Characterizes constituents of n-qubit graph quantum ML models and supplies a toolbox enabling integration with classical models, generalization of prior GQML approaches, and classical pre-training.
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Derives improved mode-independent sample complexity bounds O(η log η) for fermionic classical shadows on particle-preserving operators and Slater determinant overlaps.
RandomMeas.jl is a modular Julia package implementing randomized measurement protocols and classical shadow estimators for quantum computing applications.
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
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RandomMeas.jl: A Julia Package for Randomized Measurements in Quantum Devices
RandomMeas.jl is a modular Julia package implementing randomized measurement protocols and classical shadow estimators for quantum computing applications.