Quantum neural network autoencoder and classifier applied to an industrial case study

Abstract

Quantum computing technologies are in the process of moving from academic research to real industrial applications, with the first hints of quantum advantage demonstrated in recent months. In these early practical uses of quantum computers it is relevant to develop algorithms that are useful for actual industrial processes. In this work we propose a quantum pipeline, comprising a quantum autoencoder followed by a quantum classifier, which are used to first compress and then label classical data coming from a separator, i.e., a machine used in one of Eni's Oil Treatment Plants. This work represents one of the first attempts to integrate quantum computing procedures in a real-case scenario of an industrial pipeline, in particular using actual data coming from physical machines, rather than pedagogical data from benchmark datasets.

arXiv comment12 pages, 9 figures. To appear on Quantum Machine Intelligence

References in corpus (38)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• A variational eigenvalue solver on a quantum processor
• Quantum algorithm for solving linear systems of equations
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum Circuit Learning
• Parameterized quantum circuits as machine learning models
• Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
• Evaluating analytic gradients on quantum hardware
• The power of quantum neural networks
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• The quest for a Quantum Neural Network
• Data re-uploading for a universal quantum classifier
• Quantum autoencoders for efficient compression of quantum data
• Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
• Quantum algorithms for electronic structure calculations: particle/hole Hamiltonian and optimized wavefunction expansions
• Robust data encodings for quantum classifiers
• An Artificial Neuron Implemented on an Actual Quantum Processor
• Information-theoretic bounds on quantum advantage in machine learning
• Learning the quantum algorithm for state overlap
• Noise Resilience of Variational Quantum Compiling
• Quantum Variational Autoencoder
• Quantum Orbital-Optimized Unitary Coupled Cluster Methods in the Strongly Correlated Regime: Can Quantum Algorithms Outperform their Classical Equivalents?
• Quantum spectral methods for differential equations
• Quantum computing models for artificial neural networks
• Quantum implementation of an artificial feed-forward neural network
• Multipartite Entanglement and Global Information
• Detecting quantum entanglement with unsupervised learning
• Quantum autoencoders via quantum adders with genetic algorithms
• Noise-Resilient Variational Hybrid Quantum-Classical Optimization
• Variational learning for quantum artificial neural networks
• Quantum autoencoders with enhanced data encoding