Achieving near-term quantum advantage will require effective methods for mitigating hardware noise.
One approach is to optimize quantum circuits using compiling and machine learning, while another employs variational quantum algorithms to reduce circuit depth and potentially remove the effects of incoherent noise. More recently, quantum phase estimation has been employed for error mitigation.
Data-driven approaches to Error Mitigation (EM) are promising, with popular examples including Zero-Noise Extrapolation (ZNE) and Clifford Data Regression (CDR).
Researcher have proposed a novel, scalable error mitigation method that conceptually unifies ZNE and CDR. Their approach, called variable-noise Clifford Data Regression (vnCDR), significantly outperforms these individual methods in numerical benchmarks.
For the problem of correcting observables from random quantum circuits with 64 qubits, vnCDR improves the error by factors of 2.7 and 1.5 over ZNE and CDR, respectively.