1. App Overview

• Name: QuantumFarma
• Purpose: Promote quantum technology in agriculture and medicine, sell plant-based extracts and medicines, and enable payments via Pi Coin.
• Target Users: Pi Network users, health-conscious individuals, and patients seeking natural remedies.

2. App Structure

• Home:
  • Banner introducing QuantumFarma and quantum technology.
  • Navigation to sections: Quantum Tech, Products, Payment, Contact.
• Quantum Technology:
  • Explain quantum mechanics in farming (enhancing plant growth) and plant extract production (e.g., curcumin, neem) for cancer/virus treatments.
  • Include visuals and a short video (optional).
• Products:
  • Plant Extracts: Curcumin, lemongrass oil, reishi mushroom (e.g., 50 Pi for 100ml curcumin).
  • Cancer Medicines: Nano-curcumin capsules, reishi pills (e.g., 200 Pi for 60 capsules).
  • Antiviral Medicines: Herbal immune boosters (e.g., 150 Pi for 60 capsules).
  • Features: Product details, images, prices in Pi, “Add to Cart” and “Buy Now” buttons.
• Payment:
  • Integrate Pi Wallet for Pi Coin payments using Pi SDK/API.
  • Show cart, confirm transactions, and store order history.
• Contact:
  • Form for inquiries, company info, and social media links.

import sys import numpy as np import torch import torch.nn as nn import torch.optim as optim from qutip import *

Cơ sở dữ liệu cây thuốc (mở rộng)

herbs_database = { 'Nhân sâm (Panax ginseng)': { 'hoat_chat': ['Ginsenosides (Rb1, Rg1)', 'Polysaccharides'], 'chiet_xuat': ['Chiết xuất bằng nước sôi', 'Chiết xuất bằng cồn'], 'cong_dung': 'Bổ khí, tăng cường hệ miễn dịch' }, 'Cam thảo (Glycyrrhiza uralensis)': { 'hoat_chat': ['Glycyrrhizin', 'Flavonoids'], 'chiet_xuat': ['Decoction', 'Solvent extraction'], 'cong_dung': 'Hòa vị, giảm viêm' }, # Thêm nhiều hơn nếu cần }

AI Model: Dự đoán công dụng từ hoạt chất (PyTorch)

class HerbPredictor(nn.Module): def init(self): super(HerbPredictor, self).init() self.fc1 = nn.Linear(10, 5) # Giả sử vector hóa hoạt chất thành 10 features self.fc2 = nn.Linear(5, 2) # Output: 2 classes (ví dụ: 'Bổ khí' hoặc 'Giảm viêm')

def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = self.fc2(x)
    return x

def train_ai_model(): model = HerbPredictor() optimizer = optim.Adam(model.parameters(), lr=0.01) criterion = nn.CrossEntropyLoss()

# Dữ liệu mẫu: Hoạt chất vectorized -> label (0: Bổ khí, 1: Giảm viêm)
X = torch.tensor(np.random.rand(100, 10), dtype=torch.float32)  # Random data
y = torch.tensor(np.random.randint(0, 2, 100), dtype=torch.long)

for epoch in range(10):
    optimizer.zero_grad()
    output = model(X)
    loss = criterion(output, y)
    loss.backward()
    optimizer.step()
print("AI model trained for herb prediction.")
return model

def predict_with_ai(model, herb_name): if herb_name in herbs_database: # Giả sử vectorize hoạt chất input_vec = torch.tensor(np.random.rand(1, 10), dtype=torch.float32) output = model(input_vec) pred = torch.argmax(output).item() cong_dung_pred = ['Bổ khí dự đoán', 'Giảm viêm dự đoán'][pred] print(f"Dự đoán công dụng cho {herb_name} bằng AI: {cong_dung_pred}")

Quantum Simulation: QML cho dự đoán tương tác hoạt chất (QuTiP)

def quantum_predict(herb_name): if herb_name in herbs_database: # Simulate QSVM: Quantum circuit đơn giản qc = Qobj(np.array([[0, 1], [1, 0]])) # Pauli-X gate state = basis(2, 0) result = qc * state prob = abs(result.full())**2 pred = np.argmax(prob) print(f"Quantum simulation cho {herb_name}: Tương tác hoạt chất dự đoán (0/1): {pred}")