Extending and Defending Attacks on Reset Operations in Quantum Computers

May 16, 2024·
Jerry tan
Chuanqi Xu
Chuanqi Xu
,
Theodoros trochatos
,
Jakub szefer
· 0 min read
Abstract
The development of quantum computers has been advancing rapidly in recent years. As quantum computers become more widely accessible, potentially malicious users could try to execute their code on the machines to leak information from other users, interfere with or manipulate the results of other users, or reverse engineer the underlying quantum computer architecture and its intellectual property. Among different security threats, previous work has demonstrated information leakage across the reset operations, and it then proposed a secure reset operation could be an enabling technology that allows the sharing of a quantum computer among different users or different quantum programs of the same user. In this study, we delve deeper into the reset attack, aiming to augment its efficacy and capabilities and the countermeasure to protect from this attack. First, we propose a set of new extended reset attacks that could be more stealthy by hiding the intention of the attacker’s circuit. This work shows various concealing circuits and how attackers can retrieve information from the execution of a previous shot of a circuit, even if the concealing circuit is used between the reset operation (of the victim, after the shot of the circuit is executed) and the measurement (of the attacker). Second, based on the uncovered new possible attacks, this work proposes a set of heuristic checks that could be applied at transpile time to check for the existence of malicious circuits that try to steal information via the attack on the reset operation. Unlike run-time protection or added secure reset gates, this work proposes a complimentary, compile-time security solution to the attacks on reset operation.
Type
Publication
2024 International Symposium on Secure and Private Execution Environment Design (SEED). Best Paper
Authors
Chuanqi Xu
Authors
Chuanqi Xu (he/him)
Research Scientist
I am a Research Scientist at Meta working on Meta’s Generative Ads Recommendation Model (GEM). My focus is on designing and implementing novel transfer learning paradigms to amplify the impact of foundation models within production environments. Additionally, I am working on optimizing the efficiency and performance of GEM’s ecosystem. Previously, I earned my PhD at Yale University. My research there sat at the intersection of quantum computing and security, where I designed novel attacks and defenses for quantum computers. Before this, I completed my undergraduate studies at University of Science and Technology of China (USTC), where I studied and researched on theoretical and computational condensed matter physics.