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

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

Published on: September 8, 2023

Systems-Level Support for Hybrid Quantum-Classical Learning: A Systematic Review with a Medical Imaging Translation

Maqsudur Rahman^1,2, Pintu Chandra Paul², Amena Begum²

¹Department of Computer Science, Boise State University, Boise, ID 83725, USA.

Journal of Imaging

|June 25, 2026

Summary

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This summary is machine-generated.

This study reviews hybrid quantum-classical systems, focusing on challenges in medical imaging. It identifies strengths in orchestration and resource allocation, but highlights gaps in clinical deployment and remote workflows.

Area of Science:

Quantum Computing Systems
Hybrid Quantum-Classical Machine Learning
Medical Imaging Informatics

Background:

Hybrid quantum-classical learning pipelines integrate classical hardware with quantum processing units (QPUs), introducing complex systems-level challenges.
Existing surveys often focus on application-level quantum machine learning, neglecting critical runtime and systems mechanisms.
Medical imaging serves as a valuable use case for understanding these hybrid systems, providing a translational lens.

Purpose of the Study:

To systematically review runtime and systems mechanisms for hybrid quantum-classical workloads.
To identify challenges and evidence gaps in deploying these systems, particularly in regulated settings like medical imaging.
To provide design guidelines for future hybrid quantum-classical imaging pipelines.

Keywords:

hybrid quantum-classical learning image encoding medical imaging memory management quantum machine learning reproducibility runtime systems scheduling systematic review

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Last Updated: Jun 26, 2026

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

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

Published on: September 8, 2023

Main Methods:

A PRISMA-aligned systematic literature review was conducted, screening 364 records and synthesizing 40 studies (2020-2025).
Studies were coded by systems layer, application grounding (direct medical, medically motivated, generic systems), noisy-label relevance, and evaluation maturity.
Evidence was categorized as direct, indirect, or interpretive to assess its relevance and applicability.

Main Results:

The reviewed corpus shows strong evidence for hybrid orchestration, qubit allocation, classical-quantum data movement, and containerized reproducibility.
Direct medical data evaluation was present in 8 studies, with 12 being medically motivated and 20 generic systems studies.
Significant evidence gaps exist for realistic clinical operation, end-to-end remote quantum processing unit (QPU) workflows, multi-tenant isolation, and noisy-label retraining.

Conclusions:

Hybrid quantum-classical systems offer promising avenues for fields like medical imaging, but require further development in systems engineering.
Addressing challenges in clinical deployment, remote access, and robust retraining loops is crucial for practical adoption.
The study provides a foundational evidence map and cross-layer design guidelines for building reliable hybrid quantum-classical imaging pipelines in regulated environments.