Robust and Secure Hybrid Quantum-Classical Computation on Untrusted Cloud-Based Quantum Hardware

TL;DR

This paper addresses the challenge of ensuring trustworthy hybrid quantum-classical computations on untrusted cloud-based quantum hardware by modeling adversarial tampering and proposing strategies for reliable execution.

Contribution

It introduces methods to detect and mitigate hardware tampering in hybrid quantum algorithms, including adaptive splitting and re-initialization techniques.

Findings

Maximum performance degradation of 40% due to tampering

Achieving comparable performance requires 20X more iterations on untrusted hardware

Proposed methods improve reliability with up to 45% performance recovery

Abstract

Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy or untrustworthy or unreliable vendors, to begin offering quantum computers as hardware as a service at various price or performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable or trustworthy hardware. Less trusted vendors may tamper with the results and or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of approximately 40%. To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of approximately 30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of approximately 45%.