Performance Optimization¶
This guide covers advanced techniques for optimizing the performance of the Probabilistic Quantum Reasoner across different hardware platforms and use cases.
Performance Analysis Framework¶
Profiling Tools¶
import time
import memory_profiler
import cProfile
import pstats
from contextlib import contextmanager
from probabilistic_quantum_reasoner import ProbabilisticQuantumReasoner
class PerformanceProfiler:
"""Comprehensive performance profiling for PQR."""
def __init__(self):
self.measurements = {}
self.memory_usage = {}
@contextmanager
def profile_time(self, operation_name):
"""Profile execution time of an operation."""
start_time = time.perf_counter()
start_memory = memory_profiler.memory_usage()[0]
try:
yield
finally:
end_time = time.perf_counter()
end_memory = memory_profiler.memory_usage()[0]
self.measurements[operation_name] = {
'execution_time': end_time - start_time,
'memory_delta': end_memory - start_memory,
'peak_memory': max(memory_profiler.memory_usage())
}
def profile_function(self, func, *args, **kwargs):
"""Profile a function with detailed statistics."""
profiler = cProfile.Profile()
start_time = time.perf_counter()
profiler.enable()
result = func(*args, **kwargs)
profiler.disable()
end_time = time.perf_counter()
# Analyze profile data
stats = pstats.Stats(profiler)
stats.sort_stats('cumulative')
return {
'result': result,
'total_time': end_time - start_time,
'stats': stats,
'top_functions': stats.get_stats_profile().func_profiles
}
def benchmark_inference(self, network, reasoner, num_trials=100):
"""Benchmark inference performance."""
times = []
memory_usage = []
for _ in range(num_trials):
with self.profile_time("inference_trial"):
result = reasoner.infer(
network=network,
query=list(network.nodes.keys())[:3], # First 3 nodes
evidence={}
)
times.append(self.measurements["inference_trial"]["execution_time"])
memory_usage.append(self.measurements["inference_trial"]["memory_delta"])
return {
'mean_time': np.mean(times),
'std_time': np.std(times),
'min_time': np.min(times),
'max_time': np.max(times),
'mean_memory': np.mean(memory_usage),
'total_memory': np.sum(memory_usage)
}
# Usage example
profiler = PerformanceProfiler()
# Profile network creation
with profiler.profile_time("network_creation"):
network = create_large_network(num_nodes=50)
# Profile inference
reasoner = ProbabilisticQuantumReasoner(backend="classical")
benchmark_results = profiler.benchmark_inference(network, reasoner)
print(f"Average inference time: {benchmark_results['mean_time']:.3f}s")
print(f"Memory usage: {benchmark_results['mean_memory']:.1f} MB")
Memory Optimization¶
import gc
import weakref
from typing import Dict, Any, Optional
class MemoryOptimizer:
"""Memory optimization utilities for PQR."""
def __init__(self):
self.cache_size_limit = 1000 # Maximum cached items
self.weak_references = weakref.WeakSet()
def optimize_network_memory(self, network):
"""Optimize memory usage of a Bayesian network."""
# Convert dense arrays to sparse where appropriate
for node_name, node in network.nodes.items():
if hasattr(node, 'cpt'):
node.cpt = self._sparsify_cpt(node.cpt)
if hasattr(node, 'parents'):
# Use weak references for parent relationships
node._parent_refs = [weakref.ref(p) for p in node.parents]
# Compact string representations
self._intern_node_names(network)
return network
def _sparsify_cpt(self, cpt, threshold=1e-6):
"""Convert dense CPT to sparse representation if beneficial."""
import scipy.sparse as sp
if isinstance(cpt, np.ndarray):
# Check sparsity
zero_ratio = np.count_nonzero(cpt < threshold) / cpt.size
if zero_ratio > 0.5: # More than 50% zeros
return sp.csr_matrix(cpt)
return cpt
def _intern_node_names(self, network):
"""Intern string names to save memory."""
name_pool = {}
for node in network.nodes.values():
# Intern node name
if node.name not in name_pool:
name_pool[node.name] = sys.intern(node.name)
node.name = name_pool[node.name]
# Intern state names
if hasattr(node, 'states'):
for i, state in enumerate(node.states):
if state not in name_pool:
name_pool[state] = sys.intern(state)
node.states[i] = name_pool[state]
def cleanup_unused_objects(self):
"""Force cleanup of unused objects."""
