CohAlign: open-source tool predicts gradient noise susceptibility in quantum neural networks
ugail · x · 2026-10-23
A new PLoS One paper by Hassan Ugail (University of Bradford) and Newton Howard (RIT) introduces CohAlign, an open-source toolkit that predicts the first-order noise susceptibility of the ensemble second moment of parameter gradients in charge-conserving brickwork quantum neural networks.
Key points:
- Quantum neural networks learn via gradients, but on real hardware those gradients get washed out by noise — predicting how fast this happens before running any finite-noise experiment has been an open problem.
- CohAlign computes this prediction from only a classical description of the circuit and the noise model, avoiding costly circuit sweeps.
- The paper is open access and the toolkit is open source.
More from Research
- NTU's CodeActionBench tests whether vision-and-code agents can close the robot loop — jiqizhixin · 2026-10-23
- Same nets, 3 seeds: Adam hits 0.033 test loss vs SGD's 0.442 on regression — cneuralnetwork · 2026-10-23
- Event sequence forecasting as generative modeling reveals a realism-accuracy trade-off — Dmitry Osin · 2026-10-23
- NanoStories: 50M-story TinyStories-style dataset generated by rules in 10 minutes — Tall_Abrocoma_3533 · 2026-10-23
- FORC 2027 call for papers: differential privacy, mechanism design, AI alignment — Aaroth · 2026-10-23
- FORC 2027 Call for Papers: Foundations of Responsible Computing — Aaroth · 2026-10-23