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 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 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.
Protecting the ownership of pictures and the integrity of copyright law is now of paramount importance with the explosion in digital content, especially content that is vulnerable to manipulation and distribution without the copyright owner's permission. This research work combines post-quantum cryptography, Artificial intelligence (AI) and quantum computing aspects to introduce ground breaking quantum intelligent and secure architecture for image watermarking. The proposed system is using quantum transform methods for inserting undetectable and tamper- proof quantum watermarks into digital images. AI models are then applied to the images for change detection and immediate verification of the authenticity of the watermark. Therefore, quantum key distribution (QKD), lattice-based post-quantum cryptography and other quantum safe encryption schemes are employed to cryptographically secure the metadata of watermark for good cryptographic security. This hybrid approach has been shown to enhance the security against both classical and quantum attacks. NFT authentication, secure cloud storage of pictures, and protection for copyright in digital media are examples of some potential applications. Simulation and experimental results show increased strength of the watermark, accuracy of message about unauthorized insertion of the watermark, and enhanced cryptographic security with respect to all other methods. The article demonstrates how AI can be aligned with quantum computing in order to secure audio-visual content in post-quantum cyber security.
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.
The Flexible Representation of Quantum Images (FRQI) is widely used for encoding images into normalized quantum states for secure processing. However, it suffers from high computational and qubit complexity, limiting its scalability. In addition, quantum comparator models, often employed for qubit comparison, remain vulnerable to geometric attacks such as scaling and rotation and require a slow embedding and extraction rate. To address these challenges, this paper proposes a novel quantum watermarking technique that integrates Quantum Image Representation (QIR) with the Novel Enhanced Quantum Representation (NEQR), offering improved efficiency, robustness, and security for quantum image processing (QIP). The proposed technique initiates watermark qubit (QB) generation by integrating pixel position bits (PPB) with an arithmetic average (AA)-based logic, implemented through a quantum circuit comprising reverse parallel adder (RPA), dividing-4 (D4) models, and controlled-NOT (CNOT) gates. For watermark embedding, the proposed circuit employs RPA, quantum equal (QE) models, CNOT gates, and Pauli-X gates, embedding the watermark via a least significant bit (LSB) strategy within the NEQR-encoded image. The extraction process mirrors the embedding architecture, utilizing reverse parallel subtractor (RPS), QE models, CNOT, and Pauli-X gates to ensure accurate watermark retrieval. Experimental results demonstrate high robustness by achieving the highest peak-signal-to-noise-ratio(PSNR) values of 59.97 decibels (dB) along with a structural similarity index matrix (SSIM) of 0.9999. The proposed framework is validated using IBM Qiskit-based quantum circuit simulation on classical computing hardware and is not intended to demonstrate fault-tolerant quantum hardware execution or provable quantum computational advantage. Rather, the work focuses on simulation-level reversible quantum-circuit modelling for secure image watermarking using NEQR-based image representation and quantum-compatible reversible logic structures. Furthermore, the proposed technique is subjected to a range of image processing attacks, quantum noise and decoherence to rigorously evaluate its resilience and performance. In addition, an AI-assisted attack classification module is incorporated to automatically identify the type of distortion applied to the attacked watermarked images. A lightweight Random Forest classifier is trained to classify different attack categories, including no attack, Gaussian noise, salt-and-pepper noise, median filtering, histogram equalization, JPEG compression, cropping, rotation, and text addition. The proposed AI-assisted module achieves an overall classification accuracy of 96.56%, demonstrating its effectiveness in automatic attack identification and further strengthening the applicability of the proposed quantum watermarking framework for secure image authentication. Resource analysis shows that the watermark generation circuit requires 354 QBs (including 320 for RPA, 10 for D4, and 24 for CNOT), while both embedding and extraction consume 400 QBs each. Theoretical evaluation and empirical validation confirm the technique’s effectiveness in preserving image integrity and confidentiality, establishing it as a viable approach for secure quantum image watermarking in real time applications.
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.
High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate-set tomography (GST) data that bypasses the traditional two-step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms. Instead, a generative concept space is directly learnt from GST data, enabling conditional synthesis of quantum circuits on a desired output distribution. Our approach tokenizes GST germ circuits and embeds them into a structured latent space using a curriculum-learning-motivated strategy, starting with short circuits and progressively incorporating longer ones with diverse output statistics. The embedded sequences are processed by a set-vision transformer with permutation-invariant pooling, producing k-seed vectors that represent the learned concept space of the quantum device. Aggregating data across multiple circuits makes this latent representation inherently context-aware, capturing the shared physical noise environment (e.g., crosstalk, drift) that isolated gate metrics miss. We propose an unconditional diffusion model to sample from the concept space. During inference, a user provides a target measurement distribution, and the model generates a corresponding circuit. To ensure fidelity and robustness, the output is denoised using a diffusion model that operates on the target conditional covariance matrix. This end-to-end framework is a step towards context-aware, hardware-native circuit synthesis directly from raw GST data, which offers a new paradigm for integrating quantum control and compilation. The QMLC framework is particularly suited for near-term quantum devices with complex calibration procedures.