https://github.com/tencent-quantum-lab/tencirchem
Quantum computational chemistry based on TensorCircuit
https://github.com/tencent-quantum-lab/tencirchem
chemistry computational-chemistry cupy jax nisq pyscf python quantum-circuit quantum-computing quantum-noise quantum-simulation tensor-network theoretical-chemistry
Last synced: 11 months ago
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Quantum computational chemistry based on TensorCircuit
- Host: GitHub
- URL: https://github.com/tencent-quantum-lab/tencirchem
- Owner: tencent-quantum-lab
- License: other
- Created: 2023-03-18T06:43:59.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2024-01-17T12:32:51.000Z (about 2 years ago)
- Last Synced: 2024-04-27T00:23:27.883Z (almost 2 years ago)
- Topics: chemistry, computational-chemistry, cupy, jax, nisq, pyscf, python, quantum-circuit, quantum-computing, quantum-noise, quantum-simulation, tensor-network, theoretical-chemistry
- Language: Python
- Homepage:
- Size: 2.43 MB
- Stars: 55
- Watchers: 0
- Forks: 8
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TenCirChem
[](https://github.com/tencent-quantum-lab/TenCirChem/actions)
[](https://app.codecov.io/github/tencent-quantum-lab/TenCirChem)
[](https://pypi.org/project/tencirchem/)
[](https://tencent-quantum-lab.github.io/TenCirChem/index.html)
[](https://mybinder.org/v2/gh/tencent-quantum-lab/TenCirChem/master?labpath=docs%2Fsource%2Ftutorial_jupyter)
English | [简体中文](https://github.com/tencent-quantum-lab/TenCirChem/blob/master/README_CN.md)
TenCirChem is an efficient and versatile quantum computation package for molecular properties.
TenCirChem is based on [TensorCircuit](https://github.com/tencent-quantum-lab/tensorcircuit)
and is optimized for chemistry applications.
## Easy Installation
Getting started with TenCirChem by installing the package via pip:
```sh
pip install tencirchem
```
## Simple to Use
TenCirChem is written in pure Python, and its use is straightforward. Here's an example of calculating UCCSD:
```python
from tencirchem import UCCSD, M
d = 0.8
# distance unit is angstrom
h4 = M(atom=[["H", 0, 0, d * i] for i in range(4)])
# configuration
uccsd = UCCSD(h4)
# calculate and returns energy
uccsd.kernel()
# analyze result
uccsd.print_summary(include_circuit=True)
```
Running uccsd.kernel() in the above code determines the optimized circuit ansatz parameters and VQE energy.
TenCirChem also allows the user to supply custom parameters. Here's an example:
```python
import numpy as np
from tencirchem import UCCSD
from tencirchem.molecule import h4
uccsd = UCCSD(h4)
# evaluate various properties based on custom parameters
params = np.zeros(uccsd.n_params)
print(uccsd.statevector(params))
print(uccsd.energy(params))
print(uccsd.energy_and_grad(params))
```
For more examples and customization,
please refer to the [documentation](https://tencent-quantum-lab.github.io/TenCirChem/index.html)
## Exciting Features
TenCirChem's features include:
- Statics module
- UCC calculation with UCCSD, kUpCCGSD, pUCCD at an extremely fast speed
- Noisy circuit simulation via TensorCircuit
- Custom integrals, active space approximation, RDMs, GPU support, etc.
- Dynamics module
- Transformation from [renormalizer](https://github.com/shuaigroup/Renormalizer) models to qubit representation
- VQA algorithm based on JAX
- Built-in models: spin-boson model, pyrazine S1/S2 internal conversion dynamics
## Design principle
TenCirChem is designed to be:
- Fast
- UCC speed is 10000x faster than other packages
- Example: H8 with 16 qubits in 2s (CPU). H10 with 20 qubits in 14s (GPU)
- Achieved by analytical expansion of UCC factors and exploitation of symmetry
- Easy to hack
- Avoid defining new classes and wrappers when possible
- Example: Excitation operators are represented as `tuple` of `int`. An operator pool is simply a `list` of `tuple`
- Minimal class inheritance hierarchy: at most two levels
- Expose internal variables through class attributes
## License
TenCirChem is released under Academic Public License.
See the [LICENSE file](https://github.com/tencent-quantum-lab/TenCirChem/blob/master/LICENSE) for details.
In short, you can use TenCirChem freely for non-commercial/academic purpose
and commercial use requires a commercial license.
## Citing TenCirChem
If this project helps in your research, please cite our software whitepaper:
[TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era](https://arxiv.org/abs/2303.10825)
which is also a good introduction to the software.
## Research and Applications
### Variational basis state encoder
An efficient algorithm to encode phonon states in electron-phonon systems for quantum computation.
See [examples](https://github.com/tencent-quantum-lab/TenCirChem/tree/master/example)
and the [tutorial](https://tencent-quantum-lab.github.io/TenCirChem/tutorial_jupyter/vbe_tutorial_td.html).
Reference paper: https://arxiv.org/pdf/2301.01442.pdf (published in PRR).