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Exploration of Timing and Higher-Energy Attacks on Quantum Random Access Memory

Y.H. Tan, Chuanqi Xu, Jakub M. Szefer

Published Oct 27, 2024• 1 citation
Publisher

Abstract

This work presents the first evaluation of timing and higher-energy attacks on Quantum Random Access Memory (QRAM) circuits. By leveraging quantum principles, QRAM can efficiently store and manipulate both quantum and classical data, leading to potential significant speedups in a variety of quantum algorithms. However, as demonstrated in this work, when used in remote cloud-based quantum computers QRAM is vulnerable to different security attacks. The work demonstrates side-channel attacks, e.g., the timing attacks, as well as fault-injection-like attacks, e.g., the higher-energy attacks. This work evaluates the attacks on QRAM and different circuits that use QRAM. The work also proposes a set of defenses.

DOI
10.1145/3676536.3689916

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Abstract Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with O(log⁡N){{{\mathcal{O}}}}(\log N)O(logN) O ( log N ) circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~107 in 1D, ~1015 to ~1020 in 2D, and ~1024 in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science.
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