In the quest of large-scale quantum computers, multi-core distributed architectures are considered a compelling alternative to be explored. A crucial aspect in such approach is the stringent demand on communication among cores when qubits need to interact, which conditions the scalability potential of these architectures. In this work, we address the question of how the cost of the communication among cores impacts on the viability of the quantum multi-core approach. Methodologically, we consider a design space in which architectural variables (number of cores, number of qubits per core), application variables for several quantum benchmarks (number of qubits, number of gates, percentage of two-qubit gates) and inter-core communication latency are swept along with the definition of a figure of merit. This approach yields both a qualitative understanding of trends in the design space and companion dimensioning guidelines for the architecture, including optimal points, as well as quantitative answers to the question of beyond which communication performance levels the multi-core architecture pays off. Our results allow to determine the thresholds for inter-core communication latency in order for multi-core architectures to outperform single-core quantum processors.
In the quest of large-scale quantum computers, multi-core distributed architectures are considered a compelling alternative to be explored. A crucial aspect in such approach is the stringent demand on communication among cores when qubits need to interact, which conditions the scalability potential of these architectures. In this work, we address the question of how the cost of the communication among cores impacts on the viability of the quantum multi-core approach. Methodologically, we consider a design space in which architectural variables (number of cores, number of qubits per core), application variables for several quantum benchmarks (number of qubits, number of gates, percentage of two-qubit gates) and inter-core communication latency are swept along with the definition of a figure of merit. This approach yields both a qualitative understanding of trends in the design space and companion dimensioning guidelines for the architecture, including optimal points, as well as quantitative answers to the question of beyond which communication performance levels the multi-core architecture pays off. Our results allow to determine the thresholds for inter-core communication latency in order for multi-core architectures to outperform single-core quantum processors.
Modular quantum processor architectures are envisioned as a promising solution for the scalability of quantum computing systems beyond the Noisy Intermediate Scale Quantum (NISQ) era. Based upon interconnecting tens to hundreds of quantum processors (i.e cores) via quantum coherent and classical links, this approach unravels the pressing limitations of densely qubit-packed monolithic processors, mainly by mitigating the requirements of qubit control and enhancing qubit isolation. Therefore, this new architectural design enables executing large-scale algorithms in a distributed manner. In order to assess the performance and optimize such architectures, it is crucial to analyze the quantum state transfers occurring via inter-core communication networks, referred to as inter-core qubit traffic. This would provide insights to improve the software and hardware stack for multi-core quantum computers. To this aim, we present a characterization of the spatio-temporal inter-core qubit traffic for different large-scale quantum algorithms. The programs are compiled on an all-to-all connected multi-core architecture that supports up to around 1000 qubits. We characterize the qubit traffic based on multiple performance metrics to assess the computational process and the communication overhead. Based on the showcased results, we conclude on the parallelization and scalability of presented algorithms, qualitatively evaluate the resource requirements as we scale circuit sizes, and lay the foundations of application-oriented benchmarking of large-scale multi-core architectures.
Being a very promising technology, with impressive advances in the recent years, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Although continued progress in the fabrication and control of qubits is required, quantum computing scalability will depend as well on a comprehensive architectural design considering a distributed multi-core approach as an alternative to the traditional monolithic version, hence including a communications perspective. However, this goes beyond introducing mere interconnects. Rather, it implies consolidating the full communications stack in the quantum computer structure. In this paper, we propose a double full-stack architecture encompassing quantum computation and quantum communications, which we use to address the monolithic versus distributed question with a structured design methodology. For that, we revisit the different quantum computing layers to capture and model their essence by highlighting the open design variables and performance metrics. Using behavioral models and actual measurements from existing quantum computers, the results of simulations suggest that multicore architectures may effectively unleash the full quantum computer potential.
Despite its tremendous potential, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Among other issues, there are hard limits to the number of qubits that can be integrated into a single chip. Multicore architectures are a firm candidate for unlocking the scalability of quantum processors. Nonetheless, the vulnerability and complexity of quantum communications make this a challenging approach. A comprehensive design should imply consolidating the communications stack in the quantum computer architecture. In this article, we explain how this vision, by entangling communications and computation in the core of the design, may help to solve the open challenges. We also summarize the first results of our application of structured design methodologies backing this vision. With our work, we hope to contribute with design guidelines that may help unleash the potential of quantum computing.
Being a very promising technology, with impressive advances in the recent years, it is still unclear how quantum computing will scale to satisfy the requirements of its most powerful applications. Although continued progress in the fabrication and control of qubits is required, quantum computing scalability will depend as well on a comprehensive architectural design considering a multi-core approach as an alternative to the traditional monolithic version, hence including a communications perspective. However, this goes beyond introducing mere interconnects. Rather, it implies consolidating the full communications stack in the quantum computer architecture. In this paper, we propose a double full-stack architecture encompassing quantum computation and quantum communications, which we use to address the monolithic versus multi-core question with a structured design methodology. For that, we revisit the different quantum computing layers to capture and model their essence by highlighting the open design variables and performance metrics. Using behavioral models and actual measurements from existing quantum computers, the results of simulations suggest that multi-core architectures may effectively unleash the full quantum computer potential.
Modular architectures are a promising approach to scaling quantum computers beyond the limits of monolithic designs. However, non-local communications between different quantum processors might significantly impact overall system performance. In this work, we investigate the role of the network infrastructure in modular quantum computing architectures, focusing on coherence loss due to communication constraints. We analyze the impact of classical network latency on quantum teleportation and identify conditions under which it becomes a bottleneck. Additionally, we study different network topologies and assess how communication resources affect the number and parallelization of inter-core communications. Finally, we conduct a full-stack evaluation of the architecture under varying communication parameters, demonstrating how these factors influence the overall system performance. The results show that classical communication does not become a bottleneck for systems exceeding one million qubits, given current technology assumptions, even with modest clock frequencies and parallel wired interconnects. Additionally, increasing quantum communication resources generally shortens execution time, although it may introduce additional communication overhead. The optimal number of quantum links between QCores depends on both the algorithm being executed and the chosen inter-core topology. Our findings offer valuable guidance for designing modular architectures, enabling scalable quantum computing.
Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This article delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a 4.9× and 1.6× improvement in terms of execution time and non-local communications, respectively, compared to the best-performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.