Resource-optimal quality verification of quantum computers

Ensuring accurate quality characterization of near-term quantum computers (QCs) is crucial for optimizing quantum applications. Quality specifications of a QC can be consumed in noise simulations, helping users determine the suitability of their applications. A circuit optimizer can use these specs to select the most appropriate qubits and edges for a given application, maximizing the computation efficiency. However, the device properties can drift, leading to variability in QC quality.

Given the current scarcity of quantum computing resources, time spent on characterizing a QC takes away from its availability for running applications. This trade-off highlights the need for resource-efficient quality verification strategies. In this presentation, we introduce a framework designed to optimize QC quality verification. Our approach balances quantum resources and target accuracy in hypothesis testing for QC verification. The framework is applicable for assessing both individual properties and sets of properties, and it supports continuous verification protocols. With this method, we provide a resource-optimal approach for QC quality characterizations, maintaining accuracy of the specs while preserving valuable capacity of quantum resources.