Noisy Variational Quantum Algorithm Simulation via Knowledge Compilation for Repeated Inference

This artifact demonstrates a new way to perform quantum circuit simulation.
We convert quantum circuits into probabilistic graphical models, which are then compiled into a format that enables efficient repeated queries.

The artifact consists of a Docker image which includes Google Cirq, a quantum programming framework, which we have extended to use our proposed approach as a quantum circuit simulation backend.
Also in the Docker image are two quantum circuit simulators based on existing approaches which we compare against as evaluation baselines.

We offer the Docker image via three routes: a hosted version on Docker Hub provides the latest version of our software and requires minimal setup; a Dockerfile is provided to show how to replicate our environment from scratch; and finally a stable archival version is available on Zenodo.

With minimal setup, you can run test cases in our Docker container showing the validity of our approach.
We test our quantum circuit simulation approach using the randomized test harness that Google Cirq uses to test its quantum circuit simulation back ends.
We also demonstrate correct simulation results for a benchmark suite of quantum algorithms.

The Docker image contains performance benchmarking experiments that replicate results of our paper at reduced input problem sizes.
The experiment scripts generate PDFs showing graphs that plot simulation wall clock time against input quantum circuit sizes.
The input problem sizes are large enough to show that our proposed approach achieves a speedup versus existing simulation tools.