Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.
Portable consumer electronics is most demanding in every segment of electronic industry and to satisfy the needs of low power electronics, comprehensive approaches and techniques have been proposed by various researchers.Reversible logic is one among emerging and competent technologies with profound applications in fields of computer graphics, optical information processing, quantum computing, DNA computing, ultra low power CMOS design and communication.ALU is a fundamental component of all processing units.Portability in computing system highly demands for reversible logic based ALU.Many researchers have proposed exact synthesis approaches of ALU design based on reversible logic but few have come up with reduced quantum cost without long computation overhead.Here in this paper heuristic approach has been used which not only provides solution for large number of variables but also avoids sufferings caused by long computation overhead.The main goal of this paper is to propose reversible logic based ALU and further it is optimized by Ant Colony Optimization (ACO) algorithm combined with Depth First Search (DFS) in terms of reduced quantum cost.
Considering the relentless progress in the miniaturization of electronic devices and the need to reduce energy consumption, technical challenges in the synthesis of circuit design solutions have become evident. According to Moore's Law, the reduction of transistor sizes to the atomic scale faces physical limits, which complicate further development. Additionally, reducing transistor sizes causes current leakage, leading to increased thermal noise, which can disrupt the proper functioning of digital devices. A promising solution to these problems is the application of reversible logic in circuit design. Reversible logic allows for a reduction in energy and information losses because logical reversible operations are performed without loss. The research synthesized optimal reversible circuits based on reversible gates using evolutionary algorithms and compare them with existing analogues. The focus of this study is on logical circuits built using reversible gates, which can significantly reduce energy losses, which is critical for modern and future electronic devices. The synthesis of reversible circuits is closely related to quantum computing, where quantum gates also possess a reversible nature. This enables the use of synthesis methods to create quantum reversible logical computing devices, which in turn promotes the development of quantum technologies. The study focuses on the application of evolutionary artificial intelligence algorithms, specifically genetic algorithms and ant colony optimization algorithms, for the optimal synthesis of reversible circuits. As a result, a detailed description of the key concepts of the improved algorithms, simulation results, and comparison of the two methods is provided. The efficiency of the reversible device synthesis was evaluated using the proposed implementation of the genetic algorithm and the ant colony optimization algorithm. The obtained results were compared to existing analogs and verified using the Qiskit framework in the IBM quantum computing laboratory. The conclusions describe the developed algorithms, which demonstrate high efficiency in solving circuit topology optimization problems. A genetic algorithm was developed, featuring multi-component mutation and a matrix approach to chromosome encoding combined with Tabu search to avoid local optima. The ant colony optimization algorithms were improved, including several changes to the proposed data representation model, structure, and operational principles of the synthesis algorithm, enabling effective synthesis of devices on the NCT basis along with Fredkin gates. An improved structure for storing and using pheromones was developed to enable multi-criteria navigation in the solution space.
Abstract Ant colony optimisation (ACO) is a commonly used meta‐heuristic to solve complex combinatorial optimisation problems like the travelling salesman problem (TSP), vehicle routing problem (VRP) etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed‐up can be achieved by using parallelism offered by quantum computing. Existing quantum algorithms to solve ACO are either quantum‐inspired classical algorithms or hybrid quantum‐classical algorithms. Since all these algorithms need the intervention of classical computing, leveraging the true potential of quantum computing on real quantum hardware remains a challenge. This study's main contribution is to propose a fully quantum algorithm to solve ACO, enhancing the quantum information processing toolbox in the fault‐tolerant quantum computing (FTQC) era. We have solved the single source single destination (SSSD) shortest‐path problem using our proposed adaptive quantum circuit for representing the dynamic pheromone‐updating strategy in real IBMQ devices. Our quantum ACO technique can be further used as a quantum ORACLE to solve complex optimisation problems in a fully quantum setup with significant speed up upon the availability of more qubits.
Reversible logic has been considered as a promising direction in the field of low power design and quantum computing. However, synthesizing reversible circuits is totally different from that of irreversible circuits. Therefore, several algorithms have been devoted to optimize the synthesis process of reversible circuits in terms of the Quantum Cost (QC). Binary Decision Diagram (BDD) based synthesis has been found as one of the dominant algorithms in reversible logic synthesis, due to its scalability in synthesizing large-scale circuits within a reasonable synthesis time that is bounded by the BDD size. However, the QC of the synthesized circuit is strongly correlated with the used BDD size in the synthesis process, wherein, each BDD node is substituted by a cascade or reversible gates. As a result, exploiting effective BDD reordering algorithms combined with BDD reduction rules turns out into less QC. In this paper, we propose a BDD reordering methodology wherein recently published swarm based optimization algorithms are integrated and compared in terms of the resultant BDD size. Experimental results show that Moth-Flame optimization (MFO) algorithm outperforms other algorithms on the public benchmarks.
In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper.In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm.In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here.The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithms and overcomes some limitations of the traditional ACO algorithm. However, due to the hardware resource limitations of currently available quantum computers, such as the limited number of qubits, lack of high-fidelity gating operation, and low noisy tolerance, the practical application of the QACO is quite challenging. In this paper, we introduce a hybrid quantum-classical algorithm by combining the clustering algorithm with QACO algorithm, so that this extended QACO can handle large-scale optimization problems, which makes the practical application of QACO based on available quantum computation resource possible. To verify the effectiveness and performance of the algorithm, we tested the developed QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks. The developed QACO algorithm shows better performance under multiple data set. In addition, the developed QACO algorithm also manifests the robustness to noise of calculation process, which is typically a major barrier for practical application of quantum computers. Our work shows that the combination of the clustering algorithm with QACO has effectively extended the application scenario of QACO in current NISQ era of quantum computing.