Abstract
Quantum computing offers significant acceleration capabilities over its classical counterpart in various application domains. Consequently, there has been substantial focus on improving quantum computing capabilities. However, to date, the security implications of these quantum computing platforms have been largely overlooked. With the emergence of cloud-based quantum computing services, it is critical to investigate the extension of classical computer security threats to the realm of quantum computing.In this study, we investigated timing-based side-channel vulnerabilities within IBM's cloud-based quantum service. The proposed attack effectively subverts the confidentiality of the executed quantum algorithm, using a more realistic threat model compared to existing approaches. Our experimental results, conducted using IBM's quantum cloud service, demonstrate that with just 10 measurements, it is possible to identify the underlying quantum computer that executed the circuit. Moreover, when evaluated using the popular Grover circuit, we showcase the ability to leak the quantum oracle with a mere 500 measurements. These findings underline the pressing need to address timing-based vulnerabilities in quantum computing platforms and advocate for enhanced security measures to safeguard sensitive quantum algorithms and data.
| Original language | English |
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| Title of host publication | GLSVLSI 2025 - Proceedings of the Great Lakes Symposium on VLSI 2025 |
| Publisher | Association for Computing Machinery |
| Pages | 252-257 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798400714962 |
| DOIs | |
| State | Published - Jun 29 2025 |
| Event | 35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025 - New Orleans, United States Duration: Jun 30 2025 → Jul 2 2025 |
Publication series
| Name | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
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Conference
| Conference | 35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025 |
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| Country/Territory | United States |
| City | New Orleans |
| Period | 06/30/25 → 07/2/25 |
Funding
This research is partially supported by NSF grant #2413049