January 2023

Abstracts of the Quantum Center Lunch Seminar

Date: Thursday, January 26, 2023
Place: ETH Zurich, Hönggerberg, HPF G 6
Time: 12:00 - 13:30

Realizing a deep reinforcement learning agent for real-time quantum feedback

Kevin Reuer - Quantum Device Lab (Wallraff group), ETH Zurich

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data. We study the agent's performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control.

Electronic Poiseuille Flow in Hexagonal Boron Nitride En-capsulated Graphene FETs

Tathagata Paul - Quantum Devices (Perrin group), ETH Zurich and Empa

In most conductors, diffusive scattering from defects and lattice vibrations (phonons) leads to an Ohmic transport. Charge carriers traveling across the channel suffer many momentum relaxing collisions lead-ing to a constant drift velocity along the direction of the applied electric field. Alternatively, transport is ballistic, when the channel dimensions are the smallest length scale in the system. However, a third and relatively unexplored transport regime emerges when electron-electron interactions are sufficiently strong to induce a correlated and momentum-conserving flow such that charge carriers behave similarly to the Hagen-Poiseuille flow of a classical fluid. In the current work, we investigate the electronic signa-tures of such a viscous charge flow in high-mobility graphene FETs. In two complementary measurement schemes, we monitor differential resistance of graphene for different channel widths and for different effective electron temperatures. We observe a width dependence of channel conductivity and a mini-mum resistivity at elevated electron temperatures, indicative of the presence of charge hydrodynamics. By combining both approaches, the presence of viscous effects is verified in a temperature range starting from 178K and extending up to room temperature. Our experimental findings are supported by finite element calculations of the graphene channel, which also provide design guidelines for device geome-tries that exhibit increased viscous effects. The presence of viscous effects at room temperature opens up avenues for functional hydrodynamic devices such as geometric rectifiers like a Tesla valve and charge amplifiers based on electronic Venturi effect.