Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning (ML) methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a ‘graybox’ approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised ML models. Our approach combines physics principles with high-accuracy ML and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.

Experimental graybox quantum system identification and control / Youssry, Akram; Yang, Yang; Chapman, Robert J.; Haylock, Ben; Lenzini, Francesco; Lobino, Mirko; Peruzzo, Alberto. - In: NPJ QUANTUM INFORMATION. - ISSN 2056-6387. - ELETTRONICO. - 10:1(2024). [10.1038/s41534-023-00795-5]

Experimental graybox quantum system identification and control

Lobino, Mirko
Penultimo
;
2024-01-01

Abstract

Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning (ML) methods, which provide efficient control solutions but no control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a ‘graybox’ approach to construct a physical model of a quantum system and use it to design optimal control. We report superior performance over model fitting, while generating unitaries and Hamiltonians, which are quantities not available from the structure of standard supervised ML models. Our approach combines physics principles with high-accuracy ML and is effective with any problem where the required controlled quantities cannot be directly measured in experiments. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.
2024
1
Youssry, Akram; Yang, Yang; Chapman, Robert J.; Haylock, Ben; Lenzini, Francesco; Lobino, Mirko; Peruzzo, Alberto
Experimental graybox quantum system identification and control / Youssry, Akram; Yang, Yang; Chapman, Robert J.; Haylock, Ben; Lenzini, Francesco; Lobino, Mirko; Peruzzo, Alberto. - In: NPJ QUANTUM INFORMATION. - ISSN 2056-6387. - ELETTRONICO. - 10:1(2024). [10.1038/s41534-023-00795-5]
File in questo prodotto:
File Dimensione Formato  
Akram_Greybox.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 2.03 MB
Formato Adobe PDF
2.03 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400634
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact