Skip to content

ORNL — Quantum ML: SVM Speedups & Scaling

Implemented Support Vector Machine (SVM) techniques on quantum hardware with end‑to‑end evaluation, demonstrating 3.5–4.5× speedups and 11.1× scaling on a leading supercomputer.

Context

  • Software Engineering Intern, Oak Ridge National Laboratory (May–Aug 2023).
  • Prior internship (Jun–Aug 2022) established classical baselines and accuracy targets.

Problem

  • Evaluate whether quantum hardware can accelerate practical classification workloads.
  • Design fair comparisons vs. classical pipelines while controlling for I/O and orchestration overhead.

Role & Stack

  • Python for ML orchestration; quantum SDK/hardware integrations; HPC job submission & telemetry.

Architecture

Quantum SVM pipeline

Key Decisions

  • Kernel choices to minimize circuit depth while preserving separability.
  • Batching & parallelization strategies to hit 11.1× scaling.

Impact & Metrics

  • 3.5–4.5× speedups on target workloads with 11.1× scaling.
  • Reported publicly in a research preprint.
Speedup comparison chart

Code Highlights

Sanitized snippets showing dataset loaders, circuit builders, and evaluators.

What I’d Do Next

  • Error‑mitigation comparisons; hybrid classical‑quantum scheduling to reduce queue latency.

Links