116 research outputs found
Trojan Taxonomy in Quantum Computing
Quantum computing introduces unfamiliar security vulnerabilities demanding
customized threat models. Hardware and software Trojans pose serious concerns
needing rethinking from classical paradigms. This paper develops the first
structured taxonomy of Trojans tailored to quantum information systems. We
enumerate potential attack vectors across the quantum stack from hardware to
software layers. A categorization of quantum Trojan types and payloads is
outlined ranging from reliability degradation, functionality corruption,
backdoors, and denial-of-service. Adversarial motivations behind quantum
Trojans are analyzed. By consolidating diverse threats into a unified
perspective, this quantum Trojan taxonomy provides insights guiding threat
modeling, risk analysis, detection mechanisms, and security best practices
customized for this novel computing paradigm.Comment: 6 pages, 2 figure
Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing
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.Comment: 7 pages, VLSI
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