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

TL;DR

This paper benchmarks various noise mitigation techniques, including a new linear rescaling method, for variational quantum eigensolvers on up to 20 qubits, achieving near-accurate ground-state energies despite hardware noise.

Contribution

It introduces a novel linear rescaling error mitigation method and compares its performance with existing techniques on real quantum hardware.

Findings

Error mitigation enables energy estimates within 10% of true values for circuits with 25 layers.

Several techniques effectively recover ground-state energies on noisy quantum hardware.

The new method performs competitively with existing approaches in practical quantum simulations.

Abstract

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