Experimentally Realizable Continuous-variable Quantum Neural Networks
- URL: http://arxiv.org/abs/2306.02525v2
- Date: Wed, 7 Jun 2023 17:06:28 GMT
- Title: Experimentally Realizable Continuous-variable Quantum Neural Networks
- Authors: Shikha Bangar, Leanto Sunny, Kubra Yeter-Aydeniz, George Siopsis
- Abstract summary: Continuous-variable (CV) quantum computing has shown great potential for building neural network models.
Previous work on CV neural network protocols required the implementation of non-Gaussian operators in the network.
We built a CV hybrid quantum-classical neural network protocol that can be realized experimentally with current photonic quantum hardware.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Continuous-variable (CV) quantum computing has shown great potential for
building neural network models. These neural networks can have different levels
of quantum-classical hybridization depending on the complexity of the problem.
Previous work on CV neural network protocols required the implementation of
non-Gaussian operators in the network. These operators were used to introduce
non-linearity, an essential feature of neural networks. However, these
protocols are hard to execute experimentally. We built a CV hybrid
quantum-classical neural network protocol that can be realized experimentally
with current photonic quantum hardware. Our protocol uses Gaussian gates only
with the addition of ancillary qumodes. We implemented non-linearity through
repeat-until-success measurements on ancillary qumodes. To test our neural
network, we studied canonical machine learning and quantum computer problems in
a supervised learning setting -- state preparation, curve fitting, and
classification problems. We achieved high fidelity in state preparation of
single-photon (99.9%), cat (99.8%), and Gottesman-Kitaev-Preskill (93.9%)
states, a well-fitted curve in the presence of noise at a cost of less than 1%,
and more than 95% accuracy in classification problems. These results bode well
for real-world applications of CV quantum neural networks.
Related papers
- Quantum Convolutional Neural Network: A Hybrid Quantum-Classical Approach for Iris Dataset Classification [0.0]
We present a hybrid quantum-classical machine learning model for classification tasks, integrating a 4-qubit quantum circuit with a classical neural network.
The model was trained over 20 epochs, achieving a perfect 100% accuracy on the Iris dataset test set on 16 epoch.
This work contributes to the growing body of research on hybrid quantum-classical models and their applicability to real-world datasets.
arXiv Detail & Related papers (2024-10-21T13:15:12Z) - CTRQNets & LQNets: Continuous Time Recurrent and Liquid Quantum Neural Networks [76.53016529061821]
Liquid Quantum Neural Network (LQNet) and Continuous Time Recurrent Quantum Neural Network (CTRQNet) developed.
LQNet and CTRQNet achieve accuracy increases as high as 40% on CIFAR 10 through binary classification.
arXiv Detail & Related papers (2024-08-28T00:56:03Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows.
Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems.
We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
arXiv Detail & Related papers (2023-08-22T09:11:53Z) - Quantum Neural Network for Quantum Neural Computing [0.0]
We propose a new quantum neural network model for quantum neural computing.
Our model circumvents the problem that the state-space size grows exponentially with the number of neurons.
We benchmark our model for handwritten digit recognition and other nonlinear classification tasks.
arXiv Detail & Related papers (2023-05-15T11:16:47Z) - Variational Quantum Neural Networks (VQNNS) in Image Classification [0.0]
This paper investigates how training of quantum neural network (QNNs) can be done using quantum optimization algorithms.
In this paper, a QNN structure is made where a variational parameterized circuit is incorporated as an input layer named as Variational Quantum Neural Network (VQNNs)
VQNNs is experimented with MNIST digit recognition (less complex) and crack image classification datasets which converge the computation in lesser time than QNN with decent training accuracy.
arXiv Detail & Related papers (2023-03-10T11:24:32Z) - Problem-Dependent Power of Quantum Neural Networks on Multi-Class
Classification [83.20479832949069]
Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood.
Here we investigate the problem-dependent power of QCs on multi-class classification tasks.
Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
arXiv Detail & Related papers (2022-12-29T10:46:40Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - A Hybrid Quantum-Classical Neural Network Architecture for Binary
Classification [0.0]
We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit.
On simulated hardware, we observe that the hybrid neural network achieves roughly 10% higher classification accuracy and 20% better minimization of cost than an individual variational quantum circuit.
arXiv Detail & Related papers (2022-01-05T21:06:30Z) - A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems.
We numerically demonstrate that the MNIST image dataset satisfies such conditions.
We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
arXiv Detail & Related papers (2021-07-19T23:41:03Z) - Recurrent Quantum Neural Networks [7.6146285961466]
Recurrent neural networks are the foundation of many sequence-to-sequence models in machine learning.
We construct a quantum recurrent neural network (QRNN) with demonstrable performance on non-trivial tasks.
We evaluate the QRNN on MNIST classification, both by feeding the QRNN each image pixel-by-pixel; and by utilising modern data augmentation as preprocessing step.
arXiv Detail & Related papers (2020-06-25T17:59:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.