Custom Backend Development¶
This guide walks you through creating custom backends for the Probabilistic Quantum Reasoner, enabling integration with new quantum hardware, simulators, or specialized classical computing resources.
Backend Architecture Overview¶
The backend system in PQR follows a layered architecture:
Application Layer
↓
ProbabilisticQuantumReasoner
↓
Backend Interface
↓
Backend Implementation
↓
Hardware/Simulator LayerBase Backend Classes¶
Backend Interface¶
All backends must implement the base Backend interface:
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Optional
import numpy as np
class Backend(ABC):
"""Abstract base class for all backends."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
self.config = config or {}
self.backend_type = "unknown"
self.supports_quantum = False
self.supports_classical = True
self._initialize()
@abstractmethod
def _initialize(self):
"""Initialize backend-specific resources."""
pass
@abstractmethod
def infer(self, network, query: List[str], evidence: Dict[str, Any]) -> Dict[str, Any]:
"""Perform inference on the network."""
pass
@abstractmethod
def measure(self, network, nodes: List[str]) -> Dict[str, Any]:
"""Measure specified nodes in the network."""
pass
def validate_network(self, network) -> bool:
"""Validate that the network is compatible with this backend."""
return True
def get_capabilities(self) -> Dict[str, Any]:
"""Return backend capabilities."""
return {
"backend_type": self.backend_type,
"supports_quantum": self.supports_quantum,
"supports_classical": self.supports_classical,
"max_qubits": getattr(self, 'max_qubits', None),
"available_gates": getattr(self, 'available_gates', [])
}
Quantum Backend Base Class¶
For quantum backends, extend the QuantumBackend class:
from probabilistic_quantum_reasoner.backends import Backend
class QuantumBackend(Backend):
"""Base class for quantum backends."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.supports_quantum = True
self.max_qubits = 30 # Default limit
self.available_gates = ["H", "X", "Y", "Z", "CNOT", "RX", "RY", "RZ"]
self.shots = self.config.get("shots", 1000)
self.noise_model = self.config.get("noise_model", None)
@abstractmethod
def create_circuit(self, num_qubits: int):
"""Create a quantum circuit with specified number of qubits."""
pass
@abstractmethod
def execute_circuit(self, circuit, shots: Optional[int] = None):
"""Execute a quantum circuit and return measurement results."""
pass
@abstractmethod
def get_state_vector(self, circuit):
"""Get the quantum state vector of a circuit."""
pass
def apply_noise_model(self, circuit):
"""Apply noise model to the circuit."""
if self.noise_model:
return self.noise_model.apply(circuit)
return circuit
Implementing a Custom Quantum Backend¶
Example: Custom Quantum Simulator¶
Let's create a custom quantum backend using a simple matrix-based simulator:
import numpy as np
from typing import Dict, List, Any, Optional
from probabilistic_quantum_reasoner.backends import QuantumBackend
class CustomQuantumSimulator(QuantumBackend):
"""Custom quantum simulator backend using numpy matrices."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.backend_type = "custom_quantum_simulator"
self.max_qubits = 20 # Reasonable limit for numpy simulation
# Quantum gate definitions
self._define_gates()
def _initialize(self):
"""Initialize the custom simulator."""
self.circuit_cache = {}
self.execution_count = 0
def _define_gates(self):
"""Define quantum gates as numpy matrices."""
# Pauli gates
self.gates = {
"I": np.array([[1, 0], [0, 1]], dtype=complex),
"X": np.array([[0, 1], [1, 0]], dtype=complex),
"Y": np.array([[0, -1j], [1j, 0]], dtype=complex),
"Z": np.array([[1, 0], [0, -1]], dtype=complex),
"H": np.array([[1, 1], [1, -1]], dtype=complex) / np.sqrt(2),
}
# Two-qubit gates
self.gates["CNOT"] = np.array([
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1],
[0, 0, 1, 0]
], dtype=complex)
def create_circuit(self, num_qubits: int):
"""Create a custom quantum circuit."""
