Amazon Braket Backend Integration¶
Amazon Braket is AWS's fully managed quantum computing service that provides access to quantum computers from multiple hardware providers. SuperQuantX integrates with Braket to enable cloud-based quantum computing with enterprise-grade security and scalability.
Overview¶
The Amazon Braket backend in SuperQuantX provides:
- Multi-Vendor Hardware Access: IonQ, Rigetti, Oxford Quantum Computing, and more
- Cloud-Scale Simulation: High-performance quantum simulators
- Enterprise Integration: AWS IAM, VPC, and compliance features
- Hybrid Algorithms: Classical-quantum workload orchestration
- Cost Management: Pay-per-use pricing with detailed billing
Installation¶
# Install SuperQuantX with Braket support
pip install superquantx[braket]
# Or install Amazon Braket SDK directly
pip install amazon-braket-sdk boto3
AWS Setup¶
Configure AWS Credentials¶
# Option 1: AWS CLI
aws configure
# Enter your AWS Access Key ID, Secret Access Key, and region
# Option 2: Environment variables
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
export AWS_DEFAULT_REGION=us-west-2
IAM Permissions¶
Create IAM policy with required Braket permissions:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"braket:*",
"s3:GetObject",
"s3:PutObject",
"s3:ListBucket",
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "*"
}
]
}
Quick Start¶
Basic Usage¶
import superquantx as sqx
# Get Braket backend (uses local simulator by default)
backend = sqx.get_backend('braket')
print(f"Backend: {backend}")
# Create Bell state circuit
circuit = backend.create_circuit(2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure_all()
# Execute on local simulator
result = backend.run(circuit, shots=1000)
print(f"Bell state results: {result.get_counts()}")
Cloud Simulation¶
# Use AWS cloud simulators
backend = sqx.BraketBackend(
device_arn='arn:aws:braket:::device/quantum-simulator/amazon/sv1',
s3_bucket='my-braket-bucket',
s3_prefix='my-experiments',
shots=1000
)
result = backend.run(circuit, shots=1000)
print(f"Cloud simulation results: {result.get_counts()}")
Quantum Hardware Access¶
# Access IonQ quantum computer
ionq_backend = sqx.BraketBackend(
device_arn='arn:aws:braket:::device/qpu/ionq/ionQdevice',
s3_bucket='my-braket-bucket',
s3_prefix='ionq-experiments',
shots=100 # Reduced shots for cost control
)
# Execute on real quantum hardware
try:
result = ionq_backend.run(circuit, shots=100)
print(f"IonQ hardware results: {result.get_counts()}")
except Exception as e:
print(f"Hardware execution requires active device: {e}")
Device Selection¶
Available Devices¶
def list_braket_devices():
"""List all available Braket devices."""
try:
from braket.aws import AwsDevice
from braket.device_schema import DeviceActionType
print("Available Amazon Braket Devices:")
print("=" * 40)
# Get all devices
devices = AwsDevice.get_devices()
# Group by provider
providers = {}
for device in devices:
provider = device.provider_name
if provider not in providers:
providers[provider] = []
providers[provider].append(device)
for provider, device_list in providers.items():
print(f"\n{provider}:")
for device in device_list:
status = "ONLINE" if device.is_available else "OFFLINE"
device_type = device.type.value
print(f" {device.name} ({device_type}): {status}")
print(f" ARN: {device.arn}")
# Show device properties
if hasattr(device, 'properties'):
props = device.properties
if hasattr(props, 'paradigm'):
if hasattr(props.paradigm, 'qubitCount'):
print(f" Qubits: {props.paradigm.qubitCount}")
if hasattr(props.paradigm, 'connectivity'):
connectivity = props.paradigm.connectivity
if hasattr(connectivity, 'fullyConnected'):
if connectivity.fullyConnected:
print(f" Connectivity: Fully connected")
else:
print(f" Connectivity: Limited")
print()
except ImportError:
print("Braket device listing requires: pip install amazon-braket-sdk")
except Exception as e:
print(f"Error listing devices: {e}")
list_braket_devices()
Device-Specific Configuration¶
def configure_for_device(device_type='simulator'):
"""Configure backend for specific device types."""
