From Bits to Qubits: Challenges in Classical-Quantum Integration

2025-01-31

AI Summary

This study addresses the challenge of efficient classical-to-quantum data encoding. We systematically compare three mainstream quantum image encoding paradigms—Phase Encoding, Qubit Lattice, and Flexible Representation of Quantum Images (FRQI)—under a unified dimensional framework, quantitatively evaluating their resource complexity in terms of qubit count, quantum gate count, and state fidelity. Our analysis reveals fundamental trade-offs among scalability, hardware overhead, and information fidelity. Results show that FRQI achieves superior image storage density but incurs high gate complexity; Phase Encoding minimizes gate resources, making it particularly suitable for near-term intermediate-scale quantum (NISQ) devices; and Qubit Lattice offers a balanced compromise between structural flexibility and resource efficiency. We propose a reproducible, application-driven evaluation framework for quantum encoding selection, providing both theoretical foundations and practical guidelines for designing encoding schemes tailored to real-world quantum applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Computer Vision: Representation Learning for Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Applications of cryptography
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

While quantum computing holds immense potential for tackling previously intractable problems, its current practicality remains limited. A critical aspect of realizing quantum utility is the ability to efficiently interface with data from the classical world. This research focuses on the crucial phase of quantum encoding, which enables the transformation of classical information into quantum states for processing within quantum systems. We focus on three prominent encoding models: Phase Encoding, Qubit Lattice, and Flexible Representation of Quantum Images (FRQI) for cost and efficiency analysis. The aim of quantifying their different characteristics is to analyze their impact on quantum processing workflows. This comparative analysis offers valuable insights into their limitations and potential to accelerate the development of practical quantum computing solutions.

Problem

• Quantum Computing
• Information Conversion
• Practical Applications

Innovation

• Quantum Information Conversion
• Method Comparison
• Cost and Efficiency Evaluation