# QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks

@article{Qi2021QTNVQCAE, title={QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks}, author={Jun Qi and Chao-Han Huck Yang and Pin-Yu Chen}, journal={ArXiv}, year={2021}, volume={abs/2110.03861} }

The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The architecture of QTN is composed of a parametric tensor-train network for feature extraction and a tensor product encoding for…

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## References

SHOWING 1-10 OF 59 REFERENCES

### Quantum embeddings for machine learning

- Computer Science
- 2020

This work proposes to train the first part of the circuit with the objective of maximally separating data classes in Hilbert space, a strategy it calls quantum metric learning, which provides a powerful analytic framework for quantum machine learning.

### SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size

- Computer ScienceArXiv
- 2016

This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet).

### Quantum Machine Learning in Feature Hilbert Spaces.

- Computer SciencePhysical review letters
- 2019

This Letter interprets the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space and shows how it opens up a new avenue for the design of quantum machine learning algorithms.

### Expressive power of parametrized quantum circuits

- Computer Science
- 2020

It is proved that PQCs with a simple structure already outperform any classical neural network for generative tasks, unless the polynomial hierarchy collapses, and employed them as an application for Bayesian learning.

### Quantum speed-ups in reinforcement learning

- Computer ScienceNanoScience + Engineering
- 2021

This work quantizes the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication, and demonstrates that this feature enables a speed-up in the agent's learning process.

### Inside quantum black boxes

- Physics
- 2021

On the face of it, characterizing quantum dynamics in the exponentially large Hilbert space of a many-body system might require prohibitively many experiments. In fact, the locality of physical…

### QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits

- Computer Science2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
- 2022

Extensively evaluated with 12 quantum machine learning (QML) and variational quantum eigensolver (VQE) benchmarks on 14 quantum computers, QuantumNAS significantly outperforms noise-unaware search, human, random, and existing noise-adaptive qubit mapping baselines.

### Highly accurate protein structure prediction with AlphaFold

- Biology, Computer ScienceNature
- 2021

This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods.

### Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

- Computer ScienceQuantum
- 2022

A training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm and shows when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments.