Physicists commonly classify material phases as one or the other. Machine learning is a powerful tool for pattern recognition and thus could help identify phases of matter. However, machine learning needs a bridge to the quantum world, where the physics of atoms, electrons, and particles differs from that of larger objects or galaxies. Now, scientists have provided a bridge, which they call the quantum loop topography technique. This is a machine-learning algorithm based on neural networks. It detects with high efficiency an exotic phase where electricity is conducted around the material's surface but not through the middle.
By employing quantum-compatible machine learning techniques, they developed a method of extracting a rare Higgs boson signal from copious noise data. Higgs is the particle that was predicted to imbue elementary particles with mass and was discovered at the Large Hadron Collider in 2012. The new quantum machine learning method is found to perform well even with small datasets, unlike the standard counterparts. Despite the central role of physics in quantum computing, until now, no problem of interest for physics researchers has been resolved by quantum computing techniques. In this new work, the researchers successfully extracted meaningful information about Higgs particles by programming a quantum annealer--a type of quantum computer capable of only running optimization tasks--to sort through particle-measurement data littered with errors.
In the early '90s, Elizabeth Behrman, a physics professor at Wichita State University, began working to combine quantum physics with artificial intelligence--in particular, the then-maverick technology of neural networks. Most people thought she was mixing oil and water. "I had a heck of a time getting published," she recalled. "The neural-network journals would say, 'What is this quantum mechanics?' and the physics journals would say, 'What is this neural-network garbage?'"
At some point in time between the rapid jump in facial recognition abilities of my iPhone and the Google DeepMind defeat of world champion Lee Sedol in the ancient game of Go, I began paying attention to developments in artificial intelligence. Both of these achievements occurred years ahead of predictions by computer scientists who are familiar with the extraordinary challenges posed by machine learning. Pictures of faces are packed with a huge amount of complex information – information that is changing over time, and involves different lighting conditions, image quality, and camera angles. The extraction of a simple quantitative feature (e.g., my name) from a database of photos seems like a Herculean task. The game of Go is another complex challenge.
The same type of artificial intelligence that mastered the ancient game of Go could help wrestle with the amazing complexity of quantum systems containing billions of particles. Google's AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marvelling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar machine-learning tool to crack one of the knottiest problems in quantum physics. Now, he has built just such a neural network – which could turn out to be a game changer in understanding quantum systems. Go is far more complex than chess, in that the number of possible positions on a Go board could exceed the number of atoms in the universe.