if num_qubits > self.max_qubits:
raise ValueError(f"Too many qubits: {num_qubits} > {self.max_qubits}")
return CustomQuantumCircuit(num_qubits, self)
def execute_circuit(self, circuit, shots: Optional[int] = None):
"""Execute the quantum circuit."""
if shots is None:
shots = self.shots
# Get final state vector
state_vector = self.get_state_vector(circuit)
# Simulate measurements
probabilities = np.abs(state_vector) ** 2
# Sample from probability distribution
num_states = len(probabilities)
measurements = []
for _ in range(shots):
outcome = np.random.choice(num_states, p=probabilities)
# Convert to bitstring
bitstring = format(outcome, f'0{circuit.num_qubits}b')
measurements.append(bitstring)
# Count outcomes
counts = {}
for measurement in measurements:
counts[measurement] = counts.get(measurement, 0) + 1
self.execution_count += 1
return counts
def get_state_vector(self, circuit):
"""Compute the state vector by applying all gates."""
# Start with |00...0⟩ state
state = np.zeros(2 ** circuit.num_qubits, dtype=complex)
state[0] = 1.0
# Apply each gate in sequence
for gate_info in circuit.gates:
state = self._apply_gate(state, gate_info, circuit.num_qubits)
return state
def _apply_gate(self, state, gate_info, num_qubits):
"""Apply a single gate to the state vector."""
gate_name, qubits, params = gate_info
if gate_name in ["X", "Y", "Z", "H"]:
# Single-qubit gate
qubit = qubits[0]
gate_matrix = self.gates[gate_name]
full_matrix = self._construct_full_matrix(gate_matrix, qubit, num_qubits)
return full_matrix @ state
elif gate_name == "CNOT":
# Two-qubit gate
control, target = qubits
gate_matrix = self.gates["CNOT"]
full_matrix = self._construct_two_qubit_matrix(
gate_matrix, control, target, num_qubits
)
return full_matrix @ state
elif gate_name.startswith("R"):
# Rotation gate
angle = params[0] if params else 0
qubit = qubits[0]
if gate_name == "RX":
gate_matrix = np.array([
[np.cos(angle/2), -1j*np.sin(angle/2)],
[-1j*np.sin(angle/2), np.cos(angle/2)]
], dtype=complex)
elif gate_name == "RY":
gate_matrix = np.array([
[np.cos(angle/2), -np.sin(angle/2)],
[np.sin(angle/2), np.cos(angle/2)]
], dtype=complex)
elif gate_name == "RZ":
gate_matrix = np.array([
[np.exp(-1j*angle/2), 0],
[0, np.exp(1j*angle/2)]
], dtype=complex)
full_matrix = self._construct_full_matrix(gate_matrix, qubit, num_qubits)
return full_matrix @ state
else:
raise ValueError(f"Unknown gate: {gate_name}")
def _construct_full_matrix(self, gate_matrix, target_qubit, num_qubits):
"""Construct full matrix for single-qubit gate."""
matrices = []
for i in range(num_qubits):
if i == target_qubit:
matrices.append(gate_matrix)
else:
matrices.append(self.gates["I"])
# Tensor product of all matrices
result = matrices[0]
for matrix in matrices[1:]:
result = np.kron(result, matrix)
return result
def _construct_two_qubit_matrix(self, gate_matrix, control, target, num_qubits):
"""Construct full matrix for two-qubit gate."""
# This is a simplified implementation
# For a full implementation, need to handle arbitrary control/target positions
dim = 2 ** num_qubits
result = np.eye(dim, dtype=complex)
# Apply CNOT logic manually (simplified for demonstration)
for i in range(dim):
bitstring = format(i, f'0{num_qubits}b')
bits = [int(b) for b in bitstring]
if bits[control] == 1: # Control is |1⟩
# Flip target bit
new_bits = bits.copy()
new_bits[target] = 1 - new_bits[target]
j = int(''.join(map(str, new_bits)), 2)
# Swap amplitudes
result[i, i] = 0
result[i, j] = 1
return result
def infer(self, network, query: List[str], evidence: Dict[str, Any]) -> Dict[str, Any]:
"""Perform inference using the custom simulator."""
