# The power of quantum neural networks

@article{Abbas2020ThePO, title={The power of quantum neural networks}, author={Amira Abbas and David Sutter and Christa Zoufal and Aur{\'e}lien Lucchi and Alessio Figalli and Stefan Woerner}, journal={ArXiv}, year={2020}, volume={abs/2011.00027} }

Fault-tolerant quantum computers offer the promise of dramatically improving machine learning through speed-ups in computation or improved model scalability. In the near-term, however, the benefits of quantum machine learning are not so clear. Understanding expressibility and trainability of quantum models-and quantum neural networks in particular-requires further investigation. In this work, we use tools from information geometry to define a notion of expressibility for quantum and classical…

## 127 Citations

The dilemma of quantum neural networks

- Computer ScienceArXiv
- 2021

Through systematic numerical experiments, it is observed that current quantum neural networks fail to provide any benefit over classical learning models and are forced to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.

Quantum Federated Learning with Quantum Data

- Computer ScienceArXiv
- 2021

This paper proposes the first fully quantum federated learning framework that can operate over quantum data and share the learning of quantum circuit parameters in a decentralized manner, and is the first to combine Google’s TensorFlow Federated and Tensor Flow Quantum in a practical implementation.

Equivalence of quantum barren plateaus to cost concentration and narrow gorges

- PhysicsArXiv
- 2021

This work analytically proves the connection between three different landscape features that have been observed for PQCs: exponentially vanishing gradients, exponential cost concentration about the mean, and the exponential narrowness of minina.

Quantum machine learning with differential privacy

- Computer ScienceArXiv
- 2021

This study develops a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm and demonstrates potential to be efficiently implemented on near-term quantum devices (noisy intermediate-scale quantum [NISQ]).

Accelerating variational quantum algorithms with multiple quantum processors

- Computer ScienceArXiv
- 2021

An efficient distributed optimization scheme, called QUDIO, that can be readily mixed with other advanced VQAs-based techniques to narrow the gap between the state of the art and applications with quantum advantage.

Single-component gradient rules for variational quantum algorithms

- Computer ScienceQuantum Science and Technology
- 2022

This work provides a comprehensive picture of the family of gradient rules that vary parameters of quantum gates individually, and proposes a generalized PSR that expresses all members of the aforementioned family as special cases, and introduces a novel perspective for approaching new gradient rules.

Hybrid Quantum-Classical Graph Convolutional Network

- Computer Science, PhysicsArXiv
- 2021

This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data that demonstrates an advantage over classical multilayer perceptron and Convolutional neural networks in the aspect of number of parameters.

Federated Quantum Machine Learning

- Computer ScienceEntropy
- 2021

The distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training, and demonstrates a promising future research direction for scaling and privacy aspects.

QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer

- Computer ScienceArXiv
- 2021

Results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size ≥ 100 sentences are presented.

Ju n 20 21 Quantum Natural Gradient for Variational Bayes

- Computer Science
- 2021

It is proved that, under standard conditions, the VB algorithm with quantum natural gradient is guaranteed to converge and the classical-quantum-classical handoffs are sufficiently economical to preserve computational efficiency.

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