Warsaw Quantum Computing Group

We invite you to attend (remote-only) Episode XXXII of the Warsaw Quantum Computing Group meetings!

22.11, 18:00 CET

Manuel Rudolph & Michał Stęchły

„ORQVIZ: Visualizing High-Dimensional Landscapes in Variational Quantum Algorithms"

Registration form: https://docs.google.com/forms/d/e/1FAIpQLSeC1lMo5IsQo9yoq0y9O20YF7e7VERTW0gUnnE7wfRtz81MrQ/viewform

Abstract: In this talk, Manuel and Michał will present the results of their recent work on visualizing loss landscapes of variational quantum algorithm using a new library called “orqviz”. They (mostly Manuel) will talk about how using multiple visualization techniques allowed to get a better understanding of certain quantum algorithms. They (mostly Michał) will also show how one can use orqviz in their research and what were some challenges associated with creating this library.

Paper abstract (arXiv: 2111.04695): Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near to mid-term quantum computers. There has been an increasing effort to study the intricacies of VQAs, such as the presence or absence of barren plateaus and the design of good quantum circuit ansätze. Many of these studies can be linked to the loss landscape that is optimized as part of the algorithm, and there is high demand for quality software tools for flexibly studying these loss landscapes. In our work, we collect a variety of techniques that have been used to visualize the training of deep artificial neural networks and apply them to visualize the high-dimensional loss landscapes of VQAs. We review and apply the techniques to three types of VQAs: the Quantum Approximate Optimization Algorithm, the Quantum Circuit Born Machine, and the Variational Quantum Eigensolver. Additionally, we investigate the impact of noise due to finite sampling in the estimation of loss functions. For each case, we demonstrate how our visualization techniques can verify observations from past studies and provide new insights. This work is accompanied by the release of the open-source Python package orqviz, which provides code to compute and flexibly plot 1D and 2D scans, Principal Component Analysis scans, Hessians, and the Nudged Elastic Band algorithm. orqviz enables flexible visual analysis of high-dimensional VQA landscapes and can be found at: github.com/zapatacomputing/orqviz.


Manuel Rudolph: Quantum Application Scientist, Zapata Computing

Manuel studied Physics at the University of Heidelberg in 2014-2020 and graduated with a Master’s degree. He did his Master thesis, Exploring and Benchmarking Quantum-assisted Neural Networks with Qubit Layers, in collaboration with the Honda Research Institute Europe in Frankfurt. After graduating, he started an internship at Zapata and then joined as full-time Quantum Application Scientist for quantum machine learning and generative modelling.

Michał Stęchły: Quantum Software Engineer, Zapata Computing

Michał is a quantum software engineer at Zapata Computing, where he writes scientific software for quantum computing. His main interests are variational algorithms, optimization, software design and data visualization. Apart from that he’s active in the quantum community and involved in organizations such as Quantum Open-Source Foundation, Unitary Fund or Q4Climate.

This meeting is organized by the Quantum AI Foundation.

Strategic Partners: Snarto, Cogit