# Quantum Machine Learning Implementations: Proposals and Experiments

arXiv: https://arxiv.org/abs/2303.06263 · HTML: https://mlsanity.com/paper/2303.06263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [A quantum-inspired classical algorithm for recommendation systems](https://mlsanity.com/paper/1807.04271/) (2018)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)
- [Quantum autoencoders via quantum adders with genetic algorithms](https://mlsanity.com/paper/1709.07409/) (2017)
- [Experimental Implementation of a Quantum Autoencoder via Quantum Adders](https://mlsanity.com/paper/1807.10643/) (2018)
- [Basic protocols in quantum reinforcement learning with superconducting circuits](https://mlsanity.com/paper/1701.05131/) (2017)
- [Quantum machine learning and quantum biomimetics: A perspective](https://mlsanity.com/paper/2004.12076/) (2020)
- [Measurement-based adaptation protocol with quantum reinforcement learning](https://mlsanity.com/paper/1803.05340/) (2018)
- [Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer](https://mlsanity.com/paper/1811.07594/) (2018)

## Built on by (most cited first, 1 of 1)

- [Quantum neural network with ensemble learning to mitigate barren plateaus and cost function concentration](https://mlsanity.com/paper/2402.06026/) (2024)

## Abstract (arXiv)

This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their experimental realizations in the platforms of quantum photonics and superconducting circuits. The field of quantum machine learning could be among the first quantum technologies producing results that are beneficial for industry and, in turn, to society. Therefore, it is necessary to push forward initial quantum implementations of this technology, in Noisy Intermediate-Scale Quantum Computers, aiming for achieving fruitful calculations in machine learning that are better than with any other current or future computing paradigm.