if device_type == 'ionq':
return sqx.BraketBackend(
device_arn='arn:aws:braket:::device/qpu/ionq/ionQdevice',
s3_bucket='my-braket-bucket',
s3_prefix='ionq-jobs',
shots=100, # Cost control
max_parallel_threads=1
)
elif device_type == 'rigetti':
return sqx.BraketBackend(
device_arn='arn:aws:braket:::device/qpu/rigetti/Aspen-M-3',
s3_bucket='my-braket-bucket',
s3_prefix='rigetti-jobs',
shots=1000,
poll_timeout_seconds=86400 # 24 hours for queue
)
elif device_type == 'sv1':
# State vector simulator (up to 34 qubits)
return sqx.BraketBackend(
device_arn='arn:aws:braket:::device/quantum-simulator/amazon/sv1',
s3_bucket='my-braket-bucket',
s3_prefix='sv1-jobs',
shots=0 # Exact simulation
)
elif device_type == 'tn1':
# Tensor network simulator (up to 50 qubits)
return sqx.BraketBackend(
device_arn='arn:aws:braket:::device/quantum-simulator/amazon/tn1',
s3_bucket='my-braket-bucket',
s3_prefix='tn1-jobs',
shots=1000
)
else:
# Local simulator
return sqx.BraketBackend(
device_arn='local:braket/braket.devices.LocalSimulator',
shots=1000
)
# Test different devices
devices = ['simulator', 'sv1', 'tn1']
for device in devices:
try:
backend = configure_for_device(device)
print(f"✅ {device} backend configured successfully")
except Exception as e:
print(f"❌ {device} configuration failed: {e}")
Working with Circuits¶
Native Braket Gates¶
def demonstrate_braket_gates():
"""Show Braket-native gate operations."""
backend = sqx.get_backend('braket')
# Create circuit with native Braket gates
circuit = backend.create_circuit(4)
# Single-qubit gates
circuit.h(0) # Hadamard
circuit.x(1) # Pauli-X
circuit.y(2) # Pauli-Y
circuit.z(3) # Pauli-Z
# Rotation gates
import numpy as np
circuit.rx(np.pi/4, 0) # Rx rotation
circuit.ry(np.pi/3, 1) # Ry rotation
circuit.rz(np.pi/6, 2) # Rz rotation
# Phase gates
circuit.s(0) # S gate
circuit.t(1) # T gate
# Two-qubit gates
circuit.cx(0, 1) # CNOT
circuit.cz(1, 2) # Controlled-Z
circuit.swap(2, 3) # SWAP
# Parametric gates
circuit.phaseshift(np.pi/4, 0) # Phase shift
# Add measurements
circuit.measure_all()
# Execute circuit
result = backend.run(circuit, shots=1000)
print(f"Braket gates demonstration: {result.get_counts()}")
return circuit
demo_circuit = demonstrate_braket_gates()
Advanced Circuit Features¶
def braket_advanced_circuits():
"""Demonstrate advanced Braket circuit features."""
from braket.circuits import Circuit
from braket.circuits.gates import *
import numpy as np
print("Advanced Braket Circuit Features")
print("=" * 35)
# 1. Parameterized circuits
def create_parameterized_ansatz(params, n_qubits=4, depth=2):
"""Create parameterized ansatz for VQE."""
circuit = Circuit()
param_idx = 0
# Initial layer
for i in range(n_qubits):
circuit.ry(params[param_idx], i)
param_idx += 1
# Entangling layers
for layer in range(depth):
# Two-qubit gates
for i in range(n_qubits - 1):
circuit.cnot(i, i + 1)
# Parameterized single-qubit gates
for i in range(n_qubits):
circuit.ry(params[param_idx], i)
param_idx += 1
circuit.rz(params[param_idx], i)
param_idx += 1
return circuit
# Generate parameters
n_params = 4 + 2 * 2 * 4 # Initial + depth * 2 * qubits
params = np.random.random(n_params) * 2 * np.pi
ansatz = create_parameterized_ansatz(params)
print(f"Parameterized ansatz created with {n_params} parameters")
# 2. Noise modeling
def add_noise_to_circuit(circuit):
"""Add noise to circuit."""
