Quantum State Preparation for Medical Data: Comprehensive Methods, Implementation Challenges, and Clinical Prospects

TL;DR

This paper reviews methods for encoding complex medical data into quantum states, discusses challenges and potential advantages, and provides a framework for assessing quantum approaches in medical applications.

Contribution

It offers a comprehensive survey of quantum state preparation techniques for medical data, analyzing theoretical, algorithmic, and practical aspects, and proposes a structured assessment framework.

Findings

Quantum advantages depend on data structures like spatial and temporal correlations.

Current hardware limits implementations to small-scale problems.

Emerging methods show promise for near-term quantum medical applications.

Abstract

Quantum computing holds transformative potential for medical applications, yet efficiently preparing quantum states from complex medical data remains a fundamental challenge. This survey provides a comprehensive examination of current approaches for encoding medical information into quantum systems, analyzing theoretical principles, algorithmic advancements, and practical limitations. It discusses tensor network decomposition, variational quantum algorithms, quantum machine learning techniques, and specialized error mitigation strategies for medical computing. The findings indicate that quantum advantages in medicine rely on leveraging inherent data structures such as spatial correlations in imaging, temporal patterns in physiological signals, and hierarchical biological organization. While current hardware restricts implementations to small-scale problems, emerging methods show potential for near-term use. The study provides a structured framework for assessing when quantum state preparation outperforms classical approaches in medicine, along with implementation guidelines and performance benchmarks.