This release is a pre-release and may not be stable for production use.
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TensorCircuit is the next generation of quantum circuit simulators with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.
TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.
Getting Started
Please begin with Quick Start.
For more information and introductions, please refer to helpful example scripts and full documentation. API docstrings and test cases in tests are also informative.
The following are some minimal demos.
- Circuit manipulation:
import tensorcircuit as tc
c = tc.Circuit(2)
c.H(0)
c.CNOT(0,1)
c.rx(1, theta=0.2)
print(c.wavefunction())
print(c.expectation_ps(z=[0, 1]))
print(c.sample())
- Runtime behavior customization:
tc.set_backend("tensorflow")
tc.set_dtype("complex128")
tc.set_contractor("greedy")
- Automatic differentiations with jit:
def forward(theta):
c = tc.Circuit(2)
c.R(0, theta=theta, alpha=0.5, phi=0.8)
return tc.backend.real(c.expectation((tc.gates.z(), [0])))
g = tc.backend.grad(forward)
g = tc.backend.jit(g)
theta = tc.array_to_tensor(1.0)
print(g(theta))
Install
The package is purely written in Python and can be obtained via pip as:
pip install tensorcircuit
And we recommend you install this package with tensorflow also installed as:
pip install tensorcircuit[tensorflow]
Other optional dependencies include [torch], [jax] and [qiskit].
For nightly build of tensorcircuit with new features, try:
pip uninstall tensorcircuit
pip install tensorcircuit-nightly
We also have Docker support.
Advantages
-
Tensor network simulation engine based
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JIT, AD, vectorized parallelism compatible, GPU support
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Efficiency
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Time: 10 to 10^6 times acceleration compared to tfq or qiskit
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Space: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)
-
-
Elegance
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Flexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisions
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API design: quantum for humans, less code, more power
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Citing TensorCircuit
This project is released by Tencent Quantum Lab and is currently maintained by Shi-Xin Zhang with contributions from the lab and open source community.
If this project helps in your research, please cite our software whitepaper:
TensorCircuit: a Quantum Software Framework for the NISQ Era
which is also a good introduction for the software.
Contributing
For contribution guidelines and notes, see CONTRIBUTING.
We welcome issues, PRs, and discussions from everyone, and these are all hosted on GitHub.
Researches and Applications
DQAS
For the application of Differentiable Quantum Architecture Search, see applications. Reference paper: https://arxiv.org/pdf/2010.08561.pdf.
VQNHE
For the application of Variational Quantum-Neural Hybrid Eigensolver, see applications. Reference paper: https://arxiv.org/pdf/2106.05105.pdf and https://arxiv.org/pdf/2112.10380.pdf.
VQEX - MBL
For the application of VQEX on MBL phase identification, see the tutorial. Reference paper: https://arxiv.org/pdf/2111.13719.pdf.
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Release files for tensorcircuit-nightly 0.4.1.dev20221004
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
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|---|---|---|---|---|
| tensorcircuit_nightly-0.4.1.dev20221004-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 460.7 kB
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