# Clear weak references to deleted objects
self.weak_references = weakref.WeakSet([
obj for obj in self.weak_references if obj is not None
])
# Force garbage collection
collected = gc.collect()
print(f"Garbage collected {collected} objects")
return collected
def monitor_memory_usage(self, func, *args, **kwargs):
"""Monitor memory usage during function execution."""
import psutil
import os
process = psutil.Process(os.getpid())
# Initial memory
initial_memory = process.memory_info().rss / 1024 / 1024 # MB
# Execute function
result = func(*args, **kwargs)
# Final memory
final_memory = process.memory_info().rss / 1024 / 1024 # MB
return {
'result': result,
'memory_delta': final_memory - initial_memory,
'peak_memory': final_memory
}
# Memory optimization example
optimizer = MemoryOptimizer()
# Optimize existing network
optimized_network = optimizer.optimize_network_memory(large_network)
# Monitor memory during inference
memory_result = optimizer.monitor_memory_usage(
reasoner.infer,
network=optimized_network,
query=["target_node"],
evidence={}
)
print(f"Memory delta: {memory_result['memory_delta']:.1f} MB")
Quantum Circuit Optimization¶
Circuit Depth Reduction¶
from probabilistic_quantum_reasoner.optimization import CircuitOptimizer
class QuantumCircuitOptimizer:
"""Optimize quantum circuits for better performance."""
def __init__(self):
self.optimization_passes = [
'single_qubit_merge',
'commutative_cancellation',
'redundant_gate_removal',
'circuit_depth_reduction'
]
def optimize_circuit(self, circuit, optimization_level=3):
"""Optimize quantum circuit based on optimization level."""
optimized_circuit = circuit.copy()
if optimization_level >= 1:
optimized_circuit = self._merge_single_qubit_gates(optimized_circuit)
if optimization_level >= 2:
optimized_circuit = self._cancel_commutative_gates(optimized_circuit)
optimized_circuit = self._remove_redundant_gates(optimized_circuit)
if optimization_level >= 3:
optimized_circuit = self._reduce_circuit_depth(optimized_circuit)
optimized_circuit = self._optimize_two_qubit_gates(optimized_circuit)
return optimized_circuit
def _merge_single_qubit_gates(self, circuit):
"""Merge consecutive single-qubit gates."""
new_gates = []
gate_buffer = {} # qubit -> list of gates
for gate_info in circuit.gates:
gate_name, qubits, params = gate_info
if len(qubits) == 1: # Single-qubit gate
qubit = qubits[0]
if qubit not in gate_buffer:
gate_buffer[qubit] = []
gate_buffer[qubit].append(gate_info)
else:
# Flush single-qubit gate buffer
for q, gates in gate_buffer.items():
if gates:
merged_gate = self._merge_single_qubit_sequence(gates)
if merged_gate:
new_gates.append(merged_gate)
gate_buffer.clear()
# Add two-qubit gate
new_gates.append(gate_info)
# Flush remaining gates
for q, gates in gate_buffer.items():
if gates:
merged_gate = self._merge_single_qubit_sequence(gates)
if merged_gate:
new_gates.append(merged_gate)
circuit.gates = new_gates
return circuit
def _merge_single_qubit_sequence(self, gate_sequence):
"""Merge a sequence of single-qubit gates into one."""
if not gate_sequence:
return None
# Compute combined rotation matrix
combined_matrix = np.eye(2, dtype=complex)
qubit = gate_sequence[0][1][0]
for gate_name, qubits, params in gate_sequence:
gate_matrix = self._get_gate_matrix(gate_name, params)
combined_matrix = gate_matrix @ combined_matrix
# Convert back to rotation parameters
if np.allclose(combined_matrix, np.eye(2)):
return None # Identity operation, can be removed
# Decompose into rotation gates (simplified)
return self._matrix_to_rotation_gates(combined_matrix, qubit)
def _cancel_commutative_gates(self, circuit):
"""Cancel commuting gates that result in identity."""
