Automated closed-loop optimizations скачать в хорошем качестве

Automated closed-loop optimizations

This webinar demonstrates how new features in Boulder Opal allow you to automate performance enhancements in quantum hardware through closed-loop experimental optimization. Whether you build quantum computers or quantum sensors, or you perform basic physics research, it’s simple to use closed-loop optimization to deliver greater performance from hardware even when there are major uncertainties in your system. We will present a variety of experimental case studies including demonstrations on ultra-cold atoms, quantum memories, and superconducting quantum computers. And look out for a sneak peak of our results using closed-loop optimization to improve the performance of two-qubit gates on IBM-Q. You will learn: 1. How to use Boulder Opal to perform automated closed-loop hardware optimization in your experiment. 2. How different algorithms and approaches to performing efficient closed-loop hardware optimization compare in efficiency and effectiveness. 3. What kinds of parameters can be optimized autonomously: including atomic-gas cooling and transport, storage efficiency of quantum memories, and quantum logic operation fidelity. 4. How to integrate automated closed-loop optimization with cloud quantum computers to design better quantum logic gates - even if you don’t know much about designing quantum logic gates!