Harnessing Quantum Extreme Learning Machines for image classification

Abstract

Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine learning techniques for image classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyse different phases of the quantum extreme learning machine process, from the dataset preparation to the image final classification. In particular, we have tested different encodings, together with Principal Component Analysis, the use of Auto-Encoders, as well as the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. Additionally, while different encodings can lead to significantly different performances, Hamiltonians with varying degrees of connectivity exhibit the same discrimination rate, provided they are interacting.

17 pages, 8 figures

References in corpus (24)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• QuTiP 2: A Python framework for the dynamics of open quantum systems
• Quantum computational advantage using photons
• The quest for a Quantum Neural Network
• Next Generation Reservoir Computing
• Quantum advantage in learning from experiments
• Quantum computing with neutral atoms
• Quantum machine learning for image classification
• Hybrid quantum-classical reservoir computing of thermal convection flow
• Potential and limitations of quantum extreme learning machines
• Experimental property-reconstruction in a photonic quantum extreme learning machine
• Dissipation as a resource for Quantum Reservoir Computing
• Assessing Quantum Computing Performance for Energy Optimization in a Prosumer Community
• Feedback-driven quantum reservoir computing for time-series analysis
• Quantum reservoir computation utilising scale-free networks
• On fundamental aspects of quantum extreme learning machines
• Exploring quantum mechanical advantage for reservoir computing
• Quantum Capsule Networks
• Variational Gibbs State Preparation on NISQ devices
• Variational Quantum Algorithms for the Allocation of Resources in a Cloud/Edge Architecture
• Benchmarking the role of particle statistics in Quantum Reservoir Computing
• State estimation with quantum extreme learning machines beyond the scrambling time
• Image Classification with Rotation-Invariant Variational Quantum Circuits
• Quantum Reservoir Computing for Speckle-Disorder Potentials

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• Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics
• Quantum reservoir computing for predicting and characterizing chaotic maps