Authors : Olatinwo Adenike Sola; Salako Najeem Abiodun; Olubudo Paul Ajibola

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/mv75rfjf

DOI : https://doi.org/10.38124/ijisrt/26sep077

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Quantum systems in near-term devices operate in noisy, open-system regimes where decoherence and control imperfections are unavoidable. This study presents a physics-informed neural-network framework for modelling, noiseaware simulation, and residual-based control of open quantum systems, validated against a closed-form Lindblad solution for a driven, amplitude-damped qubit rather than a numerical reference. Across three random seeds, the trained network reproduces population and coherence dynamics with a mean RMSE of 0.00060 and a standard deviation of 0.00054. The mixed-state fidelity exceeds 0.999999 over the evaluation trajectory, while the squared Bloch-vector norm remains within the physical bound for the main modelling benchmark. Removing the Fourier-feature input encoding produces a 70-fold degradation in RMSE, confirming a strong representational benefit for the oscillatory dynamics. Direct comparison against fourth-order Runge-Kutta integration shows the classical solver to be approximately five orders of magnitude faster and two orders more accurate at this scale; the value of the proposed framework for a single qubit therefore lies in data-free, physics-constrained modelling rather than computational speed. A residual-based state-transfer control demonstration and a control-effort sweep further reveal a clear fidelity-versus-energy trade-off and a marked reduction in seed-to-seed variation as regularisation increases. These results support physics-constrained learning as a faithful approach to singlequbit open-system modelling and control, while motivating further validation on multi-qubit systems before broader scalability claims are made.

Keywords : Decoherence, Fourier Features, Lindblad Master Equation, Open Quantum Systems, Physics-Informed Neural Networks, Quantum Control, Quantum Simulation, Scientific Machine Learning.

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