A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
- URL: http://arxiv.org/abs/2407.16398v1
- Date: Tue, 23 Jul 2024 11:38:06 GMT
- Title: A Quantum Leaky Integrate-and-Fire Spiking Neuron and Network
- Authors: Dean Brand, Francesco Petruccione,
- Abstract summary: We introduce a new software model for quantum neuromorphic computing.
We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN) and a quantum spiking convolutional neural network (QSCNN)
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing -- a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.
Related papers
- Let the Quantum Creep In: Designing Quantum Neural Network Models by
Gradually Swapping Out Classical Components [1.024113475677323]
Modern AI systems are often built on neural networks.
We propose a framework where classical neural network layers are gradually replaced by quantum layers.
We conduct numerical experiments on image classification datasets to demonstrate the change of performance brought by the systematic introduction of quantum components.
arXiv Detail & Related papers (2024-09-26T07:01:29Z) - Hybrid Quantum-Classical Normalizing Flow [5.85475369017678]
We propose a hybrid quantum-classical normalizing flow (HQCNF) model based on parameterized quantum circuits.
We test our model on the image generation problem.
Compared with other quantum generative models, such as quantum generative adversarial networks (QGAN), our model achieves lower (better) Fr'echet distance (FID) score.
arXiv Detail & Related papers (2024-05-22T16:37:22Z) - 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) - 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) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - 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) - QFCNN: Quantum Fourier Convolutional Neural Network [4.344289435743451]
We propose a new hybrid quantum-classical circuit, namely Quantum Fourier Convolutional Network (QFCN)
Our model achieves exponential speed-up compared with classical CNN theoretically and improves over the existing best result of quantum CNN.
We demonstrate the potential of this architecture by applying it to different deep learning tasks, including traffic prediction and image classification.
arXiv Detail & Related papers (2021-06-19T04:37:39Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - Quantum Deformed Neural Networks [83.71196337378022]
We develop a new quantum neural network layer designed to run efficiently on a quantum computer.
It can be simulated on a classical computer when restricted in the way it entangles input states.
arXiv Detail & Related papers (2020-10-21T09:46:12Z) - On the learnability of quantum neural networks [132.1981461292324]
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme.
We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
arXiv Detail & Related papers (2020-07-24T06:34:34Z)
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.