Learning a quantum computer's capability
This work addresses the need for scalable predictive models to help researchers and stakeholders decide which quantum computers to build and use, representing an incremental improvement in quantum characterization and benchmarking.
The paper tackles the problem of predicting a quantum computer's capability for running circuits by proposing a hardware-agnostic method using convolutional neural networks (CNNs) to model success rates, achieving approximately 1% average absolute prediction error for processors with stochastic Pauli errors and 2-5% error for cloud-access systems.
Accurately predicting a quantum computer's capability -- which circuits it can run and how well it can run them -- is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and use. In this work, we propose a hardware-agnostic method to efficiently construct scalable predictive models of a quantum computer's capability for almost any class of circuits, and demonstrate our method using convolutional neural networks (CNNs). Our CNN-based approach works by efficiently representing a circuit as a three-dimensional tensor and then using a CNN to predict its success rate. Our CNN capability models obtain approximately a $1\%$ average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around $2-5\%$), and we highlight the challenges to building better neural network capability models.