Quantum computing is not a replacement for classical high-performance computing (HPC). Today’s most realistic quantum applications are hybrid: they combine a Quantum Processing Unit (QPU) with powerful classical compute resources for orchestration, optimization, and post-processing.
This page provides a lightweight overview of why quantum and HPC belong together, where the main system bottlenecks appear, and how real platforms mitigate them.
Why Quantum and HPC Belong Together¶
QPU execution is typically:
specialized (useful for specific subroutines)
serial (limited throughput)
noise-limited (short coherence times)
hardware-constrained (connectivity and qubit count)
HPC systems provide mature, scalable infrastructure for the parts quantum devices do not handle well: large classical parallel workloads, fast data movement, compilation, and scheduling.
In practice, the QPU behaves like an accelerator, while the HPC system provides the surrounding workflow.
Where HPC Is Used Today¶
In modern hybrid workflows, classical HPC resources are heavily used for:
Compilation and transpilation
Mapping circuits to hardware is computationally expensive and parallelizable.Batch execution and parameter sweeps
Variational methods (e.g., VQE/QAOA) require running many circuit instances.Circuit cutting / knitting
Larger circuits are decomposed into subcircuits that fit on available devices, then recombined classically.Post-processing and error mitigation
Aggregation, mitigation, and uncertainty estimation are classical tasks well suited to HPC.
Key Constraints¶
Hybrid quantum–HPC workflows face several system-level constraints:
Latency between the classical host and remote QPUs can dominate time-to-solution.
Queue time variability makes tightly coupled feedback loops inefficient.
Scheduling mismatch can waste classical resources if large allocations sit idle while waiting on a QPU.
How Platforms Address These Issues¶
Current platforms typically mitigate these constraints through:
Asynchronous or serverless execution to decouple classical resources from QPU queue time
Batching and session-style execution to amortize overhead
Multi-backend execution when work can be split across devices
Careful orchestration (sometimes via scheduler integration) to reduce idle time
These approaches optimize for practical time-to-solution, not theoretical runtime.
Scope of This Project¶
This project focuses on practical, reproducible hybrid workflows, emphasizing:
HPC concepts that matter for quantum integration (MPI, OpenMP, schedulers)
Containerized environments for portability and reproducibility
Clear separation of classical orchestration vs. quantum acceleration
Further Reading¶
For a deeper discussion of bottlenecks, scheduling tradeoffs, and emerging system architectures in quantum–HPC integration, see the accompanying Medium article:
Hybrid Quantum–HPC Systems: Bottlenecks, Tradeoffs, and Practical Solutions