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Quantum revolution: TUM and Google show new dimensions of physics!
The Technical University of Munich is researching quantum computing for fundamental physical processes with Princeton and Google Quantum AI.
Quantum revolution: TUM and Google show new dimensions of physics!
Advances in quantum computing are constantly increasing and are leading to significant developments in research. A research team from Technical University of Munich (TUM), Princeton University and Google Quantum AI has recently shown how quantum computers can be used to simulate fundamental physical processes. This is a critical step, especially since traditional supercomputers are often overwhelmed by computing and testing the complex theoretical models that describe the fundamental forces of nature.
The publication in the journal Nature proves that quantum computers are able to directly simulate such processes. This could enable deeper insights into particle physics, quantum materials and the nature of space and time in the future. Comprehensively understanding how nature works at the most fundamental level is an ambitious goal. For this purpose, Google's quantum processor was used, a superconducting chip that works with qubits to study fundamental interactions and the behavior of so-called strings.
Development and challenges of quantum algorithms
The challenge is to select the appropriate algorithm for the respective hardware, as different quantum hardware has specific advantages and disadvantages. While superconducting qubits enable fast calculations, ion traps are slower but more accurate, making them suitable for certain applications, such as molecular simulations. Through close collaboration with various hardware providers, a software stack is being developed that integrates all components for the operation and development of quantum computers.
Quantum computing as a driver of innovation
Scientists are also researching quantum algorithms for machine learning, which are considered a promising application of quantum computing. This includes applications such as classification, data generation and unsupervised learning. These studies are currently exploring Noisy Intermediate-Scale Quantum (NISQ) algorithms, which represent a fundamental challenge because current noisy quantum processors do not yet enable effective error correction methods.
The aim of this research is to develop methods for characterizing and mitigating errors on noisy quantum hardware. By developing new protocols, libraries and algorithms for various platforms, the aim is to advance innovation in the symbiosis of hardware and software and to enable practical applications in machine learning.
The findings from these various projects and research efforts show that quantum computers represent a key technology for the future to overcome challenges in industry and make processes more efficient. Companies, including the automotive industry, can develop quantum-based solutions to optimize their processes without being quantum experts themselves.