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IEEE International Conference on Quantum Computing and Engineering (QCE)

Quantum Circuit Cutting: A Security Methodology

George Typaldos, Theodoros Trochatos, Jakub M. Szefer

Published Aug 30, 2025• 0 citations
Publisher

Abstract

Quantum computing research has experienced tremendous advancements in recent years, and real-world applications that utilize quantum hardware are continuously emerging. Large companies are investing in quantum computing for a wide range of applications, such as finance, drug discovery, secure communications, etc. As quantum computer deployment is expected to increase and be further commercialized in the upcoming years, it is vital to develop secure systems and protocols able to protect confidential data from untrusted parties. Quantum circuit cutting is a technique originally developed to compensate for the NISQ state of quantum technology, as it breaks a large quantum circuit into several smaller subcircuits that can fit into available QPUs. However, the same method can also be applied to provide security guarantees and protect the user's data from malicious third parties that own the quantum hardware. In this paper, we present a methodology for employing circuit cutting to efficiently use available QPUs and hide the submitted algorithm from potentially malicious cloud providers. We assume variable provider intelligence and introduce two obfuscation techniques, namely, virtual subcircuits and virtual cuts, to provide a modular obfuscation level.

DOI
10.1109/qce65121.2025.00054

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Quantum computing research has experienced tremendous advancements in recent years, and real-world applications that utilize quantum hardware are continuously emerging. Large companies are investing in quantum computing for a wide range of applications, such as finance, drug discovery, secure communications, etc. As quantum computer deployment is expected to increase and be further commercialized in the upcoming years, it is vital to develop secure systems and protocols able to protect confidential data from untrusted parties. Quantum circuit cutting is a technique originally developed to compensate for the NISQ state of quantum technology, as it breaks a large quantum circuit into several smaller subcircuits that can fit into available QPUs. However, the same method can also be applied to provide security guarantees and protect the user's data from malicious third parties that own the quantum hardware. In this paper, we present a methodology for employing circuit cutting to efficiently use available QPUs and hide the submitted algorithm from potentially malicious cloud providers. We assume variable provider intelligence and introduce two obfuscation techniques, namely, virtual subcircuits and virtual cuts, to provide a modular obfuscation level.
Circuit cutting is a novel technique in the field of quantum computing that offers a promising approach for breaking up large quantum circuits into smaller ones that can be executed on smaller quantum computers. Circuit cutting offers a scalable and hybrid computing approach, by combining classical and quantum computers to run large quantum circuits. At the same time, quantum circuits represent the fundamental building blocks of quantum algorithms, and obfuscation techniques are crucial for protecting the sensitive information and intellectual property encoded in these circuits. This paper constitutes the first security analysis of circuit cutting techniques as a means to protect quantum circuits, and not just for cutting bigger quantum circuits to fit on smaller devices. As cloud providers are in control of quantum computers and give access to users, utilizing cloud-based quantum computers for executing quantum circuits involves considerable security risks. This work demonstrates that quantum circuits can be effectively obfuscated by using quantum circuit cutting techniques. If users are worried about an adversary who attempts to reverse-engineer the original quantum algorithm, circuit cutting can be used as a security measure. To further provide configurable obfuscation level, i.e. number of possible circuits that the cloud provider would have to guess, this work proposes a novel dummy-subcircuits technique. This technique introduces dummy subcircuits with additional qubit cut points to create more confusion for the cloud provider and further help to obfuscate the user's original circuits with limited cost.
Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by quantum companies, like IBM, as part of their scaling roadmap. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine qubit counts and the quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum excution runtime and the classical post-processing overhead as the qubit count increases renders it impractical for larger quantum systems with current implementation strategies. As we transition from noisy quantum hardware to fault-tolerance, our work provides useful guidance for the design of quantum software and runtime systems.
