SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
Operator sampling for shot-frugal optimization in variational algorithms
6 Pith papers cite this work. Polarity classification is still indexing.
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σ-VQE uses low-depth circuits and an energy-selective cost function to preferentially prepare quantum many-body scar states on NISQ devices.
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.
In noisy quantum settings, Krylov subspace methods are limited by statistical fluctuations rather than ill-conditioning, and new imaginary and unitary filters can validate solutions without reference to the true eigenspectrum.
Optimized q-sc-EOM on quantum hardware yields accurate excited-state energies for challenging molecular bond-breaking cases after reducing measurement scaling to O(N^5) and applying readout and symmetry error mitigation.
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.
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PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a software library extending automatic differentiation to hybrid quantum-classical systems for variational quantum algorithms.