Quantum machine-learning phase prediction of high-entropy alloys
Quantum machine-learning phase prediction of high-entropy alloys
复制标题
高熵合金的量子机器学习相预测
DOI：
10.1016/j.mattod.2023.02.014
复制
发表时间：
2023-03
期刊：
影响因子：
24.2
通讯作者：
P. Brown;H. Zhuang
中科院分区：
文献类型：
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作者：
P. Brown;H. Zhuang
在广阔的成分空间中发现新的高熵合金（HEAs）需要经典计算机的能力不断增长，以训练机器学习模型。HEA数据的指数级增长将在可预见的未来带来挑战，使机器学习过程非常耗时。量子计算机使用量子叠加和干涉来执行计算，在处理大数据和加速机器学习模型中无处不在的优化算法方面具有巨大潜力。在这里，我们采用量子计算机模拟器和量子处理器来为未来新HEA发现的挑战做准备。我们首先训练一个经典的人工神经网络（ANN），它使用HEA的组成作为输入和相应的相位作为输出，以预测相位选择。然后，我们应用量子计算机模拟器，实现混合量子-经典机器学习算法来完成相同的监督机器学习任务。我们发现，由此产生的测试精度是从经典的人工神经网络计算相媲美。最后，我们应用量子处理器来执行混合量子-经典机器学习计算，并获得略低的精度归因于量子比特在量子处理器中的脆弱性。我们的工作启动了在嘈杂的中间尺度量子（NISQ）时代采用初出茅庐的量子计算机来发现新的HEAs。
Discovering new high-entropy alloys (HEAs) in the vast compositional space requires a growing power of classical computers for training machine learning models. The exponential increase of HEA data will pose a challenge in making the machine learning process prohibitively time consuming in the foreseeable future. Quantum computers, which use quantum superposition and interference to perform computations, hold great potential in handling big data and accelerating the optimization algorithms ubiquitous in machine learning models. Here we adopt a quantum computer simulator and quantum processors to prepare for the future challenge in new HEA discovery. We first train a classical artificial neural network (ANN), which uses HEAs' compositions as inputs and the corresponding phases as outputs, to predict phase selection. We then apply a quantum computer simulator that implements a hybrid quantum–classical machine learning algorithm to accomplish the same supervised machine learning task. We find that the resulting testing accuracy is comparable to that from classical ANN calculations. We finally apply quantum processors to perform the hybrid quantum–classical machine learning calculations and obtain slightly lower accuracy ascribed to the fragile nature of quantum bits in quantum processors. Our work initiates the adoption of fledgling quantum computers in the noisy intermediate-scale quantum (NISQ) era for discovering new HEAs.