Parallel-sequential circuits provide a tunable family of quantum circuit layouts that numerically outperform brickwall, sequential, and log-depth circuits for 1D ground-state preparation under realistic noise models.
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A hierarchy of SDPs yields lower bounds on spectral gaps of frustration-free Hamiltonians that encompass and improve upon Knabe's bound on 1D spin chains.
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
citing papers explorer
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State preparation with parallel-sequential circuits
Parallel-sequential circuits provide a tunable family of quantum circuit layouts that numerically outperform brickwall, sequential, and log-depth circuits for 1D ground-state preparation under realistic noise models.
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A Hierarchy of Spectral Gap Certificates for Frustration-Free Spin Systems
A hierarchy of SDPs yields lower bounds on spectral gaps of frustration-free Hamiltonians that encompass and improve upon Knabe's bound on 1D spin chains.
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Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.