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Fast Library for Quantum Computing in PyTorch
Speedup Research on Parameterized Quantum Circuit, QML and ML for Quantum
![](https://static.wixstatic.com/media/c837a6_eb322a4179e54d788eef6896e67f05d0~mv2.jpg/v1/fill/w_98,h_55,al_c,q_80,usm_0.66_1.00_0.01,blur_2,enc_auto/c837a6_eb322a4179e54d788eef6896e67f05d0~mv2.jpg)
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
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All the operations are implemented with PyTorch native operations
![Screen Shot 2022-09-17 at 12.21.10 AM.png](https://static.wixstatic.com/media/77d3d2_873fa96094ef421f9ccd5b588e5fc338~mv2.png/v1/fill/w_100,h_169,al_c,q_85,usm_0.66_1.00_0.01,blur_2,enc_auto/Screen%20Shot%202022-09-17%20at%2012_21_10%20AM.png)
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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