Quantum field lens coding and classification algorithm to predict measurement outcomes

dc.contributor.authorAlipour, Philip B.
dc.contributor.authorGulliver, Thomas Aaron
dc.date.accessioned2023-11-06T17:55:19Z
dc.date.available2023-11-06T17:55:19Z
dc.date.copyright2023en_US
dc.date.issued2023
dc.descriptionP. B. A. acknowledges Dr. W. Chen endorsement from the Institute of Theoretical Physics at ETH Zürich, for comments on condensed matter physics. P. B. A. and T. A. G. thank Dr. T. Lu for his comments on the scalar field, dimensions and photonics, Dr. M. Laca for his comments on (3), (4), (5), (6), (7), (8), (9), (10), Dr. N. Neumann remarks from [52], [53], [54] on QAI classifiers, and the late Dr. F. Diacu for his input on phase transitions to parameterize their quantum probabilities in the proposed method.en_US
dc.description.abstractThis study develops a method to implement a quantum field lens coding and classification algorithm for two quantum double-field (QDF) system models: 1- a QDF model, and 2- a QDF lens coding model by a DF computation (DFC). This method determines entanglement entropy (EE) by implementing QDF operators in a quantum circuit. The physical link between the two system models is a quantum field lens coding algorithm (QF-LCA), which is a QF lens distance-based, implemented on real N-qubit machines. This is with the possibility to train the algorithm for making strong predictions on phase transitions as the shared objective of both models. In both system models, QDF transformations are simulated by a DFC algorithm where QDF data are collected and analyzed to represent energy states and transitions, and determine entanglement based on EE. The method gives a list of steps to simulate and optimize any thermodynamic system on macro and micro-scale observations, as presented in this article: •The implementation of QF-LCA on quantum computers with EE measurement under a QDF transformation. •Validation of QF-LCA as implemented compared to quantum Fourier transform (QFT) and its inverse, QFT−1. •Quantum artificial intelligence (QAI) features by classifying QDF with strong measurement outcome predictions.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipP. B. A. also acknowledges the University of Victoria Canada, for financial support.en_US
dc.identifier.citationAlipour, P. B. & Gulliver, T. A. (2023). Quantum field lens coding and classification algorithm to predict measurement outcomes. MethodsX, 10, 102136. https://doi.org/10.1016/j.mex.2023.102136en_US
dc.identifier.urihttps://doi.org/10.1016/j.mex.2023.102136
dc.identifier.urihttp://hdl.handle.net/1828/15587
dc.language.isoenen_US
dc.publisherMethodsXen_US
dc.subjectQuantum double-fielden_US
dc.subjectQDF Transformationen_US
dc.subjectQDF Lens codingen_US
dc.subjectDF Computationen_US
dc.subjectEntanglement entropyen_US
dc.subjectN-qubit machinesen_US
dc.subjectQuantum fourier transformen_US
dc.subjectQuantum artificial intelligenceen_US
dc.subjectQuantum lens distance-based classificationen_US
dc.titleQuantum field lens coding and classification algorithm to predict measurement outcomesen_US
dc.typeArticleen_US

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