Hamiltonian learning
Who wouldn't want a quantum devices without errors? We developed a machine learning algorithm that identifies the parameters of a quantum device from a small amount of measurements and utilizes them to bring the device into the desired error free state.
Over the past twenty years, we witnessed a dramatic advancement in the development of quantum technologies. Today, the first quantum computers are available to users via cloud technology and quantum simulators reached sizes, for which their physics can no longer be simulated on classical computers. However, there are two outstanding challenges that must be addressed for these technologies to be widely applicable: the first is correcting errors and the second is the verification that quantum devices perform the intended tasks.
In this work, we bring these two important directions of research together and create a new paradigm for error correction through verification of a quantum device. Our algorithm can be trained on a classical computer and then be deployed on real quantum devices. This new approach employs precise engineering driven by machine learning and opens a new field of research that may lead to the reformulation of quantum error correction.
Reference
Valenti A, van Nieuwenburg EPL, Huber SD, Greplova E. Hamiltonian learning for quantum error correction, external page Physical Review Research 1, 033092 (2019)