Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data

• URL: http://arxiv.org/abs/2411.07276v1
• Date: Mon, 11 Nov 2024 15:34:53 GMT
• Title: Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data
• Authors: Arpita Ghosh, MD Muhtasim Fuad, Seemanta Bhattacharjee,
• Abstract summary: We focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility.
• Score: 0.0
• License:
• Abstract: The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to contribute to the advancement of quantum machine learning methodologies, offering valuable insights into their potential for addressing complex classification challenges in various domains.

Related papers

• Machine Learning and Quantum Intelligence for Health Data Scenarios [0.0]
  Traditional machine learning algorithms often face challenges in high-dimensional or limited-quality datasets. Quantum Machine Learning leverages quantum properties, such as superposition and entanglement, to enhance pattern recognition and classification. This paper explores QML's application in healthcare, focusing on quantum kernel methods and hybrid quantum-classical networks for heart disease prediction and COVID-19 detection.
  arXiv  Detail & Related papers  (2024-10-28T01:04:43Z)
• Quantum reservoir computing on random regular graphs [0.0]
  Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines input-driven many-body quantum systems with classical learning techniques. We study information localization, dynamical quantum correlations, and the many-body structure of the disordered Hamiltonian. Our findings thus provide guidelines for the optimal design of disordered analog quantum learning platforms.
  arXiv  Detail & Related papers  (2024-09-05T16:18:03Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• 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)
• Benchmarking quantum machine learning kernel training for classification tasks [0.0]
  This work performs a benchmark study of Quantum Kernel Estimation (QKE) and Quantum Kernel Training (QKT) with a focus on classification tasks. Two quantum feature mappings, namely ZZFeatureMap and CovariantFeatureMap, are analyzed in this context. Experimental results indicate that quantum methods exhibit varying performance across different datasets.
  arXiv  Detail & Related papers  (2024-08-17T10:53:06Z)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• Coreset selection can accelerate quantum machine learning models with provable generalization [6.733416056422756]
  Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning. We present a unified approach: coreset selection, aimed at expediting the training of QNNs and quantum kernels.
  arXiv  Detail & Related papers  (2023-09-19T08:59:46Z)
• 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)
• The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning [52.77024349608834]
  Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. Key issue is how to address the inherent non-linearity of classical deep learning. We introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning.
  arXiv  Detail & Related papers  (2022-12-22T16:06:24Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Generalization Metrics for Practical Quantum Advantage in Generative Models [68.8204255655161]
  Generative modeling is a widely accepted natural use case for quantum computers. We construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Our simulation results show that our quantum-inspired models have up to a $68 times$ enhancement in generating unseen unique and valid samples.
  arXiv  Detail & Related papers  (2022-01-21T16:35:35Z)

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.