Distributed computing can scale the capacity of quantum computing through small quantum processors interconnected by quantum communication links. In contrast to monolithic quantum computers, it enables a large-scale, robust computing environment for practical, real-world tasks. Distributed quantum computing must overcome challenges in task distribution and resource optimization in order to obtain highfidelity results. Key tasks include managing quantum communication costs, mitigating communication noise, achieving high-fidelity entanglement, and optimizing qubit allocation. To solve these challenges, we present HiFi-DisQ, a distributed quantum computing framework that aims to provide high fidelity distributed quantum circuit performance by minimizing non-local operations, pattern-matching and circuit remapping algorithms. The proposed framework improves the circuit performance and provides faster transpilation and effective resource optimization. Our method shows$5 \%-20 \%$improvement in fidelity and$4 \%-14 \%$reduction in communication cost.
Distributed computing can scale the capacity of quantum computing through small quantum processors interconnected by quantum communication links. In contrast to monolithic quantum computers, it enables a large-scale, robust computing environment for practical, real-world tasks. Distributed quantum computing must overcome challenges in task distribution and resource optimization in order to obtain highfidelity results. Key tasks include managing quantum communication costs, mitigating communication noise, achieving high-fidelity entanglement, and optimizing qubit allocation. To solve these challenges, we present HiFi-DisQ, a distributed quantum computing framework that aims to provide high fidelity distributed quantum circuit performance by minimizing non-local operations, pattern-matching and circuit remapping algorithms. The proposed framework improves the circuit performance and provides faster transpilation and effective resource optimization. Our method shows5%−20%improvement in fidelity and4%−14%reduction in communication cost.
Abstract The quantum approximate optimization algorithm (QAOA) is a promising approach for solving combinatorial optimization problems on real quantum devices. As QAOA scales to tackle larger problem instances, the limited qubit capacity of single-chip systems becomes a critical bottleneck. To overcome this limitation, distributed quantum computing (DQC) provides a scalable solution. However, when QAOA circuits are executed in such systems, their performance is significantly hindered by the high cost of remote communication. Motivated by this challenge, we propose HiQ - DF , a QAOA circuit design framework tailored for DQC systems. By employing a hierarchical optimization strategy, HiQ - DF enables comprehensive multi-objective optimization during circuit construction. Experimental results on QAOA circuits solving MaxCut instances show that our framework significantly outperforms baseline methods, achieving an average reduction of 26.12% in EPR pair usage (up to 36.85%), 26.44% in circuit latency (up to 35.27%), and 39.63% in circuit depth (up to 49.3%).
As quantum computing transitions into the Noisy Intermediate-Scale Quantum (NISQ) era, scaling monolithic quantum processors remains a significant challenge due to decoherence and operational noise. This dissertation proposes enhancing the connectivity of distributed quantum systems as a scalable solution, focusing on three major research tasks: architectural co-design, operation scheduling optimization, and network topology design. First, to address the physical qubit limits of single-chip processors, we investigate the co-design of network topology and logical qubit allocation for Distributed Quantum Computing (DQC). By modeling qubit interaction as a graph partition problem, we propose a joint optimization framework to minimize inter-QPU communication overhead. Our results demonstrate that the proposed linearized formulation, TACO-L, significantly outperforms traditional two-stage methods in executing complex quantum circuits. Second, we address the challenge of maintaining high-fidelity long-distance entanglements through the optimization of Swapping and Purification Schemes (SPS). We prove that in Binary systems, any optimal swapping and purification tree (OSPT) must follow a Restricted SPT (RT) pattern, where purification is integrated at the link level prior to swapping. Furthermore, we introduce the gradient-based GradTree algorithm, which enables an efficient search for low-cost, high-fidelity scheduling in complex noise environments. Finally, this dissertation formulates the first general Topology Design Problem (TDP) for quantum networks, jointly optimizing quantum memory allocation, optical channel deployment, and entanglement scheduling. By analyzing flow-based and path-based formulations, we prove the optimality of the Relaxed Complete Swapping Tree (RCST) in minimizing the expected cost of entanglement distribution. Collectively, these contributions provide a comprehensive theoretical and algorithmic framework for building scalable, cost-effective global quantum internet architectures.
Quantum computing communication interfaces are emerging as a fundamental technology for enabling seamless interaction between quantum processors, classical computing systems, cloud infrastructures, and distributed quantum networks. The development of efficient communication interfaces is essential to address challenges such as quantum state transmission, synchronization, error correction, latency, scalability, and interoperability across heterogeneous computing environments. This paper presents a comprehensive study of quantum computing communication interfaces by examining their architecture, communication protocols, hardware components, and software integration mechanisms. The proposed framework incorporates quantum-classical hybrid communication, quantum networking principles, secure quantum key distribution, adaptive resource management, and intelligent error mitigation techniques to improve communication reliability and computational efficiency. Furthermore, the study investigates the role of emerging technologies such as quantum internet, photonic interconnects, edge-cloud integration, and artificial intelligence in enhancing interface performance. Performance evaluation demonstrates that the proposed communication interface architecture achieves reduced communication latency, improved transmission fidelity, enhanced scalability, optimized resource utilization, and stronger security compared with conventional quantum communication approaches. The findings indicate that intelligent quantum communication interfaces can significantly accelerate the practical deployment of distributed quantum computing systems and future large-scale quantum applications.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.
This paper proposes a novel approach to distributed computing heterogeneity optimization driven by quantum entanglement. Traditional distributed computing systems face significant challenges in efficiently utilizing diverse computing resources, leading to performance bottlenecks. We introduce a framework that leverages the unique properties of quantum entanglement to establish a quantum entanglement network, enabling high-fidelity, low-latency communication and computation across heterogeneous nodes. The core idea involves distributing computational tasks across these nodes based on their specific capabilities, utilizing entangled states to facilitate efficient data exchange and collaborative processing. The theoretical framework is presented, outlining the key mathematical concepts and optimization strategies. We demonstrate the potential of this approach to overcome the limitations of conventional methods and achieve significant improvements in distributed computing performance. The proposed system utilizes entanglement to achieve a level of computational coordination previously unattainable, offering a fundamentally new paradigm for distributed problem-solving. The focus here is on the theoretical foundations and potential benefits of the entanglement-based approach.
Quantum computing has made substantial progress in recent years; however, its scalability remains constrained on a monolithic quantum processing unit (QPU). Distributed quantum computing (DQC) offers a pathway by coordinating multiple QPUs to execute large-scale circuits. Yet, DQC still faces practical barriers, as its realization depends on advances in hardware-level components such as quantum transducers and high-fidelity entanglement-distribution modules. While these technologies continue to improve, mature DQC platforms remain unavailable. In the meantime, researchers need to assess the benefits of DQC and evaluate emerging DQC designs, but the software ecosystem lacks a circuit-level simulator that models heterogeneous backends, noisy connections, and distributed execution. To fill this gap, this paper proposes SimDisQ, the first end-to-end circuit-level DQC simulator, composed of a set of novel DQC-oriented automated simulation toolkits and communication noise models that can interoperate with existing toolkits in mainstream quantum software ecosystems. Leveraging circuit-level simulation capabilities, SimDisQ enables quantitative exploration of architectural design trade-offs, communication fidelity constraints, and new circuit optimization challenges introduced by DQC, providing a foundation for future research in this promising direction. Benchmarking experiments using SimDisQ respond to a couple of open questions in the community; for example, noisy simulation of superconducting and trapped-ion qubits, with a reasonable entanglement-distribution fidelity, reveal that heterogeneous QPUs can indeed yield higher execution fidelity.