As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud-based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive information about concurrently running quantum circuits. We systematically analyze simulator behavior using the QASMBench benchmark suite, profiling timing and memory characteristics across various circuit executions. Our experimental results show that timing profiles exhibit circuit-dependent patterns that can be effectively classified using pattern recognition techniques, enabling the adversary to infer circuit identities and compromise user confidentiality. We were able to achieve 88 % to 99.9 % identification rate of quantum circuits based on different datasets. This work highlights previously unexplored security risks in quantum simulation environments and calls for stronger isolation mechanisms to protect user workloads.
As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud-based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive information about concurrently running quantum circuits. We systematically analyze simulator behavior using the QASMBench benchmark suite, profiling timing and memory characteristics across various circuit executions. Our experimental results show that timing profiles exhibit circuit-dependent patterns that can be effectively classified using pattern recognition techniques, enabling the adversary to infer circuit identities and compromise user confidentiality. We were able to achieve 88 % to 99.9 % identification rate of quantum circuits based on different datasets. This work highlights previously unexplored security risks in quantum simulation environments and calls for stronger isolation mechanisms to protect user workloads.
As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud-based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive information about concurrently running quantum circuits. We systematically analyze simulator behavior using the QASMBench benchmark suite, profiling timing and memory characteristics across various circuit executions. Our experimental results show that timing profiles exhibit circuit-dependent patterns that can be effectively classified using pattern recognition techniques, enabling the adversary to infer circuit identities and compromise user confidentiality. We were able to achieve 88% to 99.9% identification rate of quantum circuits based on different datasets. This work highlights previously unexplored security risks in quantum simulation environments and calls for stronger isolation mechanisms to protect user workloads
Our aim in this paper is to present experimental evidence of a side-channel attack on superconducting cloud-based quantum computers, demonstrating that a quantum circuit can be analyzed indirectly using data gathered before and after its execution on a quantum computer. We hope that this can motivate further research into hardware and system stability of quantum computers as well as into more-nuanced systems-level attacks. Although much work has been done on establishing error correcting codes to address issues of decoherence, and schemes can be utilized to leverage quantum effects for the sake of intrusion detection over quantum communication channels, there is a lack of literature in the field regarding side-channel attacks on transpiled quantum circuits, particularly those which leverage quantum effects to gather information about quantum circuits running in the cloud. We explored issues of decoherence by examining quantum circuit behavior both before and after a specified circuit was executed in order to ascertain decalibration rates and potential sources of decoherence: our data clearly shows that information on an interim circuit can be acquired in this manner. Using convenient data classification techniques and various circuit diagrams consisting of one quantum gate and several measurements, we were able to distinguish between these various circuits when executed at position n in the interim phase by training our classifier on data from probing circuits executed at queue positions n-1 and n+1. This shows that there is sufficient information residing in the data before and after a circuit is executed on a cloud-based superconducting quantum computer to allow an attacker to ascertain information about an otherwise unknown third-party circuit and shows potential to form the basis of a side-channel attack in cases where queue positions can be examined to target a third party’s transpiled circuit during experimentation. This side channel attack merits further examination and extrapolation to identify more-complex circuits, and deeper statistical consideration to determine potential sources of information leakage and mitigation strategies.
With the rapidly growing interest in quantum computing also grows the importance of securing these quantum computers from various physical attacks. Constantly increasing qubit counts and improvements to the fidelity of the quantum computers hold great promise for the ability of these computers to run novel algorithms with highly sensitive intellectual property. However, in today's cloud-based quantum computer setting, users lack physical control over the computers. Physical attacks, such as those perpetrated by malicious insiders in data centers, could be used to extract sensitive information about the circuits being executed on these computers. Indeed, this work shows for the first time that power-based side-channel attacks could be deployed against quantum computers. Such attacks can be used to recover information about the control pulses sent to these computers. By analyzing these control pulses, attackers can reverse-engineer the equivalent gate-level description of the circuits, and possibly even the secret algorithms being run. This work introduces five new types of attacks, and evaluates them using control pulse information available from cloud-based quantum computers. This work demonstrates how and what circuits could be recovered, and then in turn how to defend from the newly demonstrated side-channel attacks on quantum~computers.
The rapidly growing interest in quantum computing also increases the importance of securing these computers from various physical attacks. Constantly increasing qubit counts and improvements to the fidelity of the quantum computers hold great promise for the ability of these computers to run novel algorithms with highly sensitive intellectual property. However, in today's cloud-based quantum computer setting, users lack physical control over the computers. Physical attacks, such as those perpetrated by malicious insiders in data centers, could be used to extract sensitive information about the circuits being executed on these computers. This work shows the first exploration and study of power-based side-channel attacks in quantum computers. The explored attacks could be used to recover information about the control pulses sent to these computers. By analyzing these control pulses, attackers can reverse-engineer the equivalent gate-level description of the circuits, and the algorithms being run, or data hard-coded into the circuits. This work introduces five new types of attacks, and evaluates them using control pulse information available from cloud-based quantum computers. This work demonstrates how and what circuits could be recovered, and then in turn how to defend from the newly demonstrated side-channel attacks on quantum computing systems.
Abstract With the growing deployment of quantum computing, side-channel attacks have emerged as one of the most serious security threats for cloud quantum service providers. This study investigates whether it is possible to infer the execution order and start times of quantum gates—and ultimately reconstruct the victim circuit—solely from measurements of the power consumption of quantum controllers. While most previous research has focused on reconstructing quantum circuits composed of standard basis gates, the possibility of reconstructing users’ custom quantum circuits from the power leakage of quantum controllers has received little attention. Our approach is evaluated under two observation models: per-channel power and total power. In the per-channel setting, the power trace of each channel is analyzed separately and compared with simulated pulse templates for different gates, in order to identify the most likely gate type and its start time. In this setting, template-based power comparison accurately determines both the gate and its onset time, and simple decision rules reduce misclassification between similar gates. In the total-power setting, an optimization model is employed to recover the logical ordering of the circuit with good accuracy, without requiring access to individual channel traces. The results show that even with a single measurement, circuit reconstruction is feasible provided that the pulse shapes and the device topology are known.
As quantum computing rapidly advances, its nearterm applications are becoming increasingly evident.However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models.In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial.The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential.In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services.In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model.The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim.Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm.When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7% accuracy.