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.
Self-correcting quantum many-body control using rein- forcement learning with tensor networks.Nature Machine Intelligence, 5(7):780–791, Jul 2023
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Optimal parameters in a two-parameter family of time-dependent dissipation rates are determined to maximize speed-up in multi-step Pontus-Mpemba protocols for open quantum systems.
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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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Optimal speed-up of multi-step Pontus-Mpemba protocols
Optimal parameters in a two-parameter family of time-dependent dissipation rates are determined to maximize speed-up in multi-step Pontus-Mpemba protocols for open quantum systems.