An example of use of Variational Methods in Quantum Machine Learning

Abstract

This paper introduces a deep learning system based on a quantum neural network for the binary classification of points of a specific geometric pattern (Two-Moons Classification problem) on a plane. We believe that the use of hybrid deep learning systems (classical + quantum) can reasonably bring benefits, not only in terms of computational acceleration but in understanding the underlying phenomena and mechanisms; that will lead to the creation of new forms of machine learning, as well as to a strong development in the world of quantum computation. The chosen dataset is based on a 2D binary classification generator, which helps test the effectiveness of specific algorithms; it is a set of 2D points forming two interspersed semicircles. It displays two disjointed data sets in a two-dimensional representation space: the features are, therefore, the individual points' two coordinates, $x_1$ and $x_2$. The intention was to produce a quantum deep neural network with the minimum number of trainable parameters capable of correctly recognising and classifying points.

References in corpus (28)

• Quantum Computing in the NISQ era and beyond
• Quantum Mechanics helps in searching for a needle in a haystack
• Quantum algorithm for solving linear systems of equations
• Variational Quantum Algorithms
• A Quantum Approximate Optimization Algorithm
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum machine learning in feature Hilbert spaces
• Quantum principal component analysis
• Circuit-centric quantum classifiers
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Quantum Networks for Elementary Arithmetic Operations
• Quantum algorithms for supervised and unsupervised machine learning
• Hybrid quantum-classical algorithms and quantum error mitigation
• Quantum autoencoders for efficient compression of quantum data
• Robust data encodings for quantum classifiers
• Towards Quantum Machine Learning with Tensor Networks
• Quantum generalisation of feedforward neural networks
• Implementing a distance-based classifier with a quantum interference circuit
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• Suppression of photon shot noise dephasing in a tunable coupling superconducting qubit
• A quantum algorithm to train neural networks using low-depth circuits
• Adequacy of Si:P Chains as Fermi-Hubbard Simulators
• Loading Classical Data into a Quantum Computer
• A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification
• Quantum storage in quantum ferromagnets
• Binary classification of proteins by a Machine Learning approach
• Skin Cancer Classification using Inception Network and Transfer Learning
• A new method for binary classification of proteins with Machine Learning