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Updated: Jun 6, 2025

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

Quantum Machine Learning-Quo Vadis?

Andreas Wichert¹

¹Department of Computer Science and Engineering, INESC-ID & Instituto Superior Técnico, University of Lisbon, 2744-016 Porto Salvo, Portugal.

Entropy (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

Quantum machine learning (QML) offers potential big data advantages but faces real-world application challenges. Constraints like data encoding bottlenecks question the feasibility of promised quantum speed-ups.

Keywords:

HHL amplitude encoding basis encoding input destruction problem quantum kernels quantum machine learning variational algorithm

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Area of Science:

Quantum Computing
Machine Learning
Data Mining

Background:

Quantum machine learning (QML) gained popularity, promising exponential speed-ups and compressed data representation for big data applications.
Classical machine learning algorithms face limitations when ported to quantum computing due to quantum physical constraints.
Key challenges include the input-output problem and normalized vector representation, hindering direct algorithm translation.

Purpose of the Study:

To critically evaluate the practical applicability of quantum machine learning for real-world scenarios.
To assess the realistic advantages of quantum machine learning algorithms beyond theoretical speed-ups.
To investigate the feasibility of promised exponential or quadratic speed-ups on actual quantum computers.

Main Methods:

Analysis of theoretical speed-ups in quantum machine learning literature.
Identification of quantum physical constraints impacting algorithm implementation.
Focus on the input destruction problem as a primary bottleneck in data encoding.

Main Results:

Theoretical analyses often overlook the input destruction problem, a critical barrier for data encoding in quantum machine learning.
Direct porting of classical algorithms is not feasible due to quantum physical constraints.
The practical advantages and realistic speed-ups of QML remain questionable under current constraints.

Conclusions:

The feasibility of applying quantum machine learning to real-world problems is uncertain due to significant implementation challenges.
Marginalizing or ignoring quantum physical constraints like data encoding bottlenecks may lead to unrealistic performance expectations.
Further research is needed to address these constraints for practical quantum machine learning advancements.