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Code and data corresponding to the paper "Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer"

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fhvqe

Variational Quantum Eigensolver for the Fermi-Hubbard model. Code developed by Phasecraft Ltd and corresponding to the paper

Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer. Stasja Stanisic, Jan Lukas Bosse, Filippo Maria Gambetta, Raul A. Santos, Wojciech Mruczkiewicz, Thomas E. O'Brien, Eric Ostby and Ashley Montanaro. arXiv:2112.02025

Installation

Recommended: using anaconda or similar virtual environment software. Requires:

  • python (3.9.7)
  • cirq (0.11.1)
  • openfermion (1.1.0)
  • numpy (1.21.2)
  • gitpython
  • pandas

Installation of cirq from github recommended.

python -m pip install git+https://github.com/quantumlib/Cirq.git

Usage

The configuration files for various experiments can be found in the config directory.

The vqe_experiment module takes three command-line arguments. One tells it the location of the configuration file: -f / --config. The other gives whether the experiment is run as simulation (-s / --simulation), exact (-e / --exact) or on chip. These settings overwrite the setting in the configuration file.

Assuming we want to run vqe_experiment.py, with configuration file config.json, and simulated, a call would be something like:

python -m fhvqe.vqe_experiment -s -f config.json

Similarly for exact, a call would be something like:

python -m fhvqe.vqe_experiment -e -f config.json

There can be multiple configuration files given at the same time.

python -m fhvqe.vqe_experiment -e -f config1.json -f config2.json

Finally, there are various debug flags throughout the code that can be switched off when the experiment is running on the actual device (or generally, for performance purposes) by adding a call to -O.

python -O -m fhvqe.vqe_experiment -f config.json

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Code and data corresponding to the paper "Observing ground-state properties of the Fermi-Hubbard model using a scalable algorithm on a quantum computer"

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