Quantum computing is a potential paradigm to overcome the current computational bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (especially quantum architecture search for parameterized quantum circuits) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
Quantum computing is a potential paradigm to overcome the current computational bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (especially quantum architecture search for parameterized quantum circuits) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
Quantum computing is a promising paradigm that may overcome the current computational power bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum architecture. Quantum circuit compilation (QCC) aims to generate feasible mappings such that a quantum circuit can be executed in a given hardware platform with acceptable confidence in outcomes. Physical constraints of a NISQ computer change frequently, requiring QCC process to be repeated often. When a circuit cannot directly be executed on a quantum hardware due to its physical limitations, it is necessary to modify the circuit by adding new quantum gates and auxiliary qubits, increasing its space and time complexity. An inefficient QCC may significantly increase error rate and circuit latency for even the simplest algorithms. In this article, we present artificial intelligence (AI)-based and heuristic-based methods recently reported in the literature that attempt to address these QCC challenges. We group them based on underlying techniques that they implement, such as AI algorithms including genetic algorithms, genetic programming, ant colony optimization and AI planning, and heuristics methods employing greedy algorithms, satisfiability problem solvers, dynamic, and graph optimization techniques. We discuss performance of each QCC technique and evaluate its potential limitations.
Abstract: The evolution of quantum computing depends not only on advancements in hardware but also on breakthroughs in compiler design that can translate high-level quantum algorithms into efficient, executable circuits. This research introduces a novel approach to quantum compiler design that leverages AI guidance in tandem with superalgebraic symmetries to optimize gate synthesis, resource usage, and noise resilience. By embedding superalgebraic representations—mathematical structures that unify bosonic and fermionic transformations—into the compilation pipeline, and training AI models to recognize optimal symmetry-preserving transformations, we construct a quantum compiler that is adaptive, structure-aware, and scalable. This framework enhances circuit depth minimization, gate reduction, and fidelity preservation in real-time quantum compilation workflows. Keywords: Quantum Compiler, Superalgebras, Artificial Intelligence, Gate Optimization, Quantum Circuits, Symmetry-Preserving Transformations, Qubit Mapping, Quantum Machine Learning
This paper presents a novel approach to quantum circuit synthesis and optimization, addressing the significant challenges in designing efficient quantum algorithms. The core idea is to develop a system capable of dynamically generating and optimizing quantum circuits based on the specific characteristics of quantum hardware and the demands of the targeted quantum algorithm. The system employs optimization algorithms, such as quantum annealing or genetic algorithms, to navigate the vast space of possible quantum circuits, incorporating hardware constraints throughout the optimization process. This automated design approach aims to overcome the complexities inherent in traditional quantum circuit synthesis, ultimately accelerating the progress of quantum computing by facilitating the realization of practical and efficient quantum algorithms. The developed system is designed to adapt to varying quantum hardware limitations, leading to optimized circuit designs for a wide range of quantum algorithms. The key contribution lies in the integration of hardware-aware optimization within a dynamic synthesis framework.
Artificial Intelligence (AI) has been evolving at a very rapid pace, with increase in compute power, thus reshaping how complex problems are handled and interpreted from vast amounts of information. AI and newly emerging Quantum Computing have each made significant strides in recent years. Quantum computing has surfaced as a promising new paradigm that leverages on unique quantum phenomena such as like superposition and entanglement to potentially deliver dramatic gains in computational power. Increasingly, researchers are combining these distinct fields into "quantum AI," exploring ways to harness quantum devices for data-intensive tasks, advanced optimisation, and even tentative investigations into emergent machine "awareness." Already researchers have established the quantum advantage over classical AI. This paper outlines the fundamental aspects of AI with respect to quantum computing. It also surveys possible benefits for deep learning and optimization, and considers key concerns related to hardware readiness, algorithmic design, and ethical frameworks. By mapping current progress and pinpointing ongoing hurdles, we provide an entry-level guide to quantum AI's prospects and the steps required for its real-world deployment.
The rapid development of Quantum Computing (QC) as a promising computing paradigm has garnered significant attention for its ability to harness quantum mechanical properties for computation. With classical computing facing limitations outlined by Moore’s Law, QC emerges as a potential alternative for tackling complex computational problems. Yet, to unlock its full potential, robust and optimized compilers are pivotal, especially in addressing challenges posed by circuits with numerous qubits. In this systematic literature review, we analyze 18 articles to identify proposed optimizations for quantum compilers, exploring their applications, performance impacts, and emerging trends. Our primary goal is to offer valuable insights into the recent advancements and challenges in QC compiler optimizations. This will be achieved through the clustering of optimization groups, ultimately facilitating further progress in the development of highly optimized quantum algorithms and circuits.