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Workshop 2.2 – Hybrid Quantum Computing via QMware Cloud
30. November 2021 @ 2:00 pm - 5:00 pm UTC+0
The state–of–the–art noisy intermediate–scale quantum devices, although imperfect, enable computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, present quantum computations are restricted to exploring specific simplified protocols, whereas the implementation of full–scale quantum algorithms aimed at solving concrete large–scale problems arising in data analysis and numerical modeling remains a challenge.
We establish cutting–edge quantum cloud QMware that consolidates high–performance classical computing, hybrid quantum computing, and machine learning. Leveraging the impressive power of the QMware hardware and software stack, we show the ability to solve
large–scale problems.
We will present several state–of–the–art hybrid quantum/classical algorithms that are suitable for the NISQ era. First, we demonstrate how to solve optimization problems like MAX–CUT based on a hybrid algorithm called Quantum Approximation Optimization Algorithm (QAOA).
In a second demo we show a hybrid quantum/classical algorithm for solving one of the emergent pressing problems, linear systems of equations, that utilizes quantum phase estimation, one of the exemplary core protocols for quantum computing. Demo part ends
with a brief explanation / demo how Machine Learning can be leveraged by Hybrid Quantum algorithms. The considered algorithms clearly indicate the advantages of hybrid quantum data processing.
We establish cutting–edge quantum cloud QMware that consolidates high–performance classical computing, hybrid quantum computing, and machine learning. Leveraging the impressive power of the QMware hardware and software stack, we show the ability to solve
large–scale problems.
We will present several state–of–the–art hybrid quantum/classical algorithms that are suitable for the NISQ era. First, we demonstrate how to solve optimization problems like MAX–CUT based on a hybrid algorithm called Quantum Approximation Optimization Algorithm (QAOA).
In a second demo we show a hybrid quantum/classical algorithm for solving one of the emergent pressing problems, linear systems of equations, that utilizes quantum phase estimation, one of the exemplary core protocols for quantum computing. Demo part ends
with a brief explanation / demo how Machine Learning can be leveraged by Hybrid Quantum algorithms. The considered algorithms clearly indicate the advantages of hybrid quantum data processing.
Source: https://az659834.vo.msecnd.net/eventsairwesteuprod/production-abbey-public/2183ea889ce9476d941748697963600eGoals of the Workshop Conceptual understanding of hybrid quantum algorithms and their role in solving relevant practical problems of today.
Schedule of the Tutorial 2:00-2:20 Introduction 2:20-3:00 QMware cloud preview, software stack and high-level architecture
Workshop 2.2 at EQTC2021, Ireland 3:00-3:30 Evolving a hybrid QAOA algorithm to solve optimization problems 3:30-4:00 Demo of a linear equation solver 4:00-4:30 Demo of a sample that integrates quantum circuits into classical ML algorithms 4:30-5:00 Q&A