By name: stochastic-physical-neural-networks description: > Stochastic Physical Neural Networks (PNNs) methodology using single-electron and single-photon stochastic neurons. Training via empirical backward pass with few trials achieves >97% MNIST accuracy. Use when: physical neural networks, stochastic neurons, single-electron tunneling, quantum dot neurons, single-photon neurons, PNN training strategies, MNIST classification, noise-resilient deep learning, arXiv:2604.10861, stochastic physical computing, quantum neurons.
Stochastic Physical Neural Networks
Train physical neural networks where neurons are realized by stochastic activation switches — single-electron tunneling or single-photon processes.
Electronic Stochastic Neuron
- Implementation: Single-electron tunneling through a quantum dot
- Basis: Charge state of the quantum dot
- Stochasticity: Inherent tunneling statistics
Photonic Stochastic Neuron
- Implementation: Single-photon source driving one of two modes via controllable beam-splitter interaction
- Basis: Occupation of the undriven mode
- Stochasticity: Photon detection statistics
Training Strategies
| Strategy | Forward Pass | Backward Pass | Key Finding |
|---|---|---|---|
| True probability | Expected values | True gradients | Lower accuracy |
| Empirical outputs | Sampled values | Empirical gradients | >97% MNIST accuracy |
Key Insight
Using empirical outputs in the backward pass (not true probabilities) achieves significantly higher accuracy with fewer trials per layer.
Noise Robustness
- Maintains >97% test accuracy under high noise and model uncertainty
- Works with single-hidden-layer architecture
- Simplicity enables practical implementation
Training Protocol
- Build single-hidden-layer stochastic PNN with electronic or photonic neurons
- Vary number of trials per layer to control forward-pass stochasticity
- Use empirical outputs (sampled values) for gradient estimation in backward pass
- Train on target task — monitor convergence under noise conditions
Architectural Simplicity
Unlike DNNs requiring backpropagation through time, stochastic PNNs:
- No BPTT needed
- Only simple readout training
- Natural noise resilience
- Compatible with quantum hardware constraints
Activation Keywords
stochastic PNN, physical neural network, single-electron neuron, single-photon neuron, quantum dot neuron, stochastic neuron training, empirical backward pass, MNIST stochastic, Dou Kumara Burns