New Trends in Quantum Machine Learning

L. Buffoni and F. Caruso

https://arxiv.org/abs/2108.09664

https://pennylane.ai/

A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.

Quantum Circuit Learning

K. Mitarai et al.

https://arxiv.org/abs/1803.00745

A Quantum Approximate Optimization Algorithm

E. Farhi at al.

https://arxiv.org/abs/1411.4028

Power and limitations of single-qubit native quantum neural networks

Z. Yu et al.

https://arxiv.org/abs/2205.07848

Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

Y. Kwak et al.

https://arxiv.org/abs/2108.06849

https://recruit.gmo.jp/engineer/jisedai/blog/quantum_machine_learning/

Quantum Machine Learning

J. Biamonte et al.

https://arxiv.org/abs/1611.09347

Variational Quantum Algorithms

M. Cerezo et al.

https://arxiv.org/abs/2012.09265

Parameterized quantum circuits as machine learning models

M. Benedetti et al.

https://arxiv.org/abs/1906.07682

Noisy intermediate-scale quantum (NISQ) algorithms

K. Bharti et al.

https://arxiv.org/abs/2101.08448

Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms

S. Sim et al.

https://arxiv.org/abs/1905.10876

Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability

T. Hubregtsen et al.

https://arxiv.org/abs/2003.09887

Barren plateaus in quantum neural network training landscapes

J. R. McClean

https://arxiv.org/abs/1803.11173

An initialization strategy for addressing barren plateaus in parametrized quantum circuits

E. Grant et al.

https://arxiv.org/abs/1903.05076

Towards Quantum Advantage on Noisy Quantum Computers

I. Akhalwaya et al.

https://arxiv.org/abs/2209.09371

NISQ-TDA