Assessing Projected Quantum Kernels for the Classification of IoT Data

• URL: http://arxiv.org/abs/2505.14593v1
• Date: Tue, 20 May 2025 16:45:58 GMT
• Title: Assessing Projected Quantum Kernels for the Classification of IoT Data
• Authors: Francesco D'Amore, Luca Mariani, Carlo Mastroianni, Francesco Plastina, Luca Salatino, Jacopo Settino, Andrea Vinci,
• Abstract summary: A major challenge in the development of Quantum Machine Learning (QML) algorithms is the lack of datasets specifically designed for quantum algorithms.<n>In this work, we utilize a dataset generated by Internet-of-Things (IoT) devices in a format directly compatible with the proposed quantum algorithms.<n>Among quantum-inspired machine learning algorithms, the Projected Quantum Kernel (PQK) stands out for its elegant solution of projecting the data encoded in the Hilbert space into a classical space.
• Score: 1.4637460398319744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of quantum computing for machine learning is among the most exciting applications of quantum technologies. Researchers are developing quantum models inspired by classical ones to find some possible quantum advantages over classical approaches. A major challenge in the development and testing of Quantum Machine Learning (QML) algorithms is the lack of datasets specifically designed for quantum algorithms. Existing datasets, often borrowed from classical machine learning, need modifications to be compatible with current noisy quantum hardware. In this work, we utilize a dataset generated by Internet-of-Things (IoT) devices in a format directly compatible with the proposed quantum algorithms, eliminating the need for feature reduction. Among quantum-inspired machine learning algorithms, the Projected Quantum Kernel (PQK) stands out for its elegant solution of projecting the data encoded in the Hilbert space into a classical space. We detail how a PQK approach can be employed to construct a prediction model on IoT data. We compare PQK with common Quantum Kernel methods and their classical counterparts, while also investigating the impact of various feature maps in encoding classical IoT data into quantum computers.

Related papers

• LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder [7.945302052915863]
  A potential application of quantum machine learning is to harness the power of quantum computers for generating classical data. We propose LatentQGAN, a novel quantum model that uses a hybrid quantum-classical GAN coupled with an autoencoder.
  arXiv  Detail & Related papers  (2024-09-22T23:18:06Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Quantum Hamiltonian Embedding of Images for Data Reuploading Classifiers [0.9374652839580181]
  One of the first considerations is the design of the quantum machine learning model itself. Recent research has started questioning whether quantum advantage via speedup is the right goal for quantum machine learning. In this paper, we take an alternative approach by incorporating the design of classical deep learning algorithms to the design of quantum neural networks.
  arXiv  Detail & Related papers  (2024-07-19T06:31:22Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Classical Verification of Quantum Learning [42.362388367152256]
  We develop a framework for classical verification of quantum learning. We propose a new quantum data access model that we call "mixture-of-superpositions" quantum examples. Our results demonstrate that the potential power of quantum data for learning tasks, while not unlimited, can be utilized by classical agents.
  arXiv  Detail & Related papers  (2023-06-08T00:31:27Z)
• Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators [0.0]
  Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model. We propose quantum machine learning with Kerrnon Parametric Hilberts (KPOs) as another promising quantum computing device.
  arXiv  Detail & Related papers  (2023-05-01T07:01:45Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• 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)
• Machine learning applications for noisy intermediate-scale quantum computers [0.0]
  We develop and study three quantum machine learning applications suitable for NISQ computers. These algorithms are variational in nature and use parameterised quantum circuits (PQCs) as the underlying quantum machine learning model. We propose a variational algorithm in the area of approximate quantum cloning, where the data becomes quantum in nature.
  arXiv  Detail & Related papers  (2022-05-19T09:26:57Z)
• Entangled Datasets for Quantum Machine Learning [0.0]
  We argue that one should instead employ quantum datasets composed of quantum states. We show how a quantum neural network can be trained to generate the states in the NTangled dataset. We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits.
  arXiv  Detail & Related papers  (2021-09-08T02:20:13Z)
• 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)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)

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.