From Bits to Qubits: Challenges in Classical-Quantum Integration

TL;DR

This paper compares three quantum encoding models—Phase Encoding, Qubit Lattice, and FRQI—to evaluate their efficiency and limitations, aiming to advance practical quantum computing integration with classical data.

Contribution

It provides a comparative analysis of three quantum encoding methods, highlighting their characteristics, limitations, and potential to improve quantum-classical data interfacing.

Findings

Qubit Lattice offers high efficiency for certain data types.

FRQI provides flexible image representation but has higher complexity.

Phase Encoding is simple but limited in data capacity.

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.