Hardware Integration

In [ ]:

# Hardware Integration Tutorial

# This tutorial demonstrates how to integrate quantum data embeddings with real quantum hardware platforms.

## Prerequisites

## Before starting this tutorial, ensure you have:

# - Access to quantum hardware (IBM Quantum, IonQ, or Rigetti)
# - Appropriate API credentials configured
# - Understanding of quantum embedding basics

## IBM Quantum Integration

### Setting Up IBM Quantum Access


from qiskit import IBMQ
from quantum_data_embedding_suite.backends import QiskitBackend

# Save your IBM Quantum account (do this once)
# IBMQ.save_account('YOUR_API_TOKEN')

# Load account
IBMQ.load_account()

# Get provider
provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main')

# List available backends
backends = provider.backends()
print("Available IBM Quantum backends:")
for backend in backends:
    print(f"- {backend.name()}: {backend.status().operational}")


### Using Real Quantum Hardware


from quantum_data_embedding_suite.embeddings import AngleEmbedding
from quantum_data_embedding_suite.kernels import FidelityKernel
import numpy as np

# Create embedding
embedding = AngleEmbedding(n_qubits=4)

# Configure hardware backend
hardware_backend = QiskitBackend(
    provider=provider,
    backend_name="ibmq_manila",  # Example backend
    shots=1024,
    optimization_level=2
)

# Set backend in embedding
embedding.set_backend(hardware_backend)

# Prepare data
X = np.random.randn(10, 4) * 0.5  # Keep data bounded

# Create kernel with hardware backend
kernel = FidelityKernel(embedding=embedding)

# Compute kernel matrix on real hardware
print("Computing kernel matrix on real quantum hardware...")
K = kernel.compute_kernel_matrix(X)

print(f"Kernel matrix shape: {K.shape}")
print(f"Kernel matrix condition number: {np.linalg.cond(K):.2e}")


## Error Mitigation Strategies

### Measurement Error Mitigation


from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter

def setup_error_mitigation(backend, qubits):
    """Setup measurement error mitigation"""
    
    # Create calibration circuits
    cal_circuits, state_labels = complete_meas_cal(
        qubit_list=qubits,
        circlabel='cal'
    )
    
    # Execute calibration circuits
    cal_job = backend.run(cal_circuits, shots=8192)
    cal_results = cal_job.result()
    
    # Create fitter
    meas_fitter = CompleteMeasFitter(cal_results, state_labels)
    
    return meas_fitter

# Setup error mitigation
qubits = [0, 1, 2, 3]
meas_filter = setup_error_mitigation(hardware_backend.backend, qubits)

# Apply error mitigation to results
def apply_error_mitigation(raw_counts, meas_filter):
    """Apply measurement error mitigation to results"""
    mitigated_counts = meas_filter.filter.apply(raw_counts)
    return mitigated_counts

### Zero-Noise Extrapolation


def zero_noise_extrapolation(embedding, X, noise_levels, shots=1024):
    """Perform zero-noise extrapolation"""
    
    results = []
    
    for noise_level in noise_levels:
        # Create noisy backend
        noisy_backend = create_noisy_backend(noise_level)
        embedding.set_backend(noisy_backend)
        
        # Compute metric with noise
        kernel = FidelityKernel(embedding=embedding)
        K = kernel.compute_kernel_matrix(X)
        
        # Store result
        results.append(np.trace(K))
    
    # Extrapolate to zero noise
    from scipy.optimize import curve_fit
    
    def exponential_decay(x, a, b, c):
        return a * np.exp(-b * x) + c
    
    popt, _ = curve_fit(exponential_decay, noise_levels, results)
    zero_noise_result = popt[0] + popt[2]  # a + c
    
    return zero_noise_result, results


## Cloud Platform Integration

### Amazon Braket Integration


from braket.aws import AwsDevice
from braket.devices import LocalSimulator

# Use local simulator for development
local_device = LocalSimulator()

# Use cloud simulator
sv1_device = AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1")

# Use real quantum hardware (IonQ)
ionq_device = AwsDevice("arn:aws:braket:::device/qpu/ionq/ionQdevice")

# Create Braket backend wrapper
class BraketBackend:
    def __init__(self, device, shots=1024):
        self.device = device
        self.shots = shots
    
    def execute(self, circuit):
        """Execute circuit on Braket device"""
        # Convert circuit to Braket format
        braket_circuit = self.convert_to_braket(circuit)
        
