HPC – Quantum Synergies

GPU-Accelerated Quantum Simulation as the "Quantum Dev Loop"

In practice, most quantum R&D cycles rely heavily on simulation (state-vector, tensor-network, noise models), especially for:

• Algorithm prototyping
• Benchmarking
• Error model exploration
• Compilation / transpilation experiments
• Verifying results before running on scarce quantum hardware

With modern GPUs, this becomes a native HPC workload. GPU-accelerated quantum simulators (state-vector, tensor-network) run on the Kubernetes platform and leverage the GPU stack (e.g., CUDA-Q / cuQuantum), with targets like ≥32-qubit simulations per GPU.

Hybrid Quantum-Classical Workflows (HPC as Orchestrator)

Even when you run on real quantum hardware, you almost always do hybrid loops:

This is exactly where HPC shines:

• Parallel parameter sweeps
• Batched circuit execution
• Multi-start optimization
• Large-scale sampling
• Experiment tracking and reproducibility
• Throughput management

The platform direction already anticipates this style of operation by packaging quantum tooling into the same managed environment where Kubernetes workflows, CI/CD, and GPU workloads live.

Multi-Backend Portability (Avoid Vendor Lock-in)

A major accelerator for research and commercialization is being able to move between:

The planned CUDA-Q approach explicitly includes integrations to multiple hardware providers (ion-trap and superconducting options). This enables:

• Comparative benchmarking
• Robustness testing
• Algorithm portability checks
• Procurement optionality