This is the official application formular for the quantum hackathon. Please answer the following question and you will get answer if you are selected to join the hackfest. The selection process will end August 1st. You will get an invitation in first week of August. In case of questions please contact us: firstname.lastname@example.org
Short details:We will start friday morning 10am with a short introduction and the possible topics for the 24h challange, you can of course also pitch your own case. The location is a free and creative space in the middle of Frankfurt's East Harbour with lots of restaurantes and cool places around. We will provide food and drinks for the day and will go to dinner altogether. There are many hostels/hotels/airbnb around, just google the location or look for the recommendations. Location you need to organize on your own. More information you will find below the questions.
Possible places to stay:https://www.25hours-hotels.com/hotels/frankfurt/the-goldmanhttps://www.accorhotels.com/de/hotel-9066-ibis-budget-frankfurt-city-ost/index.shtmlhttps://www.hotelbb.de/de/frankfurt-city-osthttps://www.motel-one.com/de/hotels/frankfurt/hotel-frankfurt-east-side/https://www.aohostels.com/de/frankfurt/frankfurt-ostend/https://www.marriott.de/hotels/travel/fraoh-moxy-frankfurt-east/https://www.melia.com/en/hotels/germany/frankfurt/innside-frankfurt-ostend/index.htmlMore expensive:https://hamptoninn3.hilton.com/en/hotels/hessee/hampton-by-hilton-frankfurt-city-centre-east-FRAHXHX/index.html
These days are intended to bring together creative and bright minds and hackers from different backgrounds to play around with quantum algorithms and explore interesting applications. We intend to make this as free as possible so teams will be allocated on Friday after a small introduction of the topic and challenges. Our team will act as floating coaches during the 2 days if you have any questions, fears, need for structuring or clarity.On Saturday every team will present their solution/ideas/code/business model to the group and we will evaluate the best team. The price is the presentation of the solution on our Quantum Computing Germany Meetup v.04 which will take place after the hackathon.
We are going to provide drinks, breakfast for the 2 days and dinner for friday evening. Travel and accommodation you need to organize yourself. There is no strict timetable that needs to be followed but the general structure would be as following:
Friday 20.09.201909:30 Open doors at INMLocation: https://goo.gl/maps/ULprdoXEtNenL6KL610:00 Start the day with a breakfast, coffee, drinks and introduction round of all participants11:00 Brief introduction of JoS QUANTUM, quantum computing applications in finance and the possible challenges to work on. If you want to bring a topic yourself to be worked on you are more than welcome, just please let us know a few days before.12:00 Start with your team and work on the problem set. You have a lot of space in the institute to maximize creativity.20:00 Dinner at an italian restaurant close to the institute:Location: https://goo.gl/maps/ng1kaB7CD9J1LKLB6If you need some fresh air, drinks or party in the evening visit this awesome place just 5min away from INM:Website: https://www.schwedlersee.de/
Saturday 21.09.201910:00 Breakfast will be provided. You can keep working or enjoy your coffee on the terrace.16:00 Presentation of solutions in front of the other teams and our jury18:00 This Is The End
JoS QUANTUM has no intention to hold IP on solutions, everything will be open sourced via Gitlab if this is the will of the contributors. You can use any software framework you wish, here is a non-exhaustive list:IBM Qiskit:https://qiskit.org/Rigetti Forest:https://www.rigetti.com/forestD-Wave leap:https://cloud.dwavesys.com/leap/login/?next=/leap/Continuous variable framework from Xanadu:https://github.com/XanaduAI/
Background information:Quantum computing is seen to be able to solve NP-hard problems when hardware will overcome some current challenges like error rates, number of qubits and coding on qbit level. Some very special kind of problems are expected to be solved in the next 24 months to show practical and not only theoretical advantage.
In finance there are different topics around optimization and heuristic methods to find optimal states for a system with many degrees of freedom and constraints. Classically there are several numerical methods like simulated annealing to find ground states of a system. The solutions are often not perfect and computational intensive, sometime take hours to days and will not provide optimal solution. Using quantum computer or quantum/digital annealer, problems need to be mapped to an Ising model to find the solution of the problem in the ground state. Adiabatic quantum annealing is a method to cool down the system, so it evolves to a state of lowest energy, in best case and after several runs global minima/maxima. Solving optimization problems can also be achieved using quantum algorithms like QAOA (Quantum Approximate Optimization Algorithm) which is a polynomial time algorithm for finding a “good” solution to an optimization problem. For each problem instance user specifies the driver Hamiltonian, cost Hamiltonian, and the approximation order of the algorithm.
Also stochastic processes can take a lot of resources to simulate different paths of possible outcomes. For representative samples including tail-events one needs a large amount of samples. For risk management and trading Monte Carlo methods are often used when analytical solutions are not possible. Using quantum computing there is an approach to sample quadratically faster than classical methods, which can provide an advantage to credit, market and operational risk methods. For example large portfolios with dynamically changing correlations and non-linearities like defaults in a financial network lead to very complex and long running simulations. If runtime can be shortened from many days to an overnight calculation during end of day process, more adequate results can lead to better pricing, lower capital requirements and lower total cost of ownership. Quantum machine learning is also theoretically showing a drastic speedup of training methods, which can be seen as optimization problems e.g. in boltzmann machines.