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  3. Real-time Sign-problem-suppressed Quantum Monte Carlo Algorithm For Noisy Quantum Circuit Simulations.
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  3. Real-time Sign-problem-suppressed Quantum Monte Carlo Algorithm For Noisy Quantum Circuit Simulations.

Related Experiment Video

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Real-Time Sign-Problem-Suppressed Quantum Monte Carlo Algorithm for Noisy Quantum Circuit Simulations.

Tong Shen1,2,3, Daniel A Lidar1,2,3,4,5

  • 1University of Southern California, Department of Electrical and Computer Engineering, Los Angeles, California 90089, USA.

Physical Review Letters
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a real-time quantum Monte Carlo algorithm to simulate open quantum systems. This method efficiently handles noise and improves classical simulations for quantum computing and annealing.

Related Experiment Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Quantum Physics
  • Computational Science

Background:

  • Simulating open quantum systems is crucial for understanding quantum computation and annealing.
  • Existing methods like quantum trajectory methods struggle with noise and non-Markovian dynamics.

Purpose of the Study:

  • To develop a novel real-time quantum Monte Carlo algorithm for simulating open quantum systems.
  • To address the sign problem and improve efficiency in classical simulations of quantum dynamics.

Main Methods:

  • The algorithm uses stochastic compression and evolution of the density matrix.
  • Population dynamics are employed to suppress the sign problem in Markovian and non-Markovian systems.
  • The method is applied to various noisy quantum circuits.

Main Results:

  • Demonstrated significant speedups and improved scaling compared to quantum trajectory methods.
  • Achieved convergence to exact solutions, even in challenging non-Markovian regimes.
  • Successfully simulated a broad class of noisy-circuit Liouvillians.

Conclusions:

  • The new quantum Monte Carlo algorithm enhances the efficiency of classical simulations for gate-based quantum computing and quantum annealing.
  • This approach offers a robust method for simulating general open quantum system dynamics.
  • The algorithm's ability to handle non-Markovian dynamics represents a significant advancement.