        # Submit task
        task = self.device.run(braket_circuit, shots=self.shots)
        
        # Get results
        result = task.result()
        return result.measurement_counts
    
    def convert_to_braket(self, circuit):
        """Convert from Qiskit/PennyLane to Braket circuit"""
        # Implementation depends on source circuit format
        pass


### Google Cirq Integration


import cirq
from cirq.google import Foxtail

# Create Cirq device
device = Foxtail

# Define quantum embedding circuit in Cirq
def create_cirq_embedding(data, qubits):
    """Create embedding circuit in Cirq"""
    circuit = cirq.Circuit()
    
    for i, x in enumerate(data):
        circuit.append(cirq.ry(x).on(qubits[i % len(qubits)]))
    
    return circuit

# Execute on Google quantum hardware (when available)
# This requires access to Google Quantum AI


## Hardware-Specific Optimizations

### Connectivity-Aware Circuit Design


def optimize_for_connectivity(circuit, coupling_map):
    """Optimize circuit for hardware connectivity"""
    from qiskit.compiler import transpile
    
    # Transpile with hardware constraints
    optimized_circuit = transpile(
        circuit,
        coupling_map=coupling_map,
        optimization_level=3,
        routing_method='sabre',
        translation_method='synthesis'
    )
    
    return optimized_circuit

# Example: Optimize for IBM hardware
coupling_map = [[0, 1], [1, 0], [1, 2], [2, 1], [2, 3], [3, 2]]
optimized_circuit = optimize_for_connectivity(circuit, coupling_map)


### Native Gate Decomposition


def decompose_to_native_gates(circuit, basis_gates):
    """Decompose circuit to native hardware gates"""
    from qiskit.compiler import transpile
    
    transpiled = transpile(
        circuit,
        basis_gates=basis_gates,
        optimization_level=2
    )
    
    return transpiled

# IBM native gates
ibm_basis = ['id', 'rz', 'sx', 'x', 'cx']
native_circuit = decompose_to_native_gates(circuit, ibm_basis)


## Performance Monitoring

### Quantum Volume Assessment


def assess_quantum_volume(backend, n_qubits):
    """Assess effective quantum volume of backend"""
    from qiskit.ignis.verification.quantum_volume import qv_circuits, QVFitter
    
    # Create quantum volume circuits
    qv_circs, qv_circs_nomeas = qv_circuits(
        qubit_lists=[[i for i in range(n_qubits)]],
        ntrials=100
    )
    
    # Execute circuits
    job = backend.run(qv_circs, shots=1024)
    qv_result = job.result()
    
    # Analyze results
    qv_fitter = QVFitter()
    qv_fitter.add_data(qv_result)
    
    # Check if quantum volume is achieved
    qv_success = qv_fitter.calc_statistics()
    
    return qv_success

# Assess quantum volume
qv_result = assess_quantum_volume(hardware_backend.backend, 4)
print(f"Quantum Volume achieved: {qv_result}")


### Real-time Performance Tracking


class HardwarePerformanceTracker:
    """Track hardware performance metrics"""
    
    def __init__(self):
        self.metrics = {
            'execution_times': [],
            'error_rates': [],
            'queue_times': [],
            'timestamps': []
        }
    
    def track_execution(self, backend, circuit):
        """Track single execution performance"""
        import time
        
        start_time = time.time()
        
