Unraveling the Quantum Mystery: Studying Quantum Processes Made Easy
Pioneering research has defined a new approach that allows quantum computers to understand and predict quantum systems with just a few simple examples.
Scientists done significant progress in the field of quantum computing, demonstrating how quantum neural networks (QNNs) can understand and predict quantum systems using just a few simple ‘produced states’. This could lead to more efficient and reliable quantum computers.
Imagine a world where computers unlock the mysteries of quantum mechanics, allowing us to study the behavior of complex materials or simulate complex molecular dynamics with unprecedented precision. Thanks to groundbreaking research led by Professor Zoe Holmes of EPFL and her team, we are closer to realizing that dream. In collaboration with researchers from Caltech, the Free University of Berlin and the Los Alamos National Laboratory, they have found a new way to teach a quantum computer to understand and predict the behavior of quantum systems, even using a few simple examples.
Quantum neural networks (QNNs) are a type of machine learning model designed to train and process information using principles inspired by quantum mechanics to mimic the behavior of quantum systems.
Like neural networks used in artificial intelligence, QNNs are made up of interconnected nodes or “neurons” that perform calculations. The difference is that in QNNs, neurons work according to the principles of quantum mechanics, allowing them to process and manipulate quantum information.
The scientists have demonstrated that training QNNs using just a few of these simple examples allows computers to effectively understand the complex dynamics of quantum systems.
Professor Holmes explains: “This means that we can study and understand quantum systems using smaller and simpler computers, such as the next generation of computers. [NISQ]which are likely to appear in the coming years, instead of waiting for the big and complex ones that may appear only in decades.”
The research opens up new possibilities for using quantum computers to solve important problems, such as studying complex new materials or modeling the behavior of molecules.
Finally, the method improves the performance of quantum computers, allowing the creation of shorter and more error-tolerant programs. By studying the behavior of quantum systems, we can simplify the programming of quantum computers, resulting in improved efficiency and reliability. “We can make quantum computers even better by making their programs shorter and less error-prone,” says Holmes.
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