# The Capacity of Quantum Neural Networks

@article{Wright2020TheCO, title={The Capacity of Quantum Neural Networks}, author={Logan G. Wright and Peter Leonard McMahon}, journal={2020 Conference on Lasers and Electro-Optics (CLEO)}, year={2020}, pages={1-2} }

Quantum neural networks (QNN) are a promising application of near-term quantum computers. We present an information theory of QNN's expressive power, which we apply to an example optical QNN based on a Gaussian Boson Sampler.

## 21 Citations

### Quantum computing models for artificial neural networks

- Computer Science
- 2021

An overview of the most recent proposals aimed at bringing together these ongoing revolutions in Machine Learning and Artificial Intelligence, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures.

### The power of quantum neural networks

- Computer ScienceArXiv
- 2020

This work is the first to demonstrate that well-designed quantum neural networks offer an advantage over classical neural networks through a higher effective dimension and faster training ability, which is verified on real quantum hardware.

### QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

- Computer ScienceFrontiers in Physics
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QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices and shows 43% better accuracy on average over the existing models on noisy quantum hardware emulators.

### Variational Learning for Quantum Artificial Neural Networks

- Computer ScienceIEEE Transactions on Quantum Engineering
- 2021

This work presents an original realization of efficient individual quantum nodes based on variational unsampling protocols, and investigates different learning strategies involving global and local layerwise cost functions, and assess their performances also in the presence of statistical measurement noise.

### Variational learning for quantum artificial neural networks

- Computer Science2020 IEEE International Conference on Quantum Computing and Engineering (QCE)
- 2020

An original realization of efficient individual quantum nodes based on variational unsampling protocols is presented, suggesting a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware.

### On the learnability of quantum neural networks

- Computer SciencePRX Quantum
- 2021

This work derives the convergence performance of QNN under the NISQ setting, and identifies classes of computationally hard concepts that can be efficiently learned by QNN, and proves that any concept class, which is efficiently learnable by a restricted quantum statistical query (QSQ) learning model, can also be efficiently learning byQNN.

### DeepQMLP: A Scalable Quantum-Classical Hybrid Deep Neural Network Architecture for Classification

- Computer Science2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID)
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It is shown that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit, and up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.

### An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States

- PhysicsArXiv
- 2022

. Neural quantum states are variational wave functions parameterised by artiﬁcial neural networks, a mathematical model studied for decades in the machine learning community. In the context of…

### Quantum reservoir computing with a single nonlinear oscillator

- Physics, Computer Science
- 2020

The results show that quantum reservoir computing in a single nonlinear oscillator is an attractive modality for quantum computing on near-term hardware and may impact the interpretation of results across quantum machine learning.

### Nonlinear input transformations are ubiquitous in quantum reservoir computing

- Computer ScienceNeuromorph. Comput. Eng.
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It is found that across the majority of schemes the input encoding implements a nonlinear transformation on the input data, which calls into question the necessity and function of further, post-input, processing.

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