from braket.circuits.noises import *
noisy_circuit = circuit.copy()
# Add depolarizing noise after each gate
for instruction in circuit.instructions:
if len(instruction.target) == 1: # Single-qubit gate
qubit = instruction.target[0]
noisy_circuit.depolarizing(qubit, probability=0.001)
elif len(instruction.target) == 2: # Two-qubit gate
for qubit in instruction.target:
noisy_circuit.depolarizing(qubit, probability=0.005)
return noisy_circuit
# Create test circuit and add noise
test_circuit = Circuit()
test_circuit.h(0).cnot(0, 1).ry(np.pi/4, 0)
noisy_circuit = add_noise_to_circuit(test_circuit)
print(f"Added noise to circuit with {len(test_circuit.instructions)} instructions")
# 3. Result types
def demonstrate_result_types():
"""Show different measurement result types."""
from braket.circuits import observables
circuit = Circuit()
circuit.h(0).cnot(0, 1)
# Probability measurement
circuit.probability(target=[0, 1])
# Expectation value measurement
circuit.expectation(observable=observables.Z(), target=0)
# Variance measurement
circuit.variance(observable=observables.X(), target=0)
# Sample measurement (default)
circuit.sample(observable=observables.Z(), target=[0, 1])
return circuit
result_circuit = demonstrate_result_types()
print(f"Circuit with multiple result types created")
braket_advanced_circuits()
Cost Management¶
Cost Estimation¶
def estimate_braket_costs():
"""Estimate costs for different Braket services."""
print("Amazon Braket Cost Estimation")
print("=" * 32)
# Current pricing (as of 2024 - check AWS for latest)
pricing = {
'sv1': {
'per_task': 0.075, # $0.075 per task
'per_shot': 0.00019 # $0.00019 per shot
},
'tn1': {
'per_task': 0.275, # $0.275 per task
'per_shot': 0.00019 # $0.00019 per shot
},
'dm1': {
'per_task': 0.075,
'per_shot': 0.00019
},
'ionq': {
'per_task': 0.00, # Included in shot pricing
'per_shot': 0.01 # $0.01 per shot
},
'rigetti': {
'per_task': 0.00375, # $0.00375 per task
'per_shot': 0.00035 # $0.00035 per shot
}
}
# Example workloads
workloads = {
'Development': {'tasks': 100, 'shots_per_task': 1000},
'Research': {'tasks': 50, 'shots_per_task': 5000},
'Production': {'tasks': 20, 'shots_per_task': 10000}
}
for workload_name, workload in workloads.items():
print(f"\n{workload_name} Workload:")
print(f" Tasks: {workload['tasks']}")
print(f" Shots per task: {workload['shots_per_task']}")
print(f" Total shots: {workload['tasks'] * workload['shots_per_task']:,}")
print(f"\n Cost estimates:")
for device, prices in pricing.items():
task_cost = workload['tasks'] * prices['per_task']
shot_cost = workload['tasks'] * workload['shots_per_task'] * prices['per_shot']
total_cost = task_cost + shot_cost
print(f" {device:8}: ${total_cost:8.2f} "
f"(${task_cost:.2f} tasks + ${shot_cost:.2f} shots)")
estimate_braket_costs()
Cost Optimization¶
def optimize_braket_usage():
"""Demonstrate cost optimization strategies."""
print("Braket Cost Optimization Strategies")
print("=" * 38)
# Strategy 1: Use local simulator for development
def development_workflow():
"""Use free local simulator for development."""
local_backend = sqx.BraketBackend(
device_arn='local:braket/braket.devices.LocalSimulator',
shots=10000 # No cost for shots
)
print("✅ Strategy 1: Use local simulator for development")
print(" - Free execution")
print(" - Fast iteration")
print(" - Good for circuit testing")
return local_backend
# Strategy 2: Optimize shot counts
def optimize_shots(circuit, target_accuracy=0.01):
"""Determine optimal shot count for accuracy."""
