  We present a method to improve the convergence of variational algorithms
based on hidden inverses to mitigate coherent errors. In the context of error
mitigation, this means replacing the on hardware implementation of certain
Hermitian gates with their inverses. Doing so results in noise cancellation and
a more resilient quantum circuit. This approach improves performance in a
variety of two-qubit error models where the noise operator also inverts with
the gate inversion. We apply the mitigation scheme on superconducting quantum
processors running the variational quantum eigensolver (VQE) algorithm to find
the H$_{\rm 2}$ ground-state energy. When implemented on superconducting
hardware we find that the mitigation scheme effectively reduces the energy
fluctuations in the parameter learning path in VQE, reducing the number of
iterations for a converged value. We also provide a detailed numerical
simulation of VQE performance under different noise models and explore how
hidden inverses \& randomized compiling affect the underlying loss landscape of
the learning problem. These simulations help explain our experimental hardware
outcomes, helping to connect lower-level gate performance to
application-specific behavior in contrast to metrics like fidelity which often
do not provide an intuitive insight into observed high-level performance.
