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Quantum Data Centres in the Presence of Noise

Kenneth McRorie Campbell

Published Dec 1, 2025• 0 citations
OpenAlex

Abstract

Distributed quantum computing is a promising way of overcoming the scalability challenges of quantum computers, but establishing quantum links over large distances remains difficult. Therefore, in this thesis, we explore quantum data centres (QDCs), in which multiple quantum processing units (QPUs) are housed together within the same warehouse and linked together over short links. By considering such a setting, we avoid the challenges of long-distance quantum communications while retaining the scalability of distributed architectures. We focus primarily on understanding and quantifying the role of noise in QDCs. We compare the impact of different types of noise in the QDC setting and explore various types of inter-QPU gate. Both individual inter-QPU gates and an assortment of larger distributed quantum circuits are investigated. Based on our findings, we evaluate strategies for the handling of noise, considering both quantum error detection and entanglement distillation based solutions. We also provide a preliminary investigation into the use of QDCs for generating entangled resource states suitable for quantum communications or measurement based quantum computing applications. A major contribution of this thesis is the development of a novel simulation framework for distributed quantum computing, which formed the basis of many of the results obtained. The framework enables users to easily specify distributed quantum circuits or import circuits designed for a single-QPU and convert them to a distributed circuit. Many of the complications of interpreting a distributed quantum circuit, such as the management of communication qubits are also handled automatically. Moreover, the framework's built-in interpreter can interpret common building blocks of distributed quantum computing, such as inter-QPU gates.

