A Survey of Quantum Learning Theory

2017/01/24 by Srinivasan Arunachalam, Arunachalam, Srinivasan, Ronald de Wolf +1 · 6 citations
Computer Science · #Computability, Logic, AI Algorithms #Computational Complexity (cs.CC) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.1701.06806

openalex publication_date 2017/01/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Citations

• Quantum algorithms for supervised and unsupervised machine learning
• Strengths and Weaknesses of Quantum Computing
• Quantum mechanical computers
• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• Quantum Support Vector Machine for Big Data Classification
• On the Power of Quantum Computation
• Simulating physics with computers
• Quantum Perceptron Models
• Quantum Recommendation Systems
• Quantum machine learning

Cited by

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• Enhancing the reachability of variational quantum algorithms via input-state design
• Data Complexity: a threshold between Classical and Quantum Machine Learning -- Part I
• Quantum statistical query learning
• Pushing the Limits of LLMs in Quantum Operations
• Noisy intermediate-scale quantum algorithms

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