In the noisy intermediate-scale quantum (NISQ) era, as the scale of existing quantum hardware continues to expand, the demand for effective methods to schedule quantum gates and minimize the number of operations has become increasingly urgent. To address this demand, the Quantum Firefly Algorithm (QFA) has been designed by incorporating quantum information into the traditional firefly algorithm. This integration enables fireflies to explore multiple positions simultaneously, thereby increasing search space coverage and utilizing quantum tunneling effects to escape local optima. Through wave function evolution and collapse mechanisms described by the Schrödinger equation, a balance between exploring new solutions and exploiting known solutions is achieved by the QFA. Additionally, random perturbation steps are incorporated into the algorithm to enhance search diversity and prevent the algorithm from being trapped in local optima. In quantum circuit scheduling problems, the QFA optimizes quantum gate operation sequences by evaluating the fitness of scheduling schemes, reducing circuit depth and movement operations, while improving parallelism. Experimental results demonstrate that, compared to traditional algorithms, the QFA reduces SWAP gates by an average of 44% and CNOT gates by an average of 16%. When compared to modern algorithms, it reduces SWAP gates by an average of 7% and CNOT gates by an average of 12%.
In the noisy intermediate-scale quantum (NISQ) era, as the scale of existing quantum hardware continues to expand, the demand for effective methods to schedule quantum gates and minimize the number of operations has become increasingly urgent. To address this demand, the Quantum Firefly Algorithm (QFA) has been designed by incorporating quantum information into the traditional firefly algorithm. This integration enables fireflies to explore multiple positions simultaneously, thereby increasing search space coverage and utilizing quantum tunneling effects to escape local optima. Through wave function evolution and collapse mechanisms described by the Schrödinger equation, a balance between exploring new solutions and exploiting known solutions is achieved by the QFA. Additionally, random perturbation steps are incorporated into the algorithm to enhance search diversity and prevent the algorithm from being trapped in local optima. In quantum circuit scheduling problems, the QFA optimizes quantum gate operation sequences by evaluating the fitness of scheduling schemes, reducing circuit depth and movement operations, while improving parallelism. Experimental results demonstrate that, compared to traditional algorithms, the QFA reduces SWAP gates by an average of 44% and CNOT gates by an average of 16%. When compared to modern algorithms, it reduces SWAP gates by an average of 7% and CNOT gates by an average of 12%.
In the noisy medium-scale quantum era, quantum computers are constrained by a limited number of qubits, restricted physical topological structures, and interference from environmental noise, making efficient and stable circuit scheduling a significant challenge. To improve the feasibility of quantum computing, it is essential to optimize the scheduling of quantum gates and the insertion of SWAP gates, reducing running time and enhancing computational efficiency. We propose a collaborative optimization framework that integrates the Quantum Exchange Lock Parallel Scheduler (QELPS) with the Full-level Joint Optimization SWAP Algorithm (FJOSA). In QELPS, SWAP conflict characteristics are used to adjust the layout of quantum gates across different levels while considering physical constraints and dynamically adapting to the circuit’s execution state. Quantum lock parallel technology enables the selective postponement of certain quantum gates, minimizing circuit depth and mitigating inefficiencies caused by excessive SWAP gate insertions. Meanwhile, FJOSA employs a cross-layer optimization strategy that combines heuristic algorithms with cost functions to improve gate scheduling at a global level. This approach effectively reduces quantum gate conflicts found in traditional methods and optimizes execution order, leading to better computational efficiency and circuit performance. Experimental results show that, compared to the traditional 2QAN algorithm, QELPS and FJOSA reduce additional gate insertions by 85.59% and 89.38%, respectively, while decreasing running time by 56.32% and 66.47%. These improvements confirm that the proposed method significantly enhances circuit scheduling efficiency and reduces resource consumption, making it a promising approach for optimizing quantum computation.
In response to the current challenges in quantum circuit mapping algorithms, such as low mapping quality, insufficient diversity, and the difficulty in balancing mapping quality and diversity, a Quantum Multi-Strategy Fusion Mapping Optimization Algorithm (QMS-FMO) is proposed in this paper. This algorithm enhances population diversity by integrating the neighboring gate strategy of Genetic Algorithm-based Quantum Circuit Mapping (GAQCM), the quantum state characteristics of QPSO, and the parameter adjustment strategy of GSQPSO. The fitness function, which comprehensively considers SWAP gates, CNOT gate costs, particle position information, and quantum behavioral factors, is designed with an adaptive adjustment mechanism to accurately evaluate mapping solutions. During solution updating, individuals are selected using the GAQCM selection strategy, and position update formulas from QPSO and GSQPSO are combined with crossover and mutation operations based on qubit coupling strength, leveraging quantum characteristics to avoid local optima. Experimental results demonstrate that, compared with the t|ket> and Qiskit compilers, QMS-FMO reduces the number of inserted SWAP gates by 46.1% compared to t|ket> and 65.2% compared to Qiskit for specific qubit scales, significantly improving the quality and efficiency of quantum circuit mapping.
