Predicting Features of Quantum Systems from Very Few Measurements
This addresses the challenge of characterizing quantum architectures with very few measurements, offering a scalable solution for quantum engineering.
The paper tackles the problem of predicting features of large-scale quantum systems with minimal measurements by introducing an efficient method called classical shadow, which accurately predicts M linear functions from only order of log(M) measurements, independent of system size, as validated numerically up to 162 qubits.
Predicting features of complex, large-scale quantum systems is essential to the characterization and engineering of quantum architectures. We present an efficient approach for constructing an approximate classical description, called the classical shadow, of a quantum system from very few quantum measurements that can later be used to predict a large collection of features. This approach is guaranteed to accurately predict M linear functions with bounded Hilbert-Schmidt norm from only order of log(M) measurements. This is completely independent of the system size and saturates fundamental lower bounds from information theory. We support our theoretical findings with numerical experiments over a wide range of problem sizes (2 to 162 qubits). These highlight advantages compared to existing machine learning approaches.