Convergence condition of simulated quantum annealing for closed and open systems

@article{Kimura2022ConvergenceCO,
  title={Convergence condition of simulated quantum annealing for closed and open systems},
  author={Yusuke Kimura and Hidetoshi Nishimori},
  journal={Physical Review A},
  year={2022}
}
Simulated quantum annealing is a generic classical protocol to simulate some aspects of quantum annealing and is sometimes regarded as a classical alternative to quantum annealing in finding the ground state of a classical Ising model. We derive a generic condition for simulated quantum annealing to converge to thermal equilibrium at a given, typically low, temperature. Both closed and open systems are treated. We rewrite the classical master equation for simulated quantum annealing into an… 

Convergence condition of simulated quantum annealing with a non-stoquastic catalyst

The Ising model with a transverse field and an antiferromagnetic transverse interaction is represented as a matrix in the computational basis with non-zero off-diagonal elements with both positive

References

SHOWING 1-10 OF 42 REFERENCES

Mathematical foundation of quantum annealing

Quantum annealing is a generic name of quantum algorithms that use quantum-mechanical fluctuations to search for the solution of an optimization problem. It shares the basic idea with quantum

Comparative study of the performance of quantum annealing and simulated annealing.

It is pointed out that the present mapping can be extended to accommodate explicit time dependence of temperature, which is used to justify the quantum-mechanical analysis of simulated annealing by Somma, Batista, and Ortiz.

Convergence of Quantum Annealing with Real-Time Schrodinger Dynamics(General)

Convergence conditions for quantum annealing are derived for optimization problems represented by the Ising model of a general form. Quantum fluctuations are introduced as a transverse field and/or

Simulated quantum annealing as a simulator of nonequilibrium quantum dynamics

Simulated quantum annealing based on the path-integral Monte Carlo is one of the most common tools to simulate quantum annealing on classical hardware. Nevertheless, it is in principle highly

Quantum and classical annealing in a continuous space with multiple local minima

Abstract The protocol of quantum annealing is applied to an optimization problem with a one-dimensional continuous degree of freedom, a variant of the problem proposed by Shinomoto and Kabashima. The

Quantum annealing in the transverse Ising model

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. Quantum fluctuations cause transitions between

Convergence theorems for quantum annealing

We prove several theorems to give sufficient conditions for convergence of quantum annealing, which is a protocol to solve generic optimization problems by quantum dynamics. In particular, the

Rigorous convergence condition for adiabatic and diabatic quantum annealing

. We derive a generic bound on the rate of decrease of transverse field for adiabatic and diabatic quantum annealing to converge to the ground state of a generic Ising model when quantum annealing is

Study of Optimization Problems by Quantum Annealing

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. The idea is tested by the two models, the

Quantum approach to classical statistical mechanics.

A new approach to study the thermodynamic properties of d-dimensional classical systems by reducing the problem to the computation of ground state properties of a d- dimensional quantum model, which allows the scope of standard optimization methods by unifying them under a general framework.