One-shot dynamical resource theory

• URL: http://arxiv.org/abs/2012.02781v1
• Date: Fri, 4 Dec 2020 18:57:42 GMT
• Title: One-shot dynamical resource theory
• Authors: Xiao Yuan, Pei Zeng, Minbo Gao and Qi Zhao
• Abstract summary: We consider tasks of one-shot resource distillation and dilution with a single copy of the resource. For any target of unitary channel or pure state preparation channel, we establish a universal strategy to determine upper and lower bounds on rates that convert between any given resource and the target. Our results are applicable to general dynamical resource theories with potential applications in quantum communication, fault-tolerant quantum computing, and quantum thermodynamics.
• Score: 16.046979670252814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A fundamental problem in resource theory is to study the manipulation of the resource. Focusing on a general dynamical resource theory of quantum channels, here we consider tasks of one-shot resource distillation and dilution with a single copy of the resource. For any target of unitary channel or pure state preparation channel, we establish a universal strategy to determine upper and lower bounds on rates that convert between any given resource and the target. We show that the rates are related to resource measures based on the channel robustness and the channel hypothesis testing entropy, with regularization factors of the target resource measures. The strategy becomes optimal with converged bounds when the channel robustness is finite and measures of the target resource collapse to the same value. The single-shot result also applies to asymptotic parallel manipulation of channels to obtain asymptotic resource conversion rates. We give several examples of dynamical resources, including the purity, classical capacity, quantum capacity, non-uniformity, coherence, and entanglement of quantum channels. Our results are applicable to general dynamical resource theories with potential applications in quantum communication, fault-tolerant quantum computing, and quantum thermodynamics.

Related papers

• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation [80.78352800340032]
  Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms. efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity. We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
  arXiv  Detail & Related papers  (2024-01-13T17:16:38Z)
• Entropic and operational characterizations of dynamic quantum resources [3.2074558838636262]
  We provide new methods for characterizing general closed and convex quantum resource theories. We propose a resource-theoretic generalization of the quantum conditional min-entropy. We show that every well-defined robustness-based measure of a channel can be interpreted as an operational advantage of the channel over free channels in a communication task.
  arXiv  Detail & Related papers  (2021-12-13T18:58:36Z)
• One-Shot Yield-Cost Relations in General Quantum Resource Theories [5.37133760455631]
  We establish a relation between the one-shot distillable resource yield and dilution cost. We show that our techniques provide strong converse bounds relating the distillable resource and resource dilution cost in the regime.
  arXiv  Detail & Related papers  (2021-10-05T17:59:30Z)
• Quantifying Qubit Magic Resource with Gottesman-Kitaev-Preskill Encoding [58.720142291102135]
  We define a resource measure for magic, the sought-after property in most fault-tolerant quantum computers. Our formulation is based on bosonic codes, well-studied tools in continuous-variable quantum computation.
  arXiv  Detail & Related papers  (2021-09-27T12:56:01Z)
• Asymptotically Consistent Measures of General Quantum Resources: Discord, Non-Markovianity, and Non-Gaussianity [1.90365714903665]
  In this paper, we establish an alternative axiom, of resource measures, which quantify resources without contradicting the rates of the resource transformation. Results show that consistent resource measures are widely applicable to the quantitative analysis of various quantum-dimensional properties.
  arXiv  Detail & Related papers  (2021-03-09T19:08:36Z)
• Dynamical Resources [7.734726150561088]
  Quantum channels play a key role in the manipulation of quantum resources. We show how to manipulate dynamical resources with free superchannels. These results serve as the framework for the study of concrete examples of theories of dynamical resources.
  arXiv  Detail & Related papers  (2020-12-31T19:08:09Z)
• One-Shot Manipulation of Dynamical Quantum Resources [0.0]
  We develop a unified framework to characterize one-shot transformations of dynamical quantum resources in terms of resource quantifiers. Our framework encompasses all dynamical resources represented as quantum channels. We show that our conditions become necessary and sufficient for broad classes of important theories.
  arXiv  Detail & Related papers  (2020-12-03T19:09:14Z)
• Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations [91.02019381927236]
  We introduce a novel method to steer the agents toward a stable population state, fulfilling the given resource constraints. The proposed method is a decentralized resource pricing method based on the resource loads resulting from the augmentation of the game's Lagrangian.
  arXiv  Detail & Related papers  (2020-10-21T10:11:17Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)
• A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission [98.47527781626161]
  We study a resource allocation problem for joint caching and transmission in small cell networks. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties.
  arXiv  Detail & Related papers  (2020-04-13T09:07:17Z)

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.