Classical machine learning has proven valuable since its implementation on classical computers became feasible. On the other hand, quantum computation claims to present an exponential advantage over any classical algorithm for specialized tasks. Thus, adapting the machine learning paradigm to the quantum realm is a promising way forward.
We start the talk with a general introduction to the...
In this talk, we investigate the application of machine learning to
an NP-hard problem in quantum information theory, the separability
problem of classifying a quantum state as entangled or
separable. This problem arises for entangled
quantum systems of dimension three or higher, where no exact solution
is currently known. We demonstrate that neural networks can accurately classify mixtures
of...
It is well known that quantum laws are fundamentally different and are currently being used to boost the performance of computers, including machine learning algorithms. We elaborate on the differences and challenges from different perspectives. Furthermore, we point out that with the recent trend in research to publish the computer code along with the research results, a causal link between...
Since the 2010s, when deep learning became feasible, machine learning (ML) has been experiencing ever-growing attention. The ability to teach large ML models gave rise to various neural network architectures, such as convolutional neural networks or generative adversarial networks. Around the same time, technological advancements allowed us to also direct our attention to quantum computing...
