A matrix decomposition into linear combinations of non-unitaries produces an LCU for any Carleman-linearized polynomial system and yields an O(α² Q²) term count for the 3D lattice Boltzmann equation independent of spatial or temporal grid points.
Aaronson, Read the fine print, Nature Physics 11, 291 (2015)
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HQRN creates an exact functional match to classical residual networks on basis inputs while using quantum correlations for better performance on mixed states in digit recognition and entanglement classification.
SparQSim is a sparse-state quantum simulator in C++ supporting QRAM that outperforms dense Schrödinger simulators on high-sparsity benchmark circuits and produces consistent results for quantum linear system solvers.
citing papers explorer
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Quantum Data Loading for Carleman Linearized Systems: Application to the Lattice-Boltzmann Equation
A matrix decomposition into linear combinations of non-unitaries produces an LCU for any Carleman-linearized polynomial system and yields an O(α² Q²) term count for the 3D lattice Boltzmann equation independent of spatial or temporal grid points.
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Bridge the Gap between Classical and Quantum Neural Networks with Residual Connections
HQRN creates an exact functional match to classical residual networks on basis inputs while using quantum correlations for better performance on mixed states in digit recognition and entanglement classification.
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SparQSim: Simulating Scalable Quantum Algorithms via Sparse Quantum State Representations
SparQSim is a sparse-state quantum simulator in C++ supporting QRAM that outperforms dense Schrödinger simulators on high-sparsity benchmark circuits and produces consistent results for quantum linear system solvers.