Quantum computing is widely regarded as a promising technology due to its potential to offer exponential computational advantages over classical systems. However, practical deployment and realization of most quantum algorithms remain constrained by the limitations of current quantum architectures– most notably, the limited number of physical qubits available in quantum processing units (QPUs). Therefore, efficient utilization of quantum resources has become a crucial concern in the field. Thus, in this paper, we explore the nature of qubit reuse. Based on our findings, we formulate an optimization problem and propose a novel algorithm to minimize the number of qubits for scheduled quantum circuits. Finally, extensive simulation results on various practical benchmarks manifest that our algorithm can significantly outperform the existing approach in qubit reuse.
Quantum computing is widely regarded as a promising technology due to its potential to offer exponential computational advantages over classical systems. However, practical deployment and realization of most quantum algorithms remain constrained by the limitations of current quantum architectures– most notably, the limited number of physical qubits available in quantum processing units (QPUs). Therefore, efficient utilization of quantum resources has become a crucial concern in the field. Thus, in this paper, we explore the nature of qubit reuse. Based on our findings, we formulate an optimization problem and propose a novel algorithm to minimize the number of qubits for scheduled quantum circuits. Finally, extensive simulation results on various practical benchmarks manifest that our algorithm can significantly outperform the existing approach in qubit reuse.
The practical applications of quantum computing is currently limited by the small number of available qubits. Recent advances in quantum hardware have introduced midcircuit measurements and resets, enabling the reuse of measured qubits and thus reducing the qubit requirements for executing quantum algorithms. In this work, we present a systematic study of dynamic quantum circuit compilation, a process that transforms static quantum circuits into their dynamic equivalents with fewer qubits through qubit reuse. We establish the first graph-based framework for optimizing qubit-reuse compilation. In particular, we characterize the task of finding the optimal compilation strategy for maximizing qubit reuse using binary integer programming and provide efficient heuristic algorithms for devising general compilation strategies. We conduct a thorough analysis of quantum circuits with practical relevance and offer their optimal qubit-reuse compilation strategies. We also perform a comparative analysis against state-of-the-art approaches, demonstrating the superior performance of our methods in both structured and random quantum circuits. Our framework lays a rigorous foundation for understanding dynamic quantum circuit compilation via qubit reuse, holding significant promise for the practical implementation of large-scale quantum algorithms on quantum computers with limited resources.
The burgeoning field of quantum computing hinges on the efficient execution of quantum algorithms. However, realizing this potential necessitates overcoming significant challenges related to hardware heterogeneity. Current quantum hardware comprises a diverse range of platforms, each possessing unique characteristics – varying qubit connectivity, gate fidelities, and coherence times. A naive approach to scheduling quantum algorithms across these platforms will likely result in suboptimal performance due to the inherent limitations of each device. This work proposes a novel resource-aware scheduling algorithm designed to mitigate these issues. The algorithm dynamically analyzes both the available resources of a quantum hardware platform and the specific algorithmic requirements of a given quantum algorithm. The core of the algorithm involves a cost function that balances resource utilization with algorithmic performance, guiding the selection of the most suitable hardware platform and execution strategy. This approach represents a step towards a more practical and efficient deployment of quantum algorithms, paving the way for leveraging the strengths of diverse quantum computing architectures. The presented method offers a framework for optimizing quantum computation, acknowledging the complex interplay between hardware and algorithm.
Qubit reuse offers a promising way to reduce the hardware demands of quantum circuits, but current approaches are largely restricted to reordering measurements and applying qubit resets. In this work, we present an approach to further optimize quantum circuits by fully utilizing the potential of dynamic quantum circuits-more precisely by moving measurements and introducing dynamic circuit primitives such as classically controlled gates in a way that forges entirely new pathways for qubit reuse. This significantly reduces the number of required qubits for a variety of circuits, creating new opportunities for running complex circuits on near-term devices with limited qubit counts. We show that the proposed approach drastically outperforms existing methods, reducing qubit requirements where previous approaches are unable to do so for popular quantum circuits such as Quantum Phase Estimation (QPE), Quantum Fourier Transform~(QFT), and Variational Quantum Eigensolver (VQE) ansätze, as well as leading to improvements of up to 95% for sparse random circuits.
Quantum computing holds significant potential across various fields such as cybersecurity, finance, and health-care, among many others. However, we currently face multiple limitations such as the low availability of computers, the underutilization of available qubits, and the ever-increasing number of users, which results in prolonged waiting times. This, combined with the cost of executions, limits access to quantum computing. To address these challenges, we propose a scheduling process to optimize task execution on quantum computers. The proposed scheduling process unifies multiple circuits to enhance the efficiency of resource utilization in quantum machines. This approach allows for better use of available qubits and reduces the associated execution costs. We validate the proposed method employing various quantum algorithms by systematically examining the scheduling process through a comparative case study between two quantum service providers: IBM Quantum and Amazon Braket. This validation has shown that the scheduling process does not generate a significant impact on the results, ensuring similar results to single circuit executions with a fraction of the cost.
Recent advances in quantum computing introduce the ability to reuse qubits through mid-circuit measurements, thereby enhancing the efficiency of quantum devices with limited computational resources.However, identifying optimal reuse opportunities in quantum circuits remains challenging due to the intricate dependencies between quantum gates.Existing frameworks address this by either directly searching for reuse opportunities or converting circuits into directed acyclic graphs (DAGs).Unfortunately, these frameworks may require exponential search complexity or may not always ensure optimal results due to their non-deterministic property.To overcome these challenges, we propose QR-Map (Qubit Reuse Map), a map-based framework that abstracts computational dependencies for efficient qubit reuse.By extracting and aligning two-qubit gates, QR-Map facilitates dependency detection and ensures qubit savings without incurring excessive idle time.This approach achieves an optimal balance between gate serialization depth and crosstalk reduction.Evaluations with various quantum circuit benchmarks demonstrate that quantum circuits optimized with QR-Map achieve average reductions of 20% in qubit usage, 25% in circuit depth, and 22% in SWAP insertions compared to those optimized with the state-of-the-art framework.
Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures. Existing qubit reuse algorithms face a trade-off between optimality and scalability, with some achieving optimal reuse but limited scalability due to computational complexities, while others exhibit reduced runtime at the expense of optimality. This paper introduces GidNET (Graph-based Identification of qubit NETwork), an algorithm for optimizing qubit reuse in quantum circuits. By analyzing the circuit's Directed Acyclic Graph (DAG) representation and its corresponding candidate matrix, GidNET identifies higher-quality pathways for qubit reuse more efficiently. Through a comparative study with established algorithms, notably QNET [1], GidNET not only achieves a consistent reduction in compiled circuit widths by a geometric mean of 4.4%, reaching up to 21% in larger circuits, but also demonstrates enhanced computational speed and scaling, with average execution time reduction of 97.4% (i.e., 38.5× geometric mean speedup) and up to 99.3% (142.9× speedup) across various circuit sizes. Furthermore, GidNET consistently outperforms Qiskit in circuit width reduction, achieving an average improvement of 59.3%, with maximum reductions of up to 72% in the largest tested circuits. These results demonstrate GidNET's ability to improve circuit width and runtime, offering a solution for quantum computers with limited numbers of qubits.