Distributed Quantum Information Processing: A Review of Recent Progress

• URL: http://arxiv.org/abs/2510.15630v1
• Date: Fri, 17 Oct 2025 13:20:36 GMT
• Title: Distributed Quantum Information Processing: A Review of Recent Progress
• Abstract summary: Distributed quantum information processing seeks to overcome the scalability limitations of monolithic quantum devices.<n>We review the theoretical foundations of distributed quantum protocols and examine the experimental platforms and algorithmic applications that realize them in practice.
• Score: 3.6574512199850453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed quantum information processing seeks to overcome the scalability limitations of monolithic quantum devices by interconnecting multiple quantum processing nodes via classical and quantum communication. This approach extends the capabilities of individual devices, enabling access to larger problem instances and novel algorithmic techniques. Beyond increasing qubit counts, it also enables qualitatively new capabilities, such as joint measurements on multiple copies of high-dimensional quantum states. The distinction between single-copy and multi-copy access reveals important differences in task complexity and helps identify which computational problems stand to benefit from distributed quantum resources. At the same time, it highlights trade-offs between classical and quantum communication models and the practical challenges involved in realizing them experimentally. In this review, we contextualize recent developments by surveying the theoretical foundations of distributed quantum protocols and examining the experimental platforms and algorithmic applications that realize them in practice.

Related papers

• Reinforcement Learning for Quantum Technology [0.0]
  Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL)<n>We discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems.<n>We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits.
  arXiv  Detail & Related papers  (2026-01-26T20:47:48Z)
• Advancing Quantum State Preparation Using Decision Diagram with Local Invertible Maps [5.328178128965817]
  We propose a family of efficient Quantum State Preparation (QSP) algorithms tailored to different numbers of available ancilla qubits.<n>Our approach exploits the power of Local Invertible Map Decision Diagrams (LimTDDs) to reduce quantum circuit complexity.
  arXiv  Detail & Related papers  (2025-07-23T03:34:44Z)
• Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications [59.0413662882849]
  Quantum computing is poised to redefine the algorithmic foundations of communication systems.<n>This article outlines the fundamentals of quantum computing in a style familiar to the communications society.<n>We highlight a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers.
  arXiv  Detail & Related papers  (2025-06-25T22:25:47Z)
• From Bits to Qubits: Challenges in Classical-Quantum Integration [0.0]
  This research focuses on the crucial phase of quantum encoding, which enables the transformation of classical information into quantum states for processing within quantum systems.<n>The aim of quantifying their different characteristics is to analyze their impact on quantum processing.
  arXiv  Detail & Related papers  (2025-01-31T05:51:04Z)
• Comprehensive Survey of QML: From Data Analysis to Algorithmic Advancements [2.5686697584463025]
  Quantum Machine Learning represents a paradigm shift at the intersection of Quantum Computing and Machine Learning.<n>The field faces significant challenges, including hardware constraints, noise, and limited qubit coherence.<n>This survey aims to provide a foundational resource for advancing Quantum Machine Learning toward practical, real-world applications.
  arXiv  Detail & Related papers  (2025-01-16T13:25:49Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• 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)
• Multimodal deep representation learning for quantum cross-platform verification [60.01590250213637]
  Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms. We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities. We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
  arXiv  Detail & Related papers  (2023-11-07T04:35:03Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [88.57261102552016]
  The anticipated applications of quantum computers span across science and industry.<n>We present a survey of several potential application areas of quantum algorithms.<n>We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• Entanglement-Assisted Quantum Networks: Mechanics, Enabling Technologies, Challenges, and Research Directions [66.27337498864556]
  This paper presents a comprehensive survey of entanglement-assisted quantum networks. It provides a detailed overview of the network structure, working principles, and development stages. It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
  arXiv  Detail & Related papers  (2023-07-24T02:48:22Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)

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.