A digital quantum processor simulates the 1D Fermi-Hubbard model on up to 120 qubits, observing spin-charge separation and achieving quantitative agreement with TDVP while running up to 3000 times faster in wall-clock time for long evolutions.
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Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
A tunable collision model for non-Markovian dynamics is used to study coherent transport, revealing that Markovian environments can sometimes enhance transport by reducing information loss.
Light cone cancellation decomposes VQE circuits for Max-Cut into smaller subcircuits, yielding higher approximation ratios on simulated noisy backends up to 100 qubits compared to standard VQE.
citing papers explorer
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Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor
A digital quantum processor simulates the 1D Fermi-Hubbard model on up to 120 qubits, observing spin-charge separation and achieving quantitative agreement with TDVP while running up to 3000 times faster in wall-clock time for long evolutions.
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Recurrent Quantum Feature Maps for Reservoir Computing
Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
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Tunable non-Markovian dynamics in a collision model: an application to coherent transport
A tunable collision model for non-Markovian dynamics is used to study coherent transport, revealing that Markovian environments can sometimes enhance transport by reducing information loss.
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Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut
Light cone cancellation decomposes VQE circuits for Max-Cut into smaller subcircuits, yielding higher approximation ratios on simulated noisy backends up to 100 qubits compared to standard VQE.