new_gates = []
i = 0
while i < len(circuit.gates):
gate = circuit.gates[i]
# Look for canceling pair
if i + 1 < len(circuit.gates):
next_gate = circuit.gates[i + 1]
if self._gates_cancel(gate, next_gate):
i += 2 # Skip both gates
continue
new_gates.append(gate)
i += 1
circuit.gates = new_gates
return circuit
def _gates_cancel(self, gate1, gate2):
"""Check if two gates cancel each other."""
gate1_name, qubits1, params1 = gate1
gate2_name, qubits2, params2 = gate2
# Same gate on same qubits
if gate1_name == gate2_name and qubits1 == qubits2:
# Check if they're inverses
if gate1_name in ['X', 'Y', 'Z', 'H']:
return True # Self-inverse gates
elif gate1_name.startswith('R') and params1 and params2:
return abs(params1[0] + params2[0]) < 1e-10 # Opposite rotations
return False
def _reduce_circuit_depth(self, circuit):
"""Reduce circuit depth by parallelizing commuting gates."""
# Group gates by dependency
dependency_graph = self._build_dependency_graph(circuit)
# Topological sort to find parallelizable operations
parallel_layers = self._extract_parallel_layers(dependency_graph)
# Reconstruct circuit with optimized ordering
new_gates = []
for layer in parallel_layers:
new_gates.extend(layer)
circuit.gates = new_gates
return circuit
def _build_dependency_graph(self, circuit):
"""Build dependency graph for circuit gates."""
dependencies = {}
qubit_last_gate = {} # qubit -> last gate index
for i, (gate_name, qubits, params) in enumerate(circuit.gates):
dependencies[i] = []
# Add dependencies on previous gates affecting same qubits
for qubit in qubits:
if qubit in qubit_last_gate:
dependencies[i].append(qubit_last_gate[qubit])
qubit_last_gate[qubit] = i
return dependencies
def _extract_parallel_layers(self, dependency_graph):
"""Extract layers of parallelizable gates."""
layers = []
remaining_gates = set(dependency_graph.keys())
while remaining_gates:
# Find gates with no remaining dependencies
ready_gates = []
for gate_idx in remaining_gates:
if all(dep not in remaining_gates for dep in dependency_graph[gate_idx]):
ready_gates.append(gate_idx)
if not ready_gates:
# Circular dependency or error
ready_gates = list(remaining_gates)
layers.append(ready_gates)
remaining_gates -= set(ready_gates)
return layers
# Circuit optimization example
circuit_optimizer = QuantumCircuitOptimizer()
# Create test circuit
circuit = backend.create_circuit(num_qubits=4)
circuit.add_hadamard(0)
circuit.add_hadamard(0) # Redundant - will be removed
circuit.add_cnot(0, 1)
circuit.add_rotation("RY", 2, np.pi/4)
circuit.add_rotation("RY", 2, -np.pi/4) # Cancels previous rotation
print(f"Original circuit depth: {circuit.depth()}")
# Optimize circuit
optimized_circuit = circuit_optimizer.optimize_circuit(circuit, optimization_level=3)
print(f"Optimized circuit depth: {optimized_circuit.depth()}")
Noise-Aware Optimization¶
class NoiseAwareOptimizer:
"""Optimize circuits considering quantum noise."""
def __init__(self, noise_model):
self.noise_model = noise_model
self.gate_fidelities = self._compute_gate_fidelities()
def _compute_gate_fidelities(self):
"""Compute fidelities for different gates under noise."""
fidelities = {}
# Single-qubit gate fidelities
fidelities['H'] = 0.999
fidelities['X'] = 0.999
fidelities['Y'] = 0.999
fidelities['Z'] = 0.9995 # Virtual Z-gate, higher fidelity
fidelities['RX'] = 0.998
fidelities['RY'] = 0.998
fidelities['RZ'] = 0.9995
# Two-qubit gate fidelities
fidelities['CNOT'] = 0.99
fidelities['CZ'] = 0.992
return fidelities
def optimize_for_noise(self, circuit):
"""Optimize circuit considering noise characteristics."""
