Skip to content

Latest commit

 

History

History
128 lines (83 loc) · 4.6 KB

README.md

File metadata and controls

128 lines (83 loc) · 4.6 KB
Raw

Qforte

Python Package using Conda codecov Documentation Status

QForte is comprehensive development tool for new quantum simulation algorithms that also contains black-box implementations of a wide variety of existing algorithms. It incorporates functionality for handling molecular Hamiltonians, fermionic encoding, automated ansatz construction, time evolution, state-vector simulation, operator averaging, and computational resource estimation. QForte requires only a classical electronic structure package as a dependency.

Black Box Algorithm Implementations

  • Disentangled (Trotterized) unitary coupled cluster variational quantum eigensolver (dUCCVQE)

    • QForte will treat up to hex-tuple particle-hole excitations (SDTQPH) or generalized singled and doubles (GSD)

  • Adaptive derivative-assembled pseudo Trotterized VQE (ADAPT-VQE)

  • Disentangled (factorized) unitary coupled cluster projective quantum eigensolver (dUCCPQE)

    • QForte will treat up to hex-tuple particle-hole excitations (SDTQPH)

  • Selected projective quantum eigensolver (SPQE)

  • Single reference Quantum Krylov diagonalization (SRQK)

  • Multireference selected quantum Krylov diagonalization (MRSQK)

  • Quantum imaginary time evolution (QITE)

  • Quantum Lanczos (QL)

  • Pilot implementation of Quantum phase estimation (QPE)

Install Dependencies (Recommended)

create and activate qforte environment:

conda create -n qforte_env python
conda activate qforte_env

install required packages:

conda install psi4 -c conda-forge
conda install scipy>=1.11

Installation (For Development)

git clone --recurse-submodules https://github.com/evangelistalab/qforte.git
cd qforte
python setup.py develop

To supply custom arguments to cmake for installation, you can either edit setup.py or CMakeLists.txt.

run tests:

cd tests
pytest

Getting Started

QForte's state-vector simulator can be used for simple tasks, such as the construction of Bell states, and is the backbone for implementation of all the black-box algorithms. Below are a few examples, more detailed descriptions of QForte's features and algorithms can be found in the release article (https://arxiv.org/abs/2108.04413) and in the Tutorial notebooks.

import qforte

# Construct a Bell state.
computer = qforte.Computer(2)
computer.apply_gate(qforte.gate('H',0))
computer.apply_gate(qforte.gate('cX',1,0))

## Run black-box algorithms for LiH molecule. ##
from qforte import *

# Define the geometry list.
geom = [('Li', (0., 0., 0.0)), ('H', (0., 0., 1.50))]

# Get the molecule object that now contains the fermionic and qubit Hamiltonians.
LiHmol = system_factory(build_type='psi4', mol_geometry=geom, basis='STO-3g', run_fci=1)

# Run the dUCCSD-VQE algorithm for LiH.
vqe_alg = UCCNVQE(LiHmol)
vqe_alg.run(opt_thresh=1.0e-2, pool_type='SD')

# Run the single reference QK algorithm for LiH.
srqk_alg = SRQK(LiHmol)
srqk_alg.run()

# Get ground state energies predicted by the algorithms, compare to FCI. 
vqe_gs_energy = vqe_alg.get_gs_energy()
srqk_gs_energy = srqk_alg.get_gs_energy()
fci_energy = LiHmol.fci_energy

Publications

QForte has been used to implement the novel algorithms presented in the following publications:

  1. Stair, Nicholas H., and Francesco A. Evangelista. Simulating Many-Body Systems with a Projective Quantum Eigensolver. PRX Quantum 2.3 (2021): 030301. https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.2.030301
  1. Stair, Nicholas H., Renke Huang, and Francesco A. Evangelista. A Multireference Quantum Krylov Algorithm for Strongly Correlated Electrons. Journal of chemical theory and computation 16.4 (2020): 2236-2245. https://pubs.acs.org/doi/10.1021/acs.jctc.9b01125

QForte's release article:

  1. Stair, Nicholas H., and Francesco A. Evangelista. Qforte: an efficient state simulator and quantum algorithms library for molecular electronic structure. arXiv preprint arXiv:2108.04413 (2021). https://arxiv.org/abs/2108.04413

Copyright

Copyright (c) 2019, The Evangelista Lab