Quantum Zeno Monte Carlo for computing observables

The recent development of logical quantum processors marks a pivotal transition from the noisy intermediate-scale quantum (NISQ) era to the fault-tolerant quantum computing (FTQC) era. These devices have the potential to address classically challenging problems with polynomial computational time using quantum properties. However, they remain susceptible to noise, necessitating noise resilient algorithms. We introduce Quantum Zeno Monte Carlo (QZMC), a classical-quantum hybrid algorithm that demonstrates resilience to device noise and Trotter errors while showing polynomial computational cost for a gapped system. QZMC computes static and dynamic properties without requiring initial state overlap or variational parameters, offering reduced quantum circuit depth.

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Publication Details

Journal
npj Quantum Information
Published
2025-03-12
DOI
https://doi.org/10.1038/s41534-025-01002-3
Citations
1
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
2.48
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article

Quantum Zeno Monte Carlo for computing observables

Sangkook Choi, Mancheon Han, Hyowon Park
1 citations
npj Quantum Information
Quantum Computing Algorithms and Architecture
2.48
article

Quantum Zeno Monte Carlo for computing observables

Sangkook Choi, Mancheon Han, Hyowon Park
article en
1 citations

Abstract

The recent development of logical quantum processors marks a pivotal transition from the noisy intermediate-scale quantum (NISQ) era to the fault-tolerant quantum computing (FTQC) era. These devices have the potential to address classically challenging problems with polynomial computational time using quantum properties. However, they remain susceptible to noise, necessitating noise resilient algorithms. We introduce Quantum Zeno Monte Carlo (QZMC), a classical-quantum hybrid algorithm that demonstrates resilience to device noise and Trotter errors while showing polynomial computational cost for a gapped system. QZMC computes static and dynamic properties without requiring initial state overlap or variational parameters, offering reduced quantum circuit depth.

npj Quantum InformationVol. 11(1)
Korea Institute for Advanced Study (KR), Argonne National Laboratory (US), University of Illinois Chicago (US)
Openalex Percentile: Top 11%
Quantum Computing Algorithms and Architecture
2.48
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Quantum Zeno Monte Carlo for computing observables — Sangkook Choi, Mancheon Han, et al. · npj Quantum Information (2025) | TGRS Research Map | TGRS