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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Quantum field lens coding and classification algorithm to predict measurement outcomes.

Philip B Alipour¹, T Aaron Gulliver¹

¹Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, V8W 2Y2, Canada.

|April 24, 2023

Summary

This summary is machine-generated.

This study introduces a quantum field lens coding algorithm (QF-LCA) for quantum double-field (QDF) systems. The method uses entanglement entropy (EE) to predict phase transitions and classify quantum states on quantum computers.

Keywords:

DF Computation Entanglement entropy QDF Lens coding QDF Transformation Quantum artificial intelligence Quantum double-field Quantum field lens coding and classification (QF-LCC)Quantum fourier transform Quantum lens distance-based classification

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Area of Science:

Quantum Computing
Quantum Information Theory
Thermodynamics

Background:

Quantum double-field (QDF) systems present complex behaviors.
Simulating and analyzing these systems requires advanced computational methods.
Understanding entanglement entropy (EE) is crucial for characterizing quantum states.

Purpose of the Study:

To develop and implement a quantum field lens coding and classification algorithm (QF-LCA) for QDF systems.
To determine entanglement entropy (EE) within these quantum systems.
To enable prediction of phase transitions and classification of quantum states using quantum artificial intelligence (QAI).

Main Methods:

Implementation of QF-LCA on quantum circuits using QDF operators.
Simulation of QDF transformations via a DF computation (DFC) algorithm.
Measurement of entanglement entropy (EE) and comparison with quantum Fourier transform (QFT).

Main Results:

Successful implementation of QF-LCA on real qubit machines.
Validation of QF-LCA against QFT and its inverse.
Demonstration of QAI capabilities for classifying QDF systems with accurate outcome predictions.

Conclusions:

The developed QF-LCA method effectively simulates and optimizes thermodynamic systems.
QF-LCA provides a robust framework for analyzing QDF systems and their entanglement properties.
The approach shows potential for advancing quantum artificial intelligence and predicting quantum system behaviors.