An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation

• URL: http://arxiv.org/abs/2601.18351v3
• Date: Thu, 29 Jan 2026 07:22:18 GMT
• Title: An Adaptive Purification Controller for Quantum Networks: Dynamic Protocol Selection and Multipartite Distillation
• Authors: Pranav Kulkarni, Leo Sünkel, Michael Kölle,
• Abstract summary: We propose an Adaptive Purification Controller (APC) that automatically optimize the entanglement distillation sequence to maximize the goodput.<n>We show that our approach mitigates the "fidelity cliffs" inherent in static protocols and reduces resource wastage in high-noise regimes.<n>We extend the controller to heterogeneous scenarios, and evaluate it for both multipartite GHZ state generation and continuous-variable systems.
• Score: 10.706268477180858
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient entanglement distribution is a cornerstone of the Quantum Internet. However, physical link parameters such as photon loss, memory coherence time, and gate error rates fluctuate dynamically, rendering static purification strategies suboptimal. In this paper, we propose an Adaptive Purification Controller (APC) that automatically optimizes the entanglement distillation sequence to maximize the goodput, i.e., the rate of delivered pairs meeting a strict fidelity threshold. By treating protocol selection as a resource allocation problem, the APC dynamically switches between purification depths and protocols (BBPSSW vs. DEJMPS) to navigate the trade-off between generation rate and state quality. Using a dynamic programming planner with Pareto pruning, simulation results show that our approach mitigates the "fidelity cliffs" inherent in static protocols and reduces resource wastage in high-noise regimes. Furthermore, we extend the controller to heterogeneous scenarios, and evaluate it for both multipartite GHZ state generation and continuous-variable systems using effective noiseless linear amplification models. We benchmark its computational overhead, showing decision latencies in the millisecond range per link in our implementation.

Related papers

• Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization [37.86747540440919]
  We consider an industrial internet of things (IIoT) network supporting dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect.<n>A joint link adaptation (LA) and device scheduling (including the order) design is provided, aiming at maximizing the total transmission rate under strict block error rate constraints.<n>We show that the proposed algorithm achieves faster convergence and higher sum-rate performance compared to existing solutions.
  arXiv  Detail & Related papers  (2025-12-29T14:32:34Z)
• TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration [64.32072516882947]
  Diffusion Policy excels in embodied control but suffers from high inference latency and computational cost.<n>We propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP)<n>TS-DP achieves up to 4.17 times faster inference with over 94% accepted drafts, reaching an inference frequency of 25 Hz.
  arXiv  Detail & Related papers  (2025-12-13T07:53:14Z)
• Trotterized Variational Quantum Control for Spin-Chain State Transfer [2.033424698590539]
  We present a hybrid variational framework for quantum optimal control aimed at high-fidelity state transfer in spin chains.<n>We study two parameterizations: a compact global scheme with a small number of shared parameters per slice, and a local scheme with site-wise angles.<n>The results make explicit an expressivity-stability trade-off and suggest a scalable route to Noisy Intermediate-Scale Quantum (NISQ) compatible control synthesis.
  arXiv  Detail & Related papers  (2025-11-12T19:38:09Z)
• PASS-Enhanced MEC: Joint Optimization of Task Offloading and Uplink PASS Beamforming [67.78883135636657]
  pinching-antenna system (PASS)-enhanced mobile edge computing (MEC) architecture is investigated.<n>PASS establishes short-distance line-of-sight (LoS) links while effectively mitigating the significant path loss and potential signal blockage.<n>We formulate a network latency minimization problem to joint optimize uplink PASS beamforming and task offloading.
  arXiv  Detail & Related papers  (2025-10-27T03:04:46Z)
• Quantum Reinforcement Learning-Guided Diffusion Model for Image Synthesis via Hybrid Quantum-Classical Generative Model Architectures [2.005299372367689]
  We introduce a quantum reinforcement learning (QRL) controller that dynamically adjusts CFG at each denoising step.<n>The controller adopts a hybrid quantum--classical actor--critic architecture.<n> Experiments on CIFAR-10 demonstrate that our QRL policy improves perceptual quality.
  arXiv  Detail & Related papers  (2025-09-17T16:47:04Z)
• From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management [31.080933663717257]
  We propose a novel machine learning technique to jointly model and predict the dynamics of the control system.<n>We show that our proposed approach reduces transmit power by over 50% while maintaining control performance comparable to baseline methods.
  arXiv  Detail & Related papers  (2025-06-19T11:08:20Z)
• The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks [56.37880529653111]
  The demand for large computation model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications.<n>In this paper, we investigate the LAIM-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment.
  arXiv  Detail & Related papers  (2025-05-14T08:18:55Z)
• Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach [0.0]
  Neural Networks (NNs) can be leveraged to parametrize control policies that yield good performance. NNs' sensitivity to small input changes poses a risk of destabilizing the closed-loop system. To address these problems, we leverage the framework of port-Hamiltonian systems to design continuous-time distributed control policies. The effectiveness of the proposed distributed controllers is demonstrated through consensus control of non-holonomic mobile robots.
  arXiv  Detail & Related papers  (2024-11-15T10:44:29Z)
• Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
  Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves. These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves. In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
  arXiv  Detail & Related papers  (2024-04-17T02:04:10Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling [4.369346338392536]
  A probabilistic graphical model is proposed, modeling the joint model parameter and multiplier evolution.<n>We address multi-objective model parameter optimization via a surrogate single objective penalty loss.
  arXiv  Detail & Related papers  (2024-03-20T16:38:26Z)
• Robust Control for Dynamical Systems With Non-Gaussian Noise via Formal Abstractions [59.605246463200736]
  We present a novel controller synthesis method that does not rely on any explicit representation of the noise distributions. First, we abstract the continuous control system into a finite-state model that captures noise by probabilistic transitions between discrete states. We use state-of-the-art verification techniques to provide guarantees on the interval Markov decision process and compute a controller for which these guarantees carry over to the original control system.
  arXiv  Detail & Related papers  (2023-01-04T10:40:30Z)
• Control of Stochastic Quantum Dynamics with Differentiable Programming [0.0]
  We propose a framework for the automated design of control schemes based on differentiable programming. We apply this approach to state preparation and stabilization of a qubit subjected to homodyne detection. Despite the resulting poor signal-to-noise ratio, we can train our controller to prepare and stabilize the qubit to a target state with a mean fidelity around 85%.
  arXiv  Detail & Related papers  (2021-01-04T19:00:03Z)
• Adaptive Subcarrier, Parameter, and Power Allocation for Partitioned Edge Learning Over Broadband Channels [69.18343801164741]
  partitioned edge learning (PARTEL) implements parameter-server training, a well known distributed learning method, in wireless network. We consider the case of deep neural network (DNN) models which can be trained using PARTEL by introducing some auxiliary variables.
  arXiv  Detail & Related papers  (2020-10-08T15:27:50Z)

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.