Stochastic Pauli-path simulator for large-scale quantum optimization

• URL: http://arxiv.org/abs/2607.17804v1
• Date: Mon, 20 Jul 2026 10:43:44 GMT
• Title: Stochastic Pauli-path simulator for large-scale quantum optimization
• Abstract summary: Pauli-path simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime.<n>Existing approaches either lack native support for gradient-based optimization or suffer from severe gradient bias.<n>Here we propose the Pauli-path simulator (SPPS), a computational framework for large-scale quantum optimization.
• Score: 57.179828941706006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pauli-based simulators offer a promising route to large-scale classical simulation of quantum circuits in the low-magic regime. Yet their applicability remains largely limited to forward simulation, making them inadequate for optimization-driven quantum tasks such as variational state preparation and parameter initialization. Existing approaches either lack native support for gradient-based optimization or suffer from severe gradient bias. Here we propose the stochastic Pauli-path simulator (SPPS), a computational framework for large-scale quantum optimization that enables unbiased stochastic gradient estimation via Pauli-path sampling across optimization iterations. Our theoretical analysis shows that the proposed simulator yields unbiased gradient estimates and admits provable convergence guarantees. We systematically evaluate our proposal, including quantum eigensolver benchmarks with up to 100 qubits and quantum neural network benchmarks with up to 40 qubits. Across these tasks, SPPS faithfully tracks optimization dynamics, converges within minutes, and broadens the role of Pauli-based simulation from forward estimation to large-scale quantum optimization.

Related papers

• Simulation of Lindbladian dynamics via adaptive variational quantum trajectory compression [6.296586170753371]
  We propose a resource-efficient algorithm for simulating Lindbladian dynamics on NISQ devices.<n>For open quantum systems with Pauli dissipations, we first derive a compact and stable mixed-unitary adjoint channel.<n>To further reduce the circuit depth required for implementing the sampled trajectories, we introduce an adaptive variational quantum trajectory compression framework.
  arXiv  Detail & Related papers  (2026-07-10T02:48:39Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Symbolic Pauli Propagation for Gradient-Enabled Pre-Training of Quantum Circuits [0.0]
  Quantum Machine Learning models typically require expensive on-chip training procedures and often lack efficient gradient estimation methods.<n>By employing Pauli propagation, it is possible to derive a symbolic representation of observables as analytic functions of a circuit's parameters.<n>The proposed approach is demonstrated on the Variational Quantum Eigensolver for obtaining the ground state of a spin model.
  arXiv  Detail & Related papers  (2025-12-18T15:44:07Z)
• A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation [0.0]
  We introduce a framework for evaluating the performance of quantum simulation algorithms.<n>Our framework provides end-to-end demonstrations of algorithmic optimizations.<n>We show a 27.1% error reduction through Pauli grouping methods, with an additional 37.6% improvement from the optimized shot distribution strategy.
  arXiv  Detail & Related papers  (2025-04-14T02:23:01Z)
• 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)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• 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)
• FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits [105.54048699217668]
  We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters. We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
  arXiv  Detail & Related papers  (2021-03-15T17:38:33Z)
• 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)
• Quantum-Enhanced Simulation-Based Optimization [0.8057006406834467]
  Simulation-based optimization seeks to optimize an objective function that is computationally expensive to evaluate exactly. Quantum Amplitude Estimation (QAE) can achieve a quadratic speed-up over classical Monte Carlo simulation.
  arXiv  Detail & Related papers  (2020-05-21T17:02:59Z)

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.