标题： GRAPE脉冲优化在具有硬件代表性噪声模型的量子门中的验证实现 显示英文标题

标题： Verified Implementation of GRAPE Pulse Optimization for Quantum Gates with Hardware-Representative Noise Models

摘要： 在嘈杂的中等规模量子（NISQ）计算机中，门保真度仍然是限制实用量子计算的主要瓶颈，这受到退相干和控制噪声的限制。量子最优控制（QOC）技术，如梯度上升脉冲工程（GRAPE）算法，提供了一种强大的方法来设计抗噪声的脉冲，以主动缓解这些影响。然而，大多数QOC实现都在理想化的仿真环境中运行，无法捕捉到物理量子硬件固有的实时参数漂移，从而造成关键的“仿真到现实”差距。在本工作中，我提出了QubitPulseOpt，一个开源且经过严格测试的Python框架，旨在通过硬件代表性最优控制来弥合这一差距。该框架展示了与IQM的Garnet量子处理器（20量子位超导设备）的API连接，并实现了使用硬件代表性参数构建高保真“数字孪生”的工作流程。使用这个仿真框架，我证明了GRAPE优化的脉冲相比标准高斯脉冲在模拟门误差方面减少了77$\times$。该框架的可靠性通过864个测试验证套件（74%的代码覆盖率）和遵循NASA JPL 10的安全部署编码标准得到保证，确立了可信赖量子控制软件的新范式。所有结果均来自经过验证的GRAPE优化，并有完整的来源记录文档。 显示英文摘要

摘要： Gate fidelity in noisy intermediate-scale quantum (NISQ) computers remains the primary bottleneck limiting practical quantum computation, constrained by decoherence and control noise. Quantum optimal control (QOC) techniques, such as the gradient ascent pulse engineering (GRAPE) algorithm, offer a powerful approach to designing noise-robust pulses that actively mitigate these effects. However, most QOC implementations operate in idealized simulation environments that fail to capture the real-time parameter drift inherent to physical quantum hardware, creating a critical ``sim-to-real'' gap. In this work, I present QubitPulseOpt, an open-source, rigorously-tested Python framework designed to bridge this gap through hardware-representative optimal control. The framework demonstrates API connectivity to IQM's Garnet quantum processor (20-qubit superconducting device) and implements a workflow that constructs a high-fidelity ``digital twin'' using hardware-representative parameters. Using this simulation framework, I demonstrate that GRAPE-optimized pulses achieve a simulated gate error reduction of 77$\times$ compared to standard Gaussian pulses. The framework's reliability is ensured through a 864-test verification suite (74\% code coverage) and adherence to NASA JPL Power-of-10 safety-critical coding standards, establishing a new paradigm for trustworthy quantum control software. All results are from verified GRAPE optimizations with full provenance documentation.