pyRiemann-qiskit: A Sandbox for Quantum Classification Experiments with Riemannian Geometry

Abstract

Quantum computing is a promising technology for machine learning, in terms of computational costs and outcomes. In this work, we intend to provide a framework that facilitates the use of quantum machine learning in the domain of brain-computer interfaces – where biomedical signals, such as brain waves, are processed.To this end, we integrated Qiskit, a well-known quantum library, with pyRiemann, a framework for the analysis of biomedical signals using Riemannian Geometry. In this paper, we describe our approach, the main elements of our implementation and our research directions. A key result is the creation of a standardised pipeline (QuantumClassifierWithDefaultRiemannianPipeline) for the binary classification of brain waves. The git repository reported in this paper also contains a complete test suite and examples to guide practitioners. We believe that this software will enable further research on the joint field of brain-computer interfaces and quantum computing.

Keywords

• information geometry
• machine learning
• time serie