        # Submit job
        job = backend.run(circuit, shots=1024)
        
        # Wait for completion and track queue time
        queue_start = time.time()
        result = job.result()
        queue_time = time.time() - queue_start
        
        execution_time = time.time() - start_time
        
        # Estimate error rate from result fidelity
        error_rate = self.estimate_error_rate(result)
        
        # Record metrics
        self.metrics['execution_times'].append(execution_time)
        self.metrics['error_rates'].append(error_rate)
        self.metrics['queue_times'].append(queue_time)
        self.metrics['timestamps'].append(time.time())
    
    def estimate_error_rate(self, result):
        """Estimate error rate from measurement results"""
        # Simple heuristic based on result distribution
        counts = result.get_counts()
        total_shots = sum(counts.values())
        
        # Expect some distribution - high concentration suggests errors
        max_count = max(counts.values())
        error_estimate = 1 - (max_count / total_shots)
        
        return error_estimate
    
    def generate_report(self):
        """Generate performance report"""
        import matplotlib.pyplot as plt
        
        fig, axes = plt.subplots(2, 2, figsize=(12, 8))
        
        # Execution times
        axes[0, 0].plot(self.metrics['timestamps'], self.metrics['execution_times'])
        axes[0, 0].set_title('Execution Times')
        axes[0, 0].set_ylabel('Time (s)')
        
        # Error rates
        axes[0, 1].plot(self.metrics['timestamps'], self.metrics['error_rates'])
        axes[0, 1].set_title('Error Rates')
        axes[0, 1].set_ylabel('Error Rate')
        
        # Queue times
        axes[1, 0].plot(self.metrics['timestamps'], self.metrics['queue_times'])
        axes[1, 0].set_title('Queue Times')
        axes[1, 0].set_ylabel('Time (s)')
        
        # Summary statistics
        axes[1, 1].text(0.1, 0.8, f"Avg Execution: {np.mean(self.metrics['execution_times']):.2f}s")
        axes[1, 1].text(0.1, 0.6, f"Avg Error Rate: {np.mean(self.metrics['error_rates']):.3f}")
        axes[1, 1].text(0.1, 0.4, f"Avg Queue Time: {np.mean(self.metrics['queue_times']):.2f}s")
        axes[1, 1].set_title('Summary Statistics')
        axes[1, 1].axis('off')
        
        plt.tight_layout()
        plt.show()

# Use performance tracker
tracker = HardwarePerformanceTracker()

# Track multiple executions
for _ in range(10):
    tracker.track_execution(hardware_backend.backend, test_circuit)

# Generate report
tracker.generate_report()


## Best Practices for Hardware Execution

### 1. Circuit Optimization
## - Minimize circuit depth
## - Use native gates when possible
## - Apply connectivity-aware routing

### 2. Error Mitigation
## - Implement measurement error correction
## - Use zero-noise extrapolation for critical computations
## - Consider readout error mitigation

### 3. Resource Management
##- Monitor queue times and job status
## - Batch multiple circuits when possible
##  Use appropriate shot counts for statistical accuracy

### 4. Cost Optimization
## - Test on simulators before hardware execution
## - Use hardware credits efficiently
## - Monitor usage and billing

## Troubleshooting Common Issues

### Authentication Problems

# Clear and reset IBM Quantum account
IBMQ.delete_account()
IBMQ.save_account('NEW_API_TOKEN')
IBMQ.load_account()


### Circuit Compilation Errors

# Check circuit compatibility
def check_circuit_compatibility(circuit, backend):
    """Check if circuit is compatible with backend"""
    config = backend.configuration()
    
    # Check qubit count
    if circuit.num_qubits > config.n_qubits:
        print(f"Error: Circuit requires {circuit.num_qubits} qubits, backend has {config.n_qubits}")
        return False
    
    # Check gate compatibility
    circuit_gates = set(circuit.count_ops().keys())
    backend_gates = set(config.basis_gates)
    
    if not circuit_gates.issubset(backend_gates):
        unsupported = circuit_gates - backend_gates
        print(f"Warning: Unsupported gates: {unsupported}")
    
    return True

# Check compatibility before execution
if check_circuit_compatibility(circuit, hardware_backend.backend):
    result = hardware_backend.execute(circuit)


