Automated quantum error mitigation based on probabilistic error reduction

• URL: http://arxiv.org/abs/2210.08611v1
• Date: Sun, 16 Oct 2022 19:09:41 GMT
• Title: Automated quantum error mitigation based on probabilistic error reduction
• Authors: Benjamin McDonough, Andrea Mari, Nathan Shammah, Nathaniel T. Stemen, Misty Wahl, William J. Zeng, Peter P. Orth
• Abstract summary: Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. We present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits.
• Score: 0.9236074230806579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, 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 (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

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