Quantum computing faces challenges such as noise, short coherence time, and limited qubit connections. These challenges worsen as quantum circuits become more complex. One major issue is the increasing depth of quantum circuits. This research proposes an optimization framework targeting depth and gate count reduction in quantum circuits, specifically for Noisy Intermediate-Scale Quantum (NISQ) devices. The proposed approach combines unitary merging of single-qubit gates, CNOT cancellation, gate commuting, and rotation gate rewriting strategies. Consecutive gates acting on the same qubit, such asRx(θ1)⋅Rx(θ2), are algebraically merged into a single rotation, while pairs of redundant CNOT gates are eliminated based on gate identity relations. The technique is implemented using Qiskit and validated across five diverse circuits including complex, random, and multi-qubit configurations. Experimental results show an average depth reduction of 33.33% and gate count reduction of 32.14%, with runtime improvement of up to 25%. For instance, an input circuit with a depth of 7 and gate count of 11 was reduced to a depth of 2 and 4 gates. All optimized circuits preserve functional correctness with a fidelityF≥0.99. High-resolution circuit diagrams are presented to visually demonstrate improvements before and after optimization. Additionally, global phase shifts such aseiπ/4are preserved or analytically characterized where relevant. This work enhances the viability of quantum computations on near-term hardware and opens pathways for future AI-driven quantum optimizations.
As quantum computing technology matures, the availability and performance of quantum devices are steadily improving. However, in the NISQ (Noisy Intermediate-Scale Quantum) era, the quantum bit error rate caused by quantum noise remains a significant constraint on computational accuracy and reliability. In quantum operating systems, quantum task scheduling is a core technology crucial for fully leveraging the performance potential of quantum processors. Currently, most multi-processor scheduling algorithms focus on task load balancing and do not directly consider the characteristics of quantum circuits and the differentiated error rates of quantum computing backends. In quantum computing, the impact of noise errors in single-qubit gates, two-qubit gates, and measurement gates on the fidelity of quantum gate circuits is crucial. Therefore, intelligently matching quantum tasks with the characteristics of backend processors to optimize computational accuracy is an urgent problem to be addressed. This paper proposes a new quantum task scheduling algorithm, which matches tasks with backends based on the weight of single-qubit gates, two-qubit gates, and measurement gates in quantum circuits, as well as the error rate characteristics of each quantum computing backend. Experimental results show that the proposed scheduling algorithm for improving the fidelity of quantum gate circuits outperforms static load balancing scheduling algorithms, resulting in a 4.39% increase in the fidelity of quantum tasks.
With the rapid development of information technology, the continuous expansion of data scale poses higher challenges to data analysis algorithms. This paper proposes a Quantum Computation Optimization Algorithm (QCOA), specifically a quantum firefly algorithm, which leverages quantum superposition, entanglement, and rotation gates to accelerate convergence and avoid local optima in optimization tasks. The proposed QCOA is experimentally tested on a large-scale IMDB dataset with one billion records using a high-performance quantum simulation environment based on Qiskit and Python. Key parameters include 50 fireflies, 32 qubits, 1000 iterations, and a random neighborhood search mechanism. The algorithm's performance is evaluated against classical baselines including SVM, KNN, and GBDT using metrics such as false positive rate, accuracy, and processing time. Results show QCOA achieves the lowest false positive rate (2.7%), highest accuracy (97.3%), and the shortest processing time (90 seconds), outperforming all classical baselines. These findings validate the practical advantages of QCOA and quantum computing in large-scale data processing and optimization, offering a promising approach for future data-intensive applications.
Abstract As the effort to scale up existing quantum hardware proceeds, it becomes necessary to schedule quantum gates in a way that minimizes the number of operations. There are three constraints that have to be satisfied: the order or dependency of the quantum gates in the specific algorithm, the fact that any qubit may be involved in at most one gate at a time, and the restriction that two-qubit gates are implementable only between connected qubits. The last aspect implies that the compilation depends not only on the algorithm, but also on hardware properties like connectivity. Here we suggest a two-step approach in which logical gates are initially scheduled neglecting connectivity considerations, while routing operations are added at a later step in a way that minimizes their overhead. We rephrase the subtasks of gate scheduling in terms of graph problems like edge-coloring and maximum subgraph isomorphism. While this approach is general, we specialize to a one-dimensional array of qubits to propose a routing scheme that is minimal in the number of exchange operations. As a practical application, we schedule the quantum approximate optimization algorithm in a linear geometry and quantify the reduction in the number of gates and circuit depth that results from increasing the efficacy of the scheduling strategies.