Applications such as simulating large quantum systems or solving large-scale linear algebra problems are immensely challenging for classical computers due their extremely high computational cost. Quantum computers promise to unlock these applications, although fault-tolerant quantum computers will likely not be available for several years. Currently available quantum devices have serious constraints, including limited qubit numbers and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which employ a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. In this review article we present an overview of the field of VQAs. Furthermore, we discuss strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage.

翻译：由于计算成本极高,模拟大型量子系统或解决大型线性代数问题等应用对古典计算机极具挑战性。量子计算机承诺解开这些应用,尽管可能无法提供几年的耐过量计算机。目前可用的量子设备存在严重的限制,包括限制电路深度的量子数量和噪音过程有限。使用传统优化器培训准流量子电路的量子系统变量数字系统(VQAs)已成为解决这些限制的一项主要战略。现在已经为研究人员设想用于量子计算机的几乎所有应用提出了VQA,它们似乎最有希望获得量子优势。然而,挑战依然存在,包括VQA的可训练性、准确性和效率。在本审查文章中,我们概述了VQA领域的情况。此外,我们讨论了克服挑战的战略以及利用它们作为量子优势的手段的令人振奋人心的前景。