Quantum computing users increasingly access quantum hardware through cloud platforms and are charged based on usage. Because these resources are scarce and expensive, users often request specific devices to ensure performance, while providers may reassign jobs to other devices to maximize throughput, potentially compromising user expectations. Thus, we present QuanGuard, an efficient fingerprinting framework for verifying whether the allocated quantum resources match user selections. QuanGuard constructs dynamic fingerprints from the noisy execution results of a probing circuit, exploiting device-specific error evolution patterns to identify hardware uniquely. We further develop an error evolution algorithm that generates user-side fingerprints for lightweight matching against assigned resources. The method requires only a single probing circuit per detection, making it highly practical for current quantum cloud platforms. Experiments on seven IBM quantum computers show that QuanGuard achieves accurate and reliable device verification with minimal overhead.
Quantum computing users increasingly access quantum hardware through cloud platforms and are charged based on usage. Because these resources are scarce and expensive, users often request specific devices to ensure performance, while providers may reassign jobs to other devices to maximize throughput, potentially compromising user expectations. Thus, we present QuanGuard, an efficient fingerprinting framework for verifying whether the allocated quantum resources match user selections. QuanGuard constructs dynamic fingerprints from the noisy execution results of a probing circuit, exploiting device-specific error evolution patterns to identify hardware uniquely. We further develop an error evolution algorithm that generates user-side fingerprints for lightweight matching against assigned resources. The method requires only a single probing circuit per detection, making it highly practical for current quantum cloud platforms. Experiments on seven IBM quantum computers show that QuanGuard achieves accurate and reliable device verification with minimal overhead.
Noisy Intermediate-Scale Quantum (NISQ) devices, while accessible via cloud platforms, face challenges due to limited availability and suboptimal quality. These challenges raise the risk of cloud providers offering fraudulent services. This emphasizes the need for users to detect such fraud to protect their investments and ensure computational integrity. This study introduces a novel dynamic fingerprinting method for detecting fraudulent service provision on quantum cloud platforms, specifically targeting machine substitution and profile fabrication attacks. The dynamic fingerprint is constructed using a single probing circuit to capture the unique error characteristics of quantum devices, making this approach practical because of its trivial computational costs. When the user examines the service, the execution results of the probing circuit act as the device-side fingerprint of the quantum device providing the service. The user then generates the user-side fingerprint by estimating the expected execution result, assuming the correct device is in use. We propose an algorithm for users to construct the user-side fingerprint with linear complexity. By comparing the device-side and user-side fingerprints, users can effectively detect fraudulent services. Our experiments on the IBM Quantum platform, involving seven devices with varying capabilities, confirm the method's effectiveness.
Quantum Computing (QC) has recently achieved significant technological progress, attracting growing interest from both academia and industry. As most users currently access QC resources through cloud platforms, concerns around the security and accountability of quantum services have become increasingly prominent. In particular, quantum noise, typically considered a source of error, can also reveal device-specific signatures that enable Quantum Device Fingerprinting (QDF). While QDF has legitimate applications such as anomaly detection and device accountability, it also carries dual-use risks, including potential misuse for unauthorized device tracking or targeted attacks. In this paper, we propose a novel Task-Driven Quantum Device Fingerprinting (TD-QDF) identification method based on quantum task outputs; we extract the fingerprint features of quantum devices from noisy quantum computing results. Unlike previous research, our method does not require any additional information (e.g., hardware details or noise information), thereby enhancing its practical applicability and accessibility. We conducted large-scale experiments using six QNN circuits on 10 IBM quantum computers, extended four classical quantum algorithms to validate generality, and demonstrated scalability on three 127-qubit processors. Specifically, the highest accuracy can reach up to 94.32% in the 3-classification device fingerprint recognition task and up to 82.4% and 61.6% in the 7-classification and 10-classification tasks, respectively. This research contributes to advancing quantum fingerprinting technologies and holds promising implications for enhancing the security and accountability of quantum computing systems and quantum cloud services.
