Research map / Learning theory and algorithms / Sample complexity
Quantum Perceptron Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use amplitude amplification to search for a misclassified training example | Online perceptron training · Separable training set | queries to training examples: N_class ∈ O(N/γ² log[1/(εγ²)]) -> N_quant ∈ O(√N/γ² log[1/(εγ²)]) | Helped |
| Use amplitude amplification to search sampled hyperplanes for one in the version space | Version space perceptron · Separable training set | queries to training data: O(N log(1/ε)/γ) -> O(N/√γ log^{3/2}[1/ε]) | Helped |
| Search the version space using quantum amplitude amplification rather than the classical version-space procedure | Version space perceptron · Separable training set | mistake bound: O(1/γ²) -> O(1/√γ log^{3/2}(1/ε)) | Helped |
| Use uniform-sampling access rather than streaming access for the classical comparison | Classical online perceptron · Separable training set | queries to training examples: O(N/γ²) | Hurt |
Closest papers by meaning
- Exploring channel distinguishability in local neighborhoods of the model space in quantum neural networks (ICLR 2025 · similarity 0.86)
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- One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective (NeurIPS 2026 · similarity 0.84)
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- Exponential Hardness of Optimization from the Locality in Quantum Neural Networks (AAAI 2024 · similarity 0.84)
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