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quant-ph2025★ 2 cited
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws within neural architectures.…
quant-ph2023★ 2 cited
Exponential Quantum Speedup for Simulation-Based Optimization Applications
Jonas Stein, Lukas Müller, Leonhard Hölscher +10
The simulation of many industrially relevant physical processes can be executed up to exponentially faster using quantum algorithms. However, this speedup can only be leveraged if…