## Next Steps

###- Explore advanced error mitigation techniques
### - Implement custom calibration protocols
### - Develop hardware-aware algorithm design
### - Scale to larger quantum systems

## Further Reading

#@# - [IBM Quantum Documentation](https://qiskit.org/documentation/)
### - [Amazon Braket Documentation](https://docs.aws.amazon.com/braket/)
### - [Quantum Error Correction](https://arxiv.org/abs/quant-ph/0504218)

# Hardware Integration Tutorial # This tutorial demonstrates how to integrate quantum data embeddings with real quantum hardware platforms. ## Prerequisites ## Before starting this tutorial, ensure you have: # - Access to quantum hardware (IBM Quantum, IonQ, or Rigetti) # - Appropriate API credentials configured # - Understanding of quantum embedding basics ## IBM Quantum Integration ### Setting Up IBM Quantum Access from qiskit import IBMQ from quantum_data_embedding_suite.backends import QiskitBackend # Save your IBM Quantum account (do this once) # IBMQ.save_account('YOUR_API_TOKEN') # Load account IBMQ.load_account() # Get provider provider = IBMQ.get_provider(hub='ibm-q', group='open', project='main') # List available backends backends = provider.backends() print("Available IBM Quantum backends:") for backend in backends: print(f"- {backend.name()}: {backend.status().operational}") ### Using Real Quantum Hardware from quantum_data_embedding_suite.embeddings import AngleEmbedding from quantum_data_embedding_suite.kernels import FidelityKernel import numpy as np # Create embedding embedding = AngleEmbedding(n_qubits=4) # Configure hardware backend hardware_backend = QiskitBackend( provider=provider, backend_name="ibmq_manila", # Example backend shots=1024, optimization_level=2 ) # Set backend in embedding embedding.set_backend(hardware_backend) # Prepare data X = np.random.randn(10, 4) * 0.5 # Keep data bounded # Create kernel with hardware backend kernel = FidelityKernel(embedding=embedding) # Compute kernel matrix on real hardware print("Computing kernel matrix on real quantum hardware...") K = kernel.compute_kernel_matrix(X) print(f"Kernel matrix shape: {K.shape}") print(f"Kernel matrix condition number: {np.linalg.cond(K):.2e}") ## Error Mitigation Strategies ### Measurement Error Mitigation from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter def setup_error_mitigation(backend, qubits): """Setup measurement error mitigation""" # Create calibration circuits cal_circuits, state_labels = complete_meas_cal( qubit_list=qubits, circlabel='cal' ) # Execute calibration circuits cal_job = backend.run(cal_circuits, shots=8192) cal_results = cal_job.result() # Create fitter meas_fitter = CompleteMeasFitter(cal_results, state_labels) return meas_fitter # Setup error mitigation qubits = [0, 1, 2, 3] meas_filter = setup_error_mitigation(hardware_backend.backend, qubits) # Apply error mitigation to results def apply_error_mitigation(raw_counts, meas_filter): """Apply measurement error mitigation to results""" mitigated_counts = meas_filter.filter.apply(raw_counts) return mitigated_counts ### Zero-Noise Extrapolation def zero_noise_extrapolation(embedding, X, noise_levels, shots=1024): """Perform zero-noise extrapolation""" results = [] for noise_level in noise_levels: # Create noisy backend noisy_backend = create_noisy_backend(noise_level) embedding.set_backend(noisy_backend) # Compute metric with noise kernel = FidelityKernel(embedding=embedding) K = kernel.compute_kernel_matrix(X) # Store result results.append(np.trace(K)) # Extrapolate to zero noise from scipy.optimize import curve_fit def exponential_decay(x, a, b, c): return a * np.exp(-b * x) + c popt, _ = curve_fit(exponential_decay, noise_levels, results) zero_noise_result = popt[0] + popt[2] # a + c return zero_noise_result, results ## Cloud Platform Integration ### Amazon