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

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' 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 Learning

Type 'help' for command list, 'examples' for tutorial examples.

2.2 第一个量子程序

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' 创建量子电路
CREATE CIRCUIT HelloQuantum
    QUBITS 2
    H(0)          ' 应用Hadamard门到量子比特0
    CNOT(0, 1)    ' 应用CNOT门，控制位0，目标位1
    MEASURE 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

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' 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

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' 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

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' 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

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' 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

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' 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

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' 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

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' 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

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' 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

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' 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 tasks
    DIM 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 accuracies
        PRINT $"{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

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' 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

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' 模型量化压缩
API ModelCompression.Quantize(
    model AS QuantumNeuralNetwork,
    bits AS Integer,  # 8, 4, 2
    calibrationData 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

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' 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：

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' 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：

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' 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：

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' 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

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' 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。