Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing

• URL: http://arxiv.org/abs/2310.17426v1
• Date: Thu, 26 Oct 2023 14:31:21 GMT
• Title: Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing
• Authors: Suryansh Upadhyay and Swaroop Ghosh
• Abstract summary: Multi-tenant computing (MTC) has emerged as a potential solution for efficient hardware utilization. This paper proposes one such threat for MTC in superconducting quantum hardware i.e., adversarial SWAP gate injection in victims program during compilation for MTC. We report a max of approximately 55 percent and a median increase of approximately 25 percent in SWAP overhead.
• Score: 2.77390041716769
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Quantum computing (QC) holds tremendous promise in revolutionizing problem-solving across various domains. It has been suggested in literature that 50+ qubits are sufficient to achieve quantum advantage (i.e., to surpass supercomputers in solving certain class of optimization problems).The hardware size of existing Noisy Intermediate-Scale Quantum (NISQ) computers have been ever increasing over the years. Therefore, Multi-tenant computing (MTC) has emerged as a potential solution for efficient hardware utilization, enabling shared resource access among multiple quantum programs. However, MTC can also bring new security concerns. This paper proposes one such threat for MTC in superconducting quantum hardware i.e., adversarial SWAP gate injection in victims program during compilation for MTC. We present a representative scheduler designed for optimal resource allocation. To demonstrate the impact of this attack model, we conduct a detailed case study using a sample scheduler. Exhaustive experiments on circuits with varying depths and qubits offer valuable insights into the repercussions of these attacks. We report a max of approximately 55 percent and a median increase of approximately 25 percent in SWAP overhead. As a countermeasure, we also propose a sample machine learning model for detecting any abnormal user behavior and priority adjustment.

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