# Rough estimate: std_error = 1/sqrt(shots)
# For 1% accuracy: 1/sqrt(shots) = 0.01 -> shots = 10,000
optimal_shots = int(1 / (target_accuracy ** 2))
print(f"✅ Strategy 2: Optimize shot counts")
print(f" - Target accuracy: {target_accuracy:.1%}")
print(f" - Recommended shots: {optimal_shots:,}")
return min(optimal_shots, 100000) # Cap at 100k for cost control
# Strategy 3: Batch multiple circuits
def batch_circuits(circuits):
"""Batch multiple circuits to reduce task overhead."""
print(f"✅ Strategy 3: Batch circuits")
print(f" - {len(circuits)} circuits in one task")
print(f" - Reduces task-based charges")
print(f" - Better resource utilization")
# In practice, you would submit all circuits together
return circuits
# Strategy 4: Choose appropriate simulators
def choose_simulator(n_qubits, circuit_depth):
"""Choose most cost-effective simulator."""
recommendations = []
if n_qubits <= 25 and circuit_depth <= 100:
recommendations.append(("SV1", "Fast and cost-effective"))
if n_qubits <= 50:
recommendations.append(("TN1", "Handles larger circuits"))
if n_qubits <= 17: # Density matrix limit
recommendations.append(("DM1", "Supports noise modeling"))
print(f"✅ Strategy 4: Choose appropriate simulator")
print(f" Circuit: {n_qubits} qubits, depth {circuit_depth}")
for sim, desc in recommendations:
print(f" - {sim}: {desc}")
return recommendations[0][0] if recommendations else "Local"
# Strategy 5: Monitor costs
def setup_cost_monitoring():
"""Set up cost monitoring and alerts."""
print(f"✅ Strategy 5: Cost monitoring")
print(f" - Use AWS Cost Explorer")
print(f" - Set up billing alerts")
print(f" - Tag resources for tracking")
print(f" - Regular cost reviews")
# Example usage
development_workflow()
print()
shots = optimize_shots(None, target_accuracy=0.02) # 2% accuracy
print()
test_circuits = [f"circuit_{i}" for i in range(5)]
batch_circuits(test_circuits)
print()
best_sim = choose_simulator(n_qubits=15, circuit_depth=50)
print()
setup_cost_monitoring()
optimize_braket_usage()
Hybrid Algorithms¶
Classical-Quantum Integration¶
def braket_hybrid_algorithm_example():
"""Demonstrate hybrid classical-quantum algorithm."""
from braket.circuits import Circuit
from braket.circuits.gates import *
import numpy as np
from scipy.optimize import minimize
print("Hybrid Algorithm: VQE with Braket")
print("=" * 35)
class BraketVQE:
"""Variational Quantum Eigensolver using Braket."""
def __init__(self, hamiltonian, backend_arn='local:braket/braket.devices.LocalSimulator'):
self.hamiltonian = hamiltonian # List of (coeff, pauli_string) tuples
self.backend = sqx.BraketBackend(device_arn=backend_arn)
self.n_qubits = len(hamiltonian[0][1]) # Length of Pauli string
def create_ansatz(self, params):
"""Create variational ansatz circuit."""
circuit = Circuit()
param_idx = 0
# Initial Y rotations
for i in range(self.n_qubits):
circuit.ry(params[param_idx], i)
param_idx += 1
# Entangling layer
for i in range(self.n_qubits - 1):
circuit.cnot(i, i + 1)
# Final rotations
for i in range(self.n_qubits):
circuit.ry(params[param_idx], i)
param_idx += 1
circuit.rz(params[param_idx], i)
param_idx += 1
return circuit
def measure_pauli_expectation(self, circuit, pauli_string):
"""Measure expectation value of Pauli string."""
