Optimising Entanglement Distillation Policies

• URL: http://arxiv.org/abs/2606.14908v1
• Date: Fri, 12 Jun 2026 19:27:58 GMT
• Title: Optimising Entanglement Distillation Policies
• Abstract summary: Entanglement distillation is a fundamental operation in quantum information processing.<n>We formulate and analyze this task as a Markov decision problem.<n>We obtain optimal deterministic policies that minimize the expected waiting time required to reach a target fidelity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Entanglement distillation is a fundamental operation in quantum information processing used to obtain higher-fidelity entangled pairs from a supply of less entangled quantum states using local operations aided by classical communication (LOCC). In a physically relevant setting, where states with an initial fidelity of $f_0$, probabilistically generated over multiple, $m$, memory pairs distributed between two parties, Alice and Bob, are pairwise distilled, the optimal policy identifies the system-configuration dependent sequence of entanglement generation and distillation operations that need to be performed in order to minimize the expected time to reach some target fidelity $f_T>f_0$. Here, we formulate and systematically analyze this task as a Markov decision problem and using a value iteration algorithm, obtain optimal deterministic policies that minimize the expected waiting time required to reach a target fidelity. Our results show that the expected waiting time under the optimal policy decreases with increasing generation probability $p$ and number of quantum memories $m$ - as expected. In contrast, it exhibits non-monotonic behavior with respect to $f_0$ for a fixed fidelity gap, $(Δf = f_T-f_0)$. While the optimal policy consistently outperforms baseline policies such as the greedy, nested and entanglement pumping policies, its relative advantage is regime-dependent, being determined by the system parameters ($p,f_0,f_T,m$), and exhibits a nontrivial dependence on the fidelity gap $Δf$. Our results highlight the value of formulating entanglement distillation as a Markov decision problem, enabling the systematic design of policies that achieve target fidelity thresholds for quantum information tasks in realistic resource-constrained settings.

Related papers

• Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent [53.828537014796574]
  Online off-policy reinforcement learning (RL) is shaped by two choices: the policy class and the update rule.<n>We propose MeanFlow Policies, a one-step generative policy class that maps noise to actions through a MeanFlow transformation.<n>Across seven MuJoCo benchmarks, S improves over Gaussian and generative baselines while retaining single-step inference efficiency.
  arXiv  Detail & Related papers  (2026-05-20T15:14:14Z)
• Holder Policy Optimisation [26.521180498291717]
  textbfHlderPO is a generalised policy optimisation framework.<n>It unifies token-level probability aggregation via the Hlder mean.<n>It achieves a state-of-the-art average accuracy of $54.9%$ across multiple mathematical benchmarks.
  arXiv  Detail & Related papers  (2026-05-12T12:45:03Z)
• POETS: Uncertainty-Aware LLM Optimization via Compute-Efficient Policy Ensembles [35.88280776565273]
  POETS is a novel framework that bridges uncertainty quantification and policy optimization.<n>Our approach is grounded in the insight that policies trained with Kullback-Leibler (KL) regularization implicitly encode an underlying reward function.<n>We demonstrate POETS achieves state-of-the-art sample efficiency across diverse scientific discovery domains.
  arXiv  Detail & Related papers  (2026-05-08T14:16:32Z)
• Adaptive Policies for Resource Generation in a Quantum Network [0.5332865877117923]
  Protocols for distributed quantum systems commonly require simultaneous availability of $n$ entangled states.<n>We derive optimal policies that minimise the expected time until $n$ entangled states are available with fidelity greater than $F_mathrmapp$.
  arXiv  Detail & Related papers  (2025-09-22T11:04:12Z)
• Quantile-Optimal Policy Learning under Unmeasured Confounding [55.72891849926314]
  We study quantile-optimal policy learning where the goal is to find a policy whose reward distribution has the largest $alpha$-quantile for some $alpha in (0, 1)$.<n>Such a problem suffers from three main challenges: (i) nonlinearity of the quantile objective as a functional of the reward distribution, (ii) unobserved confounding issue, and (iii) insufficient coverage of the offline dataset.
  arXiv  Detail & Related papers  (2025-06-08T13:37:38Z)
• Confident Natural Policy Gradient for Local Planning in $q_π$-realizable Constrained MDPs [44.69257217086967]
  The constrained Markov decision process (CMDP) framework emerges as an important reinforcement learning approach for imposing safety or other critical objectives.<n>In this paper, we address the learning problem given linear function approximation with $q_pi$-realizability.
  arXiv  Detail & Related papers  (2024-06-26T17:57:13Z)
• A safe exploration approach to constrained Markov decision processes [7.036452261968767]
  We consider discounted infinite-horizon constrained Markov decision processes (CMDPs)<n>The goal is to find an optimal policy that maximizes the expected cumulative reward while satisfying expected cumulative constraints.<n>Motivated by the application of CMDPs in online learning for safety-critical systems, we focus on developing a model-free and emphsimulator-free algorithm.
  arXiv  Detail & Related papers  (2023-12-01T13:16:39Z)
• Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees [56.848265937921354]
  Inverse reinforcement learning (IRL) aims to recover the reward function and the associated optimal policy. Many algorithms for IRL have an inherently nested structure. We develop a novel single-loop algorithm for IRL that does not compromise reward estimation accuracy.
  arXiv  Detail & Related papers  (2022-10-04T17:13:45Z)
• Efficient Policy Iteration for Robust Markov Decision Processes via Regularization [49.05403412954533]
  Robust decision processes (MDPs) provide a framework to model decision problems where the system dynamics are changing or only partially known. Recent work established the equivalence between texttts rectangular $L_p$ robust MDPs and regularized MDPs, and derived a regularized policy iteration scheme that enjoys the same level of efficiency as standard MDPs. In this work, we focus on the policy improvement step and derive concrete forms for the greedy policy and the optimal robust Bellman operators.
  arXiv  Detail & Related papers  (2022-05-28T04:05:20Z)
• Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes [70.80716221080118]
  The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning. The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
  arXiv  Detail & Related papers  (2021-12-01T23:15:09Z)
• Understanding the Effect of Stochasticity in Policy Optimization [86.7574122154668]
  We show that the preferability of optimization methods depends critically on whether exact gradients are used. Second, to explain these findings we introduce the concept of committal rate for policy optimization. Third, we show that in the absence of external oracle information, there is an inherent trade-off between exploiting geometry to accelerate convergence versus achieving optimality almost surely.
  arXiv  Detail & Related papers  (2021-10-29T06:35:44Z)
• Queueing Network Controls via Deep Reinforcement Learning [0.0]
  We develop a Proximal policy optimization algorithm for queueing networks. The algorithm consistently generates control policies that outperform state-of-arts in literature. A key to the successes of our PPO algorithm is the use of three variance reduction techniques in estimating the relative value function.
  arXiv  Detail & Related papers  (2020-07-31T01:02:57Z)
• Provably Efficient Safe Exploration via Primal-Dual Policy Optimization [105.7510838453122]
  We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation. We present an provably efficient online policy optimization algorithm for CMDP with safe exploration in the function approximation setting.
  arXiv  Detail & Related papers  (2020-03-01T17:47:03Z)

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.