In a world where computers can unlock the secrets of quantum mechanics, allowing us to study complex materials and simulate the dynamics of molecules with amazing accuracy, Professor Zoe Holmes and her team at EPFL have made a breakthrough. With the help of researchers from Caltech, the Free University of Berlin, and the Los Alamos National Laboratory, they have developed a new method to teach quantum computers how to understand and predict the behavior of quantum systems, even with just a few simple examples.
Quantum Neural Networks (QNNs)
The team focused on “quantum neural networks” (QNNs), which are machine-learning models that use principles inspired by quantum mechanics to process information and imitate the behavior of quantum systems. Similar to the neural networks in artificial intelligence, QNNs consist of interconnected nodes, or “neurons,” that perform calculations. However, QNN neurons operate based on the principles of quantum mechanics, allowing them to handle and manipulate quantum information.
“Typically, computers require many examples to learn something,” Holmes explains. “But our study shows that with just a few simple examples known as ‘product states,’ the computer can learn how a quantum system behaves, even when dealing with complex and entangled states.”
Product states are specific types of states used to describe quantum systems. For example, if a quantum system consists of two electrons, the product state is formed by considering each electron’s state independently and then combining them. These product states serve as a starting point for studying and understanding the behavior of quantum systems before diving into more intricate and entangled states.
Advancements in Quantum Computers
The researchers demonstrated that by training QNNs using just a few simple product states, computers can effectively grasp the dynamics of complex and entangled quantum systems. This breakthrough means we may be able to use smaller and simpler computers, like the near-term intermediary scale (NISQ) computers expected in the near future, to learn and understand quantum systems. We won’t have to rely on large and complex computers that may take decades to develop.
This advancement also opens up new possibilities for quantum computers to solve important problems. We can use them to study new materials and simulate the behavior of molecules. Additionally, by comprehending how quantum systems behave, we can improve the performance of quantum computers by creating shorter and more error-resistant programs. This streamlined programming leads to enhanced efficiency and reliability in quantum computers.