A quantum network comprises interconnected quantum servers capable of communication and collaboration for computational tasks. It is essential for quantum servers within this network to identify and authenticate one another. For instance, when a quantum server intends to execute a computational task on another machine, it becomes crucial for the quantum server to verify the authenticity of other quantum servers to maintain confidence in delegating computation. While several methods for fingerprinting these quantum computers have been proposed, many are resource-intensive and not currently practical. To address this, we introduce Q-ID, a lightweight fingerprinting method that accurately identifies quantum servers with negligible quantum computational demands. Q-ID operates by running a user’s task circuit at two different levels of noise, using the resulting performance gap as a unique identifier for quantum servers. Additionally, we have developed an error evolution algorithm that allows users to locally estimate this performance gap. By comparing the estimated gap with the actual one, users can effectively identify or differentiate between quantum servers in a network. Our experiments on the IBM quantum platform showcase the efficacy and benefits of our approach.
Cloud quantum platforms give users access to many backends with different qubit technologies, coupling layouts, and noise levels. The execution of a circuit, however, depends on internal allocation and routing policies that are not observable to the user. A provider may redirect jobs to more error-prone regions to conserve resources, balance load or for other opaque reasons, causing degradation in fidelity while still presenting stale or averaged calibration data. This lack of transparency creates a security gap: users cannot verify whether their circuits were executed on the hardware for which they were charged. Forensic methods that infer backend behavior from user-visible artifacts are therefore becoming essential. In this work, we introduce a Graph Neural Network (GNN)-based forensic framework that predicts per-qubit and per-qubit link error rates of an unseen backend using only topology information and aggregated features extracted from transpiled circuits. We construct a dataset from several IBM 27-qubit devices, merge static calibration features with dynamic transpilation features and train separate GNN regressors for one- and two-qubit errors. At inference time, the model operates without access to calibration data from the target backend and reconstructs a complete error map from the features available to the user. Our results on the target backend show accurate recovery of backend error rate, with an average mismatch of approximately 22% for single-qubit errors and 18% for qubit-link errors. The model also exhibits strong ranking agreement, with the ordering induced by predicted error values closely matching that of the actual calibration errors, as reflected by high Spearman correlation. The framework consistently identifies weak links and high-noise qubits and remains robust under realistic temporal noise drift.
Quantum computing (QC) has recently achieved significant technological advancements, attracting widespread attention. Current users mainly access QC resources through cloud services. However, cloud-based quantum services provide convenience while also introducing security risks. For example, attackers could steal private information or inject malicious programs into quantum devices, while quantum device fingerprinting may be the first step for these malicious intents. In this paper, we propose a novel Task-Driven Quantum Device Fingerprinting (TD-QDF) identification method based on quantum neural network (QNN) task outcomes. Unlike previous research, our method does not require any hardware details, resulting in high availability in practice. Extensive experiments involving 3 QNN circuits on 10 real IBM quantum computers show that our method can effectively identify quantum devices. This research contributes to advancing quantum fingerprinting technologies and holds promising implications for enhancing the security and accountability of quantum computing systems.
Quantum computing, despite its vast potential, is impeded by challenges like decoding and system noise, necessitating effective quantum error correction techniques. Our study investigates these techniques, focusing on their practical application. We introduce QuantoTrace, a cloud-based platform offering Error Correction as a Service (ECaaS). It enhances quantum system reliability by detecting, analysing, and rectifying errors, and implements bit-flip error correction compatible with various quantum technologies. Using 3-qubit and 5-qubit models, we demonstrated its efficacy on quantum simulators and IBM quantum hardware. Remarkably, we achieved 100% error correction accuracy on simulators and significant success rates on IBM hardware: 68.95% for error correction and 86.04% for error detection in 5-qubit systems. However, the Quantum Repetition Code (QRC) showed limitations in handling multiple-qubit errors in 5-qubit systems. These findings highlight the potential of QuantoTrace which is based on Software as a Service principles in enhancing quantum computing reliability towards further research needs in quantum error correction, especially for noisy intermediate-scale quantum (NISQ) devices. QuantoTrace aims to simplify quantum error management, making it accessible to a broad user base and facilitating optimized quantum computing strategies.