Quantum field lens coding and classification algorithm to predict measurement outcomes
This 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 operator...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221501612300136X |
| _version_ | 1797796434925322240 |
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| author | Philip B. Alipour T. Aaron Gulliver |
| author_facet | Philip B. Alipour T. Aaron Gulliver |
| author_sort | Philip B. Alipour |
| collection | DOAJ |
| description | This 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. |
| first_indexed | 2024-03-13T03:33:02Z |
| format | Article |
| id | doaj.art-0abfa3b9db2e487ba9ef239d6f85e499 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-13T03:33:02Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-0abfa3b9db2e487ba9ef239d6f85e4992023-06-24T05:17:28ZengElsevierMethodsX2215-01612023-01-0110102136Quantum field lens coding and classification algorithm to predict measurement outcomesPhilip B. Alipour0T. Aaron Gulliver1Corresponding author.; Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, V8W 2Y2, CanadaDepartment of Electrical and Computer Engineering, University of Victoria, Victoria BC, V8W 2Y2, CanadaThis 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.http://www.sciencedirect.com/science/article/pii/S221501612300136XQuantum field lens coding and classification (QF-LCC) |
| spellingShingle | Philip B. Alipour T. Aaron Gulliver Quantum field lens coding and classification algorithm to predict measurement outcomes MethodsX Quantum field lens coding and classification (QF-LCC) |
| title | Quantum field lens coding and classification algorithm to predict measurement outcomes |
| title_full | Quantum field lens coding and classification algorithm to predict measurement outcomes |
| title_fullStr | Quantum field lens coding and classification algorithm to predict measurement outcomes |
| title_full_unstemmed | Quantum field lens coding and classification algorithm to predict measurement outcomes |
| title_short | Quantum field lens coding and classification algorithm to predict measurement outcomes |
| title_sort | quantum field lens coding and classification algorithm to predict measurement outcomes |
| topic | Quantum field lens coding and classification (QF-LCC) |
| url | http://www.sciencedirect.com/science/article/pii/S221501612300136X |
| work_keys_str_mv | AT philipbalipour quantumfieldlenscodingandclassificationalgorithmtopredictmeasurementoutcomes AT taarongulliver quantumfieldlenscodingandclassificationalgorithmtopredictmeasurementoutcomes |