What is my quantum computer good for? Quantum capability learning with physics-aware neural networks
This addresses the need for fast and reliable capability assessment in quantum computing, which is crucial for stakeholders until large quantum programs can be reliably executed, representing a domain-specific incremental improvement.
The paper tackled the problem of assessing quantum computer capabilities by developing a quantum-physics-aware neural network architecture, achieving up to ~50% reductions in mean absolute error over state-of-the-art models on experimental and simulated data.
Quantum computers have the potential to revolutionize diverse fields, including quantum chemistry, materials science, and machine learning. However, contemporary quantum computers experience errors that often cause quantum programs run on them to fail. Until quantum computers can reliably execute large quantum programs, stakeholders will need fast and reliable methods for assessing a quantum computer's capability-i.e., the programs it can run and how well it can run them. Previously, off-the-shelf neural network architectures have been used to model quantum computers' capabilities, but with limited success, because these networks fail to learn the complex quantum physics that determines real quantum computers' errors. We address this shortcoming with a new quantum-physics-aware neural network architecture for learning capability models. Our architecture combines aspects of graph neural networks with efficient approximations to the physics of errors in quantum programs. This approach achieves up to $\sim50\%$ reductions in mean absolute error on both experimental and simulated data, over state-of-the-art models based on convolutional neural networks.