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NEWBASIC 2.06.7 API 帮助与用户使用手册

news 2025/11/5 7:26:11

NEWBASIC 2.06.7 API 帮助与用户使用手册

一、概述

NEWBASIC 2.06.7 是下一代量子集成编程语言，集成了多模态量子学习、终身学习系统、量子神经符号学习和量子因果推理等前沿功能。本手册提供完整的API参考和使用指南。

二、快速入门

2.1 安装与启动

' 安装NEWBASIC量子环境
INSTALL NEWBASIC-Quantum-Toolkit
CONFIGURE Quantum-Hardware --simulator="ibmq_qasm_simulator"' 启动NEWBASIC解释器
START NEWBASIC' 显示欢迎信息
Welcome to NEWBASIC 2.06.7 - Quantum Integrated Programming Language
Quantum Neural Architecture Search | Quantum Reinforcement Learning
Quantum Generative Adversarial Networks | Quantum Transfer Learning
Quantum Attention Mechanisms | Quantum Graph Neural Networks
Quantum Meta-Learning | Quantum Multimodal LearningType 'help' for command list, 'examples' for tutorial examples.

2.2 第一个量子程序

' 创建量子电路
CREATE CIRCUIT HelloQuantumQUBITS 2H(0)          ' 应用Hadamard门到量子比特0CNOT(0, 1)    ' 应用CNOT门，控制位0，目标位1MEASURE ALL   ' 测量所有量子比特
END CIRCUIT' 执行量子电路
RUN HelloQuantum WITH SHOTS=1000' 查看结果
PRINT RESULTS
' 输出: |00>: 498 |11>: 502

三、核心API参考

3.1 量子神经网络API

' 创建量子神经网络
API QuantumNeuralNetwork.Create(layers AS List(Of LayerDefinition)) AS QuantumNeuralNetwork' 示例：创建量子分类器
DIM layers = NEW List(Of LayerDefinition)()
layers.Add(NEW LayerDefinition("Input", 4))      ' 4个输入特征
layers.Add(NEW LayerDefinition("QuantumDense", 8)) ' 8量子比特的全连接层
layers.Add(NEW LayerDefinition("QuantumDense", 4)) ' 4量子比特层
layers.Add(NEW LayerDefinition("Output", 3))      ' 3分类输出DIM model = QuantumNeuralNetwork.Create(layers)' 训练模型
API model.Train(data AS Dataset, epochs AS Integer, learningRate AS Double)' 使用示例
DIM dataset = LoadDataset("quantum_data.qdata")
model.Train(dataset, epochs=100, learningRate=0.01)' 预测
API model.Predict(input AS QuantumState) AS QuantumState
DIM prediction = model.Predict(testState)

3.2 多模态学习API

' 创建多模态量子模型
API MultimodalQuantumModel.Create(textDim AS Integer, imageDim AS Integer, quantumDim AS Integer, taskType AS String) AS MultimodalQuantumModel' 示例：多模态分类
DIM multimodalModel = MultimodalQuantumModel.Create(textDim:=512,imageDim:=64*64*3, quantumDim:=10,taskType:="classification"
)' 联合训练多模态数据
API multimodalModel.JointTrain(textData AS List(Of String),imageData AS List(Of Bitmap),quantumData AS List(Of QuantumState),labels AS List(Of Integer),epochs AS Integer
)' 跨模态检索
API multimodalModel.CrossModalRetrieve(query AS Object,      ' 查询（文本、图像或量子态）targetModality AS String,  ' 目标模态："text", "image", "quantum"topK AS Integer
) AS List(Of Object)

3.3 终身学习API

' 初始化终身学习器
API LifelongLearner.Create(baseModel AS QuantumNeuralNetwork,memorySize AS Integer
) AS LifelongLearner' 示例：持续学习多个任务
DIM learner = LifelongLearner.Create(baseModel, memorySize:=1000)' 学习新任务
API learner.LearnNewTask(taskData AS Dataset,taskId AS String,epochs AS Integer
)' 增量适应
API learner.IncrementalLearn(newData AS List(Of (QuantumState, Object)),adaptationRate AS Double
) AS QuantumNeuralNetwork' 评估所有已学任务
API learner.EvaluateAllTasks() AS Dictionary(Of String, Double)

