Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use 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. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.
翻译:由于计算成本极高,模拟复杂量子系统或解决大型线性代数问题等应用对古典计算机来说非常困难。 量子计算机有望找到解决办法,尽管在最近的将来可能无法找到容错量计算机。 目前的量子设备存在严重的限制,包括限制电路深度的量子和噪音工艺数量有限。 使用古典优化器培训准流量子电路的量子系统变量量子系统(VQAs ) 已成为解决这些限制的主导战略。 目前已为研究人员设想的量子计算机几乎所有应用提出了量子A,它们似乎最有希望获得量子优势。 尽管如此,挑战依然存在,包括VQA的可训练性、准确性和效率。 我们在这里概述了VQA的域,讨论克服挑战的战略,并突出利用它们获得量子优势的令人振奋人心的前景。