# Convert Pauli string to measurements
measurement_circuit = circuit.copy()
# Add basis rotations for X and Y measurements
for i, pauli in enumerate(pauli_string):
if pauli == 'X':
measurement_circuit.ry(-np.pi/2, i) # Rotate Y basis to Z
elif pauli == 'Y':
measurement_circuit.rx(np.pi/2, i) # Rotate X basis to Z
# Z measurements need no rotation
# Add measurements
for i in range(self.n_qubits):
measurement_circuit.sample(observable=observables.Z(), target=i)
# Execute circuit
result = self.backend.run(measurement_circuit, shots=1000)
# Calculate expectation value (simplified)
# In practice, you'd need to properly handle the measurement results
# This is a placeholder for the actual calculation
return np.random.random() * 2 - 1 # Random value in [-1, 1]
def compute_energy(self, params):
"""Compute energy expectation value."""
ansatz = self.create_ansatz(params)
energy = 0.0
for coeff, pauli_string in self.hamiltonian:
expectation = self.measure_pauli_expectation(ansatz, pauli_string)
energy += coeff * expectation
return energy
def optimize(self, initial_params=None, method='COBYLA', maxiter=100):
"""Optimize VQE parameters."""
if initial_params is None:
n_params = 2 * self.n_qubits + self.n_qubits # RY + RY + RZ
initial_params = np.random.random(n_params) * 2 * np.pi
print(f"Starting VQE optimization...")
print(f" Hamiltonian terms: {len(self.hamiltonian)}")
print(f" Parameters: {len(initial_params)}")
print(f" Optimizer: {method}")
def cost_function(params):
energy = self.compute_energy(params)
print(f" Energy: {energy:.6f}")
return energy
result = minimize(
cost_function,
initial_params,
method=method,
options={'maxiter': maxiter}
)
return result
# Example: H2 molecule Hamiltonian
h2_hamiltonian = [
(-1.0523732, 'II'), # Identity
(0.39793742, 'ZI'), # Z on qubit 0
(-0.39793742, 'IZ'), # Z on qubit 1
(-0.01128010, 'ZZ'), # Z on both qubits
(0.18093119, 'XX') # X on both qubits
]
# Run VQE
vqe = BraketVQE(h2_hamiltonian)
# Optimize (using fewer iterations for demo)
result = vqe.optimize(maxiter=5)
print(f"\nVQE Results:")
print(f" Ground state energy: {result.fun:.6f}")
print(f" Optimization success: {result.success}")
print(f" Function evaluations: {result.nfev}")
# Compare with classical result
classical_energy = -1.8 # Approximate H2 ground state
quantum_energy = result.fun
if abs(quantum_energy - classical_energy) < 0.1:
print(f"✅ Quantum result close to classical expectation")
else:
print(f"⚠️ Quantum result differs from classical (normal for demo)")
braket_hybrid_algorithm_example()
Error Handling and Monitoring¶
Robust Job Management¶
def braket_job_management():
"""Demonstrate robust Braket job management."""
import time
from braket.aws import AwsDevice
from braket.circuits import Circuit
print("Braket Job Management")
print("=" * 22)
class RobustBraketExecutor:
"""Robust executor with error handling and retries."""
def __init__(self, device_arn, s3_bucket, max_retries=3):
self.device_arn = device_arn
self.s3_bucket = s3_bucket
self.max_retries = max_retries
self.job_history = []
def submit_with_retry(self, circuit, shots=1000, job_name=None):
"""Submit job with automatic retry logic."""
for attempt in range(self.max_retries):
try:
print(f"Attempt {attempt + 1}: Submitting job...")
backend = sqx.BraketBackend(
device_arn=self.device_arn,
s3_bucket=self.s3_bucket,
s3_prefix=f'attempt_{attempt}',
shots=shots
)
start_time = time.time()
result = backend.run(circuit, shots=shots)
execution_time = time.time() - start_time
# Record successful job
job_info = {
'attempt': attempt + 1,
'status': 'success',
'execution_time': execution_time,
'shots': shots,
'timestamp': time.time()
}
self.job_history.append(job_info)
print(f"✅ Job completed successfully in {execution_time:.2f}s")
return result
except Exception as e:
error_info = {
'attempt': attempt + 1,
'status': 'failed',
'error': str(e),
'timestamp': time.time()
}
self.job_history.append(error_info)
print(f"❌ Attempt {attempt + 1} failed: {e}")
if attempt < self.max_retries - 1:
wait_time = 2 ** attempt # Exponential backoff
print(f" Waiting {wait_time}s before retry...")
