Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2407.09710

DisQ: A Model of Distributed Quantum Processors (Extended Version)

Chang, Le, Saitej Yavvari, Rance Cleaveland Y, Basu, Samik, Tao, Runzhou, Li, Liyi

Published Jul 12, 2024• 1 citation
arXivHTMLPDF

Abstract

The next generation of distributed quantum processors combines single-location quantum computing and quantum networking techniques to permit large entangled qubit groups to be established through remote processors, and quantum algorithms can be executed distributively. We present DisQ, as the first formal model of distributed quantum processors, and permit the analysis of distributed quantum programs in the new computation environment. The core of DisQ is a distributed quantum programming language that combines the concepts of Chemical Abstract Machine (CHAM) and Markov Decision Processes (MDP) with the objective of providing clearly distinguishing quantum concurrent and distributed behaviors. Based on the DisQ language, we develop a simulation relation, based on classical simulation infrastructure, to check the equivalence of a quantum algorithm and its distributed versions so that users can develop the distributed version of a sequential quantum program via a simulation check.

DOI
10.48550/arxiv.2407.09710

Related papers

Modeling non-deterministic quantum programs for model checking

Kierner, Kaleb

Quantum programming introduces both probabilistic and non-deterministic behavior, making formal verification a challenging task. This work develops a framework for modeling non-deterministic quantum programs using Markov Decision Processes (MDPs). Each program configuration is represented as a combination of the remaining program and its quantum state, while transitions capture both quantum evolution and non-deterministic branching. We define operational semantics for quantum programs in terms of MDPs and prove their equivalence to established denotational semantics. This equivalence ensures consistency between high-level mathematical descriptions and executable models. The approach enables the application of model-checking techniques, originally designed for classical probabilistic systems, to quantum programs. Our results show that MDPs provide a foundation for verifying properties of non-deterministic quantum programs.

•Jan 1, 2025•0 citations

Simulated execution of hybrid quantum computing systems

Travis S. Humble, Ronadl J. Sadlier, Keith A. Britt

We use modeling and simulation to study the behavior and performance of hybrid quantum computing systems. Our approach is based on a layered design with abstract machine models, which identify the key components and interfaces for quantum processing units, quantum programming models, and hybrid execution models. We use discrete-event simulating to track the dynamical state of hierarchical abstract machine models while executing test programs, and we collect statistics on time and energy consumption to forecast the resources required by quantum processors for future scientific computation.

SPIE Quantum Information Science, Sensing, and Computation•May 14, 2018•4 citations

Towards Reliable Distributed Quantum Computing on Quantum Interconnects

Ryo Wakizaka

Quantum interconnects (QuICs), which connect multiple quantum devices to build distributed quantum systems, are essential for large-scale quantum information processing. While various software tools have been developed to analyze quantum programs running on QuICs, there are very few frameworks for formal reasoning about them on actual quantum devices. The lack of such tools may make distributed systems unreliable because of the complicated behavior of concurrent quantum processes and the physical constraints of quantum devices. This paper discusses the need for an infrastructure, including a distributed quantum programming language, to realize reliable distributed quantum computing.

International Conference on Art, Science, and Engineering of Programming (<Programming>)•Mar 13, 2023•2 citations

NetQMPI: a practical MPI-inspired library for distributed quantum computing over NetQASM SDK

F. Javier Cardama, Tomás F. Pena

Distributed quantum computing (DQC) is emerging as a practical solution to overcome the scalability limits of individual quantum processors by interconnecting them via quantum networks. However, programming such systems remains a significant challenge due to the need to coordinate quantum operations, manage entanglement, and synchronize classical communication. This paper proposes NetQMPI as a Python library that enables high-level programming of distributed quantum applications by building on top of two key foundations: NetQASM, which abstracts the physical network layer to interface with quantum nodes and simulators such as NetSquid and SimulaQron; and NetQIR, which proposes a layered abstraction model suitable for defining a quantum version of the classical Message Passing Interface (MPI). NetQMPI provides an MPI-style programming model tailored to quantum systems. It introduces primitives such as qsend and qrecv that abstract quantum communication details like entanglement generation, and protocols. Collective operations such as qscatter or qgather are also included. This allows developers to focus on distributed algorithm design while relying on NetQMPI to handle low-level operations efficiently.

IEEE International Conference on Cluster Computing Workshops (CLUSTER Workshops)•Sep 2, 2025•1 citation

Distributed quantum computing models: Study of architectures and models for the distribution of quantum computing tasks across multiple quantum nodes

Akaash Vishal Hazarika, Mahak Shah

The exploration of distributed quantum computing (DQC) represents a significant frontier in quantum information science, aiming to harness the unique properties of quantum mechanics to solve complex computational problems effectively. This paper discusses the diverse architectures and models that facilitate the distribution of quantum computing tasks across multiple quantum nodes, thereby addressing the limitations inherent in single quantum devices. DQC systems exploit the principles of superposition and entanglement to enhance computational capabilities beyond what classical computing systems can achieve. We examine various DQC architectures, including both quantum-classical hybrid systems and fully quantum distributed systems, highlighting their respective benefits and challenges. Key issues such as coherence, communication overhead, and error correction are discussed in detail, and we analyze specific use cases where DQC demonstrates superior performance relative to classical computing approaches, including optimization problems in logistics and finance, as well as quantum simulations for material science. Furthermore, we identify future research directions aimed at overcoming existing barriers to the practical implementation of DQC systems. By assessing the current landscape and future possibilities of DQC, this paper underscores the transformative potential of distributed quantum computing in various fields, paving the way for realizing more complex quantum algorithms and applications.

