8 research outputs found
Residual Matrix Product State for Machine Learning
Tensor network, which originates from quantum physics, is emerging as an
efficient tool for classical and quantum machine learning. Nevertheless, there
still exists a considerable accuracy gap between tensor network and the
sophisticated neural network models for classical machine learning. In this
work, we combine the ideas of matrix product state (MPS), the simplest tensor
network structure, and residual neural network and propose the residual matrix
product state (ResMPS). The ResMPS can be treated as a network where its layers
map the "hidden" features to the outputs (e.g., classifications), and the
variational parameters of the layers are the functions of the features of the
samples (e.g., pixels of images). This is different from neural network, where
the layers map feed-forwardly the features to the output. The ResMPS can equip
with the non-linear activations and dropout layers, and outperforms the
state-of-the-art tensor network models in terms of efficiency, stability, and
expression power. Besides, ResMPS is interpretable from the perspective of
polynomial expansion, where the factorization and exponential machines
naturally emerge. Our work contributes to connecting and hybridizing neural and
tensor networks, which is crucial to further enhance our understand of the
working mechanisms and improve the performance of both models
Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin
This study explores the potential of quantum machine learning and quantum computing for climate change detection, climate modeling, and climate digital twin. We additionally consider the time and energy consumption of quantum machines and classical computers. Moreover, we identified several use-case instances for climate change detection, climate modeling, and climate digital twin that are challenging for conventional computers but can be tackled efficiently with quantum machines or by integrating them with classical computers. We also evaluated the efficacy of quantum annealers, quantum simulators, and universal quantum computers, each designed to solve specific types and kinds of computational problems that are otherwise difficult
Machine Learning Applications for the Study and Control of Quantum Systems
In this thesis, I consider the three main paradigms of machine learning – supervised, unsupervised, and reinforcement learning – and explore how each can be employed as a tool to study or control quantum systems. To this end, I adopt classical machine learning methods, but also illustrate how present-day quantum devices and concepts from condensed matter physics can be harnessed to adapt the machine learning models to the physical system being studied. In the first project, I use supervised learning techniques from classical object detection to locate quantum vortices in rotating BoseEinstein condensates. The machine learning model achieves high accuracies even in the presence of noise, which makes it especially suitable for experimental settings. I then move on to the field of unsupervised learning and introduce a quantum anomaly detection framework based on parameterized quantum circuits to map out phase diagrams of quantum many-body systems. The proposed algorithm allows quantum systems to be directly analyzed on a quantum computer without any prior knowledge about its phases. Lastly, I consider two reinforcement learning applications for quantum control. In the first example, I use Q-learning to maximize the entanglement in discrete-time quantum walks. In the final study, I introduce a novel approach for controlling quantum many-body systems by leveraging matrix product states as a trainable machine learning ansatz for the reinforcement learning agent. This framework enables us to reach far larger system sizes than conventional neural network-based approaches.Okinawa Institute of Science and Technology Graduate Universit