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Sampling diverse near-optimal solutions via algorithmic quantum annealing.

Masoud Mohseni^1,2, Marek M Rams³, Sergei V Isakov⁴

¹Google Quantum AI, Venice, California 90291, USA.

Physical Review. E

|January 20, 2024

Summary

This summary is machine-generated.

Researchers developed a new diversity measure to quantify solutions for complex optimization problems. This metric, time-to-diversity (TTD), enhances benchmarking and reveals advantages of inhomogeneous quantum annealing schedules.

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Published on: August 13, 2012

Area of Science:

Computational physics
Quantum computing
Operations research

Background:

Hard optimization problems require diverse, high-quality solutions.
Stochastic solvers often suffer from mode collapse, limiting robustness.
A universal metric to quantify solver performance deficiencies is lacking.

Purpose of the Study:

Introduce a novel diversity measure for NP-hard optimization problems.
Develop a time-to-diversity (TTD) metric for benchmarking solvers.
Compare sampling power of quantum annealing strategies.

Main Methods:

Introduced a new diversity measure for approximate solutions.
Defined time-to-diversity (TTD) as a performance benchmark.
Utilized path-integral Monte Carlo simulations and tensor network contractions.

Main Results:

Inhomogeneous quantum annealing schedules improve time-to-solution (TTS) and TTD.
Non-equilibrium driving of quantum fluctuations enhances solution diversity by up to 40%.
Reduced fraction of hard-to-sample instances by over 25%.

Conclusions:

The new diversity measure and TTD metric offer effective benchmarking.
Quantum annealing with controlled topological defects provides an advantage.
Algorithmic quantum phase transitions enhance solution diversity for hard instances.