optimized_circuit = circuit.copy()
# Replace gates with higher-fidelity equivalents
optimized_circuit = self._replace_low_fidelity_gates(optimized_circuit)
# Minimize two-qubit gates
optimized_circuit = self._minimize_two_qubit_gates(optimized_circuit)
# Add error correction if beneficial
optimized_circuit = self._add_error_correction(optimized_circuit)
return optimized_circuit
def _replace_low_fidelity_gates(self, circuit):
"""Replace gates with higher-fidelity alternatives."""
new_gates = []
for gate_name, qubits, params in circuit.gates:
if gate_name == 'Y':
# Replace Y with Z-RX-Z sequence (higher fidelity)
new_gates.extend([
('Z', qubits, []),
('RX', qubits, [np.pi]),
('Z', qubits, [])
])
elif gate_name == 'CNOT' and len(qubits) == 2:
# Check if CZ is available and has higher fidelity
if self.gate_fidelities.get('CZ', 0) > self.gate_fidelities.get('CNOT', 0):
# Convert CNOT to CZ + single-qubit gates
control, target = qubits
new_gates.extend([
('H', [target], []),
('CZ', qubits, []),
('H', [target], [])
])
else:
new_gates.append((gate_name, qubits, params))
else:
new_gates.append((gate_name, qubits, params))
circuit.gates = new_gates
return circuit
def _minimize_two_qubit_gates(self, circuit):
"""Minimize the number of two-qubit gates."""
# Count current two-qubit gates
two_qubit_count = sum(1 for gate_name, qubits, _ in circuit.gates
if len(qubits) == 2)
if two_qubit_count <= 5: # Already minimal
return circuit
# Apply more aggressive single-qubit optimizations
optimized = circuit.copy()
# Try to decompose multi-qubit gates differently
optimized = self._decompose_for_minimal_cnots(optimized)
return optimized
def _add_error_correction(self, circuit):
"""Add error correction if circuit is long enough to benefit."""
circuit_depth = circuit.depth()
if circuit_depth > 100: # Only for deep circuits
# Add simple repetition code
return self._add_repetition_code(circuit)
return circuit
def estimate_circuit_fidelity(self, circuit):
"""Estimate overall circuit fidelity under noise."""
total_fidelity = 1.0
for gate_name, qubits, params in circuit.gates:
gate_fidelity = self.gate_fidelities.get(gate_name, 0.95)
total_fidelity *= gate_fidelity
# Account for decoherence time
circuit_time = self._estimate_circuit_time(circuit)
decoherence_factor = np.exp(-circuit_time / self.noise_model.t1_time)
return total_fidelity * decoherence_factor
# Noise-aware optimization example
from probabilistic_quantum_reasoner.noise import DepolarizingNoise
noise_model = DepolarizingNoise(probability=0.01)
noise_optimizer = NoiseAwareOptimizer(noise_model)
# Optimize for noise
noisy_circuit = create_test_circuit()
optimized_for_noise = noise_optimizer.optimize_for_noise(noisy_circuit)
original_fidelity = noise_optimizer.estimate_circuit_fidelity(noisy_circuit)
optimized_fidelity = noise_optimizer.estimate_circuit_fidelity(optimized_for_noise)
print(f"Original fidelity: {original_fidelity:.4f}")
print(f"Optimized fidelity: {optimized_fidelity:.4f}")
print(f"Improvement: {(optimized_fidelity/original_fidelity - 1)*100:.1f}%")
Classical Inference Optimization¶
Sparse Matrix Operations¶
import scipy.sparse as sp
from scipy.sparse.linalg import spsolve
class SparseInferenceEngine:
"""Inference engine optimized for sparse networks."""
def __init__(self, sparsity_threshold=0.1):
self.sparsity_threshold = sparsity_threshold
self.sparse_cache = {}
def optimize_network_for_sparsity(self, network):
"""Convert network to sparse representation."""
for node_name, node in network.nodes.items():
if hasattr(node, 'cpt'):
# Convert CPT to sparse if beneficial
cpt = node.cpt
sparsity = np.count_nonzero(cpt) / cpt.size
if sparsity < self.sparsity_threshold:
node.cpt = sp.csr_matrix(cpt)
print(f"Converted {node_name} CPT to sparse (sparsity: {sparsity:.3f})")
return network
def sparse_variable_elimination(self, network, query_vars, evidence):
"""Perform variable elimination using sparse operations."""