3.4 量子神经符号API

' 创建神经符号学习器
API NeuroSymbolicLearner.Create(neuralArch AS List(Of LayerDefinition),symbolicRules AS List(Of SymbolicRule)
) AS NeuroSymbolicLearner' 定义符号规则
DIM rules = NEW List(Of SymbolicRule)()
rules.Add(NEW SymbolicRule(pattern:="IF temperature > 30 THEN state = HOT",action:=Function(s) ClassifyAsHot(s)
))
rules.Add(NEW SymbolicRule(pattern:="IF pressure < 100 THEN check_safety",action:=Function(s) CheckSafety(s)
))' 联合训练
DIM nsl = NeuroSymbolicLearner.Create(neuralLayers, rules)
nsl.JointTrain(trainingData, epochs:=50)' 可解释预测
API nsl.ExplainPrediction(input AS QuantumState) AS Explanation
DIM explanation = nsl.ExplainPrediction(testState)
PRINT explanation.NaturalLanguage

3.5 量子因果学习API

' 创建因果模型
API QuantumCausalModel.Create(variables AS List(Of String),initialGraph AS CausalGraph
) AS QuantumCausalModel' 学习因果结构
API causalModel.LearnStructure(data AS List(Of QuantumState),testType AS String  # "quantum_pc", "quantum_lingam"
)' 因果效应估计
API causalModel.EstimateEffect(treatment AS String,outcome AS String,data AS List(Of QuantumState)
) AS Double' 反事实查询
API causalModel.Counterfactual(observedState AS QuantumState,intervention AS QuantumIntervention
) AS QuantumState

四、完整使用手册

4.1 数据准备与加载

' 1. 加载经典数据集
DIM classicalData = LoadCSV("data.csv", columns:={"feature1", "feature2", "label"},types:={"float", "float", "int"}
)' 2. 转换为量子态
DIM quantumData = classicalData.Select(Function(row)RETURN QuantumState.Encode(row.Features, encoding:="amplitude_encoding")
).ToList()' 3. 创建数据集
DIM dataset = NEW QuantumDataset(inputs:=quantumData,labels:=classicalData.Labels,trainTestSplit:=0.8,batchSize:=32
)

4.2 模型训练工作流

' 1. 定义模型架构
DIM architecture = NEW QuantumArchitecture()
architecture.AddLayer("Input", 10)          ' 10个特征
architecture.AddLayer("QuantumDense", 8)    ' 8量子比特隐藏层
architecture.AddLayer("QuantumDense", 6)     ' 6量子比特层
architecture.AddLayer("Output", 3)           ' 3分类输出' 2. 创建和编译模型
DIM model = QuantumNeuralNetwork(architecture)
model.Compile(optimizer:="quantum_adam",loss:="quantum_cross_entropy",metrics:={"accuracy", "fidelity"}
)' 3. 训练模型
model.Fit(trainData:=dataset.TrainSet,epochs:=100,validationData:=dataset.TestSet,callbacks:={"early_stopping": {"patience": 10},"model_checkpoint": {"filepath": "best_model.qnb"}}
)' 4. 评估模型
DIM metrics = model.Evaluate(dataset.TestSet)
PRINT $"测试准确率: {metrics("accuracy")}, 保真度: {metrics("fidelity")}"

4.3 高级功能使用示例

4.3.1 多模态学习示例

' 加载多模态数据
DIM textData = LoadTextData("reports.txt")
DIM imageData = LoadImages("images/", size:=(64, 64))
DIM quantumData = LoadQuantumStates("quantum_data.qst")' 创建多模态模型
DIM multimodalModel = MultimodalQuantumModel.Create(textDim:=300,  # BERT嵌入维度imageDim:=64*64*3,quantumDim:=8,taskType:="regression"
)' 训练
multimodalModel.JointTrain(textData:=textData,imageData:=imageData,quantumData:=quantumData,labels:=labels,epochs:=50
)' 跨模态检索示例
DIM queryText = "寻找相似的量子态和图像"
DIM results = multimodalModel.CrossModalRetrieve(query:=queryText,targetModality:="image",topK:=5
)

