The Quantum Learning Menagerie (A survey on Quantum learning for Classical concepts)
- URL: http://arxiv.org/abs/2602.01054v1
- Date: Sun, 01 Feb 2026 06:50:39 GMT
- Title: The Quantum Learning Menagerie (A survey on Quantum learning for Classical concepts)
- Authors: Sagnik Chatterjee,
- Abstract summary: This paper surveys various results in the field of Quantum Learning theory, specifically focusing on learning quantum-encoded classical concepts.<n>The cornerstone of this work is the emphasis on query, sample, and time separations between classical and quantum learning.
- Score: 0.5076419064097734
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper surveys various results in the field of Quantum Learning theory, specifically focusing on learning quantum-encoded classical concepts in the Probably Approximately Correct (PAC) framework. The cornerstone of this work is the emphasis on query, sample, and time complexity separations between classical and quantum learning that emerge under learning with query access to different labeling oracles. This paper aims to consolidate all known results in the area under the above umbrella and underscore the limits of our understanding by leaving the reader with 23 open problems.
Related papers
- Quantum Reinforcement Learning: Recent Advances and Future Directions [50.89638884527093]
reinforcement learning stands out as a promising yet underexplored frontier.<n>We present a comprehensive analysis of the QRL framework, including its algorithms, architectures, and supporting SDK.<n>We discuss promising use cases that may drive innovation in quantum-inspired reinforcement learning.
arXiv Detail & Related papers (2025-10-16T11:59:08Z) - Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications [59.0413662882849]
Quantum computing is poised to redefine the algorithmic foundations of communication systems.<n>This article outlines the fundamentals of quantum computing in a style familiar to the communications society.<n>We highlight a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers.
arXiv Detail & Related papers (2025-06-25T22:25:47Z) - Quantum Supervised Learning [0.5439020425819]
Recent advancements in quantum computing have positioned it as a prospective solution for tackling intricate computational challenges.
The field of quantum machine learning is still in its early stages, and there persists a level of skepticism regarding a possible near-term quantum advantage.
This paper aims to provide a classical perspective on current quantum algorithms for supervised learning.
arXiv Detail & Related papers (2024-07-24T11:05:05Z) - Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem [70.42372213666553]
The No-Free-Lunch (NFL) theorem quantifies problem- and data-independent generalization errors regardless of the optimization process.
We categorize a diverse array of quantum learning algorithms into three learning protocols designed for learning quantum dynamics under a specified observable.
Our derived NFL theorems demonstrate quadratic reductions in sample complexity across CLC-LPs, ReQu-LPs, and Qu-LPs.
We attribute this performance discrepancy to the unique capacity of quantum-related learning protocols to indirectly utilize information concerning the global phases of non-orthogonal quantum states.
arXiv Detail & Related papers (2024-05-12T09:05:13Z) - Information-theoretic generalization bounds for learning from quantum data [5.0739329301140845]
We propose a general mathematical formalism for describing quantum learning by training on classical-quantum data.
We prove bounds on the expected generalization error of a quantum learner in terms of classical and quantum information-theoretic quantities.
Our work lays a foundation for a unifying quantum information-theoretic perspective on quantum learning.
arXiv Detail & Related papers (2023-11-09T17:21:38Z) - Quantum algorithms: A survey of applications and end-to-end complexities [88.57261102552016]
The anticipated applications of quantum computers span across science and industry.<n>We present a survey of several potential application areas of quantum algorithms.<n>We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - Classical Verification of Quantum Learning [42.362388367152256]
We develop a framework for classical verification of quantum learning.
We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples.
Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents.
arXiv Detail & Related papers (2023-06-08T00:31:27Z) - A survey on the complexity of learning quantum states [23.097706741644682]
We highlight how recent results are paving the way for a highly successful theory with a range of exciting open questions.
These results include progress on quantum tomography, learning physical quantum states, alternate learning models to tomography and learning classical functions encoded as quantum states.
arXiv Detail & Related papers (2023-05-31T17:44:07Z) - The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for
Deep Quantum Machine Learning [52.77024349608834]
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.
Key issue is how to address the inherent non-linearity of classical deep learning.
We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
arXiv Detail & Related papers (2022-12-22T16:06:24Z) - A Survey on Quantum Reinforcement Learning [2.5882725323376112]
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning.
With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators.
In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage.
arXiv Detail & Related papers (2022-11-07T11:25:47Z) - On establishing learning separations between classical and quantum
machine learning with classical data [0.0]
We discuss the challenges of finding learning problems that quantum learning algorithms can learn much faster than any classical learning algorithm.
We study existing learning problems with a provable quantum speedup to distill sets of more general and sufficient conditions.
These checklists are intended to streamline one's approach to proving quantum speedups for learning problems, or to elucidate bottlenecks.
arXiv Detail & Related papers (2022-08-12T16:00:30Z) - From a quantum theory to a classical one [117.44028458220427]
We present and discuss a formal approach for describing the quantum to classical crossover.
The method was originally introduced by L. Yaffe in 1982 for tackling large-$N$ quantum field theories.
arXiv Detail & Related papers (2020-04-01T09:16:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.