# Convert factors to sparse matrices
sparse_factors = self._convert_factors_to_sparse(network, evidence)
# Determine elimination order
elimination_order = self._compute_elimination_order(network, query_vars)
# Eliminate variables one by one
for var in elimination_order:
if var not in query_vars:
sparse_factors = self._eliminate_variable_sparse(sparse_factors, var)
# Compute final marginals
return self._compute_marginals_sparse(sparse_factors, query_vars)
def _convert_factors_to_sparse(self, network, evidence):
"""Convert network factors to sparse representation."""
factors = []
for node_name, node in network.nodes.items():
if hasattr(node, 'cpt'):
factor = node.cpt
# Apply evidence
if node_name in evidence:
factor = self._apply_evidence_to_factor(factor, evidence[node_name])
# Convert to sparse if not already
if not sp.issparse(factor):
factor = sp.csr_matrix(factor)
factors.append({
'factor': factor,
'variables': [node_name] + [p.name for p in node.parents],
'name': node_name
})
return factors
def _eliminate_variable_sparse(self, factors, var):
"""Eliminate a variable using sparse operations."""
# Find factors involving the variable
relevant_factors = [f for f in factors if var in f['variables']]
other_factors = [f for f in factors if var not in f['variables']]
if not relevant_factors:
return factors
# Multiply relevant factors
product_factor = relevant_factors[0]['factor']
product_variables = relevant_factors[0]['variables']
for factor_info in relevant_factors[1:]:
product_factor = self._multiply_sparse_factors(
product_factor, factor_info['factor']
)
product_variables = list(set(product_variables + factor_info['variables']))
# Sum out the variable
marginalized_factor = self._marginalize_sparse(product_factor, var, product_variables)
marginalized_variables = [v for v in product_variables if v != var]
# Add marginalized factor to other factors
other_factors.append({
'factor': marginalized_factor,
'variables': marginalized_variables,
'name': f'marginalized_{var}'
})
return other_factors
def _multiply_sparse_factors(self, factor1, factor2):
"""Multiply two sparse factors."""
# This is a simplified implementation
# Real implementation would handle arbitrary tensor operations
if factor1.shape == factor2.shape:
return factor1.multiply(factor2)
else:
# Handle different shapes through broadcasting
return sp.kron(factor1, factor2)
def _marginalize_sparse(self, factor, var, variables):
"""Marginalize out a variable from sparse factor."""
# Simplified marginalization for sparse matrices
# Real implementation would handle arbitrary tensor marginalization
var_index = variables.index(var)
if factor.ndim == 2 and var_index == 0:
return sp.csr_matrix(factor.sum(axis=0))
elif factor.ndim == 2 and var_index == 1:
return sp.csr_matrix(factor.sum(axis=1))
else:
# For higher dimensions, convert to dense temporarily
dense_factor = factor.toarray()
marginalized = np.sum(dense_factor, axis=var_index)
return sp.csr_matrix(marginalized)
# Sparse optimization example
sparse_engine = SparseInferenceEngine(sparsity_threshold=0.15)
# Optimize network for sparsity
sparse_network = sparse_engine.optimize_network_for_sparsity(large_network)
# Perform sparse inference
start_time = time.time()
sparse_result = sparse_engine.sparse_variable_elimination(
sparse_network,
query_vars=["target"],
evidence={"obs1": "value1"}
)
sparse_time = time.time() - start_time
print(f"Sparse inference time: {sparse_time:.3f}s")
Parallel Classical Inference¶
import concurrent.futures
import multiprocessing as mp
from functools import partial
class ParallelInferenceEngine:
"""Parallel inference engine for classical networks."""
def __init__(self, num_workers=None):
self.num_workers = num_workers or mp.cpu_count()
self.chunk_size = 1000 # For batch operations
def parallel_variable_elimination(self, network, queries, evidences):
"""Perform variable elimination in parallel for multiple queries."""
