Metadata-Version: 2.1
Name: tensorcircuit
Version: 0.0.220413
Summary: Quantum circuits on top of tensor network
Home-page: https://github.com/refraction-ray/tensorcircuit
Author: refraction-ray
Author-email: shixinzhang@tencent.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

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<p align="center"> English | <a href="README_cn.md"> 简体中文 </a></p>

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](/docs/source/quickstart.rst) and [Jupyter Tutorials](/docs/source/tutorials).

For more information and introductions, please refer to helpful [example scripts](/examples) and [full documentation](/docs/source). API docstrings and test cases in [tests](/tests) are also informative.

The following are some minimal demos.

Circuit manipulation:

```python
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:

```python
tc.set_backend("tensorflow")
tc.set_dtype("complex128")
tc.set_contractor("greedy")
```

Automatic differentiations with jit:

```python
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](/CONTRIBUTING.md).

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](https://github.com/google/TensorNetwork) package. [Dockerfile](/docker) is also provided.

## Researches and applications

### DQAS

For the application of Differentiable Quantum Architecture Search, see [applications](/tensorcircuit/applications).
Reference paper: https://arxiv.org/pdf/2010.08561.pdf.

### VQNHE

For the application of Variational Quantum-Neural Hybrid Eigensolver, see [applications](/tensorcircuit/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](https://github.com/quclub/tensorcircuit-tutorials/blob/master/tutorials/vqex_mbl.ipynb).
Reference paper: https://arxiv.org/pdf/2111.13719.pdf.


