A model-free quantum stabilization framework uses sign-based Lyapunov descent, adaptive gains, and finite-difference LaSalle analogue to guarantee asymptotic stability in drift-free cases and practical ISS with unknown drift and noise.
Y., Boixo, S., Smelyanskiy, V
6 Pith papers cite this work. Polarity classification is still indexing.
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VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.
A two-stage PINN optimizes pulse sequences for silicon exchange-only spin qubits to achieve over 99% noise-averaged fidelity while shortening pulse durations by 20-40%.
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.
citing papers explorer
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Model-Free Quantum Stabilization via Finite-Difference Lyapunov Control
A model-free quantum stabilization framework uses sign-based Lyapunov descent, adaptive gains, and finite-difference LaSalle analogue to guarantee asymptotic stability in drift-free cases and practical ISS with unknown drift and noise.
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Toward General Quantum Control with Physics-Informed Large Language Models
VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
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Detection of noise correlations in two qubit systems by Machine Learning
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.
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Exchange-Only Silicon Based Spin Qubits: Charge Noise, PINN Optimised Pulse Sequences,and Gate-Level Fidelity
A two-stage PINN optimizes pulse sequences for silicon exchange-only spin qubits to achieve over 99% noise-averaged fidelity while shortening pulse durations by 20-40%.
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Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
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Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.