In recent years, we’ve grown accustomed to chatting with AI models like GPT, capable of understanding and generating human language with impressive fluency. But what if we could teach GPT to “speak” the quantum language? Our nature, at its fundamental level, is governed by quantum physics. When we probe quantum systems through measurements, we receive responses in the form of measurement outcomes—the messages from nature about its quantum behavior. These are the quantum language that nature speaks to us. Can we teach AI to understand this quantum language, i.e., training it to predict the outcomes of quantum experiments as if it were a quantum system itself?
Imagine a future where computers delve into the fundamental laws of physics, unveiling new scientific horizons with minimal human intervention. This captivating vision is inching closer to reality with the advent of an innovative artificial intelligence technique we’ve coined as the Machine Learning Renormalization Group (MLRG) at UC San Diego. MLRG algorithm aims to automate one of the most pivotal tools in physics – the renormalization group – which studies how systems change across scales. Our goal is to enable AI to learn how to efficiently coarse-grain the description of physical systems in a self-supervised manner.
As condensed matter physicists, we are fascinated by the strange collective behaviors that can emerge in quantum materials. Often times, new and exotic phases of matter arise from subtle interplay between the quantum mechanics of the electrons and the complex interactions within the material. One particular phenomenon that has intrigued us is called symmetric mass generation (SMG). This refers to a situation where massless Dirac fermions, which ordinarily behave like photons, acquire a mass gap without any symmetry breaking. Naively, one would expect that generating a mass gap requires breaking a symmetry, like the Higgs mechanism. However, SMG generates a gap while preserving all symmetries!
Superconductivity is the superhero of the materials world — a phenomenon where certain materials can conduct electricity without any resistance when cooled down to a specific temperature. Imagine a world with power lines that never lose energy or transportation systems levitating on superconducting tracks, all thanks to this unique property. The challenge, however, has always been that most materials only become superconductors at extremely low temperatures. This limitation makes them impractical for most real-world applications. But what if we could find materials that become superconductors at higher, more manageable temperatures? That’s where high-temperature superconductors come into play.
Today, I am thrilled to share with you an exciting journey into the heart of quantum mechanics, where we tackle an enduring conundrum in physics – the quantum-to-classical transition. Our research team leveraged the power of artificial intelligence to provide a fresh perspective on this age-old question. We’ve combined innovative artificial intelligence technology with quantum physics to model one of the most paradoxical concepts in quantum mechanics - Schrödinger’s Cat!
