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

Abstract

Quantum computers have the potential to help solve a range of physics and\nchemistry problems, but noise in quantum hardware currently limits our ability\nto obtain accurate results from the execution of quantum-simulation algorithms.\nVarious methods have been proposed to mitigate the impact of noise on\nvariational algorithms, including several that model the noise as damping\nexpectation values of observables. In this work, we benchmark various methods,\nincluding a new method proposed here. We compare their performance in\nestimating the ground-state energies of several instances of the 1D mixed-field\nIsing model using the variational-quantum-eigensolver algorithm with up to 20\nqubits on two of IBM's quantum computers. We find that several error-mitigation\ntechniques allow us to recover energies to within 10% of the true values for\ncircuits containing up to about 25 ansatz layers, where each layer consists of\nCNOT gates between all neighboring qubits and Y-rotations on all qubits.\n