Quantum Machine Learning has established itself as one of the most promising applications of quantum computers and Noisy Intermediate Scale Quantum (NISQ) devices. In this paper, we review the latest developments regarding the usage of quantum computing for a particular class of machine learning algorithms known as kernel methods.
We introduce a homology-based technique for the classification of multiqubit state vectors with genuine entanglement. In our approach, we associate state vectors to data sets by introducing a metric-like measure in terms of bipartite entanglement, and investigate the persistence of homologies at different scales. This leads to a novel classification of multiqubit entanglement. The relative occurrence frequency of various classes of entangled states is also shown.
We address the problem of facial expression recognition and show a possible solution using a quantum machine learning approach. In order to define an efficient classifier for a given dataset, our approach substantially exploits quantum interference. By representing face expressions via graphs, we define a classifier as a quantum circuit that manipulates the graphs adjacency matrices encoded into the amplitudes of some appropriately defined quantum states. We discuss the accuracy of the quantum classifier evaluated on the quantum simulator available on the IBM Quantum Experience cloud platform, and compare it with the accuracy of one of the best classical classifier.
Molecular Docking (MD) is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound to each other. During such analysis, 3D representations of molecules are manipulated according to their degree of freedoms: rigid roto-translation and fragment rotations along the rotatable bonds. In our work, we focused on one specific phase of the molecular docking procedure i.e. Molecular Unfolding (MU), which is used to remove the initial bias of a molecule by expanding it to an unfolded shape. The objective of the MU problem is to find the configuration that maximizes the molecular area, or equivalently, that maximizes the internal distances between atoms inside the molecule. We propose a quantum annealing approach to MU by formulating it as a High-order Unconstrained Binary Optimization (HUBO) which was possible to solve on the latest D-Wave annealing hardware (2000Q and Advantage). Results and performances obtained with quantum annealers are compared with state of art classical solvers.
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