# Create worker function
worker_func = partial(self._single_inference_worker, network)
# Execute in parallel
with concurrent.futures.ProcessPoolExecutor(max_workers=self.num_workers) as executor:
futures = [
executor.submit(worker_func, query, evidence)
for query, evidence in zip(queries, evidences)
]
results = []
for future in concurrent.futures.as_completed(futures):
results.append(future.result())
return results
def _single_inference_worker(self, network, query, evidence):
"""Worker function for single inference task."""
from probabilistic_quantum_reasoner.inference import BeliefPropagation
bp = BeliefPropagation()
return bp.infer(network, query, evidence)
def parallel_factor_multiplication(self, factors_list):
"""Multiply factors in parallel."""
if len(factors_list) <= 2:
return self._multiply_factors_sequential(factors_list)
# Divide factors into chunks
chunk_size = max(2, len(factors_list) // self.num_workers)
chunks = [factors_list[i:i+chunk_size]
for i in range(0, len(factors_list), chunk_size)]
# Multiply within chunks in parallel
with concurrent.futures.ThreadPoolExecutor(max_workers=self.num_workers) as executor:
futures = [
executor.submit(self._multiply_factors_sequential, chunk)
for chunk in chunks
]
chunk_results = [future.result() for future in futures]
# Multiply chunk results sequentially
return self._multiply_factors_sequential(chunk_results)
def _multiply_factors_sequential(self, factors):
"""Multiply a list of factors sequentially."""
if not factors:
return None
result = factors[0]
for factor in factors[1:]:
result = np.multiply(result, factor)
return result
def parallel_sampling(self, network, num_samples, batch_size=None):
"""Generate samples in parallel."""
if batch_size is None:
batch_size = max(1, num_samples // self.num_workers)
# Create batches
batches = []
remaining = num_samples
while remaining > 0:
current_batch = min(batch_size, remaining)
batches.append(current_batch)
remaining -= current_batch
# Generate samples in parallel
with concurrent.futures.ProcessPoolExecutor(max_workers=self.num_workers) as executor:
futures = [
executor.submit(self._generate_samples_batch, network, batch_size)
for batch_size in batches
]
all_samples = []
for future in futures:
all_samples.extend(future.result())
return all_samples
def _generate_samples_batch(self, network, num_samples):
"""Generate a batch of samples."""
samples = []
for _ in range(num_samples):
sample = {}
# Topological sampling
for node_name in self._topological_sort(network):
node = network.nodes[node_name]
if hasattr(node, 'sample'):
# Sample from node distribution
value = node.sample(evidence=sample)
sample[node_name] = value
else:
# Default sampling
if hasattr(node, 'states'):
value = np.random.choice(node.states)
sample[node_name] = value
samples.append(sample)
return samples
def _topological_sort(self, network):
"""Topological sort of network nodes."""
# Simplified topological sort
sorted_nodes = []
in_degree = {name: 0 for name in network.nodes}
# Compute in-degrees
for node_name, node in network.nodes.items():
if hasattr(node, 'parents'):
in_degree[node_name] = len(node.parents)
# Find nodes with no incoming edges
queue = [name for name, degree in in_degree.items() if degree == 0]
while queue:
current = queue.pop(0)
sorted_nodes.append(current)
# Update in-degrees
current_node = network.nodes[current]
if hasattr(current_node, 'children'):
for child in current_node.children:
in_degree[child.name] -= 1
if in_degree[child.name] == 0:
queue.append(child.name)
return sorted_nodes
# Parallel inference example
parallel_engine = ParallelInferenceEngine(num_workers=4)
# Multiple queries in parallel
queries = [["node1"], ["node2"], ["node3"]]
evidences = [{}, {"obs": "val1"}, {"obs": "val2"}]
parallel_results = parallel_engine.parallel_variable_elimination(
network, queries, evidences
)
print(f"Processed {len(queries)} queries in parallel")
# Parallel sampling
samples = parallel_engine.parallel_sampling(network, num_samples=10000)
print(f"Generated {len(samples)} samples in parallel")
Hardware-Specific Optimizations¶
GPU Acceleration¶
import cupy as cp
import numpy as np
class GPUOptimizedReasoner:
"""GPU-optimized quantum reasoner."""
