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TenCirChem is an efficient and versatile quantum computation package for molecular properties. TenCirChem is based on TensorCircuit and is optimized for chemistry applications.

TenCirChem-NG is fully compatible with TenCirChem with more new features and bug fixes.

Getting started with TenCirChem-NG by installing the package via pip:

pip install tencirchem-ng

Some functionality of TenCirChem-NG requires JAX. Please install JAX following installation instructions or simply by pip install tencirchem-ng[jax].

TenCirChem is written in pure Python, and its use is straightforward. Here's an example of calculating UCCSD:

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:

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

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 models to qubit representation
  • VQA algorithm based on JAX
  • Built-in models: spin-boson model, pyrazine S1/S2 internal conversion dynamics

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

TenCirChem is released under Academic Public License. See the LICENSE file for details. In short, you can use TenCirChem-NG freely for non-commercial/academic purpose and commercial use requires a commercial license.

If this project helps in your research, please cite our software whitepaper:

TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era

which is also a good introduction to the software.

An efficient algorithm to encode phonon states in electron-phonon systems for quantum computation. See examples and the tutorial. Reference paper: https://arxiv.org/pdf/2301.01442.pdf (published in PRR).