Dynamic LOCC Circuits for Automated Entanglement Manipulation
- URL: http://arxiv.org/abs/2509.07841v1
- Date: Tue, 09 Sep 2025 15:14:43 GMT
- Title: Dynamic LOCC Circuits for Automated Entanglement Manipulation
- Authors: Xia Liu, Jiayi Zhao, Benchi Zhao, Xin Wang,
- Abstract summary: We propose a general and flexible framework called dynamic LOCCNet to simulate and design LOCC protocols.<n>We demonstrate its effectiveness in two key applications: entanglement distillation and distributed state discrimination.<n>This work advances our understanding of the capabilities and limitations of LOCC while providing a powerful methodology for protocol design.
- Score: 8.101659301140087
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Due to the limited qubit number of quantum devices, distributed quantum computing is considered a promising pathway to overcome this constraint. In this paradigm, multiple quantum processors are interconnected to form a cohesive computational network, and the most natural set of free operations is local operations and classical communication (LOCC). However, designing a practical LOCC protocol for a particular task has been a tough problem. In this work, we propose a general and flexible framework called dynamic LOCCNet (DLOCCNet) to simulate and design LOCC protocols. We demonstrate its effectiveness in two key applications: entanglement distillation and distributed state discrimination. The protocols designed by DLOCCNet, in contrast to conventional ones, can solve larger-sized problems with reduced training time, making the framework a practical and scalable tool for current quantum devices. This work advances our understanding of the capabilities and limitations of LOCC while providing a powerful methodology for protocol design.
Related papers
- Addressable gate-based logical computation with quantum LDPC codes [0.0]
High-rate quantum LDPC codes can reduce error correction overhead, yet realizing high-rate fault-tolerant computation with these codes remains a central challenge.<n>We introduce a gate-based protocol for addressable single- and multi-qubit Clifford operations on individual qubits encoded within one or more quantum LDPC codes.
arXiv Detail & Related papers (2025-11-08T20:26:24Z) - CLASS: A Controller-Centric Layout Synthesizer for Dynamic Quantum Circuits [58.16162138294308]
CLASS is a controller-centric layout synthesizer designed to reduce inter-controller communication latency in a distributed control system.<n> Evaluations demonstrate that CLASS effectively reduces communication latency by up to 100% with only a 2.10% average increase in the number of additional operations.
arXiv Detail & Related papers (2025-09-19T08:11:55Z) - Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
arXiv Detail & Related papers (2025-09-12T18:41:10Z) - Planar Fault-Tolerant Quantum Computation with Low Overhead [5.232949916418351]
We introduce code craft, a framework for designing fault-tolerant logical operations on planar BB codes.<n>We show that logical operations, including controlled-NOT gates, state transfers, and Pauli measurements, can be efficiently implemented within this framework.
arXiv Detail & Related papers (2025-06-22T15:07:03Z) - Deterministic generation of multi-qubit entangled states among distant parties using indefinite causal order [1.556591713973462]
We present protocols for generating $N$-qubit entangled states across multiple network nodes.<n>The results indicate that our protocols significantly improve the efficiency of long-distance entanglement generation.
arXiv Detail & Related papers (2025-03-05T11:40:19Z) - Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures [1.8781124875646162]
This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based approach for efficient quantum circuit compilation and mapping.
In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn theses for a specific multi-core architecture.
arXiv Detail & Related papers (2024-06-17T12:09:11Z) - A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - Multi-User Entanglement Distribution in Quantum Networks Using Multipath
Routing [55.2480439325792]
We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing.
The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation.
arXiv Detail & Related papers (2023-03-06T18:06:00Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Quantum communication complexity beyond Bell nonlocality [87.70068711362255]
Efficient distributed computing offers a scalable strategy for solving resource-demanding tasks.
Quantum resources are well-suited to this task, offering clear strategies that can outperform classical counterparts.
We prove that a new class of communication complexity tasks can be associated to Bell-like inequalities.
arXiv Detail & Related papers (2021-06-11T18:00:09Z) - Practical distributed quantum information processing with LOCCNet [8.633408580670812]
We introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks.
As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation.
An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.
arXiv Detail & Related papers (2021-01-28T18:53:12Z)
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.