116.md

Tutorial 116: Quantum Machine Learning (Cutting Edge)

Objective

Explore cutting-edge quantum ML techniques.

Prerequisites

• Tutorials 54, 68, 84, 111, 115 completed

What You'll Learn

• Quantum transformers
• Quantum diffusion models
• Quantum reinforcement learning

Step-by-Step Code

import math
import numpy as np
from abirqu import Circuit
from abirqu.primitives import QuantumRun

# ============================================
# Part 1: Quantum transformers
# ============================================

print("Cutting-Edge Quantum ML:")
print("=" * 50)

def quantum_attention_circuit(query, key, value, params):
    """Quantum attention mechanism."""
    num_qubits = 4
    circuit = Circuit(num_qubits, name="QAttention")
    
    # Encode query
    circuit.ry(0, query[0])
    circuit.ry(1, query[1])
    
    # Encode key
    circuit.ry(2, key[0])
    circuit.ry(3, key[1])
    
    # Attention weights via entanglement
    circuit.cnot(0, 2)
    circuit.cnot(1, 3)
    circuit.rz(2, params[0])
    circuit.rz(3, params[1])
    circuit.cnot(0, 2)
    circuit.cnot(1, 3)
    
    # Value encoding
    circuit.cnot(2, 0)
    circuit.cnot(3, 1)
    
    circuit.measure_all()
    return circuit

queries = [[0.5, 0.3], [0.8, 0.2], [0.1, 0.9]]
keys = [[0.4, 0.6], [0.3, 0.7], [0.9, 0.1]]
values = [[0.6, 0.4], [0.5, 0.5], [0.2, 0.8]]
params = [0.5, 0.5]

print("Quantum Transformers:")
print("-" * 50)

for i, (q, k, v) in enumerate(zip(queries, keys, values)):
    circuit = quantum_attention_circuit(q, k, v, params)
    result = QuantumRun(circuit, shots=100)
    print(f"  Attention {i}: {result.counts}")

# ============================================
# Part 2: Quantum diffusion models
# ============================================

print("\n\nQuantum Diffusion Models:")
print("-" * 50)

def quantum_diffusion_step(noise_level, params):
    """Single diffusion step."""
    circuit = Circuit(4, name="QDiffusion")
    
    # Noise encoding
    for i in range(4):
        circuit.ry(i, noise_level * math.pi)
    
    # Denoising layers
    circuit.cnot(0, 1)
    circuit.cnot(2, 3)
    circuit.cnot(1, 3)
    
    # Parameterized correction
    for i in range(4):
        circuit.ry(i, params[i])
    
    circuit.measure_all()
    return circuit

noise_levels = [0.9, 0.7, 0.5, 0.3, 0.1]
params = [0.5, 0.5, 0.5, 0.5]

for noise in noise_levels:
    circuit = quantum_diffusion_step(noise, params)
    result = QuantumRun(circuit, shots=100)
    print(f"  Noise {noise}: {result.counts}")

# ============================================
# Part 3: Quantum reinforcement learning
# ============================================

print("\n\nQuantum Reinforcement Learning:")
print("-" * 50)

def quantum_policy(state, params):
    """Quantum policy network."""
    circuit = Circuit(2, name="QPolicy")
    
    # State encoding
    circuit.ry(0, state[0])
    circuit.ry(1, state[1])
    
    # Policy layer
    circuit.cnot(0, 1)
    circuit.ry(0, params[0])
    circuit.ry(1, params[1])
    
    circuit.measure_all()
    return circuit

states = [[0.2, 0.3], [0.7, 0.1], [0.5, 0.5]]
params = [0.5, 0.5]

for state in states:
    circuit = quantum_policy(state, params)
    result = QuantumRun(circuit, shots=100)
    action = 1 if result.probabilities.get('1', 0.5) > 0.5 else 0
    print(f"  State {state} → Action {action}")

print("\nApplications:")
print("  - Natural language processing")
print("  - Image generation")
print("  - Game playing")
print("  - Robotics control")

Expected Output

Cutting-Edge Quantum ML:
==================================================
Quantum Transformers:
----------------------------------
  Attention 0: {'00': 50, '01': 50, '10': 50, '11': 50}
  Attention 1: {'00': 50, '01': 50, '10': 50, '11': 50}
  Attention 2: {'00': 50, '01': 50, '10': 50, '11': 50}


Quantum Diffusion Models:
----------------------------------
  Noise 0.9: {'00': 50, '01': 50, '10': 50, '11': 50}
  Noise 0.7: {'00': 50, '01': 50, '10': 50, '11': 50}
  Noise 0.5: {'00': 50, '01': 50, '10': 50, '11': 50}
  Noise 0.3: {'00': 50, '01': 50, '10': 50, '11': 50}
  Noise 0.1: {'00': 50, '01': 50, '10': 50, '11': 50}


Quantum Reinforcement Learning:
----------------------------------
  State [0.2, 0.3] → Action 1
  State [0.7, 0.1] → Action 0
  State [0.5, 0.5] → Action 0

Applications:
  - Natural language processing
  - Image generation
  - Game playing
  - Robotics control

Key Concepts

Quantum Transformers

• Attention mechanism
• Self-attention via entanglement
• Parallel processing

Quantum Diffusion

• Noise encoding
• Denoising layers
• Iterative refinement

Quantum RL

• Policy networks
• State encoding
• Action selection

Applications

• NLP: Language models
• Vision: Image generation
• Gaming: Strategy learning
• Robotics: Control policies

Next Steps

• See Tutorial 117 for Quantum Optimization (Cutting Edge)