Quantum Perceptron Models
Authors: Ashish Kapoor, Nathan Wiebe, Krysta Svore
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane using a number of steps sublinear in the number of data points N, namely O( N). The second algorithm illustrates how the classical mistake bound of O( 1 γ2 ) can be further improved to O( 1 γ ) through quantum means, where γ denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model. |
| Researcher Affiliation | Industry | Nathan Wiebe Microsoft Research Redmond WA, 98052 nawiebe@microsoft.com Ashish Kapoor Microsoft Research Redmond WA, 98052 akapoor@microsoft.com Krysta M Svore Microsoft Research Redmond WA, 98052 ksvore@microsoft.com |
| Pseudocode | No | The paper refers to 'Algorithm 2' but does not present its steps in a structured pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not provide any statement about making its code open source or links to code repositories. |
| Open Datasets | No | The paper refers to 'training examples' and 'training set' generically (e.g., 'N separable training examples {φ1, .., φN}'), but does not specify a concrete, named public dataset with access information (e.g., a link, DOI, or formal citation). |
| Dataset Splits | No | The paper does not specify any training/validation/test dataset splits or mention a validation set. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper does not mention any software dependencies or specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |