Quantum computing is a promising paradigm to solve computationally intractable problems. Various companies such as, IBM, Rigetti and D-Wave offer quantum computers using a cloud-based platform that possess several interesting features namely, (i) quantum hardware with various number of qubits and coupling maps exist at the cloud end that offer different computing capabilities; (ii) multiple hardware with identical coupling maps exist in the suite; (iii) coupling map of larger hardware with more number of qubits can fit the coupling map of many smaller hardware; (iv) the quality of each of the hardware is distinct; (v) user cannot validate the origination of the result obtained from a quantum hardware. In other words, the user relies on the scheduler of the cloud provider to allocate the requested hardware; (vi) the queue of quantum programs at the cloud end is typically long and maximizing the throughput, which is the key to reducing costs and helping the scientific community in their explorations. The above factors motivate a new threat model with following possibilities: (a) in future, less-trustworthy quantum computers from 3rd parties can allocate poor quality hardware to save on cost or towards satisfying their falsely-advertised qubit or quantum hardware specifications; (b) the workload scheduling algorithm could have a bug or malicious code segment which will try to maximize throughput at the cost of allocation to poor fidelity hardware. Such bugs are possible for trustworthy providers; (c) a rogue employee in trusted cloud vendor could try to sabotage the vendor's reputation by degrading the user compute fidelity just by tampering with the scheduling algorithm or rerouting the program; (d) a rogue employee can steal information by redirecting the programs to a 3rd party quantum hardware where they have full control. If the allocated hardware is inferior in quality, the user will suffer from poor quality result or longer convergence time. We propose two flavors of a Quantum Physically Unclonable Function (QuPUF) to address this issue- one based on superposition and another based on decoherence. Our experiments on real quantum hardware reveal that temporal variations in qubit quality can degrade the quality of the proposed QuPUF. We add a parametric rotation to the QuPUF for stability. Experiments on real IBM quantum hardware show that the proposed QuPUF can achieve inter-die Hamming Distance (HD) of 55% and intra-HD as low as 4%, as compared to ideal cases of 50% and 0% respectively. The proposed QuPUFs can also be used as a standalone solution for any other application.
Quantum computing is a promising paradigm to solve computationally intractable problems. Various companies such as, IBM, Rigetti and D-Wave offer quantum computers using a cloud-based platform that possess several interesting features namely, (i) quantum hardware with various number of qubits and coupling maps exist at the cloud end that offer different computing capabilities; (ii) multiple hardware with identical coupling maps exist in the suite; (iii) coupling map of larger hardware with more number of qubits can fit the coupling map of many smaller hardware; (iv) the quality of each of the hardware is distinct; (v) user cannot validate the origination of the result obtained from a quantum hardware. In other words, the user relies on the scheduler of the cloud provider to allocate the requested hardware; (vi) the queue of quantum programs at the cloud end is typically long and maximizing the throughput, which is the key to reducing costs and helping the scientific community in their explorations. The above factors motivate a new threat model with following possibilities: (a) in future, less-trustworthy quantum computers from 3rd parties can allocate poor quality hardware to save on cost or towards satisfying their falsely-advertised qubit or quantum hardware specifications; (b) the workload scheduling algorithm could have a bug or malicious code segment which will try to maximize throughput at the cost of allocation to poor fidelity hardware. Such bugs are possible for trustworthy providers; (c) a rogue employee in trusted cloud vendor could try to sabotage the vendor's reputation by degrading the user compute fidelity just by tampering with the scheduling algorithm or rerouting the program; (d) a rogue employee can steal information by redirecting the programs to a 3rd party quantum hardware where they have full control. If the allocated hardware is inferior in quality, the user will suffer from poor quality result or longer convergence time. We propose two flavors of a Quantum Physically Unclonable Function (QuPUF) to address this issue- one based on superposition and another based on decoherence. Our experiments on real quantum hardware reveal that temporal variations in qubit quality can degrade the quality of the proposed QuPUF. We add a parametric rotation to the QuPUF for stability. Experiments on real IBM quantum hardware show that the proposed QuPUF can achieve inter-die Hamming Distance (HD) of 55% and intra-HD as low as 4%, as compared to ideal cases of 50% and 0% respectively. The proposed QuPUFs can also be used as a standalone solution for any other application.
