Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

Controlled-swap-based and Knowledge Navigated Quantum-inspired Computational Intelligence for Quantum Circuit Optimization

Shu–Yu Kuo, Cheng-Yen Hua, En-Tzu Hsu, Huan-Pu Chen, Jyun–Yi Shen, Chia‐Lin Liu, Yao–Hsin Chou, Hsi‐Sheng Goan

Published Jun 30, 2024• 3 citations
Publisher

Abstract

Quantum and reversible circuit optimization stand as pivotal areas in quantum computing, crucial for error mitigation, size reduction, and cost minimization. In this scenario, computational intelligence (CI) is an efficient approach for improving circuit optimization issues and constructing equivalent small-scale quantum algorithms. In this paper, we propose a novel quantum-inspired computational intelligence method to automatically synthesize quantum circuits, benefiting reliable quantum computing realization. The proposed algorithm uses the feature of quantum mechanics to enhance the exploration ability of computational intelligence. It then detects situations when encountering local optima and reacts appropriately, drawing inspiration from the controlled-swap gate to navigate candidate solutions effectively and escape local optima with preference. Given the uncertain landscape of future quantum technologies and the diverse preferable gate sets with different platforms, our approach is a flexible algorithm that can simply adapt to different gate sets. The experimental results demonstrate that our method achieves superior performance compared to previous synthesis methods, with fewer circuit costs and more stable efficiency. Furthermore, we provide all circuits synthesized by our novel method to facilitate future studies to conduct in-depth research, simplify circuits, and map to different logic gate sets. This study advances reliable quantum computing through a quantum-inspired CI to mutually benefit both realms, promoting insight into interdisciplinary collaboration.

DOI
10.1109/fuzz-ieee60900.2024.10612170

Related papers

A novel quantum-inspired evolutionary computation-based quantum circuit synthesis for various universal gate libraries

Yao–Hsin Chou, Shu–Yu Kuo, Yu-Chi Jiang +7 more

The quantum circuit is the prime factor to realize quantum computation. This paper proposes a novel quantum-inspired evolutionary computation method to synthesize quantum circuits effectively and efficiently. Recently, the Clifford+T gate library has become popular, as the T gate has a better fault-tolerant and error correction efficacy, which is essential to near-term quantum computation. The proposed quantum-inspired evolutionary computation is more flexible in constructing diverse solutions than the classical synthesis method. The entangled local search mechanism further enhances the ability to discover the optimal solutions on an efficient frontier. The encoding can be slightly changed to achieve various gate libraries circuit synthesis in the quantum or reversible Boolean circuits. The experiments demonstrate that our proposed method can be more effective in composing a compact quantum circuit than the state-of-the-art method. Meanwhile, the T-depth can attain an optimal value without ancilla bits. Furthermore, we provide an illustration to construct the function equivalent quantum circuit with the NOT, CNOT, Controlled-Square-Root-of-NOT quantum gate library (NCV). We also conduct exhaustion to prove that our synthesized circuit is an optimal solution, requiring far fewer resources in time and evaluation than exhaustion.

Genetic and Evolutionary Computation Conference Companion (GECCO Companion)•Jul 9, 2022•17 citations

Adapting Developing Quantum Circuit Synthesis with a Multi -objective Quantum- inspired Optimization

Yao–Hsin Chou, Cheng-Yen Hua, Huan-Pu Chen +3 more

It is important to construct quantum algorithms within shallow-depth quantum circuits due to the current imperfections and decoherence in quantum devices. Efficient quantum circuit synthesis and optimization are critical tasks that generate relatively small quantum circuits tailored to specific requirements. However, an efficient circuit may vary depending on the physical realization platforms of quantum computers. For example, the Clifford+T gate library has recently gained prominence for its superior fault tolerance and error correction capabilities, but the T gate dominates circuit execution time. Therefore, the objectives of synthesis algorithms in this gate set include reducing T-depth and T-count, as well as overall circuit depth and gate count. Most traditional synthesis methods produce a single optimized circuit. In contrast, this study introduces a multi-objective quantum-inspired tabu search algorithm in quantum circuit synthesis to construct a diverse set of optimized circuits based on four distinct metrics. The values of these metrics in the optimized circuit display the entanglement relationship, and the proposed method simulates the formula to assist in building the entangled optimal circuit. Results show that the construction of the Pareto front can flexibly offer a variety of functionally equivalent circuits that adapt to advancements in quantum technology. Providing multiple optimized circuit structures facilitates circuit replacement and further simplification, thereby enhancing the development of quantum computing.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•4 citations

Knowledge Navigated Quantum-inspired Tabu Search Algorithm for Reversible Circuit Synthesis

Hsing-Yu Hsu, Shan-Jung Hou, Yu-Yuan Chen +4 more

Reversible circuits are the essential building blocks of quantum computers and have zero energy dissipation. However, there is no general rule on how to synthesize an effective circuit with minimal cost. Many researchers have placed a high value on the design of algorithms for reversible circuit synthesis as it is the fundamental component to implement in many paradigms, such as Shor’s algorithm. In this paper, the knowledge navigated quantum-inspired tabu search algorithm (KNQTS) is proposed to synthesize several benchmark functions. KNQTS is a quantum-inspired algorithm with the concept of getting closer to the best solution and keeping away from the worst solution, and it has a great search ability. Furthermore, the proposed algorithm uses self-adaptive, global-best guided and Quantum-Not gate mechanisms to make all procedures more efficient and avoid sticking to the local optimum. This paper also compares the KNQTS approach’s experimental results with those obtained via other metaheuristics and previous algorithms. Finally, the result shows that the proposed KNQTS method outperforms other state-of-the-art methods and achieves the same functionality at a lower cost. The solutions are optimal or near-optimal.