# Convert network to quantum circuits
circuits = self._network_to_circuits(network, query, evidence)
results = {}
for var_name, circuit in circuits.items():
# Execute circuit
counts = self.execute_circuit(circuit)
# Convert to probabilities
total_shots = sum(counts.values())
probabilities = {}
for state, count in counts.items():
# Map quantum state to variable value
var_value = self._quantum_state_to_value(state, var_name)
prob = count / total_shots
if var_value in probabilities:
probabilities[var_value] += prob
else:
probabilities[var_value] = prob
results[var_name] = probabilities
return results
def measure(self, network, nodes: List[str]) -> Dict[str, Any]:
"""Measure specified nodes."""
# Create measurement circuit
circuit = self._create_measurement_circuit(network, nodes)
# Execute once to get single measurement
counts = self.execute_circuit(circuit, shots=1)
bitstring = list(counts.keys())[0]
# Map to node values
results = {}
for i, node_name in enumerate(nodes):
bit_value = bitstring[i]
results[node_name] = self._bit_to_value(bit_value, node_name)
return results
def _network_to_circuits(self, network, query, evidence):
"""Convert network to quantum circuits (simplified)."""
# This is a placeholder - real implementation would be more complex
circuits = {}
for var_name in query:
circuit = self.create_circuit(num_qubits=2) # Simplified
# Add gates based on network structure
circuit.add_gate("H", [0]) # Example gate
circuits[var_name] = circuit
return circuits
def _create_measurement_circuit(self, network, nodes):
"""Create circuit for measuring specific nodes."""
num_qubits = len(nodes)
circuit = self.create_circuit(num_qubits)
# Add gates based on network (simplified)
for i in range(num_qubits):
circuit.add_gate("H", [i])
return circuit
def _quantum_state_to_value(self, state, var_name):
"""Map quantum state to variable value."""
# Simplified mapping
return "state_0" if state[0] == '0' else "state_1"
def _bit_to_value(self, bit, node_name):
"""Map bit value to node value."""
return "value_0" if bit == '0' else "value_1"
class CustomQuantumCircuit:
"""Custom quantum circuit implementation."""
def __init__(self, num_qubits: int, backend):
self.num_qubits = num_qubits
self.backend = backend
self.gates = [] # List of (gate_name, qubits, params)
def add_gate(self, gate_name: str, qubits: List[int], params: Optional[List[float]] = None):
"""Add a gate to the circuit."""
if params is None:
params = []
# Validate qubits
for qubit in qubits:
if qubit >= self.num_qubits:
raise ValueError(f"Qubit {qubit} out of range")
# Validate gate
if gate_name not in self.backend.available_gates:
raise ValueError(f"Gate {gate_name} not available")
self.gates.append((gate_name, qubits, params))
def add_hadamard(self, qubit: int):
"""Add Hadamard gate."""
self.add_gate("H", [qubit])
def add_cnot(self, control: int, target: int):
"""Add CNOT gate."""
self.add_gate("CNOT", [control, target])
def add_rotation(self, gate_type: str, qubit: int, angle: float):
"""Add rotation gate."""
self.add_gate(gate_type, [qubit], [angle])
def depth(self) -> int:
"""Return circuit depth."""
return len(self.gates)
def copy(self):
"""Create a copy of the circuit."""
new_circuit = CustomQuantumCircuit(self.num_qubits, self.backend)
new_circuit.gates = self.gates.copy()
return new_circuit
Classical Backend Implementation¶
Example: GPU-Accelerated Classical Backend¶
import numpy as np
try:
import cupy as cp
CUPY_AVAILABLE = True
except ImportError:
CUPY_AVAILABLE = False
from probabilistic_quantum_reasoner.backends import Backend
class GPUClassicalBackend(Backend):
"""GPU-accelerated classical backend using CuPy."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.backend_type = "gpu_classical"
self.supports_classical = True
if not CUPY_AVAILABLE:
raise ImportError("CuPy not available for GPU acceleration")
self.device_id = self.config.get("device_id", 0)
self.memory_pool = cp.get_default_memory_pool()
def _initialize(self):
"""Initialize GPU resources."""
cp.cuda.Device(self.device_id).use()
print(f"Using GPU device {self.device_id}")
# Pre-allocate common arrays
self._setup_memory_pools()
def _setup_memory_pools(self):
"""Setup memory pools for efficient GPU memory management."""
