Machine Learning in Natural Sciences: from Quantum Physics to Nanoscience and Structural Biology

Session

Optimization and Control

OC

19 Sept 2022, 09:15

Seminarraum 1-3 (CFEL (Building 99))

47. Turning a physical system into a self-learning machine

Prof. Florian Marquardt (MPL Erlangen)

19/09/2022, 09:15

Invited Talk

30. Depth requirement reduction using sequential execution

Adrián Pérez Salinas (Leiden University)

19/09/2022, 10:00

Optimization and Control

Contributed Talk (15 min)

State-of-the-art quantum computers cannot run arbitrarily long quantum algorithms since their decoherence time is limited. The quality of the results unavoidably decays as the execution time increases. This work introduces a method to reduce the depth requirements of a circuit to be executed in a quantum device. The method consists in splitting the circuit in several stages to be applied...

33. Reducing Barren Plateaus in Quantum Algorithm Protocols

Lukas Broers (Universität Hamburg)

19/09/2022, 10:15

Optimization and Control

Contributed Talk (15 min)

Quantum machine learning has emerged as a promising utilization of near-term quantum computation devices. However, algorithmic classes such as variational quantum algorithms have been shown to suffer from barren plateaus due to vanishing gradients in their parameters spaces. We present an approach to quantum algorithm optimization that is based on trainable Fourier coefficients of Hamiltonian...

27. Characterization of Few-femtosecond Near-infrared Pulses using Machine Learning approach

Daria Kolbasova (CFEL DESY, Department of Physics UHH)

19/09/2022, 10:30

Optimization and Control

Contributed Talk (15 min)

The analysis of the absorption and emission of electromagnetic radiation is a powerful method for exploring the quantum world of atoms and molecules. The ability to use well-defined laser pulses provides an opportunity to study the underlying structure and mechanisms of these microscopic systems with a very high resolution. A large number of techniques developed up to date for the...

39. Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Dr Marin Bukov (Sofia University)

19/09/2022, 11:15

Invited Talk

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling...

20. Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Ms Friederike Metz (Okinawa Institute of Science and Technology)

19/09/2022, 12:00

Optimization and Control

Contributed Talk (15 min)

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems and consequently, to devise reliable and robust optimal control protocols. Here, we present a novel framework for efficiently controlling...

32. Online adaptive estimation of decoherence timescales for a single qubit

Muhammad Junaid Arshad (Heriot-Watt University)

19/09/2022, 12:15

Optimization and Control

Contributed Talk (15 min)

The estimation of decoherence timescales is important not only as a key performance indicator for quantum technology, but also to measure physical quantities through the change they induce in the relaxation of quantum sensors. Typically, decoherence times are estimated by fitting a signal acquired while sweeping the time delay between qubit preparation and detection on a pre-determined range....

22. Robust quantum dot charge autotuning with Bayesian Neural Networks

Victor Yon (Institut Interdisciplinaire d’Innovation Technologique (3IT), Laboratoire Nanotechnologies Nanosystèmes (LN2))

19/09/2022, 12:30

Optimization and Control

Contributed Talk (15 min)

Quantum dots must be tuned to a specific charge regime before being used for qubit operation. This calibration procedure requires measuring the stability diagram and finding the proper gate voltages to confine one electron in the dot. Currently, this operation is performed manually, which is time-consuming and therefore not desirable for a large-scale quantum system. To overcome this...