Workshop: Third International Workshop on Quantum Computing Software
Authors: Benjamin McDonough (Yale University); Andrea Mari, Nathan Shammah, Nathaniel T. Stemen, and Misty Wahl (Unitary Fund); William J. Zeng (Unitary Fund, Goldman Sachs Inc); and Peter P. Orth (Iowa State University, Ames National Laboratory)
Abstract: Current quantum computers suffer from noise that prohibits extracting useful results directly from longer computations. The figure of merit is often an expectation value, which experiences a noise induced bias. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires noise characterization and introduces an exponential sampling overhead.
Probabilistic error reduction (PER) is a related method that systematically reduces the overhead. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC. We present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography technique and exploits a noise scaling method to carry out PER. We also provide software that leverages a previously developed toolchain, employing PyGSTi for gate set tomography and Mitiq for PER.