International Journal of Science and Research Archive•Dec 30, 2024•5 citations

DisQ: A Model of Distributed Quantum Processors

Le Chang (429190), Saitej Yavvari, Rance Cleaveland Y +3 more

Lecture notes in computer science•Jan 1, 2026•0 citations
The next generation of distributed quantum processors combines single-location quantum computing and quantum networking techniques to permit large entangled qubit groups to be established through remote processors, and quantum algorithms can be executed distributively. We present DisQ, as the first formal model of distributed quantum processors, and permit the analysis of distributed quantum programs in the new computation environment. The core of DisQ is a distributed quantum programming language that combines the concepts of Chemical Abstract Machine (CHAM) and Markov Decision Processes (MDP) with the objective of providing clearly distinguishing quantum concurrent and distributed behaviors. Based on the DisQ language, we develop a simulation relation, based on classical simulation infrastructure, to check the equivalence of a quantum algorithm and its distributed versions so that users can develop the distributed version of a sequential quantum program via a simulation check.
Quantum programming introduces both probabilistic and non-deterministic behavior, making formal verification a challenging task. This work develops a framework for modeling non-deterministic quantum programs using Markov Decision Processes (MDPs). Each program configuration is represented as a combination of the remaining program and its quantum state, while transitions capture both quantum evolution and non-deterministic branching. We define operational semantics for quantum programs in terms of MDPs and prove their equivalence to established denotational semantics. This equivalence ensures consistency between high-level mathematical descriptions and executable models. The approach enables the application of model-checking techniques, originally designed for classical probabilistic systems, to quantum programs. Our results show that MDPs provide a foundation for verifying properties of non-deterministic quantum programs.
We use modeling and simulation to study the behavior and performance of hybrid quantum computing systems. Our approach is based on a layered design with abstract machine models, which identify the key components and interfaces for quantum processing units, quantum programming models, and hybrid execution models. We use discrete-event simulating to track the dynamical state of hierarchical abstract machine models while executing test programs, and we collect statistics on time and energy consumption to forecast the resources required by quantum processors for future scientific computation.
Quantum interconnects (QuICs), which connect multiple quantum devices to build distributed quantum systems, are essential for large-scale quantum information processing. While various software tools have been developed to analyze quantum programs running on QuICs, there are very few frameworks for formal reasoning about them on actual quantum devices. The lack of such tools may make distributed systems unreliable because of the complicated behavior of concurrent quantum processes and the physical constraints of quantum devices. This paper discusses the need for an infrastructure, including a distributed quantum programming language, to realize reliable distributed quantum computing.
Distributed quantum computing (DQC) is emerging as a practical solution to overcome the scalability limits of individual quantum processors by interconnecting them via quantum networks. However, programming such systems remains a significant challenge due to the need to coordinate quantum operations, manage entanglement, and synchronize classical communication. This paper proposes NetQMPI as a Python library that enables high-level programming of distributed quantum applications by building on top of two key foundations: NetQASM, which abstracts the physical network layer to interface with quantum nodes and simulators such as NetSquid and SimulaQron; and NetQIR, which proposes a layered abstraction model suitable for defining a quantum version of the classical Message Passing Interface (MPI). NetQMPI provides an MPI-style programming model tailored to quantum systems. It introduces primitives such as qsend and qrecv that abstract quantum communication details like entanglement generation, and protocols. Collective operations such as qscatter or qgather are also included. This allows developers to focus on distributed algorithm design while relying on NetQMPI to handle low-level operations efficiently.
The exploration of distributed quantum computing (DQC) represents a significant frontier in quantum information science, aiming to harness the unique properties of quantum mechanics to solve complex computational problems effectively. This paper discusses the diverse architectures and models that facilitate the distribution of quantum computing tasks across multiple quantum nodes, thereby addressing the limitations inherent in single quantum devices. DQC systems exploit the principles of superposition and entanglement to enhance computational capabilities beyond what classical computing systems can achieve. We examine various DQC architectures, including both quantum-classical hybrid systems and fully quantum distributed systems, highlighting their respective benefits and challenges. Key issues such as coherence, communication overhead, and error correction are discussed in detail, and we analyze specific use cases where DQC demonstrates superior performance relative to classical computing approaches, including optimization problems in logistics and finance, as well as quantum simulations for material science. Furthermore, we identify future research directions aimed at overcoming existing barriers to the practical implementation of DQC systems. By assessing the current landscape and future possibilities of DQC, this paper underscores the transformative potential of distributed quantum computing in various fields, paving the way for realizing more complex quantum algorithms and applications.