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Journal of Mathematical Chemistry

, Volume 57, Issue 9, pp 2154–2167 | Cite as

Inherently trap-free convex landscapes for fully quantum optimal control

  • Re-Bing WuEmail author
  • Qiuyang Sun
  • Tak-san Ho
  • Herschel Rabitz
Original Paper

Abstract

A general quantum system may be steered by a control of either classical or quantum nature and the latter scenario is particularly important in many quantum engineering problems including coherent feedback and reservoir engineering. In this paper, we consider a quantum system steered by a quantum controller and explore the underlying Q–Q (quantum–quantum) control landscape features for the expectation value of an arbitrary observable of the system, with the control being the engineered initial state of the quantum controller. It is shown that the Q–Q control landscape is inherently convex, and hence devoid of local suboptima. Distinct from the landscapes for quantum systems controlled by time-dependent classical fields, the controllability is not a prerequisite for the Q–Q landscape to be trap-free, and there are no saddle points that generally exist with a classical controller. However, the forms of Hamiltonian, the flexibility in choosing initial state of the controller, as well as the control duration, can influence the reachable optimal value on the landscape. Moreover, we show that the optimal solution of the Q–Q control landscape can be readily extracted from a de facto landscape observable playing the role of an effective “observer”. For illustration of the basic Q–Q landscape principles, we consider the Jaynes–Cummings model depicting a two-level atom in the presence of a cavity quantized radiation field.

Keywords

Quantum control Optimal control Convex optimization 

Notes

Acknowledgements

Re-Bing Wu acknowledges the support of the National Key R&D Program of China (Grants No. 2017YFA0304304) and NSFC (Grants No. 61833010 and No. 61773232). Qiuyang Sun acknowledges the support of the Princeton Plasma Science and Technology Program and the National Science Foundation (CHE-1763198). Tak-San Ho acknowledges the support of the Department of Energy (DE-FG02-02ER15344). Herschel Rabitz acknowledges the support of the Army Research Office (W911NF-19-1-0382).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of AutomationTsinghua UniversityBeijingChina
  2. 2.Department of ChemistryPrinceton UniversityPrincetonUSA

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