Quantum Learning

Jeongho Bang, James Lim, M. S. Kim +1 more

We propose a novel notion of a quantum learning machine for automatically controlling quantum coherence and for developing quantum algorithms. A quantum learning machine can be trained to learn a certain task with no a priori knowledge on its algorithm. As an example, it is demonstrated that the quantum learning machine learns Deutsch's task and finds itself a quantum algorithm, that is different from but equivalent to the original one.

Jerome R. Busemeyer, Peter Bruza

Sagnik Chatterjee

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.

Pawan Whig, Pavika Sharma, Ahmad A. Elngar +1 more

Xuan Hou

This paper has been studied in Quantum Learning Vector Quantization Neural Network. The concept of competition and model of competitive network has been studied. It analyses model of Quantum Learning Vector Quantization Neural Network and LVQ algorithm. Quantum Learning Vector Quantization Neural Network is a hybrid neural network and quantum computing theory is used in the Quantum Learning Vector Quantization Neural Network directly to build Quantum Learning Vector Quantization Neural Network. Model of Quantum Learning Vector Quantization Neural Network and Quantum-LVQ algorithms are proposed. In the study of clustering problem, experiments show that Quantum Learning Vector Quantization Neural Network have an advantage over than Learning Vector Quantization Neural Network.

Mike Hernacki