If you're not a physicist, the concept of "quantum" will likely confuse you, or simply put you off. But even for experts, the quantum world can be complex. Luckily, in a world where for nearly every challenge there's a bespoke robot ready to help, an AI now makes it easier to navigate quantum systems too.
European quantum physicists have done some amazing things over the past few decades: sent single photons to Earth orbit and back, created quantum bits that will be at the heart of computers that can crack today's encryption, and "teleported" the quantum states of photons, electrons, and atoms. But they've had less success at turning the science into technology. At least that's the feeling of some 3,400 scientists who signed the "Quantum Manifesto," which calls for a big European project to support and coordinate quantum-tech R&D. The European Commission heard them, and answered in May with a 1 billion, 10-year-long megaproject called the Quantum Technology Flagship, to begin in 2018. "Europe had two choices: either band together and compete, or forget the whole thing and let others capitalize on research done in Europe," says Anton Zeilinger, a physicist at the University of Vienna who did breakthrough work in quantum teleportation, which would be key to a future Internet secured by quantum physics.
Italian researchers recently developed the first functioning quantum neural network by running a special algorithm on an actual quantum computer. The team, lead by Francesco Tacchino of the University of Pavia in Italy, pre-published their research on ArXiv earlier this month in a research paper titled "An Artificial Neuron Implemented on an Actual Quantum Processor." Basically, they developed a single-layer artificial neural network (ANN) that runs on a quantum computer. This kind of rudimentary ANN is called a perceptron, and it's the basic building block of more robust neural networks. Previous attempts at building a perceptron on a quantum system have involved treating individual qubits as neurons in a network.
Machine learning techniques have so far proved to be very promising for the analysis of data in several fields, with many potential applications. However, researchers have found that applying these methods to quantum physics problems is far more challenging due to the exponential complexity of many-body systems. Quantum many-body systems are essentially microscopic structures made up of several interacting particles. While quantum physics studies have focused on the collective behavior of these systems, using machine learning in these investigations has proven to be very difficult. With this in mind, a team of researchers at Harvard University recently developed a quantum circuit-based algorithm inspired by convolutional neural networks (CNNs), a popular machine learning technique that has achieved remarkable results in a variety of fields.
Tomaž Prosen from the Faculty of Mathematics and Physics of the University of Ljubljana describes one thread of recent progress in non-equilibrium quantum interacting many-body systems, which started from exact solutions of boundary driven master equations of quantum spin chains and ended up in discovering new families of quamsilocal conservation laws relevant for quantum transport.