As we venture into the frontier of quantum computing and computer science, we encounter a landscape ripe with potential. Quantum mechanics, powered by qubits, enable us to solve intricate problems and open doors to transformative applications. Quantum technology has the potential to reshape industries. The disciplines of computer science, including programming, algorithms, and machine learning, are vital components in unlocking this potential. Join us for the presentations and debate.

Abstracts:

Quantum Computer Science by Jan Madsen
Quantum computing is a quickly growing field and we expect viable systems to be available soon. Denmark is one of the leading research environments for quantum research (counted as quantum researchers per capita), Current focus is on physics, i.e., how to develop quantum devices to handle qubits (and more generally using quantum mechanics for sensing and communication), or on mathematics, i.e., theoretically exploring which type of algorithms that in theory might be executed effectively on a quantum computer. 

Today, very few researchers from the Danish computer science environment are actively involved in quantum computing research. As soon as a robust and scalable hardware for the quantum computer is established, an efficient and intuitive software stack is needed to realize its full potential – this software stack must be developed by computer scientist! However, we do not need to wait for the hardware to be ready, in fact, we can already start working on the software stack from current knowledge, such that the methods and tools for quantum software development is ready. This will be crucial for the field of quantum computing to scale.
 

Quantum secure cryptography by Christian Majenz
Quantum computers pose a threat to cryptography, as crucial algorithms like RSA and Diffie-Hellman can be broken using quantum computing attacks. I will give an overview over the status of the NIST standardization effort for quantum-secure (“post-quantum”) replacements of the broken algorithms, and describe some challenges and how they are addressed.
 

Machine Learning for the control of Spin Qubit Quantum Devices by Oswin Krause
Spin Qubits are one of the prime candidates for universal quantum computing. They can be scaled up using the same semiconductor manufacturing processes that are used for CPU manufacturing. However, as spin-qubits are the smallest qubit platform, they are most affected by noise and manufacturing imprecisions. This makes their control challenging and one of the major problems in scaling up spin-qubits is the automation of the tuning processes. In this presentation, I will give a short introduction into spin-qubits as a platform and showcase how machine learning can be used to overcome its challenges.