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arXiv:2209.11872 · Hardware and Architectural Support for Security and Privacy

Robust and Secure Hybrid Quantum-Classical Computation on Untrusted Cloud-Based Quantum Hardware

Suryansh Upadhyay, Swaroop Ghosh

Published Oct 1, 2022• 19 citations
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Abstract

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 ≈ .

DOI
10.1145/3569562.3569569

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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 ≈ .
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 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.
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\approx 5≈5% on average) and maintaining low overhead costs (less than 0.5X for QAOA with single layer implementation).
Cloud computing has fundamentally transformed digital infrastructure by providing on-demand access to scalable computational resources. Yet the inherent unpredictability of workloads alongside surging demands for efficient resource distribution continues to present substantial operational hurdles. This investigation introduces a Quantum-Driven Optimization framework that synergistically combines quantum and classical computational paradigms to improve resource allocation efficacy within cloud ecosystems. The proposed methodology employs Quantum Annealing alongside Variational Quantum Eigensolver algorithms to optimize resource distribution through simultaneous minimization of energy expenditure and operational overhead while maximizing throughput and utilization rates. Empirical validation demonstrates that our framework achieves measurable improvements over conventional heuristic methodologies, specifically showing enhanced resource utilization, reduced operational costs, and accelerated task completion times. The hybrid architecture effectively distributes computational burden by delegating preprocessing and decision-making functions to classical systems while quantum processors tackle inherently complex optimization problems. The solution demonstrates scalability and adaptability across diverse cloud deployment scenarios while exhibiting reduced Service Level Agreement violations, thereby ensuring superior Quality of Service for end-users. This research contributes meaningful insights regarding the practical implementation of hybrid quantum-classical approaches for addressing contemporary resource allocation challenges within cloud computing infrastructures.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.