Catalytic entanglement transformations with noisy hardware

• URL: http://arxiv.org/abs/2505.05964v1
• Date: Fri, 09 May 2025 11:33:53 GMT
• Title: Catalytic entanglement transformations with noisy hardware
• Authors: Hemant Sharma, Aleksandr Mokeev, Jonas Helsen, Johannes Borregaard,
• Abstract summary: We benchmark catalytic EC against non-catalytic EC and distillation in the presence of state-preparation errors and operational errors.<n>We find that in the presence of low operational errors and depolarising noise, catalytic EC can provide better rates than distillation and non-catalytic EC.
• Score: 41.94295877935867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The availability of certain entangled resource states (catalyst states) can enhance the rate of converting several less entangled states into fewer highly entangled states in a process known as catalytic entanglement concentration (EC). Here, we extend catalytic EC from pure states to mixed states and numerically benchmark it against non-catalytic EC and distillation in the presence of state-preparation errors and operational errors. Furthermore, we analyse the re-usability of catalysts in the presence of such errors. To do this, we introduce a novel recipe for determining the positive-operator valued measurements (POVM) required for EC transformations, which allows for making tradeoffs between the number of communication rounds and the number of auxiliary qubits required. We find that in the presence of low operational errors and depolarising noise, catalytic EC can provide better rates than distillation and non-catalytic EC.

Related papers

• Knowledge Distillation Performs Partial Variance Reduction [93.6365393721122]
  Knowledge distillation is a popular approach for enhancing the performance of ''student'' models. The underlying mechanics behind knowledge distillation (KD) are still not fully understood. We show that KD can be interpreted as a novel type of variance reduction mechanism.
  arXiv  Detail & Related papers  (2023-05-27T21:25:55Z)
• No-go theorem for entanglement distillation using catalysis [49.24817625059456]
  We show that catalytic transformations can never allow for the distillation of entanglement from a bound entangled state. This precludes the possibility that entanglement theoryally reversible based operations under even permissive choices.
  arXiv  Detail & Related papers  (2023-05-05T12:57:59Z)
• Catalytic and asymptotic equivalence for quantum entanglement [44.99833362998488]
  Many-copy entanglement manipulation procedures allow for highly entangled pure states from noisy states.<n>We show that using an entangled catalyst cannot enhance the singlet distillation rate of a distillable quantum state.<n>Our findings provide a comprehensive understanding of the capabilities and limitations of both catalytic and state transformations of entangled states.
  arXiv  Detail & Related papers  (2023-05-05T12:57:59Z)
• Continual Detection Transformer for Incremental Object Detection [154.8345288298059]
  Incremental object detection (IOD) aims to train an object detector in phases, each with annotations for new object categories. As other incremental settings, IOD is subject to catastrophic forgetting, which is often addressed by techniques such as knowledge distillation (KD) and exemplar replay (ER) We propose a new method for transformer-based IOD which enables effective usage of KD and ER in this context.
  arXiv  Detail & Related papers  (2023-04-06T14:38:40Z)
• Catalysis in Action via Elementary Thermal Operations [0.0]
  We investigate in the framework of elementary thermal operations, leveraging the distinct features of such operations to illuminate catalytic dynamics. We establish new technical tools that enhance the computability of state transition rules for elementary thermal operations.
  arXiv  Detail & Related papers  (2022-09-30T03:59:36Z)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• Correlation in Catalysts Enables Arbitrary Manipulation of Quantum Coherence [0.0]
  We show that allowing correlation among multiple catalysts can offer arbitrary power in the manipulation of quantum coherence. This presents a new type of embezzlement-like phenomenon, in which the resource embezzlement is attributed to the correlation generated among multiple catalysts.
  arXiv  Detail & Related papers  (2021-06-23T18:00:04Z)
• Amplifying asymmetry with correlated catalysts [15.457543251048637]
  A correlated catalyst is a finite-dimensional auxiliary, which exactly preserves its reduced state while allowed to become correlated to the quantum system. We show that the power of a catalyst increases with its dimension, and further, with a large enough catalyst, a qubit state with arbitrarily small amount of asymmetry can be converted to any mixed qubit state. Our results may also apply to the constraints on coherence evolution in quantum thermodynamics, and to the distribution of timing information between quantum clocks.
  arXiv  Detail & Related papers  (2020-07-13T08:52:56Z)
• Catalyst-Assisted Probabilistic Coherence Distillation for Mixed States [0.0]
  We consider the problem of catalyst-assisted coherence distillation for mixed states under strictly incoherent operations. We present the necessary and sufficient conditions for the existence of a catalyst that increases the maximal transformation probability.
  arXiv  Detail & Related papers  (2020-01-10T15:24:17Z)

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.