Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Quantum Perceptron Models
Authors: Ashish Kapoor, Nathan Wiebe, Krysta Svore
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 14,462 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,660 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL Ashish Kapoor Microsoft Research Redmond WA, 98052 EMAIL Krysta M Svore Microsoft Research Redmond WA, 98052 EMAIL |
| 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. |