发表机构
Deutsches Elektronen-Synchrotron DESY; RWTH Aachen University（德国电子同步加速器; 亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出相干记忆玻恩机（CoMB），用固定六量子比特寄存器逐块生成量热器簇射图像，消除寄存器随图像增长及经典通道需求，训练损失线性增长，并在十二单元基准上重现能量分布、相关矩阵和总能量谱。
AI 中文摘要
模拟粒子在量热器中沉积的簇射（即探测器在多个单元中记录其能量的过程）是高能物理中最大的计算成本之一，因此需要快速的生成式替代模型。量子电路已被提出作为此类替代模型，但在量热器数据上的演示要么需要一个随图像增长的寄存器（大致每个单元一个量子比特），要么通过经典通道在图像各部分之间传递信息。我们针对该问题消除了这两个约束，采用了来自序列生成和隐量子马尔可夫模型文献的构造方法。簇射图像在固定的六量子比特寄存器上逐块生成。其中三个量子比特（即记忆）从未被测量，它们以量子态形式保存电路关于已生成块的信息，因此图像远距离部分之间的相关性以未测量的振幅形式跨越每个块边界。另外三个量子比特每个块被测量并重置一次，每次测量结果选择该块的能量模式。寄存器大小由块决定，因此向图像添加单元会增加序列的步骤，而非量子比特。我们将该模型称为相干记忆玻恩机（CoMB），玻恩机是其测量统计量构成生成分布的电路。其训练损失仅随块数线性增长，且无需枚举图像分布；一个深度为二的实例在IBM超导处理器上运行。在十二单元基准上，该模型重现了每个单元的能量分布、单元间相关矩阵以及总能量谱。移除三个记忆量子比特（其他条件不变）会产生独立的块。块间的所有相关性仅由未测量的量子比特承载，别无其他。
英文摘要
Simulating the showers that particles deposit in a calorimeter, the detector that records their energy across many cells, is one of the largest computing costs in high-energy physics, and fast generative surrogates are needed. Quantum circuits have been proposed as such surrogates, but demonstrations on calorimeter data have either required a register that grows with the image, roughly one qubit per cell, or have passed the information between parts of the image through a classical channel. We remove both constraints for this problem, using a construction drawn from the sequential-generation and hidden-quantum-Markov-model literature. A shower image is generated block by block on a fixed register of six qubits. Three of them, the memory, are never measured. They hold what the circuit knows about the blocks already generated as a quantum state, so the correlations between distant parts of the image cross each block boundary as unmeasured amplitudes. The other three are measured and reset once per block, and each outcome selects the energy pattern of one block. The register size is set by the block, so adding cells to the image adds steps to the sequence, not qubits. We call the model a coherent-memory Born machine (CoMB), a Born machine being a circuit whose measurement statistics are the generated distribution. Its training loss grows only linearly with the number of blocks and never requires enumerating the image distribution, and a depth-two instance runs on an IBM superconducting processor. On a twelve-cell benchmark the model reproduces the energy distribution of every cell, the correlation matrix between cells, and the total-energy spectrum. Removing the three memory qubits, with everything else unchanged, produces independent blocks. Every correlation between blocks is carried by the unmeasured qubits and by nothing else.
