It would be difficult to understand the inner workings of a complex machine without ever opening it up, but this is the challenge scientists face when exploring quantum systems. Traditional methods of looking into these systems often require immense resources, making them impractical for large-scale applications.
Researchers at UC San Diego, in collaboration with colleagues from IBM Quantum, Harvard and UC Berkeley, have developed a novel approach to this problem called “robust shallow shadows.” This technique allows scientists to extract essential information from quantum systems more efficiently and accurately, even in the presence of real-world noise and imperfections.
Imagine casting shadows of an object from various angles and then using those shadows to reconstruct the object. By using algorithms, researchers can enhance sample efficiency and incorporate noise-mitigation techniques to produce clearer, more detailed “shadows” to characterize quantum states.
Experimental validation on a superconducting quantum processor demonstrates that, despite realistic noise, this approach outperforms traditional single-qubit measurement techniques in accurately predicting diverse quantum state properties, such as fidelity and entanglement entropy.
The study was published March 26, 2025 in Nature Communications. Associate Professor of Physics Yi-Zhuang You is a corresponding author. This research was funded, in part, by the National Science Foundation through the Q-IDEAS HDR Institute (OAC-2118310) and the Center for Ultra Cold Atoms PFC (PHY-2317134), and the Department of Defense DARPA IMPAQT Program (HR0011-23-3-0023).
Nature Communications
Experimental study
Demonstration of robust and efficient quantum property learning with shallow shadows
26-Mar-2025