智能控制碰撞架构以实现确定性多体态工程
Intelligent Control of Collisional Architectures for Deterministic Multipartite State Engineering
AI总结：
本文提出了一种智能控制框架，用于在碰撞模型架构中确定性生成多体态，通过优化碰撞参数在噪声环境下实现高保真度。
摘要：
设计可扩展、抗噪声的控制协议以实现多体纠缠是量子技术的核心挑战，这自然要求算法合成相互作用参数，而不是手工编写的门序列。本文介绍了一种智能、受约束的控制框架，用于在重复相互作用（碰撞模型）架构中确定性地生成对称的迪克态|D_n^(m)>。该协议利用两个不相交量子比特寄存器之间的保激发部分交换碰撞，通过m个辅助“穿梭”量子比特进行调解，并将迪克态制备视为一个闭环设计问题：给定目标(n,m)，自动推断碰撞强度以在实际约束下最大化保真度。具体而言，我们制定了一个双参数、有界约束的优化问题，优化寄存器内和穿梭寄存器碰撞角度，并使用多起点策略与L-BFGS-B求解，为每个目标生成可重复的控制器配方（优化的γ_in、γ_sh和最小轮次收敛点）。这消除了投影测量的需要，并将碰撞纠缠生成扩展到任意m。关键在于我们在不完美的碰撞动态中进行优化，其中误差在整个序列中起作用，包括随机的相互作用丢失（缺失碰撞）和标准退相干通道。令人惊讶的是，在广泛的误差范围内，优化后的控制器保持高制备保真度；不完美之处主要表现为所需碰撞轮次的适度增加。这种行为反映了一种可调的竞争，其中噪声抑制相关性，而适当选择的碰撞持续补充它们，使控制算法能够以时间换取保真度。
英文摘要：
Designing scalable, noise-tolerant control protocols for multipartite entanglement is a central challenge for quantum technologies, and it naturally calls for \emph{algorithmic} synthesis of interaction parameters rather than handcrafted gate sequences. Here we introduce an intelligent, constraint-aware control framework for deterministic generation of symmetric Dicke states $|D_n^{(m)}\rangle$ in repeated-interaction (collision-model) architectures. The protocol employs excitation-preserving partial-SWAP collisions between two disjoint qubit registers, mediated by $m$ ancillary ``shuttle'' qubits, and poses Dicke-state preparation as a \emph{closed-loop design} problem: given the target $(n,m)$, automatically infer collision strengths that maximize fidelity under practical constraints. Concretely, we formulate a two-parameter, bound-constrained optimization over intra-register and shuttle--register collision angles and solve it using a multi-start strategy with L-BFGS-B, yielding a reproducible controller prescription (optimized $γ_{\mathrm{in}}$, $γ_{\mathrm{sh}}$, and minimal-round convergence points) for each target. This removes the need for projective measurements and extends collisional entanglement generation beyond the single-excitation (W-state) sector to arbitrary $m$. Crucially, we optimize \emph{within} imperfect collisional dynamics where errors act throughout the sequence, including stochastic interaction dropouts (missing collisions) and standard decoherence channels. Strikingly, across wide error ranges the optimized controller preserves high preparation fidelity; imperfections manifest primarily as a modest increase in the required number of collision rounds. This behavior reflects a tunable competition in which noise suppresses correlations while properly chosen collisions continuously replenish them, allowing the control algorithm to trade time for fidelity.
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