Tensor network simulations act as effective surrogate models for training QAOA on large 2D lattices, overcoming limits of parameter transfer from small instances and remaining classically feasible with moderate bond dimensions.
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Distributions of noisy expectation values over sets of measurement operators on random mixed states are derived combinatorially and approximated by fitted effective global-depolarizing models that match peaks in brickwork circuit simulations but deviate in tails.
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Tensor network surrogate models for variational quantum computation
Tensor network simulations act as effective surrogate models for training QAOA on large 2D lattices, overcoming limits of parameter transfer from small instances and remaining classically feasible with moderate bond dimensions.
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Distributions of Noisy Expectation Values over Sets of Measurement Operators
Distributions of noisy expectation values over sets of measurement operators on random mixed states are derived combinatorially and approximated by fitted effective global-depolarizing models that match peaks in brickwork circuit simulations but deviate in tails.