Metadata-Version: 2.1
Name: tensorcircuit
Version: 0.0.220318
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

<h1 align="center"> TENSORCIRCUIT </h1>

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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 simulator 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 [documentations](/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))
```

## Contributing

For contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).


### Install

For users, ``pip install tensorcircuit`` is enough. (Extra package installation may be required for some features.)

For developers, we suggest to first configure 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. [Dockerfiles](/docker) may also be helpful for building a good development enviroment.

## Researches and applications

### DQAS

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

### VQNHE

For 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 application of VQEX on MBL phase identification, see [tutorial](https://github.com/quclub/tensorcircuit-tutorials/blob/master/tutorials/vqex_mbl.ipynb).
Reference paper: https://arxiv.org/pdf/2111.13719.pdf.


