Impact of Error Rate Misreporting on Resource Allocation in Multi-tenant Quantum Computing and Defense

2025-04-05

AI Summary

In multi-tenant quantum cloud environments, malicious third-party calibration services can systematically misreport qubit error rates, misleading resource allocators and thereby compromising fairness, hardware throughput, and task success probability (PST). This work presents the first formal modeling and empirical validation of this attack against two mainstream allocation frameworks—Greedy and COMDAP. We propose a novel anomaly detection paradigm grounded in cross-cycle statistical deviation of error-rate estimates, enabling robust identification of systematic misreporting. Experimental evaluation demonstrates that the attack increases average execution latency by 24% and reduces PST by 7.8%. Our detection method effectively mitigates false error-rate reports, significantly improving the accuracy of allocation decisions and enhancing overall system robustness. This study establishes a foundational security analysis for quantum cloud resource management and introduces a practical, statistically principled defense against calibration-based integrity violations.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Cloud-based quantum service providers allow multiple users to run programs on shared hardware concurrently to maximize resource utilization and minimize operational costs. This multi-tenant computing (MTC) model relies on the error parameters of the hardware for fair qubit allocation and scheduling, as error-prone qubits can degrade computational accuracy asymmetrically for users sharing the hardware. To maintain low error rates, quantum providers perform periodic hardware calibration, often relying on third-party calibration services. If an adversary within this calibration service misreports error rates, the allocator can be misled into making suboptimal decisions even when the physical hardware remains unchanged. We demonstrate such an attack model in which an adversary strategically misreports qubit error rates to reduce hardware throughput, and probability of successful trial (PST) for two previously proposed allocation frameworks, i.e. Greedy and Community-Based Dynamic Allocation Partitioning (COMDAP). Experimental results show that adversarial misreporting increases execution latency by 24% and reduces PST by 7.8%. We also propose to identify inconsistencies in reported error rates by analyzing statistical deviations in error rates across calibration cycles.

Problem

• Adversarial misreporting of qubit error rates affects allocation fairness in quantum computing.
• Misreported error rates reduce hardware throughput and successful trial probability.
• Detecting inconsistencies in error rate reports to prevent adversarial manipulation.

Innovation

• Adversarial misreporting of qubit error rates
• Statistical deviation analysis for error detection
• Impact on Greedy and COMDAP allocation frameworks