The variational quantum eigensolver (VQE) is one of the most promising and widely used algorithms for exploiting the capabilities of current Noisy Intermediate-Scale Quantum (NISQ) devices. However, VQE algorithms suffer from a plethora of issues, such as barren plateaus, local minima, quantum hardware noise, and limited qubit connectivity, thus posing challenges for their successful deployment on hardware and simulators. In this work, we propose a VQE optimization strategy that builds upon recent advances in the literature, and exhibits very shallow circuit depths when applied to the specific system of interest, namely a model Hamiltonian representing a cuprate superconductor. These features make our approach a favorable candidate for generating good ground state approximations on current NISQ devices. Our findings illustrate the potential of VQE algorithmic development for leveraging the full capabilities of NISQ devices.

中文翻译：

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在 NISQ 设备上进行基态制备的 OnionVQE 优化策略


变分量子特征求解器 （VQE） 是利用当前含噪中等尺度量子 （NISQ） 设备功能的最有前途和应用最广泛的算法之一。然而，VQE 算法存在许多问题，例如贫瘠的高原、局部最小值、量子硬件噪声和有限的量子比特连接，从而为它们在硬件和模拟器上的成功部署带来了挑战。在这项工作中，我们提出了一种 VQE 优化策略，该策略建立在文献的最新进展之上，当应用于感兴趣的特定系统时，即表示铜酸盐超导体的模型哈密顿量，电路深度非常浅。这些特性使我们的方法成为在当前 NISQ 器件上生成良好基态近似值的有利候选者。我们的研究结果说明了 VQE 算法开发在利用 NISQ 设备的全部功能方面的潜力。