Research And Implementation Of Quantum Programming And Circuit Optimization

With the rapid development of the information industry and the increasing popularity of Internet technology,we are constantly entering the era of big data,which will increasingly rely on the computing power.In this context,quantum computing,as a new computing model with great computing potential,has attracted more and more attention from governments,large enterprises and research institutions.However,the research and development of quantum computers is a cross-disciplinary subject,which requires both hardware and software to promote each other and develop together.Quantum software mainly includes the development of quantum algorithms and quantum programming,which will further expand the computing power of quantum computers.However,due to the miraculous nature of quantum itself,the methods and technologies that have achieved brilliant results in the field of classical programming cannot be directly applied to quantum programing.Therefore,it is important for us to design a quantum programming language that can fully utilize the(the only truly,perhaps)parallelism of quantum computers.This thesis designs and implements a high-level quantum programming language that not only contains quantum data types,but also implements quantum loop statements and quantum conditional statements in accordance with the idea of "quantum data,classical control".Due to the fragility of the quantum state itself,the decoherence phenomenon,and the error of the quantum logic gate,reducing the quantum-circuit depth is crucial to obtain more accurate calculation results.In addition,due to physical constraints,qubits on current quantum chips are not fully connected,which poses additional problems for designing quantum programs.In the process of compiling,<QuQ> adopts a new type of quantum circuit optimization algorithms,which can adjust and optimize the original quantum program according to any given qubit arrangement.This algorithm uses “Greedy idea” to make decision.And under the premise that the compiled program can satisfy the qubit layout,the circuit depth and the number of logic gates used are reduced as much as possible,this algorithm adjusts and optimizes the original quantum programs through three steps: global qubit adjustment,local qubit adjustment and merging single-qubit logic gates.At this stage,however,due to the lack of available quantum chips with high-efficiency,it is difficult to verify the validity of this high-level quantum programming language and the reliability of this quantum circuit optimization algorithm.To this end,this thesis proposes a platform for simulating quantum computing,called <QuQ>,which is efficient,easy to use,open source and scalable.<QuQ> platform can automatically convert the input quantum program from the high-level quantum programming language to the quantum assembly language,and then to the circuit diagrams in the execution of the quantum program.After that,<QuQ> simulates the execution of quantum computing on a classical computer to calculate the input quantum programs.In order to simulate the whole calculation process more realistically,<QuQ> platform simulates the noise in the calculation process based on the single-qubit logic gate and CNOT gate errors of the latest quantum chip published by IBM,so the calculated results are closer to the actual chip.We implement our quantum circuit optimization algorithm in this platform and the experimental results show that compared with IBM's standardized circuit optimization algorithm,the proposed algorithm has obvious advantages in both compiling efficiency and the number of used logic gates.