Study on Machine Learning Application in Quantum Circuit Synthesis

Abstract

In this Research Paper we have highlighted about the Study on Machine Learning Application in Quantum Circuit Synthesis. Quantum circuit synthesis, a fundamental task in quantum computing, involves designing efficient circuits to execute quantum algorithms on quantum computers. Machine learning techniques have been increasingly applied to enhance this process. One key area where machine learning excels is in optimizing quantum circuits for specific tasks. Quantum circuit synthesis often involves a large search space of possible circuit configurations, making it computationally intensive to find optimal solutions. Machine learning algorithms, such as reinforcement learning or genetic algorithms, can efficiently navigate this search space to identify high-performing circuit configurations. These algorithms learn from past experiences and iteratively improve their performance, gradually converging towards optimal solutions. Machine learning can aid in automating the design process by predicting the performance of quantum circuits based on their characteristics. By analyzing large datasets of quantum circuits and their corresponding performance metrics, machine learning models can identify patterns and correlations, enabling the prediction of circuit performance without the need for exhaustive simulations. Additionally, machine learning techniques can assist in error mitigation in quantum circuits. Quantum computers are susceptible to various types of errors, which can degrade the performance of quantum algorithms. Machine learning algorithms can analyze error patterns and devise strategies to mitigate them, leading to more reliable quantum circuits.