A framework for engineering quantum likelihood functions for expectation estimation

  • Guoming Wang
  • Peter Johnson
  • Yudong Cao


We develop a framework for characterizing and analyzing engineered likelihood functions (ELFs), which play an important role in the task of estimating the expectation values of quantum observables. These ELFs are obtained by choosing tunable parameters in a parametrized quantum circuit that minimize the expected posterior variance of an estimated parameter. We derive analytical expressions for the likelihood functions arising from certain classes of quantum circuits and use these expressions to pick optimal ELF tunable parameters. Finally, we show applications of ELFs in the Bayesian inference framework.

Guoming Wang
Zapata Author

Guoming Wang , Ph.D.

Quantum Research Scientist
Peter Johnson
Zapata Author

Peter Johnson , Ph.D.

Lead Research Scientist & Founder
Yudong Cao
Zapata Author

Yudong Cao , Ph.D.

CTO & Founder