Quantum autoencoders via quantum adders with genetic algorithms

Abstract

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.

References in corpus (29)

• Quantum Machine Learning
• An introduction to quantum machine learning
• Multi-particle entanglement of hot trapped ions
• Quantum autoencoders for efficient compression of quantum data
• Active learning machine learns to create new quantum experiments
• Quantum generalisation of feedforward neural networks
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
• Compiling quantum circuits to realistic hardware architectures using temporal planners
• Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
• Basic protocols in quantum reinforcement learning with superconducting circuits
• Quantum Memristors
• Quantum Memristors with Superconducting Circuits
• Creation of superposition of unknown quantum states
• Genetic Algorithms for Digital Quantum Simulations
• Quantum Memristors in Quantum Photonics
• Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience
• Supervised Quantum Learning without Measurements
• Qubit-based memcapacitors and meminductors
• The Forbidden Quantum Adder
• Artificial Life in Quantum Technologies
• Biomimetic Cloning of Quantum Observables
• Experimental creation of superposition of unknown photonic quantum states
• Experimentally superposing two pure states with partial prior knowledge
• Advanced-Retarded Differential Equations in Quantum Photonic Systems
• Superposing pure quantum states with partial prior information
• Unified quantum no-go theorems and transforming of quantum states in a restricted set
• A note on superposition of two unknown states using Deutsch CTC model
• Protected State Transfer via an Approximate Quantum Adder

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• Realization of a quantum autoencoder for lossless compression of quantum data
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• Quantum machine learning and quantum biomimetics: A perspective
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• Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
• Quantum Error Correction with Quantum Autoencoders
• Experimental Implementation of a Quantum Autoencoder via Quantum Adders
• AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
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• Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Evolving Quantum Circuits
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• Resilient superconducting-element design with genetic algorithms
• Quantum neural network autoencoder and classifier applied to an industrial case study
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• Disentangling quantum autoencoder