def __init__(self, device_id=0):
cp.cuda.Device(device_id).use()
self.device_id = device_id
self.stream = cp.cuda.Stream()
def gpu_accelerated_inference(self, network, query, evidence):
"""Perform inference using GPU acceleration."""
with self.stream:
# Convert network to GPU arrays
gpu_factors = self._network_to_gpu(network)
# Apply evidence on GPU
gpu_factors = self._apply_evidence_gpu(gpu_factors, evidence)
# Perform inference on GPU
result = self._infer_on_gpu(gpu_factors, query)
# Transfer result back to CPU
cpu_result = cp.asnumpy(result)
return cpu_result
def _network_to_gpu(self, network):
"""Transfer network to GPU memory."""
gpu_factors = []
for node_name, node in network.nodes.items():
if hasattr(node, 'cpt'):
gpu_cpt = cp.asarray(node.cpt)
gpu_factors.append({
'name': node_name,
'cpt': gpu_cpt,
'variables': [node_name] + [p.name for p in node.parents]
})
return gpu_factors
def batch_quantum_simulation(self, circuits, shots_per_circuit):
"""Simulate multiple quantum circuits in parallel on GPU."""
# Batch state vector computations
batch_states = []
for circuit in circuits:
state = self._compute_state_vector_gpu(circuit)
batch_states.append(state)
# Stack states for batch processing
batch_tensor = cp.stack(batch_states)
# Batch measurement simulation
results = self._batch_measure_gpu(batch_tensor, shots_per_circuit)
return results
def _compute_state_vector_gpu(self, circuit):
"""Compute state vector on GPU."""
num_qubits = circuit.num_qubits
state = cp.zeros(2**num_qubits, dtype=cp.complex128)
state[0] = 1.0 # |00...0⟩
# Apply gates using GPU operations
for gate_name, qubits, params in circuit.gates:
gate_matrix = self._get_gate_matrix_gpu(gate_name, params)
state = self._apply_gate_gpu(state, gate_matrix, qubits, num_qubits)
return state
def _apply_gate_gpu(self, state, gate_matrix, qubits, num_qubits):
"""Apply quantum gate on GPU."""
# This is a simplified implementation
# Real implementation would use tensor contractions
if len(qubits) == 1:
# Single-qubit gate
full_matrix = self._construct_full_matrix_gpu(gate_matrix, qubits[0], num_qubits)
return full_matrix @ state
else:
# Multi-qubit gate (simplified)
return gate_matrix @ state # Placeholder
def _batch_measure_gpu(self, batch_states, shots_per_circuit):
"""Perform batch measurements on GPU."""
batch_results = []
for i, state in enumerate(batch_states):
# Compute probabilities
probabilities = cp.abs(state) ** 2
# Sample on GPU
samples = cp.random.choice(
len(probabilities),
size=shots_per_circuit[i],
p=probabilities
)
# Convert to measurement outcomes
outcomes = {}
for sample in samples:
bitstring = format(int(sample), f'0{int(cp.log2(len(state)))}b')
outcomes[bitstring] = outcomes.get(bitstring, 0) + 1
batch_results.append(outcomes)
return batch_results
# GPU optimization example
gpu_reasoner = GPUOptimizedReasoner(device_id=0)
# Batch circuit simulation
test_circuits = [create_test_circuit(i) for i in range(10)]
shots_list = [1000] * 10
gpu_results = gpu_reasoner.batch_quantum_simulation(test_circuits, shots_list)
print(f"Simulated {len(test_circuits)} circuits on GPU")
FPGA Acceleration¶
class FPGAAcceleratedBackend:
"""FPGA-accelerated backend for specific operations."""
