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.
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.
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 (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.
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.
Abstract Quantum communication has been leading the way of many remarkable theoretical results and experimental tests in physics. In this context, quantum communication complexity (QCC) has recently drawn earnest research attention as a tool to optimize the amounts of transmitted qubits and energy that are required to implement distributed computational tasks. On this matter, we introduce a novel multi-user quantum fingerprinting (QF) protocol that is ready to be implemented with existing technology. Particularly, we extend to the multi-user framework a well-known two-user coherent-state fingerprinting scheme. This generalization is highly non-trivial for a twofold reason, as it requires not only to extend the set of protocol rules but also to specify a procedure for designing the optical devices intended for the generalized protocol. Much of the importance of our work arises from the fact that the obtained QCC figures of merit allow direct comparison with the best-known classical multi-user fingerprinting protocol, of significance in the field of computer technologies and networking. Furthermore, as one of the main contributions of the manuscript, we deduce innovative analytical upper bounds on the amount of transmitted quantum information that are even valid in the two-user protocol as a particular case. These original analytical bounds are of interest for estimating the realistic protocol performance prior to experimental realizations. Ultimately, comparative results are provided to contrast different protocol implementation strategies and, importantly, to show that, under realistic circumstances, the multi-user protocol can achieve tasks that are impossible by using classical communication alone. Our work provides relevant contributions towards understanding the nature and the limitations of QF and, on a broader scope, also the limitations and possibilities of quantum-communication networks embracing a node that is accessed by multiple users at the same time.
Fingerprinting is one of the most commonly used techniques for indoor localization due to their high accuracy in the presence of the wireless channel noise. Nonetheless, they require significant storage overhead and running time. This becomes even more important when scaling such systems to a worldwide scale.Quantum computing can speedup computation of intractable problems that are hard to solve on the traditional classical computers. In this paper, we propose a realistic quantum fingerprint based algorithm that builds on quantum computing concepts to scale. In particular, the proposed fingerprinting algorithm can provide exponential enhancement in both space and running time over the classical fingerprinting techniques. We explain the details of the quantum fingerprint construction, how to encode the fingerprint signal strength vectors in quantum states, and how to calculate the quantum similarity between the online signal strength measurement and the offline ones. In addition, we study the different practical implementation and deployment issues on IBM quantum computing cloud machines.Implementation and evaluation of our algorithm in a real testbed using quantum machines confirms its ability to correctly obtain the estimated location with an exponential enhancement in both time and space compared to the traditional classical fingerprinting techniques. This highlights the promise of our algorithm to scale fingerprinting localization to be used globally.
We propose a new concept, oblivious quantum computation, which requires performing oblivious transfer with respect to the computation outcome of the quantum computation, where the secrecy of the input qubits and the program to identify the quantum gates are required. Exploiting quantum teleportation, we propose a two-server protocol for this task, which realizes an exponential improvement for the communication complexity over the simple application of two-server (quantum) oblivious transfer to the sending of the computation result. Also, we discuss delegated multiparty quantum computation, in which, several users ask multiparty quantum computation to server(s) only using classical communications. We propose a two-server protocol for the latter task as well.