Optimized non-uniform shot allocation guided by an equation-of-motion error cost function reduces measurement overhead by >2x and improves fidelity in noisy imaginary-time VQDS for 1D Ising ground states.
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The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.
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Sampling Noise and Optimized Measurement Distribution in Imaginary-Time Quantum Dynamics Simulations
Optimized non-uniform shot allocation guided by an equation-of-motion error cost function reduces measurement overhead by >2x and improves fidelity in noisy imaginary-time VQDS for 1D Ising ground states.
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The power and limitations of learning quantum dynamics incoherently
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, and demonstrates this on a 16-qubit hardware device.