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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 and Jupyter Tutorials.
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((tc.gates.z(), [1])))
print(c.perfect_sampling())
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(n=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.gates.num_to_tensor(1.0)
print(g(theta))
Install
pip install tensorcircuit.
Extra package installation may be required for some features.
Contributing
For contribution guidelines and notes, see CONTRIBUTING.
For developers, we suggest first configuring a good conda environment. The versions of dependence packages may vary in terms of development requirements. The minimum requirement is the TensorNetwork package. Dockerfile is also provided.
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.
2 maintainers
Metadata
2 maintainers
Release files for tensorcircuit 0.0.220402
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