# Pre-allocate common matrix sizes
common_sizes = [10, 100, 1000]
self.matrix_cache = {}
for size in common_sizes:
self.matrix_cache[size] = cp.zeros((size, size), dtype=cp.float32)
def infer(self, network, query: List[str], evidence: Dict[str, Any]) -> Dict[str, Any]:
"""Perform GPU-accelerated inference."""
# Convert network to GPU arrays
gpu_factors = self._network_to_gpu_factors(network)
# Apply evidence
gpu_factors = self._apply_evidence_gpu(gpu_factors, evidence)
# Perform variable elimination on GPU
results = {}
for var_name in query:
marginal = self._compute_marginal_gpu(gpu_factors, var_name)
# Convert back to CPU and normalize
marginal_cpu = cp.asnumpy(marginal)
marginal_cpu = marginal_cpu / np.sum(marginal_cpu)
# Convert to probability dictionary
var_states = network.nodes[var_name].states
prob_dict = {state: float(marginal_cpu[i])
for i, state in enumerate(var_states)}
results[var_name] = prob_dict
return results
def _network_to_gpu_factors(self, network):
"""Convert network factors to GPU arrays."""
gpu_factors = []
for node_name, node in network.nodes.items():
if hasattr(node, 'cpt'):
# Convert CPT to GPU array
cpt_gpu = cp.asarray(node.cpt, dtype=cp.float32)
gpu_factors.append({
'name': node_name,
'variables': [node_name] + [p.name for p in node.parents],
'factor': cpt_gpu
})
elif hasattr(node, 'prior'):
# Convert prior to GPU array
prior_gpu = cp.asarray(node.prior, dtype=cp.float32)
gpu_factors.append({
'name': node_name,
'variables': [node_name],
'factor': prior_gpu
})
return gpu_factors
def _apply_evidence_gpu(self, gpu_factors, evidence):
"""Apply evidence using GPU operations."""
for factor in gpu_factors:
for var_name in factor['variables']:
if var_name in evidence:
# Reduce factor by fixing evidence variable
factor['factor'] = self._fix_variable_gpu(
factor['factor'], var_name, evidence[var_name], factor
)
return gpu_factors
def _fix_variable_gpu(self, factor_gpu, var_name, value, factor_info):
"""Fix a variable to a specific value using GPU operations."""
# This is a simplified implementation
# Real implementation would handle arbitrary factor shapes
var_index = factor_info['variables'].index(var_name)
value_index = self._get_value_index(var_name, value)
# Select the slice corresponding to the evidence
# This is a simplified selection - real implementation more complex
if var_index == 0:
return factor_gpu[value_index, :]
else:
return factor_gpu[:, value_index]
def _compute_marginal_gpu(self, gpu_factors, target_var):
"""Compute marginal distribution using GPU operations."""
# Find factors involving target variable
relevant_factors = [f for f in gpu_factors if target_var in f['variables']]
if not relevant_factors:
raise ValueError(f"No factors found for variable {target_var}")
# Start with first factor
result = relevant_factors[0]['factor']
# Multiply with other factors (simplified)
for factor in relevant_factors[1:]:
result = self._multiply_factors_gpu(result, factor['factor'])
# Sum out non-target variables (simplified)
# Real implementation would handle arbitrary marginalization
if result.ndim > 1:
result = cp.sum(result, axis=tuple(range(1, result.ndim)))
return result
def _multiply_factors_gpu(self, factor1, factor2):
"""Multiply two factors using GPU operations."""
# This is a simplified multiplication
# Real implementation would handle arbitrary factor combinations
if factor1.shape == factor2.shape:
return factor1 * factor2
else:
# Broadcast and multiply
return cp.multiply.outer(factor1, factor2).flatten()
def measure(self, network, nodes: List[str]) -> Dict[str, Any]:
"""Perform measurement (classical sampling)."""
