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Topic

Quantum Field Lens Coding

Department of Electrical and Computer Engineering

Date & location

• Thursday, August 21, 2025
• 9:00 A.M.
• Virtual Defence

Examining Committee

Supervisory Committee

• Dr. Aaron Gulliver, Department of Electrical and Computer Engineering, University of Victoria (Supervisor)
• Dr. Tao Lu, Department of Electrical and Computer Engineering, UVic (Member)
• Dr. Marcelo Laca, Department of Mathematics and Statistics, UVic (Outside Member)

External Examiner

• Dr. Jagdish Chand Bansal, Department of Mathematical Sciences, South Asian University

Chair of Oral Examination

• Dr. Abdul Roudsari, School of Health Information Science, UVic

Abstract

This dissertation presents detailed topics on Quantum Field Lens Coding (QF-LC) and Thermodynamic Metrics. There had been no research on QF-LC and its thermodynamic metrics prior to the present work. QF-LC is presented as a project with research elements in form of peer-reviewed publications.

The QF-LC project comprises of 1- the quantum double-field (QDF) model, 2- QDF model’s code as the QF-LC algorithm (QF-LCA), 3- application software as the QF-LC simulator (QF-LCS), simulating a QDF system with its, 4- QF-LCA/QDF dataset. This project is developing alongside other quantum models that employ quantum technologies in thermodynamic systems, with societal and global impacts.

QF-LCA, is a QF lens distance-based algorithm implemented on N-qubit machines. This algorithm can be trained to make strong predictions on a system’s phase transition, as the aim of the QDF model and its QF-LC code. In a QDF-based system, QDF transformations are simulated by a DF computation (DFC) model to simulate systems as QF-LCS. QDF data are collected and analyzed to represent energy states, state transition (ST), and determine entanglement based on entanglement entropy (EE).

QF-LCS generates QDF datasets by its algorithm, the QF-LCA. QF-LCS, is useful for simulating systems and predict system events with high probability based on the QDF model, as evaluated in the published articles. The QF-LCS program analyzes the measurement outcome probability 𝑃 data from datasets generated by QDF circuits. Datasets are compared between the excited and ground states (ES and GS), as a 𝑃 indicator generated for measurement samples. Small dataset samples denote:

1. A particle pair’s energy state |𝑖𝑗⟩ superposing between QDF points (sublevels of a GS),
2. a single field (SF or particle state), an ES relative to a GS (a.), prior to its transform into a QDF,
3. the expected transformation of fields (ES ←→ GS) and their expected 𝑃_|𝑖𝑗⟩ value.

A strong system state prediction is achieved by computing QF lens distance-based variables associated to ST 𝑃’s from a QDF dataset. For this, a simulated QDF heat engine predicts thermal events by a QDF lens function that (de-)focuses the distribution of energy states via QF lenses, which encode the system state and produce the dataset. From this dataset, the energy path of the unfocused distribution of states is determined via particle entanglement measure. The energy path can be rerouted by focusing its distribution through QF lenses. In this heat engine, a QDF circuit samples particles and counts entangled qubit pairs.

An ST probability space doubles in prediction at the decoding step, e.g., 𝑃_|𝑖𝑗⟩≥1/3 into 𝑃_|𝑖𝑗⟩≥2/3 via (a.)–(c.), as SF 𝜅→ QDF. Field scalar 𝜅, scales a particle’s QF during interactions or diffusion of a GS matter in the system. The dataset can be used to train QF-LCA via quantum AI (QAI). The trained QF-LCA predicts and suggests an efficient energy path to choose by the QF-LCA user.

QF-LCA’s classification of EE values, distinguishes entangled states in the system. Particles not reaching a desired energy state (a target state, or TS) by observing a GS/ES 𝑃 outcome at the decoding step, can be rerouted by the engine for a TS outcome. This generates a GS/ES energy profile to access and classify states by a classifier. The profile’s data points (qubits), are inverse distance-based and labelled for a specific class. After learning the profile, the classifier decodes and predicts the next system state.

A QDF game “Alice & Bob’s Quantum Doubles,” is developed to validate the dataset as the 𝑃_|𝑖𝑗⟩’s map for a classical/quantum prediction where the 𝑃_|𝑖𝑗⟩’s and the user’s 𝑃’s correlate in their value difference, Δ𝑃. Dataset validation results are mapped to an intelligent decision simulator (IDS), as a QAI map. This maximizes system efficiency on a TS via EE of energy states (distributed in the system).

QF-LCA applications are in data science, security, forensics, particle physics, etc., such as retrieving or reconstructing information by distinguishing particle states from an evidence sample. Examples are, reconstruct damaged DNA strands of cells to predict a virus’s TS, or cancer cell, its spread and growth against healthy cells, identify forged documents from genuine based on QDF’s 𝑃_|𝑖𝑗⟩ values, and so on.