A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets

Abstract

In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete sets) and implement one-iteration learning, a novel efficient quantum perceptron algorithm based on unitary weights is proposed, where the singular value decomposition of the total weight matrix from the training set is calculated to make the weight matrix to be unitary. The example validation of quantum gates {H, S, T, CNOT, Toffoli, Fredkin} shows that our algorithm can accurately implement arbitrary quantum gates within one iteration. The performance comparison between our algorithm and other quantum perceptron algorithms demonstrates the advantages of our algorithm in terms of applicability, accuracy, and availability. For further validating the applicability of our algorithm, a quantum composite gate which consists of several basic quantum gates is also illustrated.

arXiv comment12 pages, 5 figures

References in corpus (12)

• Quantum Mechanics helps in searching for a needle in a haystack
• The quest for a Quantum Neural Network
• Quantum algorithms for supervised and unsupervised machine learning
• Artificial Neural Network Methods in Quantum Mechanics
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Quantum neural network
• An Efficient and Secure Arbitrary N-Party Quantum Key Agreement Protocol Using Bell States
• Quantum Relief Algorithm
• A Quantum Model for Autonomous Learning Automata
• Autonomous Quantum Perceptron Neural Network
• Improved Deterministic N-To-One Joint Remote Preparation of an Arbitrary Qubit via EPR Pairs
• Efficient learning algorithm for quantum perceptron unitary weights

Cited by in corpus (7)

• A Novel Quantum Visual Secret Sharing Scheme
• Quantum Searchable Encryption for Cloud Data Based on Full-Blind Quantum Computation
• A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
• Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing
• A Quantum-based Database Query Scheme for Privacy Preservation in Cloud Environment
• Quantum Privacy-Preserving Price E-Negotiation
• A Unitary Operator Construction Solution Based on Pauli Group for Maximal Dense Coding