Workshop 2.2 Hybrid Quantum Computing via QMware Cloud

The stateoftheart noisy intermediatescale 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 fullscale quantum algorithms aimed at solving concrete largescale problems arising in data analysis and numerical modeling remains a challenge.
We establish cuttingedge quantum cloud QMware that consolidates highperformance 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
largescale problems.
We will present several stateoftheart hybrid quantum/classical algorithms that are suitable for the NISQ era. First, we demonstrate how to solve optimization problems like MAXCUT 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.

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