# A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity

arXiv: https://arxiv.org/abs/2402.15333 · HTML: https://mlsanity.com/paper/2402.15333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [The Computational Limits of Deep Learning](https://mlsanity.com/paper/2007.05558/) (2020)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Quantum Natural Gradient](https://mlsanity.com/paper/1909.02108/) (2019)
- [Reinforcement learning for optimization of variational quantum circuit architectures](https://mlsanity.com/paper/2103.16089/) (2021)
- [An end-to-end trainable hybrid classical-quantum classifier](https://mlsanity.com/paper/2102.02416/) (2021)
- [Hybrid quantum-classical classifier based on tensor network and variational quantum circuit](https://mlsanity.com/paper/2011.14651/) (2020)
- [A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantage](https://mlsanity.com/paper/2006.14815/) (2020)
- [Advances in Quantum Deep Learning: An Overview](https://mlsanity.com/paper/2005.04316/) (2020)
- [Sublinear quantum algorithms for training linear and kernel-based classifiers](https://mlsanity.com/paper/1904.02276/) (2019)
- [word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement](https://mlsanity.com/paper/1911.04975/) (2019)
- [Quantum-Inspired Support Vector Machine](https://mlsanity.com/paper/1906.08902/) (2019)
- [Quantum Expectation-Maximization for Gaussian Mixture Models](https://mlsanity.com/paper/1908.06657/) (2019)
- [A Leap among Quantum Computing and Quantum Neural Networks: A Survey](https://mlsanity.com/paper/2107.03313/) (2021)
- [QuantumEyes: Towards Better Interpretability of Quantum Circuits](https://mlsanity.com/paper/2311.07980/) (2023)
- [VENUS: A Geometrical Representation for Quantum State Visualization](https://mlsanity.com/paper/2303.08366/) (2023)
- [QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation](https://mlsanity.com/paper/2210.05443/) (2022)
- [Distributed Quantum Learning with co-Management in a Multi-tenant Quantum System](https://mlsanity.com/paper/2312.08158/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum Machine Learning Architecture Search via Deep Reinforcement Learning](https://mlsanity.com/paper/2407.20147/) (2024)

## Abstract (arXiv)

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to use qubits efficiently and mitigate both present limitations and future complexities. To address this, existing quantum applications attempt to integrate classical and quantum systems in a hybrid framework. In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu. The classical component employs a tensor network for compression and feature extraction, enabling higher-dimensional data to be encoded onto logical quantum circuits with limited qubits. On the quantum side, we propose a quantum-state-fidelity-based evaluation function to iteratively train the network through a feedback loop between the two sides. co-TenQu has been implemented and evaluated with both simulators and the IBM-Q platform. Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. Additionally, it outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.