基于遗传算法的量子线路演化

演化量子电路

Evolving Quantum Circuits

本文提出用遗传算法从随机量子电路演化出稳定子量子纠错码，成功获得5量子比特完美码、Shor码和7量子比特色码，并展望其在NISQ时代用于拓扑态搜索、量子编译和硬件优化。

探索近似态制备量子电路的最优性：基于遗传算法

Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

本研究利用遗传算法优化近似态制备量子电路，在NISQ设备上以有限CNOT门显著提升Haar随机态制备保真度，并发现短近似电路优于精确电路，同时提供理论分析支持。

评估基于遗传算法的量子电路综合中的变异技术

Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis

本文研究遗传算法中不同变异策略对量子电路综合的影响，通过以保真度为主并考虑电路深度和T操作的实验，发现删除与交换策略组合效果最佳。

通过遗传编程将量子优势纳入量子电路生成

Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming

针对遗传算法生成量子电路时适应度函数未纳入量子优势的问题，提出两种整合量子优势指标的新方法，并在 Bernstein-Vazirani 与非结构化数据库搜索问题上验证其可加快收敛并生成专家级电路。

Generating efficient quantum circuits for preparing maximally multipartite entangled states

In this work we provide a method for generating quantum circuits preparing maximally multipartite entangled states using genetic programming. The presented method is faster that known realisations thanks to the applied fitness function and several modifications to the genetic programming schema. Moreover, we enrich the described method by the unique possibility to define an arbitrary structure of a system. We use the developed method to find new quantum circuits, which are simpler from known results. We also analyse the efficiency of generating entanglement in the spin chain system and in the …

Quantum Encoded Quantum Evolutionary Algorithm for the Design of Quantum Circuits

In this paper we present Quanrum Encoded Quantum Evolutionary Algorithm (QEQEA) and compare its performance against a a classical GPU accelerated Genetic Algorithm (GPUGA). The proposed QEQEA differs from existing quantum evolutionary algorithms in several points: representation of candidates circuits is using qubits and qutrits and the proposed evolutionary operators can in theory be implemented on quantum computer provided a classical control exists. The synthesized circuits are obtained by a set of measurements performed on the encoding units of quantum representation. Both algorithms are a…

基于岛屿遗传算法的量子电路综合

Synthesis of Quantum Circuits with an Island Genetic Algorithm

针对酉矩阵等效量子电路分解的难题，提出基于岛屿模型的进化搜索算法，在三类量子电路问题上验证了有效性，是一种仅受算力限制的通用方法。

DeQompile：利用遗传编程进行量子电路反编译以实现可解释的量子架构搜索

DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search

针对量子架构搜索中电路可解释性不足的问题，本文提出基于遗传编程的反编译框架DeQompile，从低层电路逆向生成可解释的高层Qiskit算法，从而推动可推广且可证明的量子算法发展。

GASP -- A Genetic Algorithm for State Preparation

The efficient preparation of quantum states is an important step in the execution of many quantum algorithms. In the noisy intermediate-scale quantum (NISQ) computing era, this is a significant challenge given quantum resources are scarce and typically only low-depth quantum circuits can be implemented on physical devices. We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state. The method uses a basis set of R_x, R_y, R_z, and CNOT gates and a genetic algorithm to systema…

遗传 Transformer 辅助量子神经网络用于最优电路设计

Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design

提出 GTQNN 混合框架，用 transformer 压缩经典数据并以 NSGA-II 搜索兼顾准确率与门数的 Pareto 最优量子电路，在四个基准上以更少资源取得竞争力性能。

灵活的遗传算法用于量子支持向量机

Flexible Genetic Algorithm for Quantum Support Vector Machines

本文提出GA-QSVM，通过遗传算法优化量子支持向量机的特征映射，实现跨数据集的泛化能力，提升量子机器学习的性能。

GAT-QNN：基于遗传算法的混合量子神经网络训练

GAT-QNN: Genetic Algorithm-Based Training of Hybrid Quantum Neural Networks

本文提出GAT-QNN框架，通过迭代采样和训练子电路并整合参数，提升混合量子神经网络在噪声和异构后端下的性能，减少计算资源并提高分类准确率。

MoG-VQE: Multiobjective genetic variational quantum eigensolver

Variational quantum eigensolver (VQE) emerged as a first practical algorithm for near-term quantum computers. Its success largely relies on the chosen variational ansatz, corresponding to a quantum circuit that prepares an approximate ground state of a Hamiltonian. Typically, it either aims to achieve high representation accuracy (at the expense of circuit depth), or uses a shallow circuit sacrificing the convergence to the exact ground state energy. Here, we propose the approach which can combine both low depth and improved precision, capitalizing on a genetically-improved ansatz for hardware…

