量子计算在玻色采样中先于计算优势的能量优势
Quantum Energetic Advantage before Computational Advantage in Boson Sampling
AI总结：
本研究通过分析玻色采样问题的能量成本，证明了量子计算在能量效率上优于经典计算，即使在经典算法仍更快的模式中，这一优势在计算优势出现前即可显现。
摘要：
理解量子计算机的能量效率对于评估其可扩展性以及确定量子技术是否能在运行时间之外超越经典计算至关重要。在本工作中，我们分析了使用现实光子量子计算架构解决玻色采样问题所需的能量，这是一种典型的量子优势任务。利用度量-噪声-资源方法，我们通过一个针对玻色采样的性能指标，建立了实验控制参数、主导噪声过程和能量资源之间的定量联系。我们估计了每个样本的能量成本，并确定了优化能量效率的操作模式。通过比较量子和最先进的经典实现的能量消耗，我们证明了量子能量优势的存在——定义为每个样本比已知最佳经典实现更低的能量成本——即使在经典算法仍更快的模式中也会在计算优势之前出现。最后，我们提出了一种实验可行的玻色采样架构，包括完整的噪声和损耗预算，该架构能够实现近-term观察到的量子能量优势。
英文摘要：
Understanding the energetic efficiency of quantum computers is essential for assessing their scalability and for determining whether quantum technologies can outperform classical computation beyond runtime alone. In this work, we analyze the energy required to solve the Boson Sampling problem, a paradigmatic task for quantum advantage, using a realistic photonic quantum computing architecture. Using the Metric-Noise-Resource methodology, we establish a quantitative connection between experimental control parameters, dominant noise processes, and energetic resources through a performance metric tailored to Boson Sampling. We estimate the energy cost per sample and identify operating regimes that optimize energetic efficiency. By comparing the energy consumption of quantum and state-of-the-art classical implementations, we demonstrate the existence of a quantum energetic advantage -- defined as a lower energy cost per sample compared to the best-known classical implementation -- that emerges before the onset of computational advantage, even in regimes where classical algorithms remain faster. Finally, we propose an experimentally feasible Boson Sampling architecture, including a complete noise and loss budget, that enables a near-term observation of quantum energetic advantage.
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