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International Conference on Computer Sciences and Convergence Information Technology

Automatic Synthesis of Quantum Teleportation Circuit

Fei Peng, Guangjun Xie, Tianhao Wu

Published Jan 1, 2009• 1 citation
Publisher

Abstract

Quantum teleportation is a typical quantum circuit. We choose it as a benchmark circuit for the problem of quantum circuit synthesis. Genetic algorithm is introduced to synthesis the circuit. A simple coding scheme and the corresponding fitness function are proposed. The algorithm is implemented using Matlab’s GA toolbox, and an optimized circuit structure is obtained.

DOI
10.1109/iccit.2009.178

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Quantum teleportation is a typical quantum circuit. We choose it as a benchmark circuit for the problem of quantum circuit synthesis. Genetic algorithm is introduced to synthesis the circuit. A simple coding scheme and the corresponding fitness function are proposed. The algorithm is implemented using Matlab’s GA toolbox, and an optimized circuit structure is obtained.
This paper applies a standard genetic algorithm to the optimization of the quantum circuits. We propose a new coding scheme, which encodes quantum gates as integers and stores circuit topology information in the position of the integers. The design of the corresponding fitness function and genetic operators are also discussed. The quantum teleportation circuit, a typical quantum computation process, is chosen as our optimization target. Finally, the experiment results are discussed, and an optimized teleportation circuit has been obtained.
Optimum design of quantum teleportation circuit is achieved based on genetic algorithm,and a novel one-dimensional quantum coding mode is proposed to store the location information of quantum gates using the location information of codes.The method is more convenient than previous ones for coding and de-coding,and has high coding efficiency.It can well express the circuit structure,and is easy to be extended to multi-bit quantum circuits.A fitness function of the genetic algorithm is also presented with two parts to determine the function and performance of the quantum circuit.Experimental results are given to show that a better circuit structure has been obtained.
Genetic algorithms were among the early choices for quantum circuit synthesis, because of their ability to evolve a given starting circuit towards one of the possible solutions. The synthesis method presented here is the first GA-based approach that dynamically adjusts its control parameters. The adaptive parameter control takes into account the analysis performed on each genetic operator, in order to automatically find an acceptable tradeoff between runtime and appropriate exploration. The experimental results prove that this method improves the synthesis runtime and the size of the circuit to be handled up to 7 qubits (previous GA-based techniques are effective only for 3-4 qubit circuits).
Abstract — Quantum and reversible circuit synthesis has been important concern of designers in recently passed decades. Considering the application of garbage bit in optimization of quantum circuit, GA-Based techniques have been introduced. This project provides an extensible infrastructure for quantum circuit automatic synthesis and optimization with the enhancement of interoperability features. The problem domain begins from complex computing model up to a synthesis of reversible quantum circuit. Optimization criterias are whole considered separately from software processing logic which cause capability of applying variable optimization critria in different problem domains. The new introduced methodology utilize PSO technique for circuit synthesis evolutionary computation in optimization step. A software library has been developed which implements this technique. It provides a simple flexible software for researchers in order to automatic synthesis of quantum circuit that implements an open source library of prerequisite of quantum circuit synthesis. Index Terms-Quantum circuits, gate, PSO, Automatic synthesis.
The design of quantum circuits is often still done manually, for instance by following certain patterns or rule of thumb. While this approach may work well for some problems, it can be a tedious task and present quite the challenge in other situations. Designing the architecture of a circuit for a simple classification problem may be relatively straightforward task, however, creating circuits for more complex problems or that are resilient to certain known problems (e.g. barren plateaus, trainability, etc.) is a different issue. Moreover, efficient state preparation or circuits with low depth are important for virtually most algorithms. In attempts to automate the process of designing circuits, different approaches have been suggested over the years, including genetic algorithms and reinforcement learning. We propose our GA4QCO framework that applies a genetic algorithm to automatically search for quantum circuits that exhibit user-defined properties. With our framework, the user specifies through a fitness function what type of circuit should be created, for instance circuits that prepare a specific target state while keeping depth at a minimum and maximizing fidelity. Our framework is designed in such a way that the user can easily integrate a custom designed fitness function. In this paper, we introduce our framework and run experiments to show the validity of the approach.
Reversible quantum logic synthesis is to study given quantum gates and quantum circuits of the constraints and limitations and find the smallest or smaller quantum cost to achieve the desired quantum logic circuits.The quantum logic gate functions of the matrix is indicated by the mathematical model. The synthesis and optimization are achieved simultaneously by the genetic algorithm as global search tool. Genetic algorithm is applied to quantum reversible logic synthesis.The fourth-order quantum circuit experiment has achieved good results,and further analysis of this method in high-level synthesis of quantum circuits and its application is completed.