Thema der Dissertation:
Learning with and about quantum computers: possibilities and limitations
Learning with and about quantum computers: possibilities and limitations
Abstract: Learning theory provides the structural framework for analysing the complexity of problems related to obtaining a description of some unknown object (such as an unknown function, a distribution, a quantum state or process) based on indirect access to it namely via data. It is not difficult to see then that a great number of central questions in quantum information and computing can be seen through the lens of learning theory. We will discuss a number of different problems that fit into this framework. Motivated by the quest for showing provable quantum advantages for quantum machine learning, we will discuss the problem of learning quantum circuit output distributions. In contrast to what was colloquially expected, we show that this task is in fact hard even for quantum learners barring any meaningful quantum advantage.
Moving forward, obtaining sufficient tomographic information about a quantum device in order to evaluate its performance is an important building block within the engineering cycle which will culminate in the development of large scale quantum computers. However, at the current point techniques such as quantum state and process tomography are way too costly to be of any practical use. With this in mind we turn our attention towards techniques to efficiently obtain sufficient tomographic information. In particular, we focus on the scenario in which we want to verify the correct operation of quantum devices in removed locations. Using the framework of cross-device verification we develop and analyse a protocol for this task based on distributed inner product estimation using Pauli-sampling.
Moving forward, obtaining sufficient tomographic information about a quantum device in order to evaluate its performance is an important building block within the engineering cycle which will culminate in the development of large scale quantum computers. However, at the current point techniques such as quantum state and process tomography are way too costly to be of any practical use. With this in mind we turn our attention towards techniques to efficiently obtain sufficient tomographic information. In particular, we focus on the scenario in which we want to verify the correct operation of quantum devices in removed locations. Using the framework of cross-device verification we develop and analyse a protocol for this task based on distributed inner product estimation using Pauli-sampling.
Time & Location
May 07, 2025 | 04:30 PM
Hörsaal A (1.3.14)
(Fachbereich Physik, Arnimallee 14, 14195 Berlin)