Research into accessible virtual reality and quantum information processing
Early-stage Researchers Kathrin Gerling and Philip Willke from KIT Receive Starting Grants from the European Research Council
IMAGE: PROFESSOR KATHRIN GERLING (PHOTO: TANJA MEISSNER, KIT) AND TENURE-TRACK PROFESSOR PHILIP WILLKE (PHOTO: PRIVATE) RECEIVE ERC STARTING GRANTS FOR THEIR PROJECTS view more
CREDIT: TANJA MEISSNER, PHILIP WILK
Contribution to the Design of Accessible Technologies
Virtual reality (VR) has an enormous potential in leisure activities, education, and work. Still, most VR systems are designed for non-disabled persons. The project AccessVR (stands for: Developing Experience-Centric Accessible Immersive Virtual Reality Technology) of Kathrin Gerling, Professor of Human-Computer Interaction and Accessibility at the Institute for Anthropomatics and Robotics (IAR) of KIT, aims to eliminate physical, digital, and experience-centric barriers and to make VR better accessible for disabled persons. In her project, the computer scientist will first study requirements to be met by user interfaces and the presentation of disability in VR. Then, customized VR prototypes will be developed. Finally, an adaptable VR platform will be designed with various workplace and entertainment scenarios. “The AccessVR project will lay the foundation for further integration of disabled people in VR, contribute to the design of accessible technologies, and support technological research that prioritizes accessibility from the very first day,” the researcher says.
Gerling heads the Human-Computer Interaction and Accessibility research group and co-directs the “Accessibility” real-world lab at KIT’s IAR. Research of her team focuses on how interactive technology can be designed to support self-determination of the people and how experience-centric accessibility can be achieved, which goes beyond the removal of barriers and aims to create enriching experience for us all.
Quantum Research Atom by Atom
Quantum mechanics is the theory that describes laws of nature in the nanoworld on the scale of atoms and molecules and opens up new technological opportunities in information processing and sensor technology, for instance. Philip Willke, Tenure-track Professor at KIT’s Physikalisches Institut (PHI), proposed ATOMQUANT to work on a new architecture based on atomic force microscopy (AFM) for quantum information processing and magnetic sensor technology on the atomic level. Here, spins – the basic units of magnets – play a central role and enable measurement of quantum mechanics properties of individual atoms and molecules. When spins are sufficiently isolated on the nanoscale, they can keep their quantum properties and remain oriented in a given direction for a long time. Within ATOMQUANT, Willke will work on improving these magnetic quantum states on surfaces by several orders of magnitude. “The results will have the potential to bring quantum research to the atomic scale. Potential quantum systems with outstanding quantum properties can be studied in situ and atom by atom,” the physicist explains.
At KIT’s PHI, Willke conducts research at the interface between quantum technologies and nanosciences. He is convinced that future challenges in science and technology will have to be addressed on the fundamental scale of matter, i.e. the atomic scale. With his team, he mainly uses scanning probe microscopy in combination with electron spin resonance to resolve and control quantum systems atom by atom.
ERC Starting Grants 2023
The European Research Council (ERC) awards Starting Grants to outstanding early-stage researchers who want to start an independent career and establish a working group of their own. Projects selected will be funded with up to about EUR 1.5 million each for a period of five years. In the recent competition of 2023, the ERC awarded Starting Grants in the total amount of EUR 628 million to 400 projects in 24 countries. 2696 proposals were submitted to ERC. The approval rate, hence, was bei 14,8 percent.
AccessVR aims to enhance the accessibility of VR technologies for disabled people. (Photo: Magali Hauser, KIT
CREDIT
Magali Hauser, KIT
Being “The Research University in the Helmholtz Association”, KIT creates and imparts knowledge for the society and the environment. It is the objective to make significant contributions to the global challenges in the fields of energy, mobility, and information. For this, about 9,800 employees cooperate in a broad range of disciplines in natural sciences, engineering sciences, economics, and the humanities and social sciences. KIT prepares its 22,300 students for responsible tasks in society, industry, and science by offering research-based study programs. Innovation efforts at KIT build a bridge between important scientific findings and their application for the benefit of society, economic prosperity, and the preservation of our natural basis of life. KIT is one of the German universities of excellence.
Machine learning contributes to better quantum error correction
Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections.
In contrast to classical computers, which operate on bits that can only take the basic values 0 and 1, quantum computers operate on “qubits”, which can assume any superposition of the computational basis states. In combination with quantum entanglement, another quantum characteristic that connects different qubits beyond classical means, this enables quantum computers to perform entirely new operations, giving rise to potential advantages in some computational tasks, such as large-scale searches, optimization problems, and cryptography.
The main challenge towards putting quantum computers into practice stems from the extremely fragile nature of quantum superpositions. Indeed, tiny perturbations induced, for instance, by the ubiquitous presence of an environment give rise to errors that rapidly destroy quantum superpositions and, as a consequence, quantum computers lose their edge.
To overcome this obstacle, sophisticated methods for quantum error correction have been developed. While they can, in theory, successfully neutralize the effect of errors, they often come with a massive overhead in device complexity, which itself is error-prone and thus potentially even increases the exposure to errors. As a consequence, full-fledged error correction has remained elusive.
In this work, the researchers leveraged machine learning in a search for error correction schemes that minimize the device overhead while maintaining good error correcting performance. To this end, they focused on an autonomous approach to quantum error correction, where a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements. They also looked at “bosonic qubit encodings”, which are, for instance, available and utilized in some of the currently most promising and widespread quantum computing machines based on superconducting circuits.
Finding high-performing candidates in the vast search space of bosonic qubit encodings represents a complex optimization task, which the researchers address with reinforcement learning, an advanced machine learning method, where an agent explores a possibly abstract environment to learn and optimize its action policy. With this, the group found that a surprisingly simple, approximate qubit encoding could not only greatly reduce the device complexity compared to other proposed encodings, but also outperformed its competitors in terms of its capability to correct errors.
Yexiong Zeng, the first author of the paper, says, “Our work not only demonstrates the potential for deploying machine learning towards quantum error correction, but it may also bring us a step closer to the successful implementation of quantum error correction in experiments.”
According to Franco Nori, “Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges. Currently, we are actively involved in a number of projects that integrate machine learning, artificial neural networks, quantum error correction, and quantum fault tolerance.”
JOURNAL
Physical Review Letters
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Approximate Autonomous Quantum Error Correction with Reinforcement Learning
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