Security and reliability are primary concerns in any computing paradigm including quantum computing. Currently users can access quantum computers through a cloud based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility it is reasonable to expect that more companies including untrusted or less trusted or unreliable vendors will begin offering quantum computers as hardware as a service at varied price or performance points. Since computing time on quantum hardware is expensive and the access queue could be long the users will be motivated to use the cheaper and readily available but unreliable or less trusted hardware. The less trusted vendors can tamper with the results providing a suboptimal solution to the user. In this paper we model this adversarial tampering and simulate its impact on a number of pure quantum and hybrid quantum classical workloads. To guarantee trustworthy computing for a mixture of trusted and untrusted hardware we propose distributing the total number of shots equally among the various hardware options. On average we note approx 30X and approx 1.5X improvement across the pure quantum workloads and a maximum improvement of approx 5X for hybrid classical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive split heuristic leveraging temporal variation in hardware quality to users advantage allowing them to identify tampered or untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware which results in a maximum improvement of approx 190X and approx 9X across the pure quantum workloads and an improvement of up to approx 2.5X for hybrid classical algorithm.
Security and reliability are primary concerns in any computing paradigm, including quantum computing. Currently, users can access quantum computers through a cloud-based platform where they can run their programs on a suite of quantum computers. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect that more companies including untrusted/less-trusted/unreliable vendors will begin offering quantum computers as hardware-as-a-service at varied price/performance points. Since computing time on quantum hardware is expensive and the access queue could be long, the users will be motivated to use the cheaper and readily available but unreliable/less-trusted hardware. The less-trusted vendors can tamper with the results, providing a sub-optimal solution to the user. For applications such as, critical infrastructure optimization, the inferior solution may have significant socio-political implications. Since quantum computers cannot be simulated in classical computers, users have no way of verifying the computation outcome. In this paper, we address this challenge by modeling adversarial tampering and simulating it's impact on both pure quantum and hybrid quantum-classical workloads. To achieve trustworthy computing in a mixed environment of trusted and untrusted hardware, we propose an equitable distribution of total shots (i.e., repeated executions of quantum programs) across hardware options. On average, we note ≈ 30X and ≈ 1.5X improvement across the pure quantum workloads and a maximum improvement of ≈ 5X for hybrid-classical algorithm in the chosen quality metrics. We also propose an intelligent run adaptive shot distribution heuristic leveraging temporal variation in hardware quality to user's advantage, allowing them to identify tampered/untrustworthy hardware at runtime and allocate more number of shots to the reliable hardware, which results in a maximum improvement of ≈ 190X and ≈ 9X across the pure quantum workloads and an improvement of up to ≈ 2.5X for hybrid-classical algorithm.
Quantum computers are currently accessible through a cloud-based platform that allows users to run their programs on a suite of quantum hardware. As the quantum computing ecosystem grows in popularity and utility, it is reasonable to expect more companies, including untrustworthy/untrustworthy/unreliable vendors, to begin offering quantum computers as hardware-as-a-service at various price/performance points. Since computing time on quantum hardware is expensive and the access queue may be long, users will be enticed to use less expensive but less reliable/trustworthy hardware. Less-trusted vendors may tamper with the results and/or parameters of quantum circuits, providing the user with a sub-optimal solution or incurring a cost of higher iterations. In this paper, we model and simulate adversarial tampering of input parameters and measurement outcomes on an exemplary hybrid quantum classical algorithm namely, Quantum Approximate Optimization Algorithm (QAOA). We observe a maximum performance degradation of . To achieve comparable performance with minimal parameter tampering, the user incurs a minimum cost of 20X higher iteration. We propose distributing the computation (iterations) equally among the various hardware options to ensure trustworthy computing for a mix of trusted and untrusted hardware. In the chosen performance metrics, we observe a maximum improvement of ≈ 30%. In addition, we propose re-initialization of the parameters after a few initial iterations to fully recover the original program performance and an intelligent run adaptive split heuristic, which allows users to identify tampered/untrustworthy hardware at runtime and allocate more iterations to the reliable hardware, resulting in a maximum improvement of ≈ .
