Keynotes

Dr. Alexander Glätzle (planqc GmbH) Quantum Computing with ultracold atoms

Quantum Computers utilizing ultracold atoms confined in optical lattices possess exceptional capabilities for solving computationally complex tasks. They offer long qubit coherence time, no manufacturing variations, and scalability to thousands of qubits, all while operating at room temperature. Established in 2022, planqc emerged as the first startup from the Munich Quantum Valley and was recently honored with the German Startup Award 2023. Notably, it has just sold its first 100-qubit quantum computer to the German Aerospace Center for 29 million euros.

Dr. Manpreet Jattana (MSQC Group, Goethe University Frankfurt) Algorithms in the era of noisy quantum hardware

Despite much progress in developing quantum hardware, we remain in an era famously termed Noisy Intermediate Scale Quantum (NISQ). Useful applications with theoretically proven quantum advantage, e.g. Shor’s algorithm, cannot yet be implemented on such hardware for solving practical problems. In this talk, I will briefly review two types of quantum computers: gate-based and annealers. I will explain how variational algorithms have become practical alternatives to traditional algorithms for universal gate-based quantum computers. Simultaneously, quantum annealers have also shown significant promise and are already successfully solving prototype optimisation problems.

Deepankar Bhagat (ING) A demonstration of quantum solutions to fraud detection and option pricing

Financial institutions are one of the prime domains of research for applications of quantum technologies. Here, we present two projects conducted at ING along with research students from various universities. First, we touch upon fraud detection by benchmarking classical supervised machine learning techniques against a quantum supervised machine learning technique (kNN vs qkNN). We find that the results are similar, but there is a major slowdown to the qKNN solution due to loading classical data into the quantum circuits. A quantum-generative adversarial network (QGAN) is used to replace the data-loading oracle and smaller runtimes are observed. The second topic is on pricing American options. Historically, path-independent derivatives were used to highlight the advantages of quantum computing solutions, for example, for European options. We provide an overview on the feasibility of quantum computing solutions for path-dependent financial derivatives as American options.

Technical support

We acknowledge technical support from Classiq (www.classiq.io) during the entire hackathon weekend. The Classiq platform will be used to model, synthesize and execute quantum circuits.