发表机构
IonQ Inc; Oak Ridge National Laboratory; Los Alamos National Laboratory（IonQ公司; 橡树岭国家实验室; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用 IonQ 的 36 量子比特处理器，通过电路压缩和纠错，模拟了 Cs2CoCl4 的磁激发谱，验证了噪声量子计算机在量子谱学中的定量应用。
AI 中文摘要
量子磁体的集体激发谱为量子计算机作为研究强关联物质工具提供了直接检验。硬件噪声限制了这些计算可用的电路深度，留下了有多少磁信息能在电路压缩中幸存的问题。利用 IonQ 的 36 量子比特 Forte Enterprise 处理器，我们模拟了描述横向场中量子磁体 Cs2CoCl4 的 36 位点 Heisenberg XXZ 链的磁激发谱和自旋动力学，所用量子电路仅包含 420 个双量子比特门。该工作流程将无辅助量子比特的局部 kick 协议与经典张量网络压缩相结合，同时编码了基态制备和时间演化。重建的动态结构因子捕捉到了从宽的双自旋子连续谱向具有磁振子特征的色散支（接近预期的临界场）的演化。我们观察到最近邻自旋关联出现有趣的场驱动符号反转，这显示了这种磁演化的互补特征。我们的纠错方法使重建的总谱与密度矩阵重正化群计算非常接近，Pearson 相关性和结构相似度均高于 0.9，归一化均方误差低于 0.15。我们还与实验的非弹性中子散射测量进行了比较，以将恢复的激发与材料的磁响应联系起来。我们的结果表明，电路压缩和纠错使噪声量子处理器能够恢复集体激发和局部磁关联，为量子谱学提供了定量基准，并为将这些方法扩展到更复杂的磁系统奠定了基础。
英文摘要
The collective excitation spectra of quantum magnets provide a direct test of quantum computers as tools for studying strongly correlated matter. Hardware noise limits the circuit depths available for these calculations, leaving the question of how much magnetic information can survive circuit compression. Using IonQ's 36 qubit Forte Enterprise processor, we simulate the magnetic excitation spectra and spin dynamics of a 36 site Heisenberg XXZ chain describing the quantum magnet Cs2CoCl4 in a transverse field using quantum circuits containing just 420 two qubit gates. The workflow combines an ancilla free local kick protocol with classical tensor network compression, encoding both ground state preparation and time evolution. The reconstructed dynamical structure factors capture the evolution from a broad two spinon continuum toward a dispersive branch with magnon character near the expected critical field. We observe an interesting field driven sign reversal of the nearest neighbor spin correlations which shows a complementary signature of this magnetic evolution. Our error mitigation methods bring the reconstructed total spectra into close agreement with density matrix renormalization group calculations, with Pearson correlation and structural similarity above 0.9 and normalized mean squared error below 0.15. We also perform comparisons with experimental inelastic neutron scattering measurements to connect the recovered excitations to the material's magnetic response. Our results demonstrate that circuit compression and error mitigation allow noisy quantum processors to recover both collective excitations and local magnetic correlations providing a quantitative benchmark for quantum spectroscopy and a basis for extending these methods to more complex magnetic systems.
