PerOpQ 0.1.1

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PerOpQ

PerOpQ is an implementation of Perturbative Optimization for Hamiltonian Simulation, based on the ideas presented in arXiv:2306.07208. The goal of the library is to provide optimized circuits to perform Hamiltonian simulations with less error than with naive Trotter decompositions. The basic idea is to evaluate the error of an ansatz unitary (decomposed into a set of one-qubit and two-qubit gates) by applying recursively the Baker–Campbell–Hausdorff (BCH) formula and truncating the resulting series at a given order. PerOpQ takes a model defined by a list of Pauli strings, a final time and a circuit depth as inputs and returns an optimized circuit as a result.

Development

This project uses Poetry for packaging and dependency management and Nox for task automation.

Recommended development setup

It is recommended to do these steps in a fresh python virtual environment

Install development tools:

pip install -r dev-tool-requirements.txt

Local development with Nox (recommended)

Nox can be used to automate various development tasks running in isolated python environments. The following Nox sessions are provided:

• pre-commit: run the configured pre-commit hooks within .pre-commit-config.yaml, this includes linting with black and pylint
• mypy: run type checks using mypy
• tests: run the unit tests
• docs-build: build the documentation

To run a session use:

nox -s <session_name>

To save time, reuse the session virtual environment using the -r option, i.e. nox -rs <session_name> (may cause errors after a dependency update).

Pre-commit can be used to run the pre-commit hooks before each commit. This is recommended. To set up the pre-commit hooks to run automatically on each commit run:

nox -s pre-commit -- install

Afterward, the pre-configured hooks will run on all changed files in a commit and the commit will be rejected if the hooks find errors. Some hooks will correct formatting issues automatically (but will still reject the commit, so that the git commit command will need to be repeated).