The toolset runs on Q-CTRL’s flagship BOULDER OPAL software
by: Praharsha Anand
Q-CTRL has announced a new AI-based toolset to facilitate the unassisted performance optimization of quantum computers.
By and large, quantum algorithms are susceptible to errors, creating a substantial barrier to progress and advancement in quantum computing. Q-CTRL’s new automated closed-loop hardware optimization tool uses custom AI agents to run quantum algorithms, resulting in fewer errors and better overall performance for end-users.
Integrated with Q-CTRL’s flagship BOULDER OPAL software for developers and R&D teams, automated closed-loop hardware optimization is also trained to obtain new experimental data/results from quantum computers while simultaneously running optimizations for algorithms. It can be used as a standalone tool or in tandem with a machine-learner online optimization package (M-LOOP) that manages quantum experiments autonomously.
Continue reading… “Q-CTRL’s new AI toolset allows quantum computers to self-optimize”