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IEEE Access•Jan 1, 2026•1 citation
Distributed quantum computing is a promising way of overcoming the scalability challenges of quantum computers, but establishing quantum links over large distances remains difficult. Therefore, in this thesis, we explore quantum data centres (QDCs), in which multiple quantum processing units (QPUs) are housed together within the same warehouse and linked together over short links. By considering such a setting, we avoid the challenges of long-distance quantum communications while retaining the scalability of distributed architectures. We focus primarily on understanding and quantifying the role of noise in QDCs. We compare the impact of different types of noise in the QDC setting and explore various types of inter-QPU gate. Both individual inter-QPU gates and an assortment of larger distributed quantum circuits are investigated. Based on our findings, we evaluate strategies for the handling of noise, considering both quantum error detection and entanglement distillation based solutions. We also provide a preliminary investigation into the use of QDCs for generating entangled resource states suitable for quantum communications or measurement based quantum computing applications. A major contribution of this thesis is the development of a novel simulation framework for distributed quantum computing, which formed the basis of many of the results obtained. The framework enables users to easily specify distributed quantum circuits or import circuits designed for a single-QPU and convert them to a distributed circuit. Many of the complications of interpreting a distributed quantum circuit, such as the management of communication qubits are also handled automatically. Moreover, the framework's built-in interpreter can interpret common building blocks of distributed quantum computing, such as inter-QPU gates.
Abstract Quantum data centres (QDCs) could overcome the scalability challenges of modern quantum computers. Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased. In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor. In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution. We investigate the relative impact of these different types of noise using a classically simulated QDC with two QPUs and compare the robustness to noise of the two main ways of implementing remote gates, cat-comm and TP-comm. We find that considering the quantity of gates or inter-QPU entangled links is often inadequate to predict the output fidelity from a quantum circuit and infer that an improved understanding of error propagation during distributed quantum circuits may represent a significant optimisation opportunity for compilation.
Quantum computers connected through classical and quantum communication channels can be combined to function as a single unit to run large quantum circuits that each device is unable to execute on their own. The distributed quantum computing paradigm is therefore often seen as a potential pathway to scaling quantum computing to capacities necessary for practical and large-scale applications. Whether connecting multiple quantum processing units (QPUs) in clusters or over networks, quantum communication requires entanglement to be generated and distributed over distances. Using entanglement, the remote CX protocol can be performed, which allows the application of the CX gate involving qubits located in different QPUs. In this work, we use a specialized simulation framework for a high-level evaluation of the impact of the protocol when executed under noise in various network configurations using different number of QPUs. We compare naive and graph partitioning qubit assignment strategies and how they affect the fidelity in experiments run on Grover, GHZ, VQC, and random circuits. The results provide insights on how QPU and network configurations or naive scheduling can degrade performance.
As quantum computers scale, single-chip architectures face inherent limitations in qubit count. This drives the need for modular quantum computing and quantum data centers (QDCs), where multiple quantum processor units (QPUs) are interconnected to enable the distributed execution of a quantum algorithm. However, evaluating distributed quantum computing (DQC) architectures is challenging. Classical simulation is limited by the exponential growth of the state vector, limiting its ability to model large systems and realistically capture hardware noise and timing. Meanwhile, implementing QDC introduces interconnect noise challenges such as transduction inefficiency and optical fiber loss. In this work, we introduce a hardware-based emulation framework by partitioning a single quantum processor’s qubit coupling map into multiple logical QPUs. We show how noise arising from transduction and optical fibers can be modeled by adding an ancilla qubit representing the environment, based on quantum collision dynamics. This model is then translated into a gate-based circuit, in which the couplings between each portion act as controllable noisy quantum communication channels. We demonstrate the framework on IBM quantum hardware by executing remote gates under controllable communication noise. To highlight the flexibility of the platform, we further replicate the implementation results of the distributed Grover’s search algorithm on an ion-trap system. Finally, we test a larger circuit, i.e., a five-qubit quantum Fourier transform (QFT), achieving reasonable fidelity across logical QPUs. Overall, the framework provides a scalable hardware-level emulation platform that captures noise sources through physical qubits and is compatible with any platform supporting the Qiskit SDK.
Scaling quantum computers, i.e., quantum processing units (QPUs) to enable the execution of large quantum circuits is a major challenge, especially for applications that should provide a quantum advantage over classical algorithms. One approach to scale QPUs is to connect multiple machines through quantum and classical channels to form clusters or even quantum networks. Using this paradigm, several smaller QPUs can collectively execute circuits that each would not be able on its own. However, communication between QPUs is costly as it requires generating and maintaining entanglement, and hence it should be used wisely. In this paper, we evaluate the architectures, and in particular the entanglement patterns, of variational quantum circuits in a distributed quantum computing (DQC) setting. That is, using Qiskit, we simulate the execution of an eight qubit circuit using four QPUs each with two computational and two communication qubits where non-local CX-gates are performed using the remote-CX protocol. We compare the performance of various circuits on a binary classification task where training is executed under ideal and testing under noisy conditions. The study provides initial results on suitable VQC architectures for the DQC paradigm, and indicates that a standard VQC baseline is not always the best choice, and alternative architectures that use entanglement between QPUs sparingly deliver better results under noise.
In recent years, advances in quantum computing have been driven by substantial improvements in both the number and quality of qubits. As the field progresses, there is growing interest in interconnecting quantum systems to enable scalable computation through Distributed Quantum Computing (DQC) architectures. Consider a distributed quantum application executed across multiple Quantum Processing Units (QPUs) within a quantum data center, where remote gates require establishing entanglement between different QPUs. The creation of such end-to-end entanglement can lead to network congestion and resource contention. To address these challenges, we propose a resource management framework that maximizes fidelity-guaranteed throughput while satisfying dependency constraints. We first formulate the problem as a Mixed-Integer Linear Programming (MILP) model to provide a performance benchmark. Building on this, we develop efficient approximate scheduling algorithms that achieve performance comparable to the optimization solver. Although a trade-off exists between execution time and network throughput, simulation results demonstrate that one of the proposed strategies, Weighted Group Least Resource First (WGLRF), closely approximates the solver’s performance across most scenarios. These findings suggest that the lightweight strategy is sufficient for current DQC settings, offering a practical solution for managing remote-gate resource contention in distributed quantum circuits and improving overall system performance.