Education and Research in Quantum Information Systems and more

There is a proposal a new Bachelor of Science program at the Department of Physics of the University of Oslo. This program is called Computational Physics and Quantum Technologies.

Tentative start fall semester 2024. The program will be administrated by the Department of Physics@UiO.

Computational physics, computational science and data science play a central role in scientific investigations and are central to innovation in most domains of our lives. These fields underpin the majority of today's technological, economic and societal feats. We have entered an era in which huge amounts of data offer enormous opportunities, but only to those who are able to harness them. The 3rd industrial revolution will alter significantly the demands on the workforce. In particular, the developments taking place in quantum technologies and quantum information systems (QIS) together with artificial intelligence (AI) and machine learning (ML) are expected to play a significant role in technology developments and innovations, and for fundamental discoveries in physics.

Artificial intelligence is built upon integrated machine learning algorithms, which in turn are fundamentally rooted in optimization and statistical learning.

Artificial intelligence (AI) and Machine learning (ML) techniques have in the last years gained considerable traction in scientific discovery. In particular, applications and techniques for so-called fast ML, that is high-performance ML methods applied to real time experimental data processing, hold great promise for enhancing scientific discoveries in many different disciplines. These developments cover a broad mix of rapidly evolving fields, from the development of ML techniques to computer and hardware architectures.

The authors discuss applications and techniques for fast machine learning (ML) in science – the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The report covers three main areas

1. applications for fast ML across a number of scientific domains;
2. techniques for training and implementing performant and resource-efficient ML algorithms;
3. and computing architectures, platforms, and technologies for deploying these algorithms.

For our research in for example particle and nuclear physics, fields which cover a huge range of energy and length scales, spanning from our smallest constituents to the physics of dense astronomical objects like supernovae and neutron stars, AI and ML techniques offer possibilities for new discoveries and deeper insights about the physics of atomic nuclei, elementary particles and dense matter. Similarly, ML algorithms are widely applied in condensed matter physics, materials science and nanotechnology, in molecular dynamics simulations of complex systems in neuroscience and in many other fields in natural science.

• Artificial Intelligence and Machine Learning in Nuclear Physics, Amber Boehnlein et al., arXiv:2112.02309 and Reviews of Modern Physics, 2022, in press
• Predicting Solid State Material Platforms for Quantum Technologies, Hebnes et al,. arXiv:2203.16203
• Mehta et al. and Physics Reports (2019).
• Machine Learning and the Physical Sciences by Carleo et al
• Particle Data Group summary on ML methods

Recent developments in quantum information systems and technologies offer the possibility to address some of the most challenging large-scale problems, whether they are represented by complicated interacting quantum mechanical systems or classical systems. Originally proposed by Feynman, the efficient simulation of for example quantum systems by other, more controllable quantum systems formed the basis for modern constructions of quantum computations. Many algorithmic and theoretical advances have followed since the initial work in this area and with recent developments in quantum computing hardware there is an additional drive to identify early practical problems on which these devices might demonstrate an advantage.

In addition to theoretical activities conducted at the Department of Physics (mainly at the Center for Computing in Science Education (CCSE) and the condensed matter group and other groups), there is a growing interest to study candidate systems for making quantum hardware. In particular, so-called point defects in semiconductors are pursued by experimenters at the center for Materials Science. With this broad list of activities at the department of physics, there is a huge potential to prepare the ground for educating physicists with the theoretical and experimental background needed for the 21st century. There is also a great interest in candidates with such a background, knowledge, skills and competences in industry and the public sector.

Establishing such an educational program will be unique in Norway and has the potential to attract excellent students. The popularity of the Computational Science and in particular the Computational Physics and Computational Materials Science study direction are clear indicattors that these are fields with the potential to attract new students.

Furthermore, Oslo Metropolitan university has recently acquired two quantum quantum computers, see https://kommunikasjon.ntb.no/pressemelding/oslomet-avduker-norges-forste-kvantedatamaskin?publisherId=15678779&releaseId=17917781 and is now establishing research and educational initiatives in quantum information systems. There are thus several interesting avenues for joint collaborations in quantum information systems and quantum technologies as well as developing joint educational programs.

1. Information Systems
2. From Classical Information theory to Quantum Information theory
3. Classical vs. Quantum Logic
4. Classical and Quantum Laboratory
5. Discipline-Based Quantum Mechanics
6. Quantum Software
7. Quantum Hardware
8. more

1. Lots of conceptual learning: superposition, entanglement, QIS and QT, etc.
2. Connecting statistics and mathematics with ML methods
3. Linking ML algorithms with quantum ML.
4. Coding is indispensable. This is a central reason why the CCSE should be involved.
5. Experience with teamwork, project management, and communication are important and highly valued.
6. Experience with engagement with industry, public sector and priority to diversity through the Computational Science program and other activities at the CCSE.
7. Mentorship should begin the moment students enroll. The experiences with the Computational Science program developed at the CCSE will be of great value, as the activities of the KURT center.

The program aims at addressing future societal needs, such as the needs for specialized candidates (Master of Science, PhDs, postdocs), but also the needs of people with a broad overview of what is possible in QIS and QT. There are not enough potential employees in AI, ML, QIS and QT. There is a clear supply gap.

