Research map / Learning theory and algorithms / Prediction and decision losses
Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Sample subnetwork nodes from optimized distribution p*_{λ,Δ} (depends on f and data) instead of uniform random features | shallow wide neural network subnetwork (lottery ticket search) · D=1, P=127, sine function, ReLU | empirical risk of subnetwork: order of magnitude advantage | Helped |
| Replace ReLU activation with sigmoid (tanh) | optimized sampling subnetwork · D=1, P=127, sine function | empirical risk advantage over random features | No clear effect |
| Quantum ridgelet transform implemented via QFT yields runtime O(D polylog P) instead of exp(O(D)) classical | ridgelet transform of D-dimensional function · theoretical runtime | runtime scaling in D | Helped |
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