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Lecture notes in networks and systems

Distributed Quantum Computing for Scalable Systems

Daniel Casado-Faulí, Parfait Atchade-Adelomou, David Pérez de Lara, Rodrigo Gil-Merino

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-05748-8_20

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As the works on quantum computing have seen substantial development in recent years, we are on the verge of seeing the fruitful result of the preliminary, early version of quantum computers. Nevertheless, in order to achieve full-fledged, large-scale quantum computers, two aspects still lacking in the existing quantum computers, i.e., scalability and reliability, will need to be more carefully considered and investigated. In this survey, we present a review of existing literature that aims to alleviate the scalability and reliability issues in quantum computers. In particular, we discuss how existing research leads to two main solutions: for advancing scalability, a distributed quantum computing (DQC) paradigm will be the primary direction instead of the general centralized quantum computing (CQC), whereas for attaining reliability, a fault-tolerant quantum computation (FTQC) approach would need to be leveraged instead of the existing noisy intermediate-scale quantum computers (NISQ). Combining both solutions, we highlight the essentiality of fault-tolerant distributed quantum computation (FT-DQC) and present related progress in the field. Furthermore, from the papers that we have gathered, we provide the taxonomy of the works to give a clearer landscape of the field and discuss the key issues in realizing FT-DQC.
The rapid advancement of quantum computing has highlighted the need for scalable and efficient software infrastructures to fully exploit its potential. Current quantum processors face significant scalability constraints due to the limited number of qubits per chip. In response, distributed quantum computing (DQC) —achieved by networking multiple quantum processor units (QPUs)— is emerging as a promising solution. To support this paradigm, robust intermediate representations (IRs) are needed to translate high-level quantum algorithms into executable instructions suitable for distributed systems. This paper presents NetQIR, an extension of Microsoft’s Quantum Intermediate Representation (QIR), specifically designed to facilitate DQC by incorporating new instruction specifications. NetQIR was developed in response to the lack of abstraction at the network and hardware layers identified in the existing literature as a significant obstacle to effectively implementing distributed quantum algorithms. Based on this analysis, NetQIR introduces new essential abstraction features to support compilers in DQC contexts. It defines network communication instructions independent of specific hardware, abstracting the complexities of inter-QPU communication. Although the proposed work allows abstraction of the underlying network, it is important to note that it is intended for the development of high-performance code on future modular quantum architectures. Leveraging the QIR framework, NetQIR aims to bridge the gap between high-level quantum algorithm design and low-level hardware execution, thus promoting modular and scalable approaches to quantum software infrastructures for distributed applications. Furthermore, its design may serve as a foundational component for future implementations of distributed quantum standards such as the Quantum Message Passing Interface (QMPI).
We employ quantum-volume random-circuit sampling to benchmark the two-QPU entanglement-assisted distributed quantum computing (DQC) and compare it with single-QPU quantum computing. We first specify a single-qubit depolarizing noise model in the random circuit. Based on this error model, we show the one-to-one correspondence of three figures of merits, namely average gate fidelity, heavy output probability, and linear cross-entropy. We derive an analytical approximation of the average gate fidelity under the specified noise model, which is shown to align with numerical simulations. The approximation is calculated based on a noise propagation matrix obtained from the extended connectivity graph of a DQC device. In numerical simulation, we unveil the scalability enhancement in DQC for the QPUs with limited connectivity. Furthermore, we provide a simple formula to estimate the average gate fidelity, which also provides us with a heuristic method to evaluate the scalability enhancement in DQC, and a guide to optimize the structure of a DQC configuration.