Machine learning is frequently listed among the most promising applications for quantum
computing. This is in fact a curious choice: the machine-learning algorithms of today are …

Topological data analysis refers to approaches for systematically and reliably computing
abstract 'shapes' of complex data sets. There are various applications of topological data …

Jonas Landman: jonas. landman@ qcware. com we trained on these small-scale datasets
require fewer parameters compared to standard classical benchmarks. While this …

Quantum computing and machine learning (ML) have received significant developments
which have set the stage for the next frontier of creative work and usefulness. This paper …

Quantum machine learning (QML) has become a promising area for real world applications
of quantum computers, but near-term methods and their scalability are still important …

Quantum algorithms for topological data analysis (TDA) seem to provide an exponential
advantage over the best classical approach while remaining immune to dequantization …

Topological invariants of a dataset, such as the number of holes that survive from one length
scale to another (persistent Betti numbers) can be used to analyse and classify data in …

The field of quantum machine learning (QML) produces many proposals for attaining
quantum speedups for tasks in machine learning and data analysis. Such speedups can …

In this paper we study the Clifford+ Toffoli universal fault-tolerant gate set. We introduce a
generating set in order to represent any unitary implementable by this gate set and with this …

In this paper we study the universal V-basis gate sets, which have also been shown to be
fault tolerant. Our methods and results can be applied to arbitrary dimensional basis gates …