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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Thermalization time in a boundary-coupled 1D chain with approximate pair-flip constraints scales exponentially with system size due to configuration-space bottlenecks.
Efficient witnesses and testing algorithms based on stabilizer Rényi entropy certify and quantify magic in mixed states, with experimental demonstration on IonQ hardware showing robustness under strong noise.
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.
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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Exponentially slow thermalization and the robustness of Hilbert space fragmentation
Thermalization time in a boundary-coupled 1D chain with approximate pair-flip constraints scales exponentially with system size due to configuration-space bottlenecks.
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Efficient witnessing and testing of magic in mixed quantum states
Efficient witnesses and testing algorithms based on stabilizer Rényi entropy certify and quantify magic in mixed states, with experimental demonstration on IonQ hardware showing robustness under strong noise.
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Quantum Convolutional Neural Networks are Effectively Classically Simulable
QCNNs are classically simulable via Pauli shadows on low-bodyness subspaces of locally-easy datasets, with explicit simulation demonstrated up to 1024 qubits for phases of matter classification.