遗传优化ansatz表达性以提升变分量子算法性能

Genetic optimization of ansatz expressibility for enhanced variational quantum algorithm performance

本文提出一种遗传算法启发的框架，用于设计高表达性、低深度的ansatze，以提升变分量子算法性能。

面向量子特征映射设计的混合遗传优化方法

Hybrid Genetic Optimisation for Quantum Feature Map Design

本研究针对量子特征映射设计中准确率评估成本高的问题，提出用核目标对齐及其近似方法替代准确率作为遗传算法适应度指标，结合COBYLA参数训练的混合方法，在9个UCI二分类任务上实现了相当准确率且训练间隔提升超20%。

变分量子电路解耦

Variational Quantum Circuit Decoupling

本文提出变分解耦算法，将 n 量子比特幺正门分解为多个独立演化的子组件，并应用于量子电路综合，数值实验表明其能实现传统变分电路无法达到的 2 和 4 量子比特门保真度。

自适应遗传算法用于脉冲级量子误差缓解

Adaptive Genetic Algorithms for Pulse-Level Quantum Error Mitigation

本文提出一种自适应遗传算法，通过动态调整脉冲参数来缓解量子噪声，无需修改电路门，并在Grover和Deutsch-Jozsa算法上验证了其提升保真度的有效性。

Evolutionary Quantum Logic Synthesis of Boolean Reversible Logic Circuits Embedded in Ternary Quantum Space using Heuristics

It has been experimentally proven that realizing universal quantum gates using higher-radices logic is practically and technologically possible. We developed a Parallel Genetic Algorithm that synthesizes Boolean reversible circuits realized with a variety of quantum gates on qudits with various radices. In order to allow synthesizing circuits of medium sizes in the higher radix quantum space we performed the experiments using a GPU accelerated Genetic Algorithm. Using the accelerated GA we compare heuristic improvements to the mutation process based on cost minimization, on the adaptive cost o…

通过演化异构集成提升量子电路的可靠性

Improving the Reliability of Quantum Circuits by Evolving Heterogeneous Ensembles

针对量子电路设计难、可靠性待提升的问题，该研究开发工具QuEEn，用进化算法生成异构量子电路集成，在Iris分类任务的理想与含噪仿真中均优于同构集成，为NISQ时代提升量子电路可靠性提供有效策略。

基于混合量子进化算法的量子预言机进化

Evolving Quantum Oracles with Hybrid Quantum-inspired Evolutionary Algorithm

量子预言机在量子计算和量子信息的研究中起着关键作用。但是用通用量子门有效地实现量子预言是一项艰巨的工作。受遗传规划的启发，本文提出了一种新的量子预言机进化方法--混合量子进化算法。该方法利用数值对量子线路进行编码，并将量子线路的代价和正确性结合到适应度函数中。为了加快个体评价中矩阵乘法的计算速度，提出了一种矩阵乘Kronecker积的快速算法。实验结果表明了该方法的有效性和一些参数的影响。并讨论了该方法的一些特点。

ZX-calculus在量子架构搜索中的应用

Application of ZX-calculus to Quantum Architecture Search

针对量子机器学习中高效量子电路设计难题，提出融合ZX-calculus与遗传编程的量子架构搜索方法，设计多种基于ZX-calculus变换规则的变异算子，实验证明其在多项指标上优于传统遗传优化，可生成更浅、门分布更均匀的电路。

AutoQML：利用遗传算法在灰度图像上自动生成和训练鲁棒量子启发分类器

AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images

本文提出AutoQML，利用多目标遗传算法自动生成并训练量子启发分类器，通过动态适应度函数和降维预处理，在灰度图像上实现高准确率与鲁棒性。

一种用于实值全局优化的基于门的量子遗传算法

A Gate-Based Quantum Genetic Algorithm for Real-Valued Global Optimization

本文提出一种基于门的量子遗传算法，利用量子电路编码个体并通过叠加与个体间纠缠提升实值全局优化的收敛性和鲁棒性。

使用里德伯原子阵列的数字-模拟量子遗传算法

Digital-analog quantum genetic algorithm using Rydberg-atom arrays

本文提出一种基于里德伯原子阵列的数字-模拟量子遗传算法，通过数字单比特门与模拟全局驱动相结合估计分子基态能量，在H₂、LiH和BeH₂上实现误差小于1%且态重叠接近1的结果。

用于量子态制备的 T-Count 优化遗传算法

T-Count Optimizing Genetic Algorithm for Quantum State Preparation

本文提出一种面向 Clifford+T 量子态制备电路的遗传算法，通过优化 T-Count 降低噪声影响，并在多类量子态上验证其高保真与容错电路生成能力。