New Framework Measures Quantum Computing Bottleneck in Practical Systems
Researchers have developed a method to assess how efficiently quantum computers can convert quantum states using limited operations—a critical constraint in real-world systems. The advance provides the first reliable way to predict when quantum processors will fail at key preparation tasks, helping companies and labs set realistic expectations for near-term quantum devices.
Originaltitel: Assessing non-Gaussian quantum state conversion with the stellar rank
State conversion is a fundamental task in quantum information processing. Quantum resource theories allow for analyzing and bounding conversions that use restricted sets of operations. In the context of continuous-variable systems, state conversions restricted to Gaussian operations are crucial for both fundamental and practical reasons, particularly in state preparation and quantum computing with bosonic codes. However, previous analysis did not consider the relevant case of approximate state conversion. In this work, we introduce a framework for assessing approximate Gaussian state conversion by extending the stellar rank to the approximate stellar rank, which serves as an operational measure of non-Gaussianity. We derive bounds for Gaussian state conversion and distillation under approximate and probabilistic conditions, yielding new no-go results for non-Gaussian state preparation and enabling a reliable assessment of the performance of Gaussian conversion protocols. We also provide an open-source Python library to compute stellar-rank-related quantities and to assess Gaussian conversion.