Entanglement Swapping for Repeater Chains with Finite Memory Sizes

• URL: http://arxiv.org/abs/2111.10994v1
• Date: Mon, 22 Nov 2021 05:20:10 GMT
• Title: Entanglement Swapping for Repeater Chains with Finite Memory Sizes
• Authors: Wenhan Dai and Don Towsley
• Abstract summary: We develop entanglement swapping protocols and memory allocation methods for quantum repeater chains. We determine the trade-off between the entanglement distribution rate and the memory size for temporal multiplexing techniques.
• Score: 19.436762526586204
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We develop entanglement swapping protocols and memory allocation methods for quantum repeater chains. Unlike most of the existing studies, the memory size of each quantum repeater in this work is a parameter that can be optimized. Based on Markov chain modeling of the entanglement distribution process, we determine the trade-off between the entanglement distribution rate and the memory size for temporal multiplexing techniques. We then propose three memory allocation methods that achieve entanglement distribution rates decaying polynomially with respect to the distance while using constant average memory slots per node. We also quantify the average number of memory slots required due to classical communication delay, as well as the delay of entanglement distribution. Our results show that a moderate memory size suffices to achieve a polynomial decay of entanglement distribution rate with respect to the distance, which is the scaling achieved by the optimal protocol even with infinite memory size at each node.

Related papers

• Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss [59.835032408496545]
  We propose a tile-based strategy that partitions the contrastive loss calculation into arbitrary small blocks. We also introduce a multi-level tiling strategy to leverage the hierarchical structure of distributed systems. Compared to SOTA memory-efficient solutions, it achieves a two-order-of-magnitude reduction in memory while maintaining comparable speed.
  arXiv  Detail & Related papers  (2024-10-22T17:59:30Z)
• Entanglement Distribution in Quantum Repeater with Purification and Optimized Buffer Time [53.56179714852967]
  We explore entanglement distribution using quantum repeaters with optimized buffer time. We observe that increasing the number of memories on end nodes leads to a higher entanglement distribution rate per memory. When imperfect operations are considered, however, we make the surprising observation that the per-memory entanglement rate decreases with increasing number of memories.
  arXiv  Detail & Related papers  (2023-05-23T23:23:34Z)
• Entanglement distribution with minimal memory requirements using time-bin photonic qudits [0.0]
  We propose a novel protocol based on $2m$-dimensional time-bin photonic qudits. By adopting the qudit protocol, the required qubit memory time is independent of the transmission loss between the nodes. Our protocol can significantly boost the performance of near-term quantum networks.
  arXiv  Detail & Related papers  (2022-10-29T09:23:04Z)
• Scheduling Quantum Teleportation with Noisy Memories [0.0]
  We quantify the effect of decoherence on fidelity at a node in a quantum network due to the storage of qubits in noisy memory platforms. We show that fidelity at a node is a linear sum of terms, exponentially decaying with time, where the decay rate depends on the decoherence rate of the memory platform. We prove that serving qubits in a Last In First Out order with pushout for buffer overflow management is optimal in terms of average fidelity.
  arXiv  Detail & Related papers  (2022-05-12T18:29:42Z)
• SreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm [60.61943386819384]
  Existing implementations of KRR require that all the data is stored in the main memory. We propose StreaMRAK - a streaming version of KRR. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum.
  arXiv  Detail & Related papers  (2021-08-23T21:03:09Z)
• MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference [1.7894377200944507]
  Machine learning networks can easily exceed available memory, increasing latency due to excessive OS swapping. We propose a memory usage predictor coupled with a search algorithm to provide optimized fusing and tiling configurations. Results show that our approach can run in less than half the memory, and with a speedup of up to 2.78 under severe memory constraints.
  arXiv  Detail & Related papers  (2021-07-14T19:45:49Z)
• Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory [75.65949969000596]
  Episodic and semantic memory are critical components of the human memory model. We develop a new principled Bayesian memory allocation scheme that bridges the gap between episodic and semantic memory. We demonstrate that this allocation scheme improves performance in memory conditional image generation.
  arXiv  Detail & Related papers  (2021-02-20T18:40:40Z)
• Neural Network Compression for Noisy Storage Devices [71.4102472611862]
  Conventionally, model compression and physical storage are decoupled. This approach forces the storage to treat each bit of the compressed model equally, and to dedicate the same amount of resources to each bit. We propose a radically different approach that: (i) employs analog memories to maximize the capacity of each memory cell, and (ii) jointly optimize model compression and physical storage to maximize memory utility.
  arXiv  Detail & Related papers  (2021-02-15T18:19:07Z)
• Memformer: A Memory-Augmented Transformer for Sequence Modeling [55.780849185884996]
  We present Memformer, an efficient neural network for sequence modeling. Our model achieves linear time complexity and constant memory space complexity when processing long sequences.
  arXiv  Detail & Related papers  (2020-10-14T09:03:36Z)
• Efficient optimization of cut-offs in quantum repeater chains [0.0]
  We develop an algorithm for computing the probability distribution of the waiting time and fidelity of entanglement produced by repeater chain protocols. We use the algorithm to optimize cut-offs in order to maximize secret-key rate between the end nodes of the repeater chain.
  arXiv  Detail & Related papers  (2020-05-11T09:17:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.