# Machine learning & artificial intelligence in the quantum domain: a review of recent progress

@article{Dunjko2018MachineL, title={Machine learning \& artificial intelligence in the quantum domain: a review of recent progress}, author={Vedran Dunjko and Hans J. Briegel}, journal={Reports on Progress in Physics}, year={2018}, volume={81} }

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research—quantum information versus machine learning (ML) and artificial intelligence (AI)—have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work…

## 492 Citations

### Headway in Quantum Domain for Machine Learning Towards Improved Artificial Intelligence

- Computer Science2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS)
- 2019

This composite review, to the best of the authors' knowledge, makes an attempt to explore the recent headway for actuating AI and ML in the quantum domain.

### Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

- Computer Science, PhysicsNatural Science
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This work employs machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.

### Quantum Computing Methods for Supervised Learning

- Computer Science, PhysicsQuantum Mach. Intell.
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This paper provides a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems, and aims to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.

### Machine Learning: Quantum vs Classical

- Computer ScienceIEEE Access
- 2020

An overview of quantum machine learning in the light of classical approaches is presented, discussing various technical contributions, strengths and similarities of the research work in this domain and elaborate upon the recent progress of different quantum machinelearning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.

### Q Learning with Quantum Neural Networks

- Computer ScienceNatural Science
- 2019

This study implements the well-known RL algorithm Q learning with a quantum neural network and evaluates it in the grid world environment and extends previous work on solving the contextual bandit problem using a quantum Neural Network.

### Reinforcement Learning with Neural Networks for Quantum Feedback

- Computer SciencePhysical Review X
- 2018

This work shows how a network-based "agent" can discover complete quantum-error-correction strategies, protecting a collection of qubits against noise, and develops two ideas: two-stage learning with teacher/student networks and a reward quantifying the capability to recover the quantum information stored in a multi-qubit system.

### Recent Advances for Quantum Neural Networks in Generative Learning

- Computer Science, PhysicsArXiv
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This paper interprets these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models, and explores their intrinsic relation and their fundamental differences.

### Experimental quantum speed-up in reinforcement learning agents

- Computer Science, PhysicsNature
- 2021

This work presents a reinforcement learning experiment where the learning process of an agent is sped up by utilizing a quantum communication channel with the environment, and shows that combining this scenario with classical communication enables the evaluation of such an improvement, and additionally allows for optimal control of the learning progress.

### Smooth input preparation for quantum and quantum-inspired machine learning

- Computer ScienceQuantum Mach. Intell.
- 2021

This work proves using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries and that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

### Quantum machine learning and quantum biomimetics: A perspective

- Computer ScienceMach. Learn. Sci. Technol.
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An overview of quantum machine learning, quantum reinforcement learning, and the field of quantum biomimetics is given, describing the related research carried out by the scientific community.

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