使用线性重标度减少多达20个量子比特的变分量子本征解的实验误差
Experimental error mitigation using linear rescaling for variational quantum eigensolving with up to 20 qubits
摘要：
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从量子模拟算法的执行中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，包括将噪声建模为可观测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。我们比较他们的表现，估计基态能量的几个实例的一维混合场伊辛模型使用变分量子本征解算器算法与两个IBM的量子计算机上的20个量子位。我们发现，几种错误缓解技术使我们能够将能量恢复到包含多达约25个反相层的电路的真实值的10%以内，其中每个层由所有相邻量子位之间的CNOT门和所有量子位上的Y旋转组成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
