Overview

This repository consists of three main components:

• env_setup/: Conda environment configuration files
• qbs/: Source code for the qbs algorithm
• reproduce/: Scripts and notebooks for reproducing the experiments in the paper

Installation

1. Set up the Conda environment

Navigate to the environment setup directory:

cd env_setup

Install the CPU-compatible environment:

conda env create -f cpu-env.yml

Activate the environment:

conda activate qbs

If the installation fails or is interrupted, remove the environment and reinstall:

conda remove -n qbs --all

2. GPU Support (Optional)

Our method is accelerated using the DDSIM backend and already runs efficiently on CPU.

GPU acceleration via qiskit-aer-gpu is optional and can speed up baseline simulations (e.g., HEA and QAOA), which involve many RX gates. However, the CPU environment alone is sufficient for reproducing all experimental results.

To enable GPU support on Linux, you must have CUDA 11.2 or later and a compatible GPU driver installed.

Option A: Preconfigured environment for CUDA 12.8

If CUDA 12.8 is available on your system, you can set up the environment directly with:

conda env create -f gpu-cuda12.8-env.yml

Option B: Manual installation for other CUDA versions

To add GPU support to an existing qbs environment, simply install the GPU simulator with:

pip install qiskit-aer-gpu

Verify Installation

Run the environment check script located in the env_setup/ directory:

python env_check.py

If your environment is correctly configured, you will see:

✅ CPU environment configured successfully!

If GPU support is also correctly set up, you will additionally see:

✅ GPU environment configured successfully!

The programs in reproduce/ will automatically detect whether GPU acceleration is available. No manual switching is required.

If the test fails, consider:

1. Ensuring the correct Conda environment is activated.

2. Making sure the Python environment is isolated from global site-packages. You may disable the user site by:

   export PYTHONNOUSERSITE=1

Troubleshooting

This section summarizes known issues that may arise during execution and provides suggested workarounds.

1. A process in the process pool was terminated abruptly while the future was running or pending.

These errors may occur when too many reproduction programs are executed simultaneously, causing excessive load on the CPU or GPU.

Solution: Run the reproduction programs one at a time, preferably in sequence. If the issue persists even with sequential execution, consider reducing parallelism by adjusting the num_processes variable in the script, or switching to a machine with more resources.

2. Output files are missing or not saved in the expected subdirectory

This usually happens when the script is executed from the wrong working directory, since output paths are relative to the execution location.

Solution: Make sure to run each script from within its corresponding subdirectory (e.g., figure_x/ or table_2/) so that output files are saved in the correct place.