The paper adapts kill chain methodology from classical IT security to quantum machine learning, organizing published QML attacks into a five-stage lifecycle with attacker roles, capabilities, and defenses.
Equivalence checking of quantum circuits via intermediary matrix product operator
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abstract
As quantum computing advances, the complexity of quantum circuits is rapidly increasing, driving the need for robust methods to aid in their design. Equivalence checking plays a vital role in identifying errors that may arise during compilation and optimization of these circuits and is a critical step in quantum circuit verification. In this work, we introduce a novel method based on Matrix Product Operators (MPOs) for determining the equivalence of quantum circuits. Our approach contracts tensorized quantum gates from two circuits into an intermediary MPO, exploiting their reversibility to determine their equivalence or non-equivalence. Our results show that this method offers significant scalability improvements over existing methods, with polynomial scaling in circuit width and depth for the practical use cases we explore. We expect that this work sets the new standard for scalable equivalence checking of quantum circuits and will become a crucial tool for the validation of increasingly complex quantum systems.
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Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
The paper adapts kill chain methodology from classical IT security to quantum machine learning, organizing published QML attacks into a five-stage lifecycle with attacker roles, capabilities, and defenses.