This work presents a routing-aware pruning strategy for quantum circuits executed on Noisy Intermediate-Scale Quantum (NISQ) devices. We propose a method to remove parametric controlled rotations whose small rotation angles do not justify the routing overhead required for their implementation. By selectively pruning such gates, the method mitigates fidelity loss arising from additional SWAP operations introduced during compilation. Our approach evaluates whether executing a gate leads to greater fidelity loss than omitting it. Simulations on benchmark circuits with realistic noise models show that the method reduces two-qubit gate counts (up to 48.6%) while improving final state fidelity (up to 47.7%), especially for larger circuits where routing costs dominate.

The scalability of quantum computing is constrained by the physical and architectural limitations of monolithic quantum processors. Modular multi-core quantum architectures, which interconnect multiple quantum cores (QCs) via classical and quantum-coherent links, offer a promising alternative to address these challenges. However, transitioning to a modular architecture introduces communication overhead, where classical communication plays a crucial role in executing quantum algorithms by transmitting measurement outcomes and synchronizing operations across QCs. Understanding the impact of classical communication on execution time is therefore essential for optimizing system performance. In this work, we introduce qcomm , an open-source simulator designed to evaluate the role of classical communication in modular quantum computing architectures. qcomm provides a high-level execution and timing model that captures the interplay between quantum gate execution, entanglement distribution, teleportation protocols, and classical communication latency. We conduct an extensive experimental analysis to quantify the impact of classical communication bandwidth, interconnect types, and quantum circuit mapping strategies on overall execution time. Furthermore, we assess classical communication overhead when executing real quantum benchmarks mapped onto a cryogenically-controlled multi-core quantum system. Our results show that, while classical communication is generally not the dominant contributor to execution time, its impact becomes increasingly relevant in optimized scenarios—such as improved quantum technology, large-scale interconnects, or communication-aware circuit mappings. These findings provide useful insights for the design of scalable modular quantum architectures and highlight the importance of evaluating classical communication as a performance-limiting factor in future systems.

In this work, we explore and propose several quantum circuit mapping strategies to optimize qubit shuttling in scalable quantum computing architectures based on silicon spin qubits. Our goal is to minimize phase errors introduced during shuttling operations while reducing the overall execution time of quantum circuits. We propose and evaluate five mapping algorithms using benchmarks from quantum algorithms. The Swap Return strategy emerged as the most robust solution, offering a superior balance between execution time and error minimization by considering future qubit interactions. Additionally, we assess the importance of initial qubit placement, demonstrating that an informed placement strategy can significantly enhance the performance of dynamic mapping approaches.

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.

Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architectures remains a significant challenge. Multi-core quantum architectures are proposed to solve the scalability problem, arising a new set of challenges in hardware, communications and compilation, among others. One of these challenges is to adapt a quantum algorithm to fit within the different cores of the quantum computer. This paper presents a novel approach for circuit partitioning using Deep Reinforcement Learning, contributing to the advancement of both quantum computing and graph partitioning. This work is the first step in integrating Deep Reinforcement Learning techniques into Quantum Circuit Mapping, opening the door to a new paradigm of solutions to such problems.