Generative quantum advantage for classical and quantum problems
This work addresses the problem of achieving quantum advantage in generative tasks for quantum computing and machine learning, representing a foundational advance rather than an incremental step.
The paper tackled the challenge of demonstrating generative quantum advantage by introducing trainable quantum models that are hard to simulate classically, and used a 68-qubit quantum processor to learn classically intractable distributions and accelerate physical simulation.
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities. While beyond-classical quantum experiments can generate samples from classically intractable distributions, their complexity has thwarted all efforts toward efficient learning. This challenge has hindered demonstrations of generative quantum advantage: the ability of quantum computers to learn and generate desired outputs substantially better than classical computers. We resolve this challenge by introducing families of generative quantum models that are hard to simulate classically, are efficiently trainable, exhibit no barren plateaus or proliferating local minima, and can learn to generate distributions beyond the reach of classical computers. Using a $68$-qubit superconducting quantum processor, we demonstrate these capabilities in two scenarios: learning classically intractable probability distributions and learning quantum circuits for accelerated physical simulation. Our results establish that both learning and sampling can be performed efficiently in the beyond-classical regime, opening new possibilities for quantum-enhanced generative models with provable advantage.