4.3.2 终身学习示例

' 初始化终身学习系统
DIM baseModel = LoadModel("base_model.qnb")
DIM lifelongLearner = LifelongLearner.Create(baseModel, memorySize:=5000)' 顺序学习多个任务
DIM tasks = {"task1", "task2", "task3", "task4"}
FOR EACH taskId IN tasksDIM taskData = LoadTaskData(taskId)lifelongLearner.LearnNewTask(taskData, taskId, epochs:=30)' 评估所有已学任务DIM accuracies = lifelongLearner.EvaluateAllTasks()PRINT $"任务 {taskId} 完成后的各任务准确率:"FOR EACH kvp IN accuraciesPRINT $"{kvp.Key}: {kvp.Value}"NEXT
NEXT' 保存最终模型
SaveModel(lifelongLearner.BaseModel, "lifelong_model.qnb")

4.3.3 量子因果推理示例

' 创建因果图
DIM causalGraph = NEW CausalGraph()
causalGraph.AddVariables({"temperature", "pressure", "reaction_rate", "yield"})
causalGraph.AddEdge("temperature", "reaction_rate")
causalGraph.AddEdge("pressure", "reaction_rate") 
causalGraph.AddEdge("reaction_rate", "yield")' 初始化因果模型
DIM causalModel = QuantumCausalModel.Create(variables:={"temperature", "pressure", "reaction_rate", "yield"},initialGraph:=causalGraph
)' 从数据学习因果结构
causalModel.LearnStructure(experimentData, testType:="quantum_pc")' 估计因果效应
DIM effect = causalModel.EstimateEffect(treatment:="temperature",outcome:="yield",data:=experimentData
)
PRINT $"温度对产量的因果效应: {effect}"' 反事实分析
DIM observedState = experimentData(0)
DIM intervention = NEW QuantumIntervention("temperature", "do(high)")
DIM counterfactualYield = causalModel.Counterfactual(observedState, intervention)
PRINT $"如果温度提高，产量会是: {counterfactualYield}"

4.4 部署与优化

' 模型量化压缩
API ModelCompression.Quantize(model AS QuantumNeuralNetwork,bits AS Integer,  # 8, 4, 2calibrationData AS List(Of QuantumState)
) AS QuantumNeuralNetwork' 示例：8位量化
DIM quantizedModel = ModelCompression.Quantize(model:=trainedModel,bits:=8,calibrationData:=calibrationSet
)' 性能基准测试
API Benchmark.Run(model AS QuantumNeuralNetwork,dataset AS Dataset,hardware AS String  # "simulator", "ibmq", "rigetti"
) AS BenchmarkResults' 部署到量子硬件
DEPLOY MODEL quantizedModel TO HARDWARE "ibmq_montreal"
WITH CONFIG {"shots": 1000,"optimization_level": 3,"resilience_level": 2
}

五、故障排除与常见问题

5.1 常见错误代码

' QERROR-101: 量子比特不足
SOLUTION: 增加量子比特数或使用更小的模型' QERROR-202: 梯度消失
SOLUTION: 使用量子梯度裁剪或调整学习率' QERROR-303: 硬件连接超时
SOLUTION: 检查网络连接或使用本地模拟器' QERROR-404: 模态不匹配
SOLUTION: 检查输入数据的维度和类型

5.2 性能优化建议

电路深度优化：

' 使用门融合技术
OPTIMIZE CIRCUIT DEPTH WITH FUSION
' 使用更浅的替代架构
USE SHALLOW_ARCHITECTURE FOR FASTER_EXECUTION

内存管理：

' 启用量子态压缩
ENABLE STATE_COMPRESSION
' 使用批处理减少内存占用
SET BATCH_SIZE = 32

分布式训练：

' 启用数据并行
ENABLE DATA_PARALLEL WITH 4 WORKERS
' 使用梯度累积
SET GRADIENT_ACCUMULATION_STEPS = 4

六、资源与支持

6.1 学习资源

官方文档: https://docs.newbasic.ai
量子教程: /tutorials/quantum_basics.nb
示例代码: /examples/ 目录
社区论坛: https://community.newbasic.ai

6.2 获取帮助

' 在NEWBASIC中获取帮助
HELP                  # 显示所有命令
HELP QuantumNeuralNetwork  # 显示特定类帮助
EXAMPLES multimodal    # 显示多模态学习示例' 诊断命令
DIAGNOSE SYSTEM       # 系统诊断
CHECK DEPENDENCIES    # 检查依赖
TEST CONNECTIVITY     # 测试量子硬件连接

本手册涵盖了NEWBASIC 2.06.7的主要功能和API，如需更详细的信息，请参考官方文档或使用内置帮助系统。

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