Scientists have made significant progress in the field of quantum calculations, demonstrating how quantum neural networks (QNN) can understand and predict quantum systems using only a few simple ‘states’. This can lead to the creation of more effective and reliable quantum computers.
A new study conducted by Professor Zoe Holmes from EPFL, in collaboration with researchers from Caltech, Free University of Berlin, and the National Laboratory of Los Amos, has brought us closer to a world where computers can reveal the secrets of quantum mechanics. They have found a new way of teaching a quantum computer to understand and predict the behavior of quantum systems, even using several simple examples.
Quantum neural networks (QNN) are machine learning models inspired by quantum mechanics, which simulate the behavior of quantum systems. Similar to neural networks used in artificial intelligence, QNN consist of interconnected components or “neurons” that perform calculations. However, in QNN, neurons operate based on the principles of quantum mechanics, enabling them to process and manipulate quantum information.
The researchers have demonstrated that QNN, with just a few simple examples, can effectively understand the complex dynamics of quantum systems. Professor Holmes explains that this means we can study and understand quantum systems using smaller and simpler computers, such as the upcoming generation of quantum computers, instead of waiting for larger and more complex ones that may take decades to develop.
This study opens up new opportunities for using quantum computers to solve important problems, such as studying complex materials or modeling the behavior of molecules. Additionally, the method improves the performance of quantum computers, allowing for the creation of shorter and more error-resistant programs. By studying the behavior of quantum systems, the programming of quantum computers can be simplified, resulting in improved efficiency and reliability.