A BSc degree with specialization is thus a good place to start. Linking this with the Physics MSc program and the Computational Science program and the study directions Computational physics and Computational materials science, will offer our various research fields top candidates as well as pointing to new research directions.

And what about AI/ML and quantum technologies towards high-school?

The rationale behind proposing a new bachelor of science (BSc) program in computational physics and quantum technologies (QT) is:

1. To attract at an earlier stage new students with an explicit interest in QIS, QT and AI and ML in physics.
2. To enhance the recruitment to fields in physics which are in high demand for students and candidates with an expertise in computations, QIS, QT, AI and ML. We expect high demands from both the private sector and the public sector for candidates with these competences, insights and skills.
3. Candidates with such a background will be of great importance for new scientific discoveries and technological innovations. At the department of physics of the university of Oslo there are several research directions whose scientific activities will benefit at large from candidates with such a background, spanning from fast ML for new discoveries to the development of QTs.

The program could offer three possible directions

1. Quantum information systems and quantum technologies
2. Artificial intelligence and machine learning in physics
3. Computational Physics

The students specialize in these directions in their last year of the BSc program.

There are several existing courses which can be included in this program. There are also courses which need to be established. At the CCSE we have the research and educational expertise to establish two to three new courses in these directions. Most likely there are potential teachers from other groups.

A separate application for establishing these courses follows. The additional courses we propose are (these are suggestions and codes are tentative) listed here. Note that we propose these courses as cloned courses. We may consider extensions of these codes in order to offer PhD variants as well.

The list here is tentative. Two of the courses, FYS4446 and FYS4447 have been taught as special topics since 2018 and we have already a large body of material. This list is a mere suggestion.

1. Classical and quantum laboratory, needs to be established, perhaps by researchers at the Center of Materials Science (LENS group), FYS2440
2. Quantum computing and software, needs to be established. This can be organized together with OsloMet and Simula Research lab.
3. Quantum hardware, needs to be established, this can be organized together with OsloMet and Simula Research lab.
4. Quantum computing and quantum machine learning, FYS3446/FYS4446 (cloned course, already developed by CCSE)
5. Advanced machine learning and data analysis for the physical sciences, FYS3447/FYS4447 (already developed by CCSE)

The last three courses are elective ones for the last semester of study. Some of these courses can also be split into modules a 5 ECTS or 7.5 ECTS. There are obviously other course alternatives. The first year is identical with the BSc program Physics and Astronomy.

The table here is an example of a suggested path for a Master of Science project, with course work the first year and thesis work the last year.

Students at the University of Oslo may choose to take parts of their degrees at a university abroad.

Students in this program have a number of interesting international exchange possibilities. The involved researchers have extensive collaborations with other researchers worldwide. These exchange possibility range from top universities in the USA, Asia and Europe as well as leading National Laboratories in the USA.

Candidates who are capable of modeling and understanding complicated systems in natural science, are in short supply in society. The computational methods and approaches to scientific problems students learn when working on their thesis projects are very similar to the methods they will use in later stages of their careers. To handle large numerical projects demands structured thinking and good analytical skills and a thorough understanding of the problems to be solved. This knowledge makes the students unique on the job market.

Career opportunities are many, from research institutes, universities and university colleges and a multitude of companies. Examples include IBM, Hydro, Statoil, and Telenor. The program gives an excellent background for further studies.

The program has also a strong international element which allows students to gain important experience from international collaborations in science, with the opportunity to spend parts of the time spent on thesis work at research institutions abroad.

• Machine Learning applied to Quantum Mechanical systems
• Quantum Engineering
• Quantum algorithms
• Quantum Machine Learning

1. be scalable
2. have qubits that can be entangled
3. have reliable initializations protocols to a standard state
4. have a set of universal quantum gates to control the quantum evolution
5. have a coherence time much longer than the gate operation time
6. have a reliable read-out mechanism for measuring the qubit states
7. and many more

1. Time-Dependent full configuration interaction theory
2. Time-dependent Coupled-Cluster theory
3. Designing quantum circuits

• MSU: Ben Hall, Jane Kim, Julie Butler, Danny Jammoa, Nicholas Cariello, Paul-Aymeric McRae, Johannes Pollanen (Expt), Niyaz Beysengulov (Expt), Dean Lee, Scott Bogner, Heiko Hergert, Matt Hirn, Huey-Wen Lin, Alexei Bazavov, Andrea Shindler and Angela Wilson
• UiO: Stian Bilek, Heine Åbø (now OsloMet), Håkon Emil Kristiansen, Øyvind Schøyen Sigmundsson, Jonas Boym Flaten, Kristian Wold (now OsloMet), Lasse Vines (Expt) Andrej Kuznetsov (Expt), David Rivas Gongora (Expt) and Marianne Bathen (Expt, now PD at ETH)

This work is supported by the U.S. Department of Energy, Office of Science, office of Nuclear Physics under grant No. DE-SC0021152 and U.S. National Science Foundation Grants No. PHY-1404159 and PHY-2013047 and the Norwegian ministry of Education and Research for PhD fellowships.