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics•Oct 9, 2022•9 citations

A novel optimization method for reversible logic circuit minimization

Matthew A. Morrison, Nagarajan Ranganathan

Programmable reversible logic is emerging as a prospective logic design style for implementation in modern nanotechnology and quantum computing with minimal impact on circuit heat generation. Recent advances in reversible logic using and quantum computer algorithms allow for improved computer architecture and arithmetic logic unit designs. We present an optimization method for reversible logic synthesis based on the Integrated Qubit (IQ) library. This method works in conjunction with existing methods to further improve quantum cost and delay of a synthesized reversible logic circuit. This algorithm runs in O(N) time, and reduces the quantum cost of synthesized circuit by up to 45 percent. In addition, the process of replacing the gates in the synthesized circuits with IQ gates uses a locally optimal technique whose major benefits include reduction of cost as well as delay.

IEEE Computer Society Annual Symposium on VLSI•Aug 1, 2013•14 citations

Quantum-Inspired Tabu Search Algorithm for reversible logic circuit synthesis

Wen-Hsin Wang, Chia-Hui Chiu, Shu–Yu Kuo +2 more

Reversible logic plays an important role in quantum computation, which is a promising research field. The reversible logic synthesis problem focuses on generating a reversible circuit automatically and finding the lowest cost when an output function is given. The synthesis of reversible logic circuits can be formulated as a combinatorial optimization problem. This paper proposes a new evolutionary algorithm for synthesizing reversible circuits based on Quantum-Inspired Tabu Search Algorithm (QTS). The proposed algorithm uses the QTS-based approach to find fewer gates and reduce the cost of reversible circuits. This method is simpler, has better performance in computational cost, and reduce the gate counts of reversible circuits. This paper also compares experimental results with other heuristic and evolutionary algorithms. The final outcome shows that the QTS-based approach performs much better than other algorithms.

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics•Oct 1, 2012•24 citations

Quantum-Inspired Evolutionary Algorithms for Circuit Optimization

Jincheng Zhang

This paper explores the application of quantum-inspired evolutionary algorithms (QIEAs) to the optimization of quantum circuits. Traditional evolutionary algorithms (EAs) often face challenges in efficiently navigating the complex design space inherent in quantum circuit design. This work proposes a novel approach that leverages quantum-inspired operators – superposition and quantum tunneling – within an adaptive evolutionary framework. The core idea is to mimic key quantum mechanical phenomena to enhance exploration capabilities and accelerate convergence. The algorithm is designed to dynamically adjust its search strategy based on the current state of the population, fostering a more targeted and effective exploration of the circuit design landscape. We demonstrate the potential of this approach to significantly improve the performance of circuit optimization, particularly in scenarios where conventional EAs struggle to find optimal solutions within reasonable computational time. The theoretical framework and the proposed algorithm are presented, followed by a discussion of its potential applications and future research directions.

