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Functional Exploitation of Noise in Utility-Scale Quantum Optimization

An AFRL-funded project exploring how noise and nonideal dynamics can be characterized, modeled, and potentially exploited for utility-scale quantum optimization.

This project focuses on the functional role of noise in quantum optimization systems, performing theory research and hero-grade experimental campaigns on quantum devices to push the envelope of noise-directed techniques.

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Project publications

3 linked records

Staff involved

Davide Venturelli Filip Maciejewski Stuart Hadfield

Figure-derived thumbnail for Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits — 2026

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Noise-Directed Adaptive Remapping for Integer Optimization: from qubits to (encoded) qudits

Stuart Hadfield, Filip B. Maciejewski, Davide Venturelli arXiv preprint arXiv:2606.28234

arXiv

Figure-derived thumbnail for Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting — 2026

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Quantum Approximate Optimization via Noise-Directed Adaptive Warm-Starting

Filip B. Maciejewski, Stuart Hadfield, Oscar Wallis, George Pennington, Sebastian Brandhofer, Stefan Woerner, Daniel J. Egger, Davide Venturelli arXiv preprint arXiv:2607.09368

arXiv Code repository Hardware: IBM

Open paper

Improving Quantum Approximate Optimization by Noise-Directed Adaptive Remapping

Filip B Maciejewski, Jacob Biamonte, Stuart Hadfield, Davide Venturelli Quantum, 9, 1906

DOI arXiv Hardware: Rigetti