AI 中文总结
本文将D-Wave量子退火器表征为封闭热力学循环，通过分析其能量交换等热力学量，补充了仅靠解质量或运行时间无法获得的信息，连接了量子优化等多领域。
AI 中文摘要
可编程量子退火器被用于优化、概率采样和模拟，但其性能通常未计入计算过程中交换的能量。本文将D-Wave量子退火器表征为一个封闭热力学循环，从初始和最终伊辛能量以及拟合输出分布得到的有效温度，我们获得了熵产生、环境能量交换、功和功率的下界。通过改变制备的分布和反向退火的转折点，我们在一维链和更高连通性实例中绘制了加热器、加速器、制冷机和发动机兼容的区域，并将相同分析应用于Advantage和Advantage2硬件。对于编码优化问题，测量的处理器能量变化状态表明，最终候选解平均而言是否改善或恶化了编程目标；对于采样，拟合温度提供了概率在低能构型中集中程度的可操作度量。因此，热力学模式补充了仅从解质量或运行时间中缺失的信息：它区分了驱动式优化、净加热和热泵作用，同时量化了它们的能量后果。该框架将量子优化、概率计算、统计物理模拟、硬件诊断和能量感知评估联系起来，且不假设仅热力学标签就能决定计算性能。
英文摘要
Programmable quantum annealers are used for optimization, probabilistic sampling, and simulation, but their performance is commonly reported without the energy exchanged during computation. Here we characterize the D-Wave quantum annealer as a closed thermodynamic cycle. From initial and final Ising energies and an effective temperature fitted to the output distribution, we obtain lower bounds on entropy production, environment energy exchange, work, and power. By varying the prepared distribution and the reverse annealing turning point, we map heater-, accelerator-, refrigerator-, and engine-compatible regimes in one dimensional chains and higher connectivity instances, and apply the same analysis to Advantage and Advantage2 hardware. For an encoded optimization problem, the measured processor energy change states whether final candidates improve or worsen the programmed objective on average. For sampling, the fitted temperature provides an operational measure of how strongly probability is concentrated among low energy configurations. The thermodynamic mode therefore adds information absent from solution quality or runtime alone: it distinguishes driven refinement, net heating, and heat pumping while quantifying their energetic consequences. This framework connects quantum optimization, probabilistic computing, statistical physics simulation, hardware diagnostics, and energy-aware assessment without assuming that a thermodynamic label alone determines computational performance.
Comments21 pages, 9 figures
