Quantum machine learning (QML) offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for QML. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.
翻译:量子机器学习(QML)为短期量子计算机的编程提供了一个强大而灵活的模式,在化学、计量学、材料科学、数据科学和数学方面的应用。在这里,一个以参数化量子电路的形式训练了一台安萨兹,以完成一项令人感兴趣的任务。然而,最近出现了一些挑战,表明由于随机性或硬件噪音造成的平坦的培训场景,深层的肛门难以训练。这激励着我们的工作,我们提出了一种为QML建造卫星的可变结构方法。我们称为VAns(可变安萨茨)的方法,在优化期间以知情的方式对生长和(显然)拆除量子门都适用一套规则。因此,VANs非常适合通过保持亚萨茨浅度来减轻可训练性和与噪音有关的问题。我们使用VAns用于浓缩物质和量子化学应用的变量子量子离子。我们在量子自动计算机中采用了一套结构方法,用于数据压缩和统一汇编问题,以显示所有情况下的成功结果。