A new compilation framework treats quantum channels as first-class objects via ChannelIR and LindFront, achieving up to 99% gate count reduction on Lindbladian benchmarks versus unoptimized and Stinespring baselines.
Schlimgen, Kade Head-Marsden, LeeAnn M
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A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
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A Compilation Framework for Quantum Simulation of Non-unitary Dynamics
A new compilation framework treats quantum channels as first-class objects via ChannelIR and LindFront, achieving up to 99% gate count reduction on Lindbladian benchmarks versus unoptimized and Stinespring baselines.
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Non-markovian neural quantum propagator and its application to the simulation of ultrafast nonlinear spectra
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.