Scalable Quantum Neural Networks for Classification

@article{Wu2022ScalableQN,
  title={Scalable Quantum Neural Networks for Classification},
  author={Jindi Wu and Zeyi Tao and Qun Li},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.07719}
}
—Many recent machine learning tasks resort to quan- tum computing to improve classification accuracy and training efficiency by taking advantage of quantum mechanics, known as quantum machine learning (QML). The variational quantum circuit (VQC) is frequently utilized to build a quantum neural network (QNN), which is a counterpart to the conventional neural network. Due to hardware limitations, however, current quantum devices only allow one to use few qubits to represent data and perform simple… 
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References

SHOWING 1-10 OF 47 REFERENCES

Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

A hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training and shows that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset.

Training deep quantum neural networks

A noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers.

Classification with Quantum Neural Networks on Near Term Processors

This work introduces a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, and shows through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.

Large-scale quantum machine learning

This work measures quantum kernels using randomized measurements to gain a quadratic speedup in computation time and quickly process large datasets and efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth.

QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity

This work proposes a novel architecture QuClassi, a quantum neural network for both binary and multi-class classification, powered by a quantum differentiation function along with a hybrid quantum-classic design, which achieves a comparable performance with 97.37% fewer parameters.

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.

Classification with Quantum Machine Learning: A Survey

This paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML), and proposes a classification scheme in the quantum world and discusses encoding methods for mapping classical data to quantum data.

Supervised learning with a quantum classifier using multi-level systems

We propose a quantum classifier, which can classify data under the supervised learning scheme using a quantum feature space. The input feature vectors are encoded in a single qu N it (a N -level

Quantum circuit learning

A classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which is hybridizing a low-depth quantum circuit and a classical computer for machinelearning, paves the way toward applications of near- term quantum devices for quantum machine learning.

Quantum Competitive Neural Network

A Quantum Competitive Neural Network that can recognize patterns and classify patterns via quantum competition that has no weights, does not need to learn and update weights, which accelerates the learning process of the network.