# Infer joint distribution
joint_result = self.infer(network, nodes, {})
# Sample from joint distribution
# Simplified - real implementation would compute actual joint
results = {}
for node_name in nodes:
node_probs = joint_result[node_name]
states = list(node_probs.keys())
probs = list(node_probs.values())
# Sample
sampled_state = np.random.choice(states, p=probs)
results[node_name] = sampled_state
return results
def _get_value_index(self, var_name, value):
"""Get index of value in variable's state space."""
# This would be implemented based on network structure
return 0 # Simplified
def get_memory_usage(self):
"""Get current GPU memory usage."""
return {
'used_bytes': self.memory_pool.used_bytes(),
'total_bytes': self.memory_pool.total_bytes()
}
def clear_cache(self):
"""Clear GPU memory cache."""
self.memory_pool.free_all_blocks()
Backend Registration and Integration¶
Registering Custom Backends¶
from probabilistic_quantum_reasoner.backends import BackendRegistry
# Register the custom backends
BackendRegistry.register("custom_quantum", CustomQuantumSimulator)
BackendRegistry.register("gpu_classical", GPUClassicalBackend)
# Use the custom backend
from probabilistic_quantum_reasoner import ProbabilisticQuantumReasoner
# Create reasoner with custom quantum backend
reasoner = ProbabilisticQuantumReasoner(
backend="custom_quantum",
backend_config={
"shots": 2000,
"max_qubits": 15
}
)
# Or create directly
custom_backend = CustomQuantumSimulator(config={"shots": 5000})
reasoner = ProbabilisticQuantumReasoner(backend=custom_backend)
Backend Factory Pattern¶
class BackendFactory:
"""Factory for creating backends with different configurations."""
@staticmethod
def create_backend(backend_type: str, **kwargs):
"""Create backend with specified configuration."""
if backend_type == "custom_quantum_optimized":
return CustomQuantumSimulator(config={
"shots": kwargs.get("shots", 10000),
"optimization_level": kwargs.get("optimization", 3),
"memory_limit": kwargs.get("memory", "8GB")
})
elif backend_type == "gpu_classical_high_performance":
return GPUClassicalBackend(config={
"device_id": kwargs.get("gpu_id", 0),
"precision": kwargs.get("precision", "float32"),
"batch_size": kwargs.get("batch_size", 1000)
})
else:
raise ValueError(f"Unknown backend type: {backend_type}")
# Use factory
backend = BackendFactory.create_backend(
"custom_quantum_optimized",
shots=20000,
optimization=2
)
Testing Custom Backends¶
Unit Tests¶
import unittest
from probabilistic_quantum_reasoner.networks import BayesianNetwork
from probabilistic_quantum_reasoner.nodes import DiscreteNode
class TestCustomQuantumSimulator(unittest.TestCase):
"""Test suite for custom quantum simulator."""
def setUp(self):
"""Set up test fixtures."""
self.backend = CustomQuantumSimulator()
self.simple_network = self._create_simple_network()
def _create_simple_network(self):
"""Create a simple test network."""
network = BayesianNetwork()
node_a = DiscreteNode(
name="A",
states=["true", "false"],
prior=[0.6, 0.4]
)
network.add_node(node_a)
return network
def test_circuit_creation(self):
"""Test quantum circuit creation."""
circuit = self.backend.create_circuit(num_qubits=3)
self.assertEqual(circuit.num_qubits, 3)
self.assertEqual(len(circuit.gates), 0)
def test_gate_addition(self):
"""Test adding gates to circuit."""
circuit = self.backend.create_circuit(num_qubits=2)
circuit.add_hadamard(0)
circuit.add_cnot(0, 1)
self.assertEqual(len(circuit.gates), 2)
self.assertEqual(circuit.gates[0][0], "H")
self.assertEqual(circuit.gates[1][0], "CNOT")
def test_state_vector_computation(self):
"""Test state vector computation."""
