Statistical Qubit Freezing Extending Physical Limit of Quantum Annealers

TL;DR

This paper introduces statistical qubit freezing (SQF), a novel algorithm that improves quantum annealing by selectively fixing qubits, significantly increasing spectral gaps and overcoming scalability limitations in quantum annealers.

Contribution

The paper presents a new algorithmic scheme, SQF, that enhances quantum annealing performance by selectively fixing deterministic qubits, extending the physical limits of quantum annealers.

Findings

SQF increases spectral gaps by up to 60% in quantum annealing.

SQF effectively overcomes fundamental scalability limitations.

Enhanced annealing performance demonstrated on D-Wave's quantum Ising machine.

Abstract

Adiabatic quantum annealers encounter scalability challenges due to exponentially fast diminishing energy gaps between ground and excited states with qubit-count increase. This introduces errors in identifying ground states compounded by a thermal noise. We propose a novel algorithmic scheme called statistical qubit freezing (SQF) that selectively fixes the state of statistically deterministic qubit in the annealing Hamiltonian model of the given problem. Applying freezing repeatedly, SQF significantly enhances the spectral gap between of an adiabatic process, as an example, by up to 60\% compared to traditional annealing methods in the standard D-Wave's quantum Ising machine solution, effectively overcoming the fundamental limitations.