Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
arXiv preprint arXiv:2205.12481 , year=
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Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.
citing papers explorer
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Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems
Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Optimizing resource allocation for accuracy in noisy variational quantum algorithms
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.