circuit = self.backend.create_circuit(num_qubits=1)
circuit.add_hadamard(0)
state = self.backend.get_state_vector(circuit)
# Should be |+⟩ = (|0⟩ + |1⟩)/√2
expected = np.array([1/np.sqrt(2), 1/np.sqrt(2)])
np.testing.assert_allclose(state, expected, atol=1e-10)
def test_circuit_execution(self):
"""Test circuit execution and measurement."""
circuit = self.backend.create_circuit(num_qubits=1)
circuit.add_hadamard(0)
counts = self.backend.execute_circuit(circuit, shots=1000)
# Should get roughly 50/50 split
total_counts = sum(counts.values())
self.assertEqual(total_counts, 1000)
# Check that we get both '0' and '1' outcomes
self.assertIn('0', counts)
self.assertIn('1', counts)
def test_bell_state(self):
"""Test Bell state creation and measurement."""
circuit = self.backend.create_circuit(num_qubits=2)
circuit.add_hadamard(0)
circuit.add_cnot(0, 1)
counts = self.backend.execute_circuit(circuit, shots=1000)
# Should get only '00' and '11' outcomes
self.assertIn('00', counts)
self.assertIn('11', counts)
self.assertNotIn('01', counts)
self.assertNotIn('10', counts)
def test_error_handling(self):
"""Test error handling."""
# Test qubit out of range
with self.assertRaises(ValueError):
circuit = self.backend.create_circuit(num_qubits=2)
circuit.add_hadamard(2)
# Test too many qubits
with self.assertRaises(ValueError):
self.backend.create_circuit(num_qubits=100)
class TestGPUClassicalBackend(unittest.TestCase):
"""Test suite for GPU classical backend."""
def setUp(self):
"""Set up test fixtures."""
if not CUPY_AVAILABLE:
self.skipTest("CuPy not available")
self.backend = GPUClassicalBackend()
def test_initialization(self):
"""Test backend initialization."""
self.assertEqual(self.backend.backend_type, "gpu_classical")
self.assertTrue(self.backend.supports_classical)
def test_memory_management(self):
"""Test GPU memory management."""
initial_usage = self.backend.get_memory_usage()
# Perform some operations
large_array = cp.zeros((1000, 1000))
final_usage = self.backend.get_memory_usage()
self.assertGreater(final_usage['used_bytes'], initial_usage['used_bytes'])
# Clear cache
self.backend.clear_cache()
# Run tests
if __name__ == "__main__":
unittest.main()
Integration Tests¶
def test_backend_integration():
"""Test integration with the main reasoner."""
# Create test network
network = BayesianNetwork()
node_a = DiscreteNode(
name="Weather",
states=["sunny", "rainy"],
prior=[0.7, 0.3]
)
node_b = DiscreteNode(
name="Mood",
states=["happy", "sad"],
parents=[node_a],
cpt=np.array([
[0.8, 0.2], # sunny -> happy/sad
[0.3, 0.7] # rainy -> happy/sad
])
)
network.add_nodes([node_a, node_b])
# Test with custom backend
custom_backend = CustomQuantumSimulator()
reasoner = ProbabilisticQuantumReasoner(backend=custom_backend)
# Perform inference
result = reasoner.infer(
network=network,
query=["Mood"],
evidence={"Weather": "sunny"}
)
print("Integration test result:")
print(f"P(Mood|Weather=sunny): {result}")
# Test measurement
measurement = reasoner.measure(
network=network,
nodes=["Weather", "Mood"]
)
print(f"Sample measurement: {measurement}")
# Run integration test
test_backend_integration()
Performance Optimization¶
Caching and Memoization¶
from functools import lru_cache
import hashlib
class OptimizedCustomBackend(CustomQuantumSimulator):
"""Optimized version with caching."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.circuit_cache = {}
self.state_cache = {}
def get_state_vector(self, circuit):
"""Cached state vector computation."""
# Create circuit hash
circuit_hash = self._hash_circuit(circuit)
if circuit_hash in self.state_cache:
return self.state_cache[circuit_hash]
# Compute state vector
state = super().get_state_vector(circuit)
# Cache result
self.state_cache[circuit_hash] = state
return state
def _hash_circuit(self, circuit):
"""Create hash