def __init__(self, fpga_device="/dev/fpga0"):
self.fpga_device = fpga_device
self.fpga_available = self._check_fpga_availability()
def _check_fpga_availability(self):
"""Check if FPGA device is available."""
try:
import os
return os.path.exists(self.fpga_device)
except:
return False
def fpga_accelerated_matrix_multiplication(self, matrices):
"""Perform matrix multiplication on FPGA."""
if not self.fpga_available:
# Fallback to CPU
return self._cpu_matrix_multiplication(matrices)
# FPGA-specific implementation
return self._fpga_multiply_matrices(matrices)
def _fpga_multiply_matrices(self, matrices):
"""FPGA matrix multiplication implementation."""
# This would interface with actual FPGA hardware
# Using placeholder implementation
result = matrices[0]
for matrix in matrices[1:]:
# Simulate FPGA operation with optimized CPU code
result = np.dot(result, matrix)
return result
def fpga_quantum_simulation(self, circuit):
"""Simulate quantum circuit on FPGA."""
if not self.fpga_available:
return None
# FPGA-specific quantum simulation
# This would use dedicated quantum simulation cores on FPGA
return self._fpga_simulate_circuit(circuit)
# FPGA example
fpga_backend = FPGAAcceleratedBackend()
if fpga_backend.fpga_available:
print("FPGA acceleration available")
else:
print("FPGA not available, using CPU fallback")
Benchmark Results¶
Performance Comparison¶
def comprehensive_benchmark():
"""Comprehensive performance benchmark."""
# Test configurations
configurations = [
{"backend": "classical", "optimization": "none"},
{"backend": "classical", "optimization": "sparse"},
{"backend": "classical", "optimization": "parallel"},
{"backend": "qiskit", "optimization": "none"},
{"backend": "qiskit", "optimization": "circuit_opt"},
{"backend": "gpu_classical", "optimization": "gpu"},
]
# Test networks of different sizes
network_sizes = [10, 25, 50, 100]
results = {}
for config in configurations:
config_name = f"{config['backend']}_{config['optimization']}"
results[config_name] = {}
for size in network_sizes:
print(f"Benchmarking {config_name} with {size} nodes...")
# Create test network
network = create_test_network(size)
# Setup reasoner with configuration
reasoner = setup_reasoner(config)
# Benchmark inference
times = []
for _ in range(10): # 10 trials
start_time = time.time()
result = reasoner.infer(
network=network,
query=list(network.nodes.keys())[:3],
evidence={}
)
end_time = time.time()
times.append(end_time - start_time)
results[config_name][size] = {
'mean_time': np.mean(times),
'std_time': np.std(times),
'min_time': np.min(times)
}
return results
# Run comprehensive benchmark
benchmark_results = comprehensive_benchmark()
# Display results
print("\nBenchmark Results:")
print("="*50)
for config, size_results in benchmark_results.items():
print(f"\n{config}:")
for size, metrics in size_results.items():
print(f" {size} nodes: {metrics['mean_time']:.3f}s ± {metrics['std_time']:.3f}s")
Best Practices Summary¶
Performance Optimization Guidelines¶
- Profile First: Always profile before optimizing
- Start with Algorithms: Algorithmic improvements usually beat micro-optimizations
- Use Appropriate Data Structures: Sparse vs dense, GPU vs CPU arrays
- Cache Wisely: Cache expensive computations but manage memory
- Parallelize When Possible: Use all available cores and accelerators
- Optimize for Your Use Case: Different applications need different optimizations
Memory Management¶
- Monitor Memory Usage: Track memory consumption during inference
- Use Weak References: Prevent memory leaks in large networks
- Clear Caches Regularly: Implement cache eviction policies
- Optimize Data Types: Use appropriate precision (float32 vs float64)
Hardware Utilization¶
- Match Backend to Hardware: Use GPU backends for GPU systems
- Batch Operations: Process multiple items together when possible
- Minimize Data Transfer: Keep data on accelerators when possible
- Use Vendor Optimizations: Leverage hardware-specific libraries
This comprehensive guide provides the foundation for optimizing PQR performance across different hardware platforms and use cases.