Circuit cutting allows quantum circuits larger than the available hardware to be executed. Cutting techniques split circuits into smaller subcircuits, run them on the hardware, and recombine results through classical post-processing. Circuit cutting techniques have been extensively researched over the last five years and it been adopted by major quantum hardware vendors as part of their scaling roadmaps. We examine whether current circuit cutting techniques are practical for orchestrating executions on fault-tolerant quantum computers. We conduct a resource estimation-based benchmarking of important quantum applications and different types of circuit cutting techniques. Our applications include practically relevant algorithms, such as Hamiltonian simulation, kernels such as quantum Fourier transform and more. To cut these applications, we use IBM's Qiskit cutting tool. We estimate resources for subcircuits using Microsoft's Azure Quantum Resource Estimator and develop models to determine the qubit, quantum and classical runtime needs of circuit cutting. We demonstrate that while circuit cutting works for small-scale systems, the exponential growth of the quantum runtime and the classical post-processing overhead as the qubit count increases renders it impractical for larger quantum systems with current implementation strategies. As we transition from noisy quantum hardware to fault-tolerance, our work provides important guidance for the design of quantum software and runtime systems.
An efficient quantum circuit (program) compiler aims to minimize the gate-count - through efficient instruction translation, routing, gate, and cancellation - to improve run-time and noise. Therefore, a high-efficiency compiler is paramount to enable the game-changing promises of quantum computers. To date, the quantum computing hardware providers are offering a software stack supporting their hardware. However, several third-party software toolchains, including compilers, are emerging. They support hardware from different vendors and potentially offer better efficiency. As the quantum computing ecosystem becomes more popular and practical, it is only prudent to assume that more companies will start offering software-as-a-service for quantum computers, including high-performance compilers. With the emergence of third-party compilers, the security and privacy issues of quantum intellectual properties (IPs) will follow. A quantum circuit can include sensitive information such as critical financial analysis and proprietary algorithms. Therefore, submitting quantum circuits to untrusted compilers creates opportunities for adversaries to steal IPs. In this paper, we present a split compilation methodology to secure IPs from untrusted compilers while taking advantage of their optimizations. In this methodology, a quantum circuit is split into multiple parts that are sent to a single compiler at different times or to multiple compilers. In this way, the adversary has access to partial information. With analysis of over 152 circuits on three IBM hardware architectures, we demonstrate the split compilation methodology can completely secure IPs (when multiple compilers are used) or can introduce factorial time reconstruction complexity while incurring a modest overhead (~ 3% to ~ 6% on average).
As quantum computing gains popularity, it’s crucial to tackle security and privacy issues upfront. One major concern is the involvement of third-party tools and hardware. With more quantum computing services available, even from less reputable sources, users might be drawn in by lower costs and easier access. However, usage of untrusted hardware could present the risk of intellectual property (IP) theft. For instance, popular algorithms like Quantum Approximate Optimization Algorithm (QAOA) encode graph properties in parameterized quantum circuits, opening the door to potential risks. For mission critical applications like power grid optimization, the graph structure can reveal the power grid and their connectivity (an IP that should be protected). To mitigate this risk, we propose an edge pruning obfuscation method for QAOA along with a split iteration methodology. The basic idea is to, (i) create two flavors of QAOA circuit each with few distinct edges eliminated from the problem graph for obfuscation, (ii) iterate the circuits alternately during optimization process to uphold the optimization quality, and (iii) send the circuits to two different untrusted hardware provider so that the adversary has access to partial graph protecting the IP. We demonstrate that combining edge pruning obfuscation with split iteration on two different hardware secures the IP and increases the difficulty of reconstruction while limiting performance degradation to a maximum of 10% (≈5\approx 5≈5% on average) and maintaining low overhead costs (less than 0.5X for QAOA with single layer implementation).
The developments in quantum computing hardware and software are focused on boosting the scalability and robustness of quantum systems. With near-term quantum computers poised to accelerate specific computational tasks, ensuring their security has become a pressing priority. Recent efforts have proposed practical solutions to ensure data confidentiality in quantum computers accessed through the cloud, particularly in the presence of untrusted quantum cloud providers by leveraging trusted hardware that enables quantum circuit obfuscation. However, these solutions overlook the detailed structural information of the quantum algorithms, which may limit the effectiveness of the obfuscation methods. In this paper, we investigate the structural constraints of quantum algorithms executed across different quantum architectures. Based on this analysis, we develop a Machine Learning (ML)-based classifier that reveals the underlying quantum algorithm of the obfuscated physical quantum circuit. We also propose a pre-processing phase to minimize the obfuscation's impact on the classifier accuracy. We evaluate our proposed ML-based classification model on different obfuscated physical quantum circuits, which are transpiled to support IBM quantum hardware. Our analysis shows the vulnerability of existing obfuscation methods to reverse engineering attacks, which can potentially expose sensitive information about quantum circuits.