Matrix product state simulations of 2D Rayleigh-Bénard convection recover Nusselt number statistics with 1.8% error and a 9-fold reduction in degrees of freedom at Ra=10^10 using bond dimensions comparable to lower Ra cases.
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Tensor-network fractional-step method simulates incompressible flows in curvilinear coordinates with up to 20x field compression and 1000x operator compression while keeping errors below 0.3% versus finite differences.
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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Quantum-Inspired Simulation of 2D Turbulent Rayleigh-B\'enard Convection
Matrix product state simulations of 2D Rayleigh-Bénard convection recover Nusselt number statistics with 1.8% error and a 9-fold reduction in degrees of freedom at Ra=10^10 using bond dimensions comparable to lower Ra cases.
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Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
Tensor-network fractional-step method simulates incompressible flows in curvilinear coordinates with up to 20x field compression and 1000x operator compression while keeping errors below 0.3% versus finite differences.
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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.