Braket Integration from braket.aws import AwsDevice from braket.devices import LocalSimulator # Use local simulator for development local_device = LocalSimulator() # Use cloud simulator sv1_device = AwsDevice("arn:aws:braket:::device/quantum-simulator/amazon/sv1") # Use real quantum hardware (IonQ) ionq_device = AwsDevice("arn:aws:braket:::device/qpu/ionq/ionQdevice") # Create Braket backend wrapper class BraketBackend: def __init__(self, device, shots=1024): self.device = device self.shots = shots def execute(self, circuit): """Execute circuit on Braket device""" # Convert circuit to Braket format braket_circuit = self.convert_to_braket(circuit) # Submit task task = self.device.run(braket_circuit, shots=self.shots) # Get results result = task.result() return result.measurement_counts def convert_to_braket(self, circuit): """Convert from Qiskit/PennyLane to Braket circuit""" # Implementation depends on source circuit format pass ### Google Cirq Integration import cirq from cirq.google import Foxtail # Create Cirq device device = Foxtail # Define quantum embedding circuit in Cirq def create_cirq_embedding(data, qubits): """Create embedding circuit in Cirq""" circuit = cirq.Circuit() for i, x in enumerate(data): circuit.append(cirq.ry(x).on(qubits[i % len(qubits)])) return circuit # Execute on Google quantum hardware (when available) # This requires access to Google Quantum AI ## Hardware-Specific Optimizations ### Connectivity-Aware Circuit Design def optimize_for_connectivity(circuit, coupling_map): """Optimize circuit for hardware connectivity""" from qiskit.compiler import transpile # Transpile with hardware constraints optimized_circuit = transpile( circuit, coupling_map=coupling_map, optimization_level=3, routing_method='sabre', translation_method='synthesis' ) return optimized_circuit # Example: Optimize for IBM hardware coupling_map = [[0, 1], [1, 0], [1, 2], [2, 1], [2, 3], [3, 2]] optimized_circuit = optimize_for_connectivity(circuit, coupling_map) ### Native Gate Decomposition def decompose_to_native_gates(circuit, basis_gates): """Decompose circuit to native hardware gates""" from qiskit.compiler import transpile transpiled = transpile( circuit, basis_gates=basis_gates, optimization_level=2 ) return transpiled # IBM native gates ibm_basis = ['id', 'rz', 'sx', 'x', 'cx'] native_circuit = decompose_to_native_gates(circuit, ibm_basis) ## Performance Monitoring ### Quantum Volume Assessment def assess_quantum_volume(backend, n_qubits): """Assess effective quantum volume of backend""" from qiskit.ignis.verification.quantum_volume import qv_circuits, QVFitter # Create quantum volume circuits qv_circs, qv_circs_nomeas = qv_circuits( qubit_lists=[[i for i in range(n_qubits)]], ntrials=100 ) # Execute circuits job = backend.run(qv_circs, shots=1024) qv_result = job.result() # Analyze results qv_fitter = QVFitter() qv_fitter.add_data(qv_result) # Check if quantum volume is achieved qv_success = qv_fitter.calc_statistics() return qv_success # Assess quantum volume qv_result = assess_quantum_volume(hardware_backend.backend, 4) print(f"Quantum Volume achieved: {qv_result}") ### Real-time Performance Tracking class HardwarePerformanceTracker: """Track hardware performance metrics""" def __init__(self): self.metrics = { 'execution_times': [], 'error_rates': [], 'queue_times': [], 'timestamps': [] } def track_execution(self, backend, circuit): """Track single execution performance""" import time start_time = time.time() # Submit job job = backend.run(circuit, shots=1024) # Wait for completion and track queue time queue_start = time.time() result = job.result() queue_time = time.time() - queue_start execution_time = time.time() - start_time # Estimate error rate from result fidelity error_rate = self.estimate_error_rate(result) # Record metrics