Variational Quantum Algorithms (VQAs) are promising candidates for finding practical applications of near- to mid-term quantum computers. To best utilize available quantum resources, it is crucial that we do not treat VQAs as “black boxes.”
We have been working on a Python library that can be used to gain a deeper understanding of a range of research topics by providing easy-to-use methods for visually representing the optimization landscape of Variational Quantum Algorithms.
The orqviz library is released together with a scientific paper which showcases these methods on a range of examples of quantum algorithms to provide a new perspective from which to tackle future research projects. The implementation of the visualization techniques has been adapted for natural application and ease of use for quantum algorithms.
Our goal is to reach a wide audience — academics, businesses and quantum enthusiasts alike — and provide value through our work and with our quantum workflow platform Orquestra®. With orqviz, we hope to provide a valuable tool for researchers that they can use in their own projects in order to progress the field of quantum computing.
- Jarrod R. McClean, Google Quantum A.I.
- Eric R. Anschuetz, M.I.T. Center for Theoretical Physics
- Asad Raza, Zapata Quantum Applications Intern