Speaker
Description
Novel technologies emerging from the second quantum revolution enable us to identify, control and manipulate individual quanta with unprecedented precision. One important area is the rapidly evolving new paradigm of quantum computing, which has the potential to revolutionize computing by operating on completely different principles. Expectations are high, as quantum computers have already solved complex problems that cannot be solved with classical computers.
A very important new branch is quantum machine learning (QML), which lies at the intersection of quantum computing and machine learning. QML combines classical Machine Learning with topics concerning Quantum Algorithms and Architectures. Many studies address hybrid quantum-classical approaches, but full quantum approaches are also investigated. The ultimate goal is to find the so-called quantum advantage, where quantum models outperform classical algorithms in terms of runtime or even solve problems that are intractable for classical computers.
However, in the current NISQ era (Noisy Intermediate-Scale Quantum computing), where noise in quantum computing challenges the accuracy of computations and the small number of qubits limits the size of the problem to be solved, it is difficult to achieve quantum advantage. Nevertheless, machine learning can be robust to noise and allows to deal with limited resources of present-day quantum computers.
In this talk, quantum machine learning will be introduced and explained with examples. Challenges and possible transfer to practical applications will be discussed.
