Privacy-Preserving Quantum Annealing for Quadratic Unconstrained Binary Optimization (QUBO) Problems

TL;DR

This paper presents a novel framework for privacy-preserving quantum annealing that obfuscates QUBO problem data, enabling secure outsourcing of quantum optimization tasks while maintaining solution accuracy.

Contribution

It introduces a new obfuscation method combining digit-wise splitting and matrix permutation to protect user privacy in quantum annealing.

Findings

The proposed method effectively conceals QUBO matrices.

High accuracy in reconstructing original solutions from obfuscated problems.

The approach is both efficient and theoretically sound.

Abstract

Quantum annealers offer a promising approach to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, which have a wide range of applications. However, when a user submits its QUBO problem to a third-party quantum annealer, the problem itself may disclose the user's private information to the quantum annealing service provider. To mitigate this risk, we introduce a privacy-preserving QUBO framework and propose a novel solution method. Our approach employs a combination of digit-wise splitting and matrix permutation to obfuscate the QUBO problem's model matrix $Q$, effectively concealing the matrix elements. In addition, based on the solution to the obfuscated version of the QUBO problem, we can reconstruct the solution to the original problem with high accuracy. Theoretical analysis and empirical tests confirm the efficacy and efficiency of our proposed technique, demonstrating its potential for preserving user privacy in quantum annealing services.