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Physics-Informed Artificial Intelligence for Inverse Design and Decoherence Mitigation in Superconducting Transmon Architectures

Year: 2026

Type: article

Abstract: Scaling superconducting quantum processors is fundamentally limited by environmentally induced decoherence arising from nanoscale fabrication defects. Conventional heuristic microwave control protocols cannot dynamically adapt to device-specific material variations, while purely data-driven deep learning approaches often violate physical constraints. Here we introduce a compact quantum physics-informed neural network (QPINN) that learns device-aware microwave control pulses for superconducting qubits. Trained on empirical defect characteristics extracted from 162 InAs nanosheet Josephson junction measurements, the model embeds the Lindblad master equation directly into its loss function to enforce physical consistency. Open-system simulations demonstrate a 42% reduction in leakage to non-computational states. Hardware experiments executed on the 156-qubit IBM Quantum processor show an increase in Ramsey dephasing time from 83.3 ± 7.8 μs to 113.0 ± 1.1 μs while maintaining gate fidelity exceeding 99.99%.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.19044883

Authors: RAMISETTY, Ravi Raja, kudipudi, gopi chand, Aravapalli, Anvith

Institutions: Woxsen School of Business, Koneru Lakshmaiah Education Foundation

Language: English

Topic: Quantum Information and Cryptography

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7136251633

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