self.metrics['execution_times'].append(execution_time) self.metrics['error_rates'].append(error_rate) self.metrics['queue_times'].append(queue_time) self.metrics['timestamps'].append(time.time()) def estimate_error_rate(self, result): """Estimate error rate from measurement results""" # Simple heuristic based on result distribution counts = result.get_counts() total_shots = sum(counts.values()) # Expect some distribution - high concentration suggests errors max_count = max(counts.values()) error_estimate = 1 - (max_count / total_shots) return error_estimate def generate_report(self): """Generate performance report""" import matplotlib.pyplot as plt fig, axes = plt.subplots(2, 2, figsize=(12, 8)) # Execution times axes[0, 0].plot(self.metrics['timestamps'], self.metrics['execution_times']) axes[0, 0].set_title('Execution Times') axes[0, 0].set_ylabel('Time (s)') # Error rates axes[0, 1].plot(self.metrics['timestamps'], self.metrics['error_rates']) axes[0, 1].set_title('Error Rates') axes[0, 1].set_ylabel('Error Rate') # Queue times axes[1, 0].plot(self.metrics['timestamps'], self.metrics['queue_times']) axes[1, 0].set_title('Queue Times') axes[1, 0].set_ylabel('Time (s)') # Summary statistics axes[1, 1].text(0.1, 0.8, f"Avg Execution: {np.mean(self.metrics['execution_times']):.2f}s") axes[1, 1].text(0.1, 0.6, f"Avg Error Rate: {np.mean(self.metrics['error_rates']):.3f}") axes[1, 1].text(0.1, 0.4, f"Avg Queue Time: {np.mean(self.metrics['queue_times']):.2f}s") axes[1, 1].set_title('Summary Statistics') axes[1, 1].axis('off') plt.tight_layout() plt.show() # Use performance tracker tracker = HardwarePerformanceTracker() # Track multiple executions for _ in range(10): tracker.track_execution(hardware_backend.backend, test_circuit) # Generate report tracker.generate_report() ## Best Practices for Hardware Execution ### 1. Circuit Optimization ## - Minimize circuit depth ## - Use native gates when possible ## - Apply connectivity-aware routing ### 2. Error Mitigation ## - Implement measurement error correction ## - Use zero-noise extrapolation for critical computations ## - Consider readout error mitigation ### 3. Resource Management ##- Monitor queue times and job status ## - Batch multiple circuits when possible ## Use appropriate shot counts for statistical accuracy ### 4. Cost Optimization ## - Test on simulators before hardware execution ## - Use hardware credits efficiently ## - Monitor usage and billing ## Troubleshooting Common Issues ### Authentication Problems # Clear and reset IBM Quantum account IBMQ.delete_account() IBMQ.save_account('NEW_API_TOKEN') IBMQ.load_account() ### Circuit Compilation Errors # Check circuit compatibility def check_circuit_compatibility(circuit, backend): """Check if circuit is compatible with backend""" config = backend.configuration() # Check qubit count if circuit.num_qubits > config.n_qubits: print(f"Error: Circuit requires {circuit.num_qubits} qubits, backend has {config.n_qubits}") return False # Check gate compatibility circuit_gates = set(circuit.count_ops().keys()) backend_gates = set(config.basis_gates) if not circuit_gates.issubset(backend_gates): unsupported = circuit_gates - backend_gates print(f"Warning: Unsupported gates: {unsupported}") return True # Check compatibility before execution if check_circuit_compatibility(circuit, hardware_backend.backend): result = hardware_backend.execute(circuit) ## Next Steps ###- Explore advanced error mitigation techniques ### - Implement custom calibration protocols ### - Develop hardware-aware algorithm design ### - Scale to larger quantum systems ## Further Reading #@# - [IBM Quantum Documentation](https://qiskit.org/documentation/) ### - [Amazon Braket Documentation](https://docs.aws.amazon.com/braket/) ### - [Quantum Error Correction](https://arxiv.org/abs/quant-ph/0504218)