const qiskit = require('@qiskit/qiskit');

// 量子隨機數生成器
async function quantumRandom() {
const { QuantumCircuit, Aer, transpile, assemble } = qiskit;
const qc = new QuantumCircuit(1, 1);

qc.h(0);  // Hadamard gate to create superposition
qc.measure(0, 0);  // Measure the qubit

const simulator = Aer.get_backend('qasm_simulator');
const compiled_circuit = transpile(qc, simulator);
const job = await simulator.run(assemble(compiled_circuit));
const result = job.result();
const counts = result.get_counts(qc);

return counts['0'] ? 0 : 1;  // Return 0 or 1 based on measurement

}

// 使用生成器函數來生成斐波那契數列
async function* quantumFibonacci(n) {
let a = 0, b = 1;
for (let i = 0; i < n; i++) {
const randomBit = await quantumRandom();
yield a + randomBit;
[a, b] = [b, a + b];
}
}

// 使用裝飾器來計算函數執行時間
function timer(func) {
return async function(...args) {
const start_time = performance.now();
const result = await func(...args);
const end_time = performance.now();
console.log(Function ${func.name} took ${(end_time - start_time).toFixed(4)} milliseconds);
return result;
};
}

const compute_fibonacci = timer(async function(n) {
const fib = quantumFibonacci(n);
const result = [];
for await (let num of fib) {
result.push(num);
}
return result;
});

// 取前10個斐波那契數
compute_fibonacci(10).then(first10Fib => console.log(first10Fib));

// 定義神經網絡類
class SimpleNN {
constructor(inputSize, hiddenSize, outputSize) {
this.model = tf.sequential();
this.model.add(tf.layers.dense({ units: hiddenSize, inputShape: [inputSize], activation: 'relu' }));
this.model.add(tf.layers.dense({ units: outputSize }));
this.model.compile({ loss: 'categoricalCrossentropy', optimizer: tf.train.sgd(0.01) });
}

async initializeWeights() {
    const weights = [];
    for (let i = 0; i < this.model.layers.length; i++) {
        const layerWeights = [];
        for (let j = 0; j < this.model.layers[i].weights.length; j++) {
            layerWeights.push(await quantumRandom());
        }
        weights.push(layerWeights);
    }
    this.model.setWeights(weights);
}

async forward(x) {
    return this.model.predict(tf.tensor2d([x], [1, x.length]));
}

}

// 初始化模型、損失函數和最佳化器
const inputSize = 10;
const hiddenSize = 5;
const outputSize = 2;
const model = new SimpleNN(inputSize, hiddenSize, outputSize);

// 初始化權重
model.initializeWeights().then(() => {
// 訓練循環（隨機資料範例）
async function train() {
for (let epoch = 0; epoch < 100; epoch++) {
const inputs = tf.randomNormal([1, inputSize]);
const labels = tf.tensor1d([1]); // 假標籤
await model.model.fit(inputs, labels, { epochs: 1 });
}
console.log("Training complete!");
}
train();
});