Status: Technical Report (ERAU, Fall 2025)
Preprint: “Parallelizing the Variational Quantum Eigensolver: From JIT Compilation to Multi-GPU Scaling” — In Preparation (with Ashton Steed)
Overview
GPU-accelerated Variational Quantum Eigensolver (VQE) for quantum chemistry simulations. Achieved 117× speedup through a 4-phase optimization pipeline, reducing H2 potential energy surface computation from 593.95s to 5.04s while maintaining near-exact accuracy.
Key Results
| Metric | Value |
|---|---|
| Speedup | 117× |
| Original Time | 593.95s |
| Optimized Time | 5.04s |
| Ground State Energy | -1.137 Ha (at equilibrium) |
| Bond Lengths Computed | 100 |
Optimization Pipeline
Phase 1: JIT Compilation
- Just-in-time compilation of quantum circuit operations
- Eliminates Python interpreter overhead
Phase 2: GPU Acceleration
- Single-GPU parallelization of quantum state evolution
- CUDA-accelerated matrix operations
Phase 3: Multi-GPU Scaling
- Distributed computation across 4× NVIDIA H100 GPUs
- Efficient memory management for large state vectors
Phase 4: MPI Parallelization
- OpenMPI distribution across 192 AMD EPYC cores
- Hybrid CPU-GPU workload balancing
Hardware
Computed on ERAU Vega HPC Cluster:
- 4× NVIDIA H100 GPUs
- 192 AMD EPYC CPU cores
- High-bandwidth interconnect
Application
Computed the H2 molecular potential energy surface—the ground state energy of molecular hydrogen across 100 different bond lengths. This is a standard benchmark for quantum chemistry methods and demonstrates the practical utility of VQE for molecular simulation.
The 117× speedup enables interactive exploration of quantum chemistry problems that would otherwise require batch processing.
Technology Stack
- Quantum Framework: PennyLane
- GPU Acceleration: JAX, CUDA
- Parallelization: OpenMPI (mpi4py)
- HPC: SLURM, Linux cluster