolver (VQE) is a key algorithm for near-term quantum computers, yet its performance is often limited by the classical optimization of circuit parameters. We propose using the velocity Verlet algorithm, inspired by classical molecular dynamics, to address this challenge. By introducing an inertial "velocity" term, our method efficiently explores complex energy landscapes. We compare its performance against standard optimizers on H$_2$ and LiH molecules. For H$_2$, our method achieves chemical accuracy with fewer quantum circuit evaluations than L-BFGS-B. For LiH, it attains the lowest final energy, demonstrating its potential for high-accuracy VQE simulations.

Related papers

• The Dual Role of Low-Weight Pauli Propagation: A Flawed Simulator but a Powerful Initializer for Variational Quantum Algorithms [0.951494089949975]
  Variational quantum algorithms (VQAs) rely on a classical pre-optimizer to tune a parameterized quantum circuit.<n>In this work, we investigate the low-weight Pauli propagation (LWPP) algorithm as a potential classical tool for simulating the VQA circuit.
  arXiv  Detail & Related papers  (2025-08-08T14:44:00Z)
• Generative flow-based warm start of the variational quantum eigensolver [0.09999629695552192]
  Flow-VQE is a generative framework leveraging conditional normalizing flows with parameterized quantum circuits.<n>By embedding a generative model into the VQE optimization loop through preference-based training, Flow-VQE enables quantum gradient-free optimization.
  arXiv  Detail & Related papers  (2025-07-02T14:00:37Z)
• 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)
• Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA [55.2480439325792]
  We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators.<n>This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
  arXiv  Detail & Related papers  (2025-03-03T17:15:23Z)
• Application of Langevin Dynamics to Advance the Quantum Natural Gradient Optimization Algorithm [43.21662368414268]
  Quantum Natural Gradient (QNG) algorithm for optimization of variational quantum circuits has been proposed recently.<n>Momentum-QNG is more effective to escape local minima and plateaus in the variational parameter space.<n>Best result is obtained for the Sherrington-Kirkpatrick model in the strong spin glass regime.
  arXiv  Detail & Related papers  (2024-09-03T15:21:16Z)
• 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 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)
• The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry [0.0]
  We study how various types of errors affect the variational quantum eigensolver (VQE) We find that the optimal way of running the hybrid classical-quantum optimization is to allow some noise in intermediate energy evaluations.
  arXiv  Detail & Related papers  (2021-11-09T06:24:52Z)
• 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)
• Improved accuracy on noisy devices by non-unitary Variational Quantum Eigensolver for chemistry applications [0.0]
  We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers. A non-unitary operator is combined with the original system Hamiltonian leading to a new variational problem with a simplified wavefunction Ansatz.
  arXiv  Detail & Related papers  (2021-01-22T20:17:37Z)
• 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)

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.