Optimization of deterministic photonic graph state generation via local operations

• URL: http://arxiv.org/abs/2401.00635v2
• Date: Tue, 23 Jul 2024 21:11:07 GMT
• Title: Optimization of deterministic photonic graph state generation via local operations
• Authors: Sobhan Ghanbari, Jie Lin, Benjamin MacLellan, Luc Robichaud, Piotr Roztocki, Hoi-Kwong Lo,
• Abstract summary: We introduce an optimization method for such protocols based on the local Clifford equivalency of states and the graph theoretical correlations of the generation cost parameters. We achieve a 50% reduction in use of the 2-qubit gates for generation of the arbitrary large repeater graph states and similar significant reductions in the total gate count for generation of random dense graphs.
• Score: 3.2106353278518105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Realizing photonic graph states, crucial in various quantum protocols, is challenging due to the absence of deterministic entangling gates in linear optics. To address this, emitter qubits have been leveraged to establish and transfer the entanglement to photons. We introduce an optimization method for such protocols based on the local Clifford equivalency of states and the graph theoretical correlations of the generation cost parameters. Employing this method, we achieve a 50% reduction in use of the 2-qubit gates for generation of the arbitrary large repeater graph states and similar significant reductions in the total gate count for generation of random dense graphs.

Related papers

• Heralded photonic graph states with inefficient quantum emitters [2.612403257963011]
  Quantum emitter-based schemes for the generation of photonic graph states offer a promising, resource efficient methodology. We present a heralded scheme for making photonic graph states that is compatible with the typically poor photon collection from state-of-the-art emitters.
  arXiv  Detail & Related papers  (2024-05-22T00:24:01Z)
• Resource-efficient and loss-aware photonic graph state preparation using an array of quantum emitters, and application to all-photonic quantum repeaters [0.8409980020848168]
  We propose an algorithm that can trade the number of emitters with the graph-state depth, while minimizing the number of emitter CNOTs. We find that our scheme achieves a far superior rate-vs.-distance performance than using the least number of emitters needed to generate the repeater graph state.
  arXiv  Detail & Related papers  (2024-02-01T16:29:07Z)
• Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation [69.43216268165402]
  We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.
  arXiv  Detail & Related papers  (2023-03-06T16:51:30Z)
• Quantum Gate Generation in Two-Level Open Quantum Systems by Coherent and Incoherent Photons Found with Gradient Search [77.34726150561087]
  We consider an environment formed by incoherent photons as a resource for controlling open quantum systems via an incoherent control. We exploit a coherent control in the Hamiltonian and an incoherent control in the dissipator which induces the time-dependent decoherence rates.
  arXiv  Detail & Related papers  (2023-02-28T07:36:02Z)
• Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs [131.53471236405628]
  We present three methods that exploit the induced graphon representation of graphs and graph signals on partitions of [0, 1]2 in the graphon space. We prove that those low dimensional representations constitute a convergent sequence of graphs and graph signals. We observe that graphon pooling performs significantly better than other approaches proposed in the literature when dimensionality reduction ratios between layers are large.
  arXiv  Detail & Related papers  (2022-12-15T22:11:34Z)
• Deterministic generation of qudit photonic graph states from quantum emitters [0.0]
  We show that our approach can be applied to generate any qudit graph state. We construct protocols to generate one- and two-dimensional qudit cluster states, absolutely maximally entangled states, and logical states of quantum error correcting codes.
  arXiv  Detail & Related papers  (2022-11-23T19:00:01Z)
• Performance analysis of quantum repeaters enabled by deterministically generated photonic graph states [6.06945682395675]
  quantum repeaters enable fast entanglement distribution rates approaching classical communication. New schemes have been proposed that employ quantum emitters to deterministically generate photonic graph states. We quantitatively analyze the performance of quantum repeaters based on two different graph states.
  arXiv  Detail & Related papers  (2022-09-23T06:13:55Z)
• Near-deterministic hybrid generation of arbitrary photonic graph states using a single quantum emitter and linear optics [0.0]
  We introduce near-deterministic solutions for the generation of graph states using the current quantum emitter capabilities. Our results should pave the way towards the practical implementation of resource-efficient quantum information processing.
  arXiv  Detail & Related papers  (2022-05-19T17:59:59Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets [81.63035727821145]
  Spectral graph convolutional networks (SGCNs) have been attracting increasing attention in graph representation learning. We propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets.
  arXiv  Detail & Related papers  (2021-08-03T17:57:53Z)
• Block-Approximated Exponential Random Graphs [77.4792558024487]
  An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions. Our methods are scalable to sparse graphs consisting of millions of nodes.
  arXiv  Detail & Related papers  (2020-02-14T11:42:16Z)

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.