AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications

• URL: http://arxiv.org/abs/2205.09115v1
• Date: Tue, 17 May 2022 19:38:13 GMT
• Title: AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
• Authors: Toshiaki Koike-Akino, Pu Wang, Ye Wang
• Abstract summary: Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. We investigate a proof-of-concept approach using automated quantum machine learning framework called AutoAnsatz to recognize human gesture.
• Score: 19.06876644658677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Commercial Wi-Fi devices can be used for integrated sensing and communications (ISAC) to jointly exchange data and monitor indoor environment. In this paper, we investigate a proof-of-concept approach using automated quantum machine learning (AutoQML) framework called AutoAnsatz to recognize human gesture. We address how to efficiently design quantum circuits to configure quantum neural networks (QNN). The effectiveness of AutoQML is validated by an in-house experiment for human pose recognition, achieving state-of-the-art performance greater than 80% accuracy for a limited data size with a significantly small number of trainable parameters.

Related papers

• Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection [3.2927352068925444]
  deepfake technologies pose challenges to privacy, security, and information integrity. This paper introduces a Quantum-Trained Convolutional Neural Network framework designed to enhance the detection of deepfake audio.
  arXiv  Detail & Related papers  (2024-10-11T20:52:10Z)
• Training-efficient density quantum machine learning [2.918930150557355]
  Quantum machine learning requires powerful, flexible and efficiently trainable models. We present density quantum neural networks, a learning model incorporating randomisation over a set of trainable unitaries.
  arXiv  Detail & Related papers  (2024-05-30T16:40:28Z)
• Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applications [18.34157974553066]
  A quantum convolution-based object detection (QCOD) is proposed to perform object detection at high speed. The QCOD utilizes our proposed fast quantum convolution that uploads input channel information and re-constructs output channels. Our experiments with KITTI autonomous driving object detection dataset verify that the proposed fast quantum convolution and QCOD are successfully operated in real object detection applications.
  arXiv  Detail & Related papers  (2023-12-28T00:38:10Z)
• Federated Quantum Long Short-term Memory (FedQLSTM) [58.50321380769256]
  Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models. No prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions. A novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed.
  arXiv  Detail & Related papers  (2023-12-21T21:40:47Z)
• Quantum Transfer Learning for Wi-Fi Sensing [19.06876644658677]
  We investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks (DNN) for the future quantum-ready society.
  arXiv  Detail & Related papers  (2022-05-17T19:16:21Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• 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)
• Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition [101.69873988328808]
  We build upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction. An input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram. The corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition.
  arXiv  Detail & Related papers  (2020-10-26T03:36:01Z)
• SeQUeNCe: A Customizable Discrete-Event Simulator of Quantum Networks [53.56179714852967]
  This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
  arXiv  Detail & Related papers  (2020-09-25T01:52:15Z)
• End-to-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics [0.9599644507730106]
  This work presents a hardware-friendly end-to-end quantum machine learning scheme that can be implemented with imperfect near-term intermediate-scale quantum (NISQ) processors. The proposal transforms the machine learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables. Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables.
  arXiv  Detail & Related papers  (2020-03-30T17:44:51Z)
• Machine learning transfer efficiencies for noisy quantum walks [62.997667081978825]
  We show that the process of finding requirements on both a graph type and a quantum system coherence can be automated. The automation is done by using a convolutional neural network of a particular type that learns to understand with which network and under which coherence requirements quantum advantage is possible. Our results are of importance for demonstration of advantage in quantum experiments and pave the way towards automating scientific research and discoveries.
  arXiv  Detail & Related papers  (2020-01-15T18:36:53Z)

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.