Ishiyama Yuki, Nagai Ryutaro, Mieda Shunsuke, Takei Yuki, Minato Yuichiro, Natsume Yutaka
Platform Laboratory for Science and Technology, Asahi Kasei Corporation, Shizuoka, Japan.
Informatics Initiative, Asahi Kasei Corporation, Tokyo, Japan.
Sci Rep. 2022 Nov 8;12(1):19003. doi: 10.1038/s41598-022-22940-4.
Quantum machine learning for predicting the physical properties of polymer materials based on the molecular descriptors of monomers was investigated. Under the stochastic variation of the expected predicted values obtained from quantum circuits due to finite sampling, the methods proposed in previous works did not make sufficient progress in optimizing the parameters. To enable parameter optimization despite the presence of stochastic variations in the expected values, quantum circuits that improve prediction accuracy without increasing the number of parameters and parameter optimization methods that are robust to stochastic variations in the expected predicted values, were investigated. The multi-scale entanglement renormalization ansatz circuit improved the prediction accuracy without increasing the number of parameters. The stochastic gradient descent method using the parameter-shift rule for gradient calculation was shown to be robust to sampling variability in the expected value. Finally, the quantum machine learning model was trained on an actual ion-trap quantum computer. At each optimization step, the coefficient of determination [Formula: see text] improved equally on the actual machine and simulator, indicating that our findings enable the training of quantum circuits on the actual quantum computer to the same extent as on the simulator.
研究了基于单体分子描述符预测聚合物材料物理性质的量子机器学习。由于有限采样，量子电路得到的预期预测值存在随机变化，先前工作中提出的方法在参数优化方面进展不足。为了在期望值存在随机变化的情况下实现参数优化，研究了在不增加参数数量的情况下提高预测精度的量子电路以及对预期预测值的随机变化具有鲁棒性的参数优化方法。多尺度纠缠重整化近似电路在不增加参数数量的情况下提高了预测精度。使用参数移位规则进行梯度计算的随机梯度下降方法被证明对期望值的采样变异性具有鲁棒性。最后，在实际的离子阱量子计算机上训练了量子机器学习模型。在每个优化步骤中，实际机器和模拟器上的决定系数[公式：见原文]均有同等程度的提高，这表明我们的研究结果能够在实际量子计算机上与在模拟器上一样程度地训练量子电路。
