Quantum circuit mapping is an essential process required by executing quantum circuits using a noisy intermediate-scale quantum (NISQ) device. Since qubits and quantum gates of a NISQ device are error-prone and variable in quality, it is crucial to choose qubits or quantum gates in a variation-aware manner to maximize the success rate of executing circuits. To this end, this article proposes a variation-aware method for quantum circuit mapping through the cooperation of multiple agents. Each agent in the proposed method can gradually construct a physical circuit that respects the device's connectivity constraints by inserting a SWAP gate at each step. Moreover, at each step, the circuit information of each agent is shared within the agent population through a communication mechanism that combines global and local information exchange, so that agents with poor fitness can get an opportunity to improve their physical circuits. The experimental results on extensive benchmark circuits confirm that the proposed method can effectively and consistently improve the overall circuit fidelity compared with the state-of-the-art methods.
Quantum circuit mapping is an essential process required by executing quantum circuits using a noisy intermediate-scale quantum (NISQ) device. Since qubits and quantum gates of a NISQ device are error-prone and variable in quality, it is crucial to choose qubits or quantum gates in a variation-aware manner to maximize the success rate of executing circuits. To this end, this article proposes a variation-aware method for quantum circuit mapping through the cooperation of multiple agents. Each agent in the proposed method can gradually construct a physical circuit that respects the device's connectivity constraints by inserting a SWAP gate at each step. Moreover, at each step, the circuit information of each agent is shared within the agent population through a communication mechanism that combines global and local information exchange, so that agents with poor fitness can get an opportunity to improve their physical circuits. The experimental results on extensive benchmark circuits confirm that the proposed method can effectively and consistently improve the overall circuit fidelity compared with the state-of-the-art methods.
Quantum circuit mapping (QCM) is a crucial preprocessing step for executing a logical circuit (LC) on noisy intermediate-scale quantum (NISQ) devices. Balancing the introduction of extra gates and the efficiency of preprocessing poses a significant challenge for the mapping process. To address this challenge, we propose the mutual-influence-aware (MIA) heuristic method by integrating an initial mapping search framework, an initial mapping generator, and a heuristic circuit mapper. Initially, the framework utilizes the generator to obtain a favorable starting point for the initial mapping search. With this starting point, the search process can efficiently discover a promising initial mapping within a few bidirectional iterations. The circuit mapper considers mutual influences of SWAP gates and is invoked once per iteration. Ultimately, the best result from all iterations is considered the QCM outcome. The experimental results on extensive benchmark circuits demonstrate that, compared to the iterated local search (ILS) method, which represents the current state-of-the-art, our MIA method introduces a similar number of extra gates while achieving nearly 95 times faster execution.
Quantum computing is currently in the era of Noisy-Intermediate-Scale Quantum (NISQ), where superconducting quantum chips can only support two-qubit gates between adjacent physical qubits. This constraint necessitates the insertion of SWAP gates during circuit mapping to ensure that the nearest neighbor requirements are met on actual quantum computing devices. In this work, based on the circuit partitioning and unitary matrix decomposition mechanism, an equivalent transformation form of quantum circuit is given to reduce the number of two-qubit gates in the target quantum circuit. Experimental results demonstrate that the decomposed equivalent circuits have a shorter depth, with an average optimization rate of 18.41%. Our proposed optimization method effectively reduces the number of two-qubit gates in quantum circuits, enhancing the efficiency of mapping on NISQ devices.
Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom, and arbitrarily long coherence time. In noisy intermediate-scale quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and, therefore, cause further decay of the performance of a quantum circuit. Thus, it is crucial to minimize the number of added gates. In this article, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best-selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters, including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. The experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device.
In the era of noisy intermediate-scale quantum (NISQ) computing, the limited connectivity between qubits is one of the common physical limitations faced by current quantum computing devices. Quantum circuit mapping methods transform quantum circuits into equivalent circuits that satisfy physical connectivity constraints by remapping logical qubits, making them executable. The optimization problem of quantum circuit mapping has NP-hard computational complexity, and existing heuristic mapping algorithms still have significant potential for optimization in terms of the number of quantum gates generated. To reduce the number of SWAP gates inserted during mapping, the solution space of the mapping problem is represented as a tree structure, and the mapping process is equivalent to traversing this tree structure. To effectively and efficiently complete the search process, a beam search framework (BSF) is proposed for solving quantum circuit mapping. By iteratively selecting, expanding, and making decisions, high-quality target circuits are generated. Experimental results show that this method can significantly reduce the number of inserted SWAP gates on medium to large circuits, achieving an average reduction of 44% compared to baseline methods, and is applicable to circuits of various sizes and complexities.
Noisy Intermediate-Scale Quantum (NISQ) computers impose severe architectural constraints that prevent direct execution of logical quantum circuits. As a result, quantum circuit mapping—transforming ideal circuits into hardware-compatible forms—has become a critical task in quantum software toolchains. This paper presents a comparative study of key mapping techniques, focusing on circuit depth, SWAP gate overhead, and fidelity awareness. Through a case analysis on a 5-qubit Quantum Fourier Transform (QFT) circuit, we demonstrate the performance trade-offs of several prominent mapping strategies, including naive greedy routing, Qiskit’s SABRE, and CQC’s t|ket>. Our findings reveal that mapping technique selection has a profound impact on circuit quality and feasibility on real NISQ hardware.
Abstract With the rapid development of quantum computing, quantum computing devices have entered the Noisy Intermediate-Scale Quantum(NISQ)era. However, current NISQ quantum computing devices still face the challenge of physical limitations. To enable quantum algorithms to be directly executed on quantum computing devices, high-level quantum gates (such as MCT gates) in quantum circuits need to be decomposed and mapped onto quantum computing devices. This paper proposes a quantum device-aware method for decomposing high-level quantum gates. This method selects an optimal sub-topology of the quantum device as the target based on the circuit scale and the goal of reducing error rates. According to the proposed MCT gate decomposition rules, an associated gate-pairs generation algorithm is designed to facilitate subsequent decomposition. The control bits of the MCT gates are partitioned based on the associated gate-pair to better match the selected sub-topology. After eliminating redundant gates through the inverse decomposition of associated gate-pairs, the mapping is performed. Experimental results show that the proposed decomposition method reduces the number of basic quantum gates in quantum circuits, with an average optimization rate of 14.6%. For benchmark circuits decomposed using the method, applying the routing algorithm for mapping requires fewer additional gates, with an average optimization rate of 20.6%. The fidelity of the circuits improved by an average of 14.1% after mapping.