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1. Preparation-free resonator array atomic memory for on-chip quantum storage

   https://doi.org/10.1063/5.0270067  

   An, Haechan; Li, Zongfeng; Hosseini, Mahdi (June 2025, APL Quantum)

   We propose a light storage technique utilizing an array of photonic resonators that mimic atomic frequency comb memory. This method can be implemented on a solid-state photonic chip without requiring spectral hole burning. By eliminating the need for long preparation cycles and leveraging tunable photonic resonances, this approach offers high bandwidth, high efficiency, and a fast duty cycle simultaneously and without compromise. 

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2. Algorithmic optimization of quantum optical storage in solids

   https://doi.org/10.1103/PhysRevResearch.6.033153  

   Lei, Yisheng; An, Haechan; Li, Zongfeng; Hosseini, Mahdi (August 2024, Physical Review Research)

   Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth. Published by the American Physical Society2024 

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3. Broadband quantum memory in atomic ensembles

   https://doi.org/10.1016/bs.aamop.2023.04.001  

   Shinbrough, Kai; Pearson Jr., Donny R.; Fang, Bin; Goldschmidt, Elizabeth A.; Lorenz, Virginia O. (June 2023, Advances in atomic molecular and optical physics)

   Broadband quantum memory is critical to enabling the operation of emerging photonic quantum technology at high speeds. Here we review a central challenge to achieving broadband quantum memory in atomic ensembles—what we call the ‘linewidth-bandwidth mismatch’ problem—and the relative merits of various memory protocols and hardware used for accomplishing this task. We also review the theory underlying atomic ensemble quantum memory and its extensions to optimizing memory efficiency and characterizing memory sensitivity. Finally, we examine the state-of-the-art performance of broadband atomic ensemble quantum memories with respect to three key metrics: efficiency, memory lifetime, and noise. 

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4. Provable Optimality of the Square-Tooth Atomic Frequency Comb Quantum Memory

   https://doi.org/10.1088/1751-8121/adba1f  

   Zang, Allen; Suchara, Martin; Zhong, Tian (February 2025, Journal of Physics A: Mathematical and Theoretical)

   Abstract Atomic frequency comb (AFC) quantum memories are a promising technology for quantum repeater networks because they enable multi-mode, long-time, and high-fidelity storage of photons with on-demand retrieval. The optimization of the retrieval efficiency of an AFC memory is important because it strongly impacts the entanglement distribution rate in quantum networks. Despite initial theoretical analyses and recent experimental demonstrations, a rigorous proof of the universally optimal configuration for the highest AFC retrieval efficiency has not been presented. In this paper we present a simple analytical proof which shows that the optimized square tooth offers the highest retrieval efficiency among all tooth shapes, under the physical constraint of finite optical depth of an atomic ensemble. The optimality still holds when the non-zero background absorption and the finite optical linewidth of atoms are considered. We further compare square, Lorentzian and Gaussian tooth shapes to reinforce the practical advantage of the square-tooth AFC in retrieval efficiency. Our proof lays rigorous foundation for the recipe of creating optimal AFC under realistic experimental conditions. 

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5. ASSASIN: Architecture Support for Stream Computing to Accelerate Computational Storage

   Zou, Chen; Chien, Andrew A. (October 2022, 55th IEEE/ACM International Symposium on Microarchitecture)

   Computational storage adds computing to storage devices, providing potential benefits in offload, data-reduction, and lower energy. Successful computational SSD architectures should match growing flash bandwidth, which in turn requires high SSD DRAM memory bandwidth. This creates a memory wall scaling problem, resulting from SSDs’ stringent power and cost constraints. A survey of recent computational SSD research shows that many computational storage offloads are suited to stream computing. To exploit this opportunity, we propose a novel general-purpose computational SSD and core architecture, called ASSASIN (Architecture Support for Stream computing to Accelerate computatIoNal Storage). ASSASIN provides a unified set of compute engines between SSD DRAM and the flash array. This eliminates the SSD DRAM bottleneck by enabling direct computing on flash data streams. ASSASIN further employs a crossbar to achieve performance even when flash data layout is uneven and preserve independence for page layout decisions in the flash translation layer. With stream buffers and scratchpad memories, ASSASIN core’s memory hierarchy and instruction set extensions provide superior low-latency access at low-power and effectively keep streaming flash data out of the in-SSD cache-DRAM memory hierarchy, thereby solving the memory wall. Evaluation shows that ASSASIN delivers 1.5x - 2.4x speedup for offloaded functions compared to state-of-the-art computational SSD architectures. Further, ASSASIN’s streaming approach yields 2.0x power efficiency and 3.2x area efficiency improvement. And these performance benefits at the level of computational SSDs translate to 1.1x - 1.5x end-to-end speedups on data analytics workloads. 

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