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Fast Library for Quantum Computing in PyTorch
Speedup Research on Parameterized Quantum Circuit, QML and ML for Quantum
Quantum Computing made easy with PyTorch on CPU/GPU
Construct, simulate and train quantum circuit the same as constructing a normal PyTorch Model
Dynamic Computation Graph
Easily obtain the intermediate data and perform debugging.
Automatic Gradient Computation
Obtain gradients on circuit parameters with back-propagation
Gate & Pulse Level Support
Simulation support across different levels.
Batch Mode Processing
Simulating a batch of quantum circuit, fully leverage the parallel computing power on GPUs/CPUs
All the operations are implemented with PyTorch native operations
Build the quantum circuit model by specifcy the quantum gates in the __init__ function and then specify the circuit structure in the forward function
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