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.
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.
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 a quantum internet, quantum processing units (QPUs) with varying architectures and capabilities may be connected through quantum communication channels, enabling new applications such as distributed quantum computing (DQC), a paradigm in which multiple QPUs execute a single circuit. However, remote operations between QPUs are expensive as they require the creation and distribution of entanglement throughout the network. It is therefore crucial to assign qubits to QPUs and partition circuits in such a way that the overall communication between QPUs is minimized. In this paper, we apply and evaluate simulated annealing and an evolutionary algorithm for this problem. We consider quantum networks with 25 nodes arranged in different topologies and QPUs with varying qubit capacities. The circuits evaluated contain 50 and 100 qubits. We show that the different metaheuristics all significantly outperform the baselines by drastically reducing the communication cost by over 40%.
Quantum computing has the potential to outperform classical computing in solving specific problems. However, the limited qubit capacity of existing Quantum Processing Units (QPUs) poses significant barriers to the practical implementation of quantum computing. Distributed quantum computing (DQC) offers a promising approach to scaling the qubit capacity of quantum systems by interconnecting multiple QPUs and enabling collaborative computation. Nevertheless, DQC necessitates implementing remote quantum gate operations that consume entangled qubit pairs, which poses a significant challenge for DQC. In this work, we formulate and investigate the remote gate scheduling (RGS) problem, considering two approaches for remote gate operations: Telegate and Teledata. We propose a hybrid heuristic algorithm that dynamically schedules quantum gate operations within a circuit, executed on distributed QPUs, while minimizing entanglement consumption. We conducted extensive simulations using real-world quantum circuits and processors to evaluate the proposed approach. The results show that our approach reduces entanglement consumption by up to 90% and 25% compared to the two baselines, Telegate-SA and Teledata-ZS, respectively. Furthermore, the execution time of our approach is significantly shorter than that of the baselines.
Quantum computers offer computational advantages for specific problems over classical systems [8], but today's devices are limited by qubit counts and susceptibility to noise. Distributed quantum computing (DQC) addresses these constraints by interconnecting quantum processors via quantum networks to enable cooperative execution of quantum algorithms [4]. DQC can be realized either through quantum teleportation [3], which transfers qubit states across nodes, or through remote gates [7], which enact quantum operations between spatially separated qubits while preserving their physical allocation. Both approaches depend on pre-shared entanglement, which must be distributed across potentially multi-hop quantum networks via entanglement swapping [11]. In realistic settings, DQC performance is influenced by factors such as entanglement fidelity, quantum memory coherence time, and routing path length, yet the impact of these parameters on computational fidelity remains insufficiently understood. This work investigates these effects through simulation of distributed Grover circuits executed over quantum networks with 1-4 routers, incorporating entanglement swapping, network delays, and varying physical constraints to assess how network-level properties affect circuit-level performance.
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.
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.