•Sep 2, 2026•0 citations
Quantum and reversible circuit optimization stand as pivotal areas in quantum computing, crucial for error mitigation, size reduction, and cost minimization. In this scenario, computational intelligence (CI) is an efficient approach for improving circuit optimization issues and constructing equivalent small-scale quantum algorithms. In this paper, we propose a novel quantum-inspired computational intelligence method to automatically synthesize quantum circuits, benefiting reliable quantum computing realization. The proposed algorithm uses the feature of quantum mechanics to enhance the exploration ability of computational intelligence. It then detects situations when encountering local optima and reacts appropriately, drawing inspiration from the controlled-swap gate to navigate candidate solutions effectively and escape local optima with preference. Given the uncertain landscape of future quantum technologies and the diverse preferable gate sets with different platforms, our approach is a flexible algorithm that can simply adapt to different gate sets. The experimental results demonstrate that our method achieves superior performance compared to previous synthesis methods, with fewer circuit costs and more stable efficiency. Furthermore, we provide all circuits synthesized by our novel method to facilitate future studies to conduct in-depth research, simplify circuits, and map to different logic gate sets. This study advances reliable quantum computing through a quantum-inspired CI to mutually benefit both realms, promoting insight into interdisciplinary collaboration.
The quantum circuit is the prime factor to realize quantum computation. This paper proposes a novel quantum-inspired evolutionary computation method to synthesize quantum circuits effectively and efficiently. Recently, the Clifford+T gate library has become popular, as the T gate has a better fault-tolerant and error correction efficacy, which is essential to near-term quantum computation. The proposed quantum-inspired evolutionary computation is more flexible in constructing diverse solutions than the classical synthesis method. The entangled local search mechanism further enhances the ability to discover the optimal solutions on an efficient frontier. The encoding can be slightly changed to achieve various gate libraries circuit synthesis in the quantum or reversible Boolean circuits. The experiments demonstrate that our proposed method can be more effective in composing a compact quantum circuit than the state-of-the-art method. Meanwhile, the T-depth can attain an optimal value without ancilla bits. Furthermore, we provide an illustration to construct the function equivalent quantum circuit with the NOT, CNOT, Controlled-Square-Root-of-NOT quantum gate library (NCV). We also conduct exhaustion to prove that our synthesized circuit is an optimal solution, requiring far fewer resources in time and evaluation than exhaustion.
It is important to construct quantum algorithms within shallow-depth quantum circuits due to the current imperfections and decoherence in quantum devices. Efficient quantum circuit synthesis and optimization are critical tasks that generate relatively small quantum circuits tailored to specific requirements. However, an efficient circuit may vary depending on the physical realization platforms of quantum computers. For example, the Clifford+T gate library has recently gained prominence for its superior fault tolerance and error correction capabilities, but the T gate dominates circuit execution time. Therefore, the objectives of synthesis algorithms in this gate set include reducing T-depth and T-count, as well as overall circuit depth and gate count. Most traditional synthesis methods produce a single optimized circuit. In contrast, this study introduces a multi-objective quantum-inspired tabu search algorithm in quantum circuit synthesis to construct a diverse set of optimized circuits based on four distinct metrics. The values of these metrics in the optimized circuit display the entanglement relationship, and the proposed method simulates the formula to assist in building the entangled optimal circuit. Results show that the construction of the Pareto front can flexibly offer a variety of functionally equivalent circuits that adapt to advancements in quantum technology. Providing multiple optimized circuit structures facilitates circuit replacement and further simplification, thereby enhancing the development of quantum computing.
Reversible circuits are the essential building blocks of quantum computers and have zero energy dissipation. However, there is no general rule on how to synthesize an effective circuit with minimal cost. Many researchers have placed a high value on the design of algorithms for reversible circuit synthesis as it is the fundamental component to implement in many paradigms, such as Shor’s algorithm. In this paper, the knowledge navigated quantum-inspired tabu search algorithm (KNQTS) is proposed to synthesize several benchmark functions. KNQTS is a quantum-inspired algorithm with the concept of getting closer to the best solution and keeping away from the worst solution, and it has a great search ability. Furthermore, the proposed algorithm uses self-adaptive, global-best guided and Quantum-Not gate mechanisms to make all procedures more efficient and avoid sticking to the local optimum. This paper also compares the KNQTS approach’s experimental results with those obtained via other metaheuristics and previous algorithms. Finally, the result shows that the proposed KNQTS method outperforms other state-of-the-art methods and achieves the same functionality at a lower cost. The solutions are optimal or near-optimal.
Programmable reversible logic is emerging as a prospective logic design style for implementation in modern nanotechnology and quantum computing with minimal impact on circuit heat generation. Recent advances in reversible logic using and quantum computer algorithms allow for improved computer architecture and arithmetic logic unit designs. We present an optimization method for reversible logic synthesis based on the Integrated Qubit (IQ) library. This method works in conjunction with existing methods to further improve quantum cost and delay of a synthesized reversible logic circuit. This algorithm runs in O(N) time, and reduces the quantum cost of synthesized circuit by up to 45 percent. In addition, the process of replacing the gates in the synthesized circuits with IQ gates uses a locally optimal technique whose major benefits include reduction of cost as well as delay.
Reversible logic plays an important role in quantum computation, which is a promising research field. The reversible logic synthesis problem focuses on generating a reversible circuit automatically and finding the lowest cost when an output function is given. The synthesis of reversible logic circuits can be formulated as a combinatorial optimization problem. This paper proposes a new evolutionary algorithm for synthesizing reversible circuits based on Quantum-Inspired Tabu Search Algorithm (QTS). The proposed algorithm uses the QTS-based approach to find fewer gates and reduce the cost of reversible circuits. This method is simpler, has better performance in computational cost, and reduce the gate counts of reversible circuits. This paper also compares experimental results with other heuristic and evolutionary algorithms. The final outcome shows that the QTS-based approach performs much better than other algorithms.
This paper explores the application of quantum-inspired evolutionary algorithms (QIEAs) to the optimization of quantum circuits. Traditional evolutionary algorithms (EAs) often face challenges in efficiently navigating the complex design space inherent in quantum circuit design. This work proposes a novel approach that leverages quantum-inspired operators – superposition and quantum tunneling – within an adaptive evolutionary framework. The core idea is to mimic key quantum mechanical phenomena to enhance exploration capabilities and accelerate convergence. The algorithm is designed to dynamically adjust its search strategy based on the current state of the population, fostering a more targeted and effective exploration of the circuit design landscape. We demonstrate the potential of this approach to significantly improve the performance of circuit optimization, particularly in scenarios where conventional EAs struggle to find optimal solutions within reasonable computational time. The theoretical framework and the proposed algorithm are presented, followed by a discussion of its potential applications and future research directions.