time.sleep(wait_time)
else:
print(f" Max retries exceeded. Job failed.")
raise e
def get_job_statistics(self):
"""Get job execution statistics."""
if not self.job_history:
return {"message": "No jobs executed yet"}
successful_jobs = [j for j in self.job_history if j['status'] == 'success']
failed_jobs = [j for j in self.job_history if j['status'] == 'failed']
stats = {
'total_jobs': len(self.job_history),
'successful_jobs': len(successful_jobs),
'failed_jobs': len(failed_jobs),
'success_rate': len(successful_jobs) / len(self.job_history) * 100,
}
if successful_jobs:
execution_times = [j['execution_time'] for j in successful_jobs]
stats['avg_execution_time'] = np.mean(execution_times)
stats['total_execution_time'] = np.sum(execution_times)
return stats
# Example usage
executor = RobustBraketExecutor(
device_arn='local:braket/braket.devices.LocalSimulator',
s3_bucket='my-braket-experiments' # Would be real S3 bucket
)
# Create test circuit
test_circuit = Circuit()
test_circuit.h(0).cnot(0, 1)
# Submit job with retry logic
try:
result = executor.submit_with_retry(test_circuit, shots=1000)
print(f"Job result: {len(result.get_counts() if hasattr(result, 'get_counts') else {})} outcomes")
except Exception as e:
print(f"Final job failure: {e}")
# Get statistics
stats = executor.get_job_statistics()
print(f"\nJob Statistics:")
for key, value in stats.items():
print(f" {key}: {value}")
braket_job_management()
Best Practices¶
Security and Compliance¶
def braket_security_best_practices():
"""Demonstrate Braket security best practices."""
print("Braket Security Best Practices")
print("=" * 32)
practices = [
{
"category": "IAM and Access Control",
"practices": [
"Use least-privilege IAM policies",
"Rotate access keys regularly",
"Use IAM roles for service-to-service access",
"Enable CloudTrail for API logging",
"Use VPC endpoints for private access"
]
},
{
"category": "Data Protection",
"practices": [
"Encrypt S3 buckets with KMS",
"Use secure S3 bucket policies",
"Enable S3 versioning for data recovery",
"Clean up intermediate results",
"Avoid storing sensitive data in circuits"
]
},
{
"category": "Cost Control",
"practices": [
"Set up billing alerts",
"Use resource tags for cost tracking",
"Implement circuit validation before submission",
"Monitor queue times and costs",
"Use simulators for development"
]
},
{
"category": "Development Workflow",
"practices": [
"Test circuits locally first",
"Use version control for quantum code",
"Implement proper error handling",
"Monitor job success rates",
"Document quantum algorithms and parameters"
]
}
]
for category_info in practices:
print(f"\n{category_info['category']}:")
for i, practice in enumerate(category_info['practices'], 1):
print(f" {i}. {practice}")
# Example secure configuration
print(f"\nExample Secure Configuration:")
print(f"```python")
print(f"backend = sqx.BraketBackend(")
print(f" device_arn='arn:aws:braket:::device/quantum-simulator/amazon/sv1',")
print(f" s3_bucket='my-secure-braket-bucket-{{region}}-{{account}}',")
print(f" s3_prefix='quantum-experiments/{{project}}/{{date}}',")
print(f" kms_key='arn:aws:kms:region:account:key/key-id',")
print(f" shots=1000,")
print(f" tags={{'Project': 'QuantumML', 'Owner': 'TeamName'}}")
print(f")")
print(f"```")
braket_security_best_practices()
Performance Optimization¶
def braket_performance_optimization():
"""Optimize performance for Braket workloads."""
print("Braket Performance Optimization")
print("=" * 33)
# 1. Circuit optimization
def optimize_circuit_for_braket(circuit):
"""Optimize circuit for Braket execution."""
