# Quantum Machine Learning: from physics to software engineering

**Authors:** Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, Ray-Kuang Lee  
**arXiv:** [2301.01851](https://arxiv.org/abs/2301.01851)  
**Date:** 2023-01-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2301.01851v1/

## Key points

- Quantum machine learning (QML) combines quantum information science and technologies with machine learning and artificial intelligence techniques.
- Classical machine learning approaches can enhance the capabilities of existing quantum computers in the noisy intermediate-scale quantum (NISQ) era.
- Quantum algorithms and computers can be used to solve key problems in classical machine learning tasks.
- Quantum neural networks can be implemented in quantum-classical training algorithms for variational circuits.
- Speedup problem for random walks on arbitrary graphs needs to be addressed for both classical and quantum algorithms.
- Machine learning enhances fundamental and applied aspects of quantum physics, such as quantum tomography and photonic quantum computing.
- Machine learning approaches contribute to knowledge discoveries in modern fundamental physics, including particle physics research at facilities like the Large Hadron Collider (LHC).
- Data mining techniques improve sensitivity in network research on gravitational wave detection and weak signal registration in astronomy.

## Summary

Quantum machine learning (QML) is an emerging and rapidly growing field of research that combines quantum information science and technologies with novel machine learning and artificial intelligence techniques. In this review article, the authors provide a comprehensive analysis of current QML methods and approaches. The aim of their work is twofold. First, they demonstrate how classical machine learning approaches can enhance the capabilities of existing quantum computers and simulators, particularly in the context of the modern noisy intermediate-scale quantum (NISQ) era. By leveraging classical machine learning techniques, researchers can optimize quantum hardware to achieve desired quantum states through the implementation of quantum devices. Secondly, the authors discuss how quantum algorithms and computers can be utilized to solve key problems in classical machine learning tasks. Quantum-inspired algorithms, which utilize a quantum approach to classical information processing, have proven to be powerful tools in software engineering for improving classical computation capacities. The authors specifically explore various capabilities of quantum neural networks that can be implemented in quantum-classical training algorithms for variational circuits. The integration of quantum computers into routine machine learning procedures is expected in the future. Therefore, it is crucial to address the speedup problem for random walks on arbitrary graphs, which are utilized in both classical and quantum algorithms. The authors also cover how machine learning enhances fundamental and applied aspects of quantum physics, including topics such as quantum tomography and photonic quantum computing. It is important to note that machine learning approaches are closely connected with knowledge discoveries in modern fundamental physics. The ability to analyze big data plays a significant role in automated scientific discovery, contributing to advancements in fields like particle physics research at facilities such as the Large Hadron Collider (LHC). Additionally, network research on gravitational wave detection and weak signal registration in astronomy relies on improved sensitivity through data mining techniques. Overall, this review article provides a detailed exploration of QML methods and their potential applications across various disciplines, highlighting the intersection between quantum information science, machine learning, and classical computing.