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Quantum computers have the potential to help solve a range of physics andchemistry problems, but noise in quantum hardware currently limits our abilityto obtain accurate results from the execution of quantum-simulation algorithms.Various methods have been proposed to mitigate the impact of noise onvariational algorithms, including several that model the noise as dampingexpectation values of observables. In this work, we benchmark various methods,including a new method proposed here. We compare their performance inestimating the ground-state energies of several instances of the 1D mixed-fieldIsing model using the variational-quantum-eigensolver algorithm with up to 20qubits on two of IBM's quantum computers. We find that several error-mitigationtechniques allow us to recover energies to within 10% of the true values forcircuits containing up to about 25 ansatz layers, where each layer consists ofCNOT gates between all neighboring qubits and Y-rotations on all qubits.

Experimental error mitigation using linear rescaling for variational quantum eigensolving with up to 20 qubits