performance-debug

Debug training slowdowns in hybrid quantum-classical PyTorch models by diagnosing first-epoch JIT compilation costs, gradient stability, and quantum-layer throughput to identify observable bottlenecks and propose minimal fixes. It analyzes your PennyLane pipeline to pinpoint performance degradation points.

Persistent slowness across all epochs is distinguished from first-epoch JIT costs by analyzing quantum circuit throughput and scheduler mistakes. The workflow identifies whether the bottleneck stems from quantum-layer execution overhead or suboptimal training loop configuration.

Dead quantum signals and non-decreasing loss in hybrid quantum nets are caused by gradient instability and scheduler mistakes. The workflow diagnoses these failure modes by evaluating gradient flow through quantum layers and proposing targeted fixes.

Yes, you can diagnose unstable accuracy in a PennyLane training pipeline by running the workflow. It identifies failure modes like unstable accuracy and non-decreasing loss, then recommends reruns or ablations to validate hypotheses.

Identify first-epoch JIT compilation costs in hybrid quantum training by applying diagnostics to the training loop to isolate initial runtime overhead from persistent bottlenecks. The workflow distinguishes slow first epoch failures from other performance issues.

You need to run ablations to validate performance fixes in hybrid quantum nets when the diagnosis requires confirming hypotheses about bottlenecks. The workflow recommends reruns or ablations when needed to verify that proposed minimal fixes resolve the issue.