Designing a Quantum Brain-Computer Interface (QBCI) is a highly speculative and futuristic concept that blends quantum computing with neural interfaces. While no real-world QBCI exists yet, we can create a conceptual simulation in Python that models how such a system might work—combining EEG-like brain signals with quantum-inspired processing.

This project will simulate brain signals, encode them into quantum states, and apply basic quantum operations using a quantum computing framework like Qiskit.

🧠 Concept Overview

• Brain Signal Simulation: Generate synthetic EEG-like signals.
• Quantum Encoding: Map signal features into quantum states.
• Quantum Processing: Apply quantum gates to simulate decision-making or classification.
• Visualization: Show results of quantum measurements.

🗂️ Folder Structure

qbci_simulator/
├── main.py
├── config/
│   └── settings.yaml
├── data/
│   └── brain_signal.npy
├── brain/
│   ├── __init__.py
│   └── signal_generator.py
├── quantum/
│   ├── __init__.py
│   ├── encoder.py
│   └── processor.py
├── visualization/
│   ├── __init__.py
│   └── results_plotter.py
├── utils/
│   ├── __init__.py
│   └── logger.py

📄 config/settings.yaml

brain:
  channels: 8
  duration: 5
  sample_rate: 128
  signal_frequency: 10
  noise_level: 0.05

quantum:
  qubits: 4
  shots: 1024

📄 brain/signal_generator.py

import numpy as np

def generate_brain_signal(channels, duration, sample_rate, signal_freq, noise_level):
    t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
    signal = np.sin(2 * np.pi * signal_freq * t)
    data = np.array([
        signal + noise_level * np.random.randn(len(t))
        for _ in range(channels)
    ])
    np.save("data/brain_signal.npy", data)
    return data

📄 quantum/encoder.py

from qiskit import QuantumCircuit
import numpy as np

def encode_signal_to_qubits(signal_segment, num_qubits):
    norm = np.linalg.norm(signal_segment)
    angles = (np.pi * signal_segment[:num_qubits]) / norm
    qc = QuantumCircuit(num_qubits)
    for i, angle in enumerate(angles):
        qc.ry(angle, i)
    return qc

📄 quantum/processor.py

from qiskit import Aer, execute
from qiskit.visualization import plot_histogram

def process_quantum_circuit(qc, shots):
    backend = Aer.get_backend("qasm_simulator")
    qc.measure_all()
    job = execute(qc, backend, shots=shots)
    result = job.result()
    counts = result.get_counts()
    return counts

📄 visualization/results_plotter.py

from qiskit.visualization import plot_histogram
import matplotlib.pyplot as plt

def show_quantum_results(counts):
    plot_histogram(counts)
    plt.title("Quantum Measurement Results")
    plt.show()

📄 utils/logger.py

from datetime import datetime

def log_event(message):
    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    print(f"[{timestamp}] {message}")

📄 main.py

import yaml
from brain.signal_generator import generate_brain_signal
from quantum.encoder import encode_signal_to_qubits
from quantum.processor import process_quantum_circuit
from visualization.results_plotter import show_quantum_results
from utils.logger import log_event

def load_config():
    with open("config/settings.yaml", "r") as f:
        return yaml.safe_load(f)

def main():
    config = load_config()
    brain_cfg = config["brain"]
    quantum_cfg = config["quantum"]

    log_event("Generating brain signal...")
    signal = generate_brain_signal(
        brain_cfg["channels"],
        brain_cfg["duration"],
        brain_cfg["sample_rate"],
        brain_cfg["signal_frequency"],
        brain_cfg["noise_level"]
    )

    log_event("Encoding signal into quantum circuit...")
    segment = signal[0][:quantum_cfg["qubits"]]
    qc = encode_signal_to_qubits(segment, quantum_cfg["qubits"])

    log_event("Processing quantum circuit...")
    counts = process_quantum_circuit(qc, quantum_cfg["shots"])

    log_event("Displaying results...")
    show_quantum_results(counts)

if __name__ == "__main__":
    main()

🧪 How to Run

1. Install dependencies:

   pip install numpy matplotlib qiskit pyyaml
   

2. Run the simulation:

   python main.py