Randomized Subsystem Descent reduces weighted Pauli weight in fermion-to-qubit mappings for Hubbard models up to 16x16 sites and molecular Hamiltonians with 54 modes.
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A new alternating stochastic optimization method for multi-objective problems that lowers computational cost per iteration by block and objective alternation while recovering classical convergence rates under convex, non-convex, and PL conditions.
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Randomized Subsystem Descent for Fermion-to-Qubit Mapping
Randomized Subsystem Descent reduces weighted Pauli weight in fermion-to-qubit mappings for Hubbard models up to 16x16 sites and molecular Hamiltonians with 54 modes.
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Stochastic block coordinate and function alternation for multi-objective optimization and learning
A new alternating stochastic optimization method for multi-objective problems that lowers computational cost per iteration by block and objective alternation while recovering classical convergence rates under convex, non-convex, and PL conditions.