End-to-End Quantum Vision Transformer: Towards Practical Quantum Speedup
in Large-Scale Models
- URL: http://arxiv.org/abs/2402.18940v2
- Date: Fri, 1 Mar 2024 06:05:47 GMT
- Title: End-to-End Quantum Vision Transformer: Towards Practical Quantum Speedup
in Large-Scale Models
- Authors: Cheng Xue, Zhao-Yun Chen, Xi-Ning Zhuang, Yun-Jie Wang, Tai-Ping Sun,
Jun-Chao Wang, Huan-Yu Liu, Yu-Chun Wu, Zi-Lei Wang, Guo-Ping Guo
- Abstract summary: This paper introduces an end-to-end Quantum Vision Transformer (QViT), which incorporates an innovative quantum residual connection technique.
Our thorough analysis of the QViT reveals a theoretically exponential complexity and empirically speedup, showcasing the model's efficiency and potential in quantum computing applications.
- Score: 20.72342380227143
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The field of quantum deep learning presents significant opportunities for
advancing computational capabilities, yet it faces a major obstacle in the form
of the "information loss problem" due to the inherent limitations of the
necessary quantum tomography in scaling quantum deep neural networks. This
paper introduces an end-to-end Quantum Vision Transformer (QViT), which
incorporates an innovative quantum residual connection technique, to overcome
these challenges and therefore optimize quantum computing processes in deep
learning. Our thorough complexity analysis of the QViT reveals a theoretically
exponential and empirically polynomial speedup, showcasing the model's
efficiency and potential in quantum computing applications. We conducted
extensive numerical tests on modern, large-scale transformers and datasets,
establishing the QViT as a pioneering advancement in applying quantum deep
neural networks in practical scenarios. Our work provides a comprehensive
quantum deep learning paradigm, which not only demonstrates the versatility of
current quantum linear algebra algorithms but also promises to enhance future
research and development in quantum deep learning.
Related papers
- Quantum Generative Adversarial Networks: Bridging Classical and Quantum
Realms [0.6827423171182153]
We explore the synergistic fusion of classical and quantum computing paradigms within the realm of Generative Adversarial Networks (GANs)
Our objective is to seamlessly integrate quantum computational elements into the conventional GAN architecture, thereby unlocking novel pathways for enhanced training processes.
This research is positioned at the forefront of quantum-enhanced machine learning, presenting a critical stride towards harnessing the computational power of quantum systems.
arXiv Detail & Related papers (2023-12-15T16:51:36Z) - XpookyNet: Advancement in Quantum System Analysis through Convolutional
Neural Networks for Detection of Entanglement [0.0]
We introduce a custom deep convolutional neural network (CNN) model explicitly tailored to quantum systems.
Our proposed CNN model, the so-called XpookyNet, effectively overcomes the challenge of handling complex numbers data.
First and foremost, quantum states should be classified more precisely to examine fully and partially entangled states.
arXiv Detail & Related papers (2023-09-07T17:52:43Z) - Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization
in Future Networks [54.03701670739067]
We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks.
Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
arXiv Detail & Related papers (2022-09-16T02:44:52Z) - Quantum Neural Architecture Search with Quantum Circuits Metric and
Bayesian Optimization [2.20200533591633]
We propose a new quantum gates distance that characterizes the gates' action over every quantum state.
Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
arXiv Detail & Related papers (2022-06-28T16:23:24Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Quantum neural networks force fields generation [0.0]
We design a quantum neural network architecture and apply it successfully to different molecules of growing complexity.
The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances.
arXiv Detail & Related papers (2022-03-09T12:10:09Z) - Quantum Annealing Formulation for Binary Neural Networks [40.99969857118534]
In this work, we explore binary neural networks, which are lightweight yet powerful models typically intended for resource constrained devices.
We devise a quadratic unconstrained binary optimization formulation for the training problem.
While the problem is intractable, i.e., the cost to estimate the binary weights scales exponentially with network size, we show how the problem can be optimized directly on a quantum annealer.
arXiv Detail & Related papers (2021-07-05T03:20:54Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - The Hintons in your Neural Network: a Quantum Field Theory View of Deep
Learning [84.33745072274942]
We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles.
On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
arXiv Detail & Related papers (2021-03-08T17:24:29Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z)
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.