Scholarlink

<p>This work explores adapting the classical Metropolis-Hastings algorithm into a quantum-enhanced framework to improve sampling efficiency in variational Monte Carlo. Utilizing variational Monte Carlo, and in particular the single-thread Monte Carlo method, we estimate the dominant eigenvalue of a symmetric 3 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> 3 matrix, serving as the toy model for the ground state energy of a three-level quantum system. We compare classical and hybrid quantum-classical implementations, where the latter employs the quantum enhanced Metropolis-Hastings algorithm. In a simulated environment, the hybrid version demonstrates faster convergence and improved estimator precision for this test case.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Variational Monte Carlo and the Quantum Enhanced Metropolis-Hastings Algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月17日

查看全文
  • Mishka Naicker,
  • Mervlyn Moodley

摘要

This work explores adapting the classical Metropolis-Hastings algorithm into a quantum-enhanced framework to improve sampling efficiency in variational Monte Carlo. Utilizing variational Monte Carlo, and in particular the single-thread Monte Carlo method, we estimate the dominant eigenvalue of a symmetric 3 \(\times \) × 3 matrix, serving as the toy model for the ground state energy of a three-level quantum system. We compare classical and hybrid quantum-classical implementations, where the latter employs the quantum enhanced Metropolis-Hastings algorithm. In a simulated environment, the hybrid version demonstrates faster convergence and improved estimator precision for this test case.