optimizations = [
"Reduce circuit depth where possible",
"Use native gate sets when available",
"Minimize measurements",
"Batch similar circuits together",
"Pre-validate circuits locally"
]
print("Circuit Optimization:")
for i, opt in enumerate(optimizations, 1):
print(f" {i}. {opt}")
return circuit
# 2. Device selection
def select_optimal_device(circuit_qubits, circuit_depth, budget='medium'):
"""Select optimal device based on circuit characteristics."""
recommendations = []
if circuit_qubits <= 12 and budget == 'low':
recommendations.append(("Local Simulator", "Free, fast for development"))
if circuit_qubits <= 34:
recommendations.append(("SV1", "Cost-effective, exact simulation"))
if circuit_qubits <= 50:
recommendations.append(("TN1", "Large-scale simulation"))
if circuit_qubits <= 32 and budget == 'high':
recommendations.append(("IonQ", "Real quantum hardware"))
print(f"Device Selection for {circuit_qubits} qubits, depth {circuit_depth}:")
for device, desc in recommendations:
print(f" ✅ {device}: {desc}")
return recommendations[0][0] if recommendations else "Local Simulator"
# 3. Execution strategies
def execution_strategies():
"""Different execution strategies for various use cases."""
strategies = {
"Development": {
"device": "Local Simulator",
"shots": 10000,
"validation": "Extensive local testing"
},
"Research": {
"device": "SV1 or TN1",
"shots": 5000,
"validation": "Cloud simulation validation"
},
"Production": {
"device": "Quantum Hardware",
"shots": 1000,
"validation": "Full pipeline testing"
}
}
print("Execution Strategies:")
for strategy, config in strategies.items():
print(f" {strategy}:")
for key, value in config.items():
print(f" {key}: {value}")
print()
# Example optimization workflow
optimize_circuit_for_braket(None)
print()
best_device = select_optimal_device(circuit_qubits=16, circuit_depth=20)
print()
execution_strategies()
braket_performance_optimization()
Resources and Support¶
Documentation and Learning¶
def braket_resources():
"""Comprehensive Braket resources and support."""
resources = {
"Official Documentation": [
"AWS Braket Developer Guide",
"Braket Python SDK Documentation",
"Quantum Computing with Amazon Braket",
"Device-specific documentation"
],
"Learning Materials": [
"Braket Tutorials and Examples",
"Quantum Computing Fundamentals",
"Hybrid Algorithm Patterns",
"Cost optimization guides"
],
"Community and Support": [
"AWS Quantum Computing Blog",
"Braket GitHub Repository",
"AWS Support (for Enterprise customers)",
"Quantum computing forums"
],
"Hardware Partner Resources": [
"IonQ Documentation and API",
"Rigetti Documentation",
"Oxford Quantum Computing",
"Hardware-specific calibration data"
]
}
print("Amazon Braket Resources")
print("=" * 25)
for category, items in resources.items():
print(f"\n{category}:")
for item in items:
print(f" • {item}")
print(f"\nKey Links:")
print(f" 📖 Docs: https://docs.aws.amazon.com/braket/")
print(f" 🐍 SDK: https://github.com/amazon-braket/amazon-braket-sdk-python")
print(f" 💡 Examples: https://github.com/amazon-braket/amazon-braket-examples")
print(f" 💰 Pricing: https://aws.amazon.com/braket/pricing/")
braket_resources()
Amazon Braket's integration with SuperQuantX provides enterprise-grade quantum computing capabilities with cloud scalability, multi-vendor hardware access, and robust security features. It's ideal for organizations looking to integrate quantum computing into their existing AWS infrastructure.
Cost Control
Always test circuits on local simulators first, then use cloud simulators, and finally quantum hardware. Use shot optimization and batching to minimize costs.
Hardware Queues
Quantum hardware devices may have significant queue times and limited availability. Plan accordingly and have fallback options.
Enterprise Features
Braket provides enterprise features like VPC support, encryption, compliance certifications, and integration with other AWS services for production quantum computing workflows.