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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.

Getting started with TenCirChem by installing the package via pip:

pip install tencirchem

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 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).