Quantum computing is an emerging computing paradigm that is expected to offer decisive advantages for critical applications. The design flow of quantum circuits poses problematic security challenges in the presence of variable errors and limited resources (qubits) to support error correction. Yet, quantum computers can be very powerful for several applications if proper hardware/software approaches are deployed.In the current embodiment, quantum circuits are expected to be used for several applications including financial analysis and machine learning in which a designer wants to protect the Intellectual Property (IP) of the quantum circuits. In this paper, we propose a watermarking approach for quantum circuits, which embeds a secret signature during the decomposition phase to verify the ownership of the IP. We evaluate the effectiveness of the proposed approach using Quantum Approximate optimization Algorithm (QAOA) used for solving a Max-Cut problem.
Quantum computing is an emerging computing paradigm that is expected to offer decisive advantages for critical applications. The design flow of quantum circuits poses problematic security challenges in the presence of variable errors and limited resources (qubits) to support error correction. Yet, quantum computers can be very powerful for several applications if proper hardware/software approaches are deployed.In the current embodiment, quantum circuits are expected to be used for several applications including financial analysis and machine learning in which a designer wants to protect the Intellectual Property (IP) of the quantum circuits. In this paper, we propose a watermarking approach for quantum circuits, which embeds a secret signature during the decomposition phase to verify the ownership of the IP. We evaluate the effectiveness of the proposed approach using Quantum Approximate optimization Algorithm (QAOA) used for solving a Max-Cut problem.
Quantum computing represents a burgeoning computational paradigm that significantly advances the resolution of contemporary intricate problems across various domains, including cryptography, chemistry, and machine learning. Quantum circuits tailored to address specific problems have emerged as critical intellectual properties (IPs) for quantum computing companies, attributing to the escalating commercial value of quantum computing. Consequently, designing watermarking schemes for quantum circuits becomes imperative to thwart malicious entities from producing unauthorized circuit replicas and unlawfully disseminating them within the market. Unfortunately, the prevailing watermarking technique reliant on unitary matrix decomposition markedly inflates the number of 2-qubit gates and circuit depth, thereby compromising the fidelity of watermarked circuits when embedding detectable signatures into the corresponding unitary matrices. In this paper, we propose an innovative multi-stage watermarking scheme for quantum circuits, introducing additional constraints across various synthesis stages to validate the ownership of IPs. Compared to the state-of-the-art watermarking technique, our multi-stage watermarking approach demonstrates, on average, a reduction in the number of 2-qubit gates by 16% and circuit depth by 6%, alongside an increase in the fidelity of watermarked circuits by 8%, while achieving a 79.4% lower probabilistic proof of authorship.
Quantum cloud platforms have become the most widely adopted and mainstream approach for accessing quantum computing resources, due to the scarcity and operational complexity of quantum hardware. In this service-oriented paradigm, quantum circuits, which constitute high-value intellectual property, are exposed to risks of unauthorized access, reuse, and misuse. Digital watermarking has been explored as a promising mechanism for protecting quantum circuits by embedding ownership information for tracing and verification. However, driven by recent advances in generative artificial intelligence, the paradigm of quantum circuit design is shifting from individually and manually constructed circuits to automated synthesis based on quantum circuit generative models (QCGMs). In such generative settings, protecting only individual output circuits is insufficient, and existing post hoc, circuit-centric watermarking methods are not designed to integrate with the generative process, often failing to simultaneously ensure stealthiness, functional correctness, and robustness at scale. These limitations highlight the need for a new watermarking paradigm that is natively integrated with quantum circuit generative models. In this work, we present the first watermarking framework for QCGMs, which embeds ownership signals into the generation process while preserving circuit fidelity. We introduce a symmetric sampling strategy that aligns watermark encoding with the model's Gaussian prior, and a synchronization mechanism that counteracts adversarial watermark attack through latent drift correction. Empirical results confirm that our method achieves high-fidelity circuit generation and robust watermark detection across a range of perturbations, paving the way for scalable, secure copyright protection in AI-powered quantum design.
Quantum circuits constitute Intellectual Property (IP) of the quantum developers and users, which needs to be protected from theft by adversarial agents e.g., the quantum cloud provider or a rogue adversary present in the cloud. This necessitates the exploration of low-overhead techniques applicable to near-term quantum devices, to trace the quantum circuits/algorithms' IP and their output. We present two such lightweight watermarking techniques to prove ownership in the event of an adversary cloning the circuit design. For the first technique a rotation gate is placed on ancilla qubits combined with other gate (s) at the output of the circuit. For the second method, a set of random gates are inserted in the middle of the circuit followed by its inverse, separated from the circuit by a barrier. These models are combined and applied on benchmark circuits and the circuit depth, 2-qubit gate count, probability of successful trials (PST) and probabilistic proof of authorship (PPA) are compared against the state-of-the-art. The PST is reduced by a miniscule 0.53% against the non-watermarked benchmarks and is up to 22.69% higher compared to existing techniques. The circuit depth has been reduced by up to 27.7% as against the state-of-the-art. The PPA is astronomically smaller than existing watermarks.
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% on average) and maintaining low overhead costs (less than 0.5X for QAOA with single layer implementation).
Variational Quantum Circuits (VQCs) have emerged as a powerful quantum computing paradigm, demonstrating a scaling advantage for problems intractable for classical computation. As VQCs require substantial resources and specialized expertise for their design, they represent significant intellectual properties (IPs). However, existing quantum circuit watermarking techniques suffer from two primary drawbacks: (1) watermarks can be removed during re-compilation of the circuits, and (2) these methods significantly increase task loss due to the extensive length of the inserted watermarks across multiple compilation stages. To address these challenges, we propose BVQC, a backdoor-based watermarking technique for VQCs that preserves the original loss in typical execution settings, while deliberately increasing the loss to a predefined level during watermark extraction. Additionally, BVQC employs a grouping algorithm to minimize the watermark task's interference with the base task, ensuring optimal accuracy for the base task. BVQC retains the original compilation workflow, ensuring robustness against re-compilation. Our evaluations show that BVQC greatly reduces Probabilistic Proof of Authorship (PPA) changes by9.89e−3and ground truth distance (GTD) by 0.089 compared to prior watermarking technologies.
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).