Quantum computing (QC) is an emerging paradigm with the potential to transform numerous application domains by addressing classically intractable problems. However, its growing presence in cyberspace has introduced new security and privacy challenges. Similar to classical computing systems, the QC stack including software and hardware relies extensively on third parties, many of which are emerging and trust-seeking or less-trusted. This stack often contains sensitive intellectual property (IP) that demands protection. Unique features of quantum systems can enable classical-style attacks: for instance, crosstalk in multitenant settings can facilitate fault-injection attacks, while malicious calibration services can misreport error rates or miscalibrate qubits to induce denial-of-service (DoS) conditions. Given the high cost and limited availability of likely trustworthy quantum hardware, users may be enticed to explore emerging and trust-seeking but cheaper and readily available quantum hardware, which can enable the stealth of IP and tampering of quantum programs and/or computation outcomes. Similarly, emerging compilation services may compromise circuit confidentiality or insert Trojans. Despite the strategic significance of QC and its potential to process sensitive information, its security and privacy concerns remain underexplored. This article presents a comprehensive overview of QC fundamentals, key vulnerabilities, recent attack vectors, and corresponding defenses, and concludes with directions for future research to strengthen the quantum security community.
Hardware security primitives are hardware-based fundamental components and mechanisms used to enhance the security of modern computing systems in general. These primitives provide building blocks for implementing security features and safeguarding against threats to ensure integrity, confidentiality, and availability of information and resources. With the high-speed development of quantum computation and information processing, a huge potential is shown in constructing hardware security primitives with quantum mechanical systems. Meanwhile, addressing potential vulnerabilities from the hardware perspective is becoming increasingly important to ensure the security properties of quantum applications. The thesis focuses on practical hardware security primitives in quantum analogue, which refer to designing and implementing hardware-based security features with quantum mechanical systems against various threats and attacks. Our research follows two questions: How can quantum mechanical systems enhance the security of existing hardware security primitives? And how can hardware security primitives protect quantum computing systems? We give the answers by studying two different types of hardware security primitives with quantum mechanical systems from constructions to applications: Physical Unclonable Function (PUF) and Trusted Execution Environments (TEE). We first propose classical-quantum hybrid constructions of PUFs called HPUF and HLPUF. When PUFs exploit physical properties unique to each individual hardware device to generate device-specific keys or identifiers, our constructions incorporate quantum information processing technologies and implement quantum-secure authentication and secure communication protocols with reusable quantum keys. Secondly, inspired by TEEs that achieve isolation properties by hardware mechanism, we propose the QEnclave construction with quantum mechanical systems. The idea is to provide an isolated and secure execution environment within a larger quantum computing system by utilising secure enclaves/processors to protect sensitive operations from unauthorized access or tampering with minimal trust assumptions. It results in an operationally simple enough QEnclave construction with performing rotations on single qubits. We show that QEnclave enables delegated blind quantum computation on the cloud server with a remote classical user under the security definitions.
Sustainable advancement is being made to improve the efficiency of the generation, transmission, and distribution of renewable energy resources, as well as managing them to ensure the reliable operation of the smart grid. Supervisory control and data acquisition (SCADA) enables sustainable management of grid communication flow through its real-time data sensing, processing, and actuation capabilities at various levels in the energy distribution framework. The security vulnerabilities associated with the SCADA-enabled grid infrastructure and management could jeopardize the smart grid operations. This work explores the potential of Quantum Physical Unclonable Functions (QPUF) for the security, privacy, and reliability of the smart grid's energy transmission and distribution framework. Quantum computing has emerged as a formidable security solution for high-performance computing applications through its probabilistic nature of information processing. This work has a quantum hardware-assisted security mechanism based on intrinsic properties of quantum hardware driven by quantum mechanics to provide tamper-proof security for quantum computing driven smart grid infrastructure. This work introduces a novel QPUF architecture using quantum logic gates based on quantum decoherence, entanglement, and superposition. This generates a unique bitstream for each quantum device as a fingerprint. The proposed QPUF design is evaluated on IBM and Google quantum systems and simulators. The deployment on the IBM quantum simulator (ibmq_qasm_simulator) has achieved an average Hamming distance of 50.07%, 51% randomness, and 86% of the keys showing 100% reliability.