torchquantum and tensorcircuit
Both are quantum software frameworks designed for simulating quantum circuits and machine learning, making them **competitors** as they offer similar functionalities for developing and deploying quantum algorithms, with `torchquantum` having broader support for real quantum computer deployment and `tensorcircuit` focusing on tensor network optimizations.
About torchquantum
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
This framework helps quantum computing researchers and algorithm designers quickly simulate quantum circuits and quantum machine learning models on classical hardware. You can input descriptions of quantum circuits or quantum neural networks using familiar PyTorch commands, and it outputs simulated quantum states, measurement results, and enables gradient calculations for optimization. It's designed for quantum algorithm researchers, quantum machine learning practitioners, and those working on quantum neural networks.
About tensorcircuit
tencent-quantum-lab/tensorcircuit
Tensor network based quantum software framework for the NISQ era
TensorCircuit helps quantum algorithm researchers and quantum computing scientists design, simulate, and test quantum circuits and algorithms. It takes your quantum circuit designs and parameters, providing simulation results like wavefunctions, expectation values, and samples. This framework is ideal for those working on quantum-classical hybrid algorithms and variational quantum algorithms, especially in the NISQ era.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work