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 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.
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.
Distributed quantum computing combines the computational power of multiple devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations through classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer qubits. The original circuit's outcome can be replicated by executing these subcircuits on separate devices and combining their results. However, the number of shots required to achieve a fixed result accuracy with circuit cutting grows exponentially with the number of cuts, posing significant costs. In contrast, quantum teleportation allows the distribution of quantum computations without an exponential increase in shots. Nevertheless, each teleportation procedure requires a pre-shared pair of maximally entangled qubits for transmitting a quantum state, and non-maximally entangled qubits cannot be used for this purpose. To address this, we propose a novel circuit cutting technique that leverages non-maximally entangled qubit pairs, effectively reducing the cost associated with circuit cutting. By considering the degree of entanglement in the pre-shared qubit pairs, our technique provides a continuum between existing circuit cutting methods and quantum teleportation, adjusting the cost of circuit cutting accordingly.
Quantum computing resources are now becoming easily accessible from various cloud providers. Although still in the Noisy Intermediate Scale Quantum regime, quantum computers hold promise to be able to execute novel algorithms and create invaluable data. However, just as with any other type of computing resource, they may be vulnerable to security attacks and should have defenses built into their design. This paper explores a particular threat of untrusted cloud providers, and how to protect user's quantum programs and data from the untrusted cloud provider. By leveraging trusted hardware in the quantum computer, a new obfuscation-based protection is developed based on switching of control pulses between different drive and control channels of the quantum computer. This work demonstrates that simple hardware modifications can enable dynamic, run-time pulse switching, which makes it extremely difficult for the cloud provider to decode what actual circuit is executed on the quantum computer. This work presents a basic architecture that employs pulse switching, and an extended architecture that includes use of dummy qubits for increased protection. The overhead of the proposed changes, as well as attack complexity for different types of user circuits and obfuscation levels is evaluated in this work.
Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative—purely classical simulations—and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.
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.
Circuit cutting is a technique for simulating large quantum circuits by partitioning them into smaller subcircuits, which can be executed on smaller quantum devices. The results from these subcircuits are then combined in classical postprocessing to accurately reconstruct the expectation value of the original circuit. Circuit cutting introduces a sampling overhead that grows exponentially with the number of gates and qubit wires that are cut. Many recently developed quasiprobabilistic circuit cutting techniques leverage classical side information, obtained from intermediate measurements within the subcircuits, to enhance the postprocessing step. In this work, we provide a formalization of general circuit cutting techniques utilizing side information through quantum instruments. With this framework, we analyze the advantage that classical side information provides in reducing the sampling overhead of circuit cutting. Surprisingly, we find that in certain scenarios, side information does not yield any reduction in sampling overhead, whereas in others it is essential for circuit cutting to be feasible at all. Furthermore, we present a lower bound for the optimal sampling overhead with side information that can be evaluated efficiently via semidefinite programming and improves on all previously known lower bounds.