This approach is based on an encoding of two binary strings into a topo- logical Hilbert space, whose inner product yields a natural Hamming distance kernel on ...

Nov 21, 2024 · This algorithm is used in [80] to define a kernelization of the Hamming distance on strings. This is obtained by encoding binary strings as some ...

This approach is based on an encoding of two binary strings into a topological Hilbert space, whose inner product yields a natural Hamming distance kernel on ...

Aug 25, 2019 · We present a novel approach to computing Hamming distance and its kernelisation within Topological Quantum Computation.

Jul 14, 2023 · The topological kernel method embeds data into a filtration, extracts topological features, uses to define a distance function, which can ...

Apr 28, 2019 · Kernel methods. Topological quantum computation. Hamming distance kernelisation via topological quantum computation (Di Pierro et al. 2017).

This document reviews recent developments in using quantum computing for a particular type of machine learning algorithm called kernel methods.

There exist several equivalent models of quantum computation like quantum circuit model, adiabatic quantum computing (AQC) model and topological quantum ...

In this work, we propose a quantum approach to defining topological kernels, which is based on constructing Betti curves, i.e. topological fingerprint of ...

Hamming distance kernelisation via topological quantum computation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial ...