https://github.com/zapatacomputing/qb-gsee-benchmark
A comprehensive suite for benchmarking Ground State Energy Estimation (GSEE) algorithms.
https://github.com/zapatacomputing/qb-gsee-benchmark
quantum-computing
Last synced: 5 months ago
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A comprehensive suite for benchmarking Ground State Energy Estimation (GSEE) algorithms.
- Host: GitHub
- URL: https://github.com/zapatacomputing/qb-gsee-benchmark
- Owner: zapatacomputing
- License: apache-2.0
- Created: 2024-07-25T17:05:48.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-07T22:06:56.000Z (over 1 year ago)
- Last Synced: 2026-01-14T19:21:45.722Z (5 months ago)
- Topics: quantum-computing
- Language: Python
- Homepage:
- Size: 886 KB
- Stars: 4
- Watchers: 6
- Forks: 4
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# QB-GSEE-Benchmark
QB-GSEE-Benchmark is a comprehensive suite for benchmarking Ground State Energy Estimation (GSEE) algorithms developed by the DARPA Quantum Benchmarking (QB) Program. This tool enables performers to run a subset of Hamiltonian instances and assess the performance of their algorithms in terms of accuracy, runtime, and hardware utilization.
## What is QB-GSEE-Benchmark?
This repository includes:
- A curated list of Hamiltonian instances for benchmarking, sourced from the [qb-gsee-problem-instances repository](https://github.com/jp7745/qb-gsee-problem-instances).
- Example code to access and process these instances.
- Scripts to evaluate and summarize the performance of GSEE algorithms.
Performers will generate solution files that detail:
- Estimated energies or accuracies
- Computation runtime
- Hardware specifications
- Other relevant metrics
These solution files can then be used with this tool to generate comprehensive performance summaries and interface with the "Bubble ML" GUI for advanced performance exploration.
## Installation
Clone this repository to get started:
```bash
git clone https://github.com/yourusername/qb-gsee-benchmark.git
cd qb-gsee-benchmark
```
## Usage
1. **Prepare the Data**:
Download and prepare the problem instances from the qb-gsee-problem-instances repository. Follow their guide on downloading associated data files.
2. **Running Benchmarks**:
Execute the benchmark scripts with your solution files:
```bash
python run_benchmark.py solution_file.json
```
3. **View Results**:
After running the benchmarks, generate a summary of performance:
```bash
python summarize_performance.py solution_file.json
```
4. **Explore with Bubble ML**:
Launch the Bubble ML GUI to visualize and explore performance details:
```bash
python bubble_ml_gui.py
```
## Contributing
Contributions to the QB-GSEE-Benchmark are welcome! Please consider the following steps:
- Fork the repository.
- Create a feature branch (`git checkout -b feature-branch`).
- Commit your changes (`git commit -am 'Add some feature'`).
- Push to the branch (`git push origin feature-branch`).
- Open a Pull Request.
## License
This project is licensed under the Apache License, Version 2.0 - see the [LICENSE](LICENSE) file for details.
## Acknowledgments
This software was developed as a part of [DARPA Quantum Benchmarking program](https://www.darpa.mil/program/quantum-benchmarking).