The innovation initiative QuantumBW launches the new "QuantumBW Colloquium" on the campus of the Fraunhofer Institute Center Stuttgart. The aim of the colloquium is to promote scientific exchange on hardware and algorithmic topics in the field of quantum computing, to present the latest developments in this research area and to promote the idea of co-development of quantum solutions.
In addition to the question of how the next generation of computers will be realized, it is also exciting to see what next-generation computers can be used for. They have promising applications in cryptography, machine learning and optimization. Due to the currently still noisy, small systems of the NISQ era (Noisy Intermediate Scale), the class of variational quantum algorithms is particularly interesting. These and other topics such as quantum error correction, barren plateaus and quantum advantage will be discussed in the colloquium on the following dates:
Current quantum devises are faulty and one needs to be able to assess the errors which occur during quantum information processing. I will discuss several methods to gain confidence about the correct functioning of quantum devices and to test quantum computations and quantum simulations.
We will describe digital, analog, and digital-analog quantum computing paradigms. Furthermore, we will discuss the possibility of reaching quantum advantage for industry use cases with current quantum computers in trapped ions, superconducting circuits, neutral atoms, and photonic systems.
Quantum computing promises advantages for a number of structured computational problems. While the idea of quantum computing is not new, only within the last a bit more than five years protagonists have set out to actually build such devices to a reasonable scale. The quantum computers we have today are still somewhat noisy and not huge - but then, such devices seemed inconceivable not very long ago, creating an exciting state of affairs. This also comes along with lots of expectations and some hype. This talk will go on a journey deciphering what we can reasonably expect from such machines in the near future. It will present some exciting perspectives concerning achieving industrially relevant applications in machine learning and optimization. It will also debunk some of the most unreasonable of expectations and provide a reality check of what can be achieved for noisy devices. Overall, this will give rise to a ride through the landscape of one of the most exciting and promising future technologies.