RandomMeas.jl is a modular Julia package implementing randomized measurement protocols and classical shadow estimators for quantum computing applications.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Engineering method using dynamic circuits to generate randomized algorithm distributions on quantum hardware, achieving 14000x speedup for Pauli measurements and enabling 10M-circuit classical shadows on 28-40 qubit hydrogen models.
Geometric partitioning of lattice Hamiltonians into local patches enables energy measurements in patch eigenbases, producing lower-variance estimators than Pauli grouping for eigenstates with rigorous guarantees even under depolarizing noise.
citing papers explorer
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Efficient implementation of randomized quantum algorithms with dynamic circuits
Engineering method using dynamic circuits to generate randomized algorithm distributions on quantum hardware, achieving 14000x speedup for Pauli measurements and enabling 10M-circuit classical shadows on 28-40 qubit hydrogen models.
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Shot-noise reduction for lattice Hamiltonians
Geometric partitioning of lattice Hamiltonians into local patches enables energy measurements in patch eigenbases, producing lower-variance estimators than Pauli grouping for eigenstates with rigorous guarantees even under depolarizing noise.