Noise-Adaptive Quantum Algorithms for Large-Scale Optimization in Post-Quantum Security Architectures

doi:10.23940/ijpe.26.07.p1.363373

Abstract

Abstract: The emergence and potential of large-scale quantum computing is a threat to existing cryptographic infrastructures but also a powerful tool for optimization. Yet, the near-term utility of quantum devices is severely limited due to noise, decoherence, and lack of confidentiality in untrusted cloud execution environments. In this paper, the authors present an algorithm named Noise-Adaptive Secure Optimization (NASA), which is a single approach that solves the crucial intersection between large-scale optimization, real-time noise adaptation, and cryptographic confidentiality of post-quantum security architectures. NASA combines four core innovations: (1) a real-time noise profiling module, which is a continuous characterization of device decoherence times, gate error rates and readout fidelities; (2) a reinforcement learning agent, which dynamically selects optimal adaptation strategies, including pulse-level corrections, Hamiltonian remapping and ansatz switching, based on instantaneous noise conditions; (3) a lightweight quantum one-time pad confidentiality layer, which obfuscates the circuit structure and measurement outcomes with minimal runtime overhead; and (4) a noise-aware objective function that balances energy minimization with fidelity estimates to ensure robust optimization. The algorithm is validated using extensive simulations and hardware experiments on standard MaxCut benchmarks and cryptanalytically-relevant Learning with Errors (LWE) instances. Results show that the success rates of the algorithms (86% on MaxCut, 79% LWE at n=10) are superior for the algorithms developed by the National Aeronautics and Space Administration (NASA) compared to non-adaptive baselines, the circuit fidelity is high (0.89), and the additional runtime overhead of the algorithms is 18% while they deliver 128 bits of cryptographic security. Furthermore, the number of qubits that can be processed in the benchmarks is scaled by the size of the problem (up to 35 qubits) that is much larger than any baseline, proving that noise-adaptive techniques are required to get meaningful results from NISQ devices in security-sensitive applications.

Key words: noise-adaptive quantum algorithms, variational quantum algorithms, post-quantum cryptography, quantum error mitigation, reinforcement learning

Pushpendra Kumar Verma, Sandeep Gupta, and Tadiwa Elisha Nyamasvisva. Noise-Adaptive Quantum Algorithms for Large-Scale Optimization in Post-Quantum Security Architectures [J]. Int J Performability Eng, 2026, 22(7): 363-373.

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