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.

量子计算机有可能帮助解决一系列物理和化学问题，但量子硬件中的噪声目前限制了我们通过执行量子模拟算法获得准确结果的能力。已经提出了各种方法来减轻噪声对变分算法的影响，包括几种将噪声建模为可观察量的阻尼期望值的方法。在这项工作中，我们对各种方法进行了基准测试，包括这里提出的一种新方法。我们在 IBM 的两台量子计算机上使用变分量子特征求解器算法（最多 20 个量子位）来比较它们在估计 1D 混合场伊辛模型的几个实例的基态能量方面的性能。所有量子位上的Y旋转。