Ginés Carrascal
Computational Scientist & Architect
IBM Quantum

Abstract:
High-Performance Computing has reached a pivotal moment with the advent of quantum computing. This talk delves into the quantum realm, exploring the foundational elements and applications of quantum computing to machine learning.
Qubits, States, Operators, and Other Secret Monsters.
At the heart of quantum computing lies the qubit, a unit of quantum information that defies classical analogs. We unravel the mysteries of qubits, their states, and the operators that manipulate them, revealing the ‘secret monsters’ of quantum mechanics that empower quantum computers to perform complex calculations.
Parameter-Dependent Quantum Circuits:
The adaptability of quantum circuits is paramount for their practical application. We examine parameter-dependent quantum circuits, which are essential for running hybrid quantum-classical algorithms. These circuits’ capacity and trainability are assessed, highlighting their potential in the noisy intermediate-scale quantum (NISQ) era.
Learning from Scratch: How to Train Your Quantum Computer
Quantum computing is not just for the experts. We present a beginner-friendly approach to quantum machine learning, demonstrating how enthusiasts can start and understand, programming from scratch the basics of a quantum learning system.
Getting the Most Out of It: Quantum Support Vector Machines
Quantum Support Vector Machines (QSVMs) represent a significant leap in machine learning. By leveraging the principles of quantum mechanics, QSVMs offer a new paradigm for data classification and pattern recognition. We explore the complexities and advantages of QSVMs, showcasing their superiority in certain applications over their classical counterparts.

More information: [ Ссылка ]

свернуть