This book chapter describes a novel approach to training machine learning systems by means of a hybrid computer setup i.e. a digital computer tightly coupled with an analog computer. As an example a reinforcement learning system is trained to balance an inverted pendulum which is simulated on an analog computer, thus demonstrating a solution to the major challenge of adequately simulating the environment for reinforcement learning.
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN …
Southern University of Science and Technology（南方科技大学）
The key to electroencephalography (EEG)-based brain-computer interface (BCI) lies in neural decoding, and its accuracy can be improved by using hybrid BCI paradigms, that is, fusing multiple paradigms. However, hybrid BCIs usually require separate processing processes for EEG signals in each paradigm, which greatly reduces the efficiency of EEG feature extraction and the generalizability of the model. Here, we propose a two-stream convolutional neural network (TSCNN) based hybrid brain-computer interface. It combines steady-state visual evoked potential (SSVEP) and motor imagery (MI) paradigms…
South China Normal University（华南师范大学）；Nanjing University（南京大学）；The University of Hong Kong（香港大学）
Incorporating nonlinearity into quantum machine learning is essential for learning a complicated input-output mapping. We here propose quantum algorithms for nonlinear regression, where nonlinearity is introduced with feature maps when loading classical data into quantum states. Our implementation is based on a hybrid quantum computer, exploiting both discrete and continuous variables, for their capacity to encode novel features and efficiency of processing information. We propose encoding schemes that can realize well-known polynomial and Gaussian kernel ridge regressions, with exponentially …
University of Florida（佛罗里达大学）；Oak Ridge National Laboratory（橡树岭国家实验室）
Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers quickly approach physical limitations that will slow performance improvements in years to come. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one o…
Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing
University of Bristol, School of Computer Science（布里斯托大学计算机科学学院）；University of Bristol, School of Engineering Mathematics and Technology（布里斯托大学工程数学与技术学院）
Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way…
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
Massachusetts Institute of Technology（麻省理工学院）；University of California at Berkeley（加州大学伯克利分校）；The University of Hong Kong（香港大学）；University of Florida（佛罗里达大学）
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder
Université de Sherbrooke（谢布鲁克大学）；Polytechnique Montréal（蒙特利尔理工学院）；Thales Digital Solutions（泰雷兹数字解决方案公司）；Institut Quantique, Université de Sherbrooke（谢布鲁克大学量子研究所）
Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot be accurately modelled may lead to the notorious reality gap between the digital simulator and the physical system. To address this challenge, we demonstrate hybrid training of optical neural networks where the weight matrix is trained with neuron activation functions computed opt…
University College London（伦敦大学学院）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Chicago（芝加哥大学）；RWTH Aachen University（亚琛工业大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）；Rutgers, the State University of New Jersey（罗格斯大学，新泽西州立大学）；Science Museum（科学博物馆）；Italian Institute of Technology（意大利理工学院）；Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities（巴伐利亚科学与人文学院莱布尼茨超级计算中心）；CERN（欧洲核子研究中心）；Argonne National Laboratory（阿贡国家实验室）；University of Amsterdam（阿姆斯特丹大学）
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here…
