Natalia Ares

Abstract:

The development of quantum devices that can be operated with great precision, and within time scales as short as ten nanoseconds, opens up possibilities both for the development of novel technologies and for answering fundamental questions in quantum mechanics and thermodynamics.

In this context, the ability to measure the movement of nanometer-thick membranes excited by electrical fluctuations has allowed us to estimate the thermodynamic cost of timekeeping [1]. Fully-suspended carbon nanotube devices allow for ultra-strong coupling between electron tunnelling and motion [2]. This interaction is allowing us to study nanoscale machines in which the working substance is one or two electrons and the piston’s motion is the nanotube’s displacement.

But control tasks become challenging as the complexity of quantum devices grows, since the dimension of the parameter space to be explored to operate and optimize such devices grows rapidly. We have thus turned to artificial intelligence algorithms, which are capable of characterizing and calibrating quantum devices in a completely automatic way and with an efficiency superior to that of humans [3]. In turn, these devices offer us a platform to explore the meaning of learning in quantum mechanics, opening the path to the development of quantum circuits with the ability to learn.

[1] A. Pearson et al. Physical Review X 11, 021029 (2021)

[2] F. Vigneau et al. arXiv:2103.15219

[3] N. Ares. Comment, Nature Reviews Materials (2021)