HPC Integration

HPC Integration#

Integrating quantum coprocessors with classical HPC clusters. MPI+quantum hybrid workflows, job scheduling, and data movement.

The Hybrid HPC Model#

Quantum processors will not replace classical HPC clusters. They will augment them, accelerating specific subroutines within larger computational workflows. The integration model mirrors how GPUs were introduced to HPC in the 2000s.

┌─────────────────────────────────────┐
│           Job Scheduler (Slurm)      │
├──────────┬──────────┬───────────────┤
│ CPU Nodes│ GPU Nodes│ QPU Access    │
│          │          │ (cloud/local) │
│ Mesh gen │ Matrix   │ Linear solve  │
│ I/O      │ multiply │ Optimization  │
│ Pre/post │ ML infer │ Sampling      │
└──────────┴──────────┴───────────────┘

Integration Patterns#

Pattern 1: QPU as Remote Accelerator#

The quantum processor is accessed via cloud API. The HPC job submits circuits, waits for results, and continues.

# Within an MPI-parallel CFD solver
from mpi4py import MPI
from qiskit_ibm_runtime import QiskitRuntimeService

comm = MPI.COMM_WORLD
rank = comm.Get_rank()

if rank == 0:
    # Only rank 0 submits quantum job
    service = QiskitRuntimeService()
    backend = service.backend("ibm_fez")
    # Submit circuit, get result
    quantum_result = run_quantum_linear_solve(A_sub, b_sub, backend)
else:
    quantum_result = None

# Broadcast result to all ranks
quantum_result = comm.bcast(quantum_result, root=0)

Pattern 2: Quantum-in-the-Loop#

Quantum subroutines are called iteratively within a classical solver loop (e.g., VQLS within each timestep of a PDE solver).

Pattern 3: Batch Quantum Preprocessing#

Quantum optimization runs as a separate batch job to precompute parameters (e.g., optimal mesh adaptivity, preconditioner selection) that the classical solver then uses.

Data Movement Challenges#

Challenge	Description	Mitigation
State preparation	Classical \(\rightarrow\) quantum encoding is \(O(N)\)	Use quantum-native problem formulations
Measurement	Full state readout requires \(O(N)\) shots	Extract only needed observables
Latency	Cloud QPU round-trip: 100+ ms	Batch jobs, overlap with classical work
Bandwidth	QPU produces \(O(\text{shots} \times \text{qubits})\) bits	Modest; not a bottleneck

Job Scheduling#

Quantum jobs require special scheduling considerations:

Calibration windows: QPU properties change over time; submit jobs after fresh calibration
Queue times: Cloud QPU queues range from seconds to hours
Shot budgets: Balance statistical accuracy against cost/time
Error mitigation: Post-processing overhead for noise reduction

Security#

All HPC-to-QPU communication should be encrypted with post-quantum algorithms. Zipminator’s ML-KEM-768 secures the channel, ensuring that quantum computation results (which may encode sensitive simulation data) are protected against HNDL attacks.