PRX Quantum· 2026Q1
Quantum Metropolis Sampling via Weak Measurement
- 2citations
- Q1SCImago
- 2026year
Short summary
A new quantum Gibbs sampler uses weak measurement to mimic the classical Metropolis algorithm, achieving the Gibbs state as an approximate fixed point without requiring complex techniques like quantum phase estimation rewinding.
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Key points
- Introduces a provably correct quantum Gibbs sampler inspired by the classical Metropolis algorithm.
- Employs weak measurement and a simplified boosted Quantum Phase Estimation (QPE).
- Avoids the Marriott-Watrous rewinding technique and the need for shift-invariant QPE.
- Achieves the Gibbs state as an approximate unique fixed point for quantum Hamiltonians.
AI-generated from the title and abstract; the full text is not read.
Abstract
Gibbs sampling is a crucial computational technique used in physics, statistics, and many other scientific fields. For classical Hamiltonians, the most commonly used Gibbs sampler is the Metropolis algorithm, known for having the Gibbs state as its unique fixed point. For quantum Hamiltonians, designing provably correct Gibbs samplers has been more challenging. [TOV+11] introduced a novel method that uses quantum phase estimation (QPE) and the Marriot-Watrous rewinding technique to mimic the classical Metropolis algorithm for quantum Hamiltonians. The analysis of their algorithm relies upon the use of a boosted and shift-invariant version of QPE which may not exist [CKBG23]. Recent efforts to design quantum Gibbs samplers take a very different approach and are based on simulating Davies generators [CKBG23,CKG23,RWW23,DLL24]. Currently, these are the only provably correct Gibbs samplers for quantum Hamiltonians. We revisit the inspiration for the Metropolis-style algorithm of [TOV+11] and incorporate weak measurement to design a conceptually simple and provably correct quantum Gibbs sampler, with the Gibbs state as its approximate unique fixed point. Our method uses a Boosted QPE which takes the median of multiple runs of QPE, but we do not require the shift-invariant property. In addition, we do not use the Marriott-Watrous rewinding technique which simplifies the algorithm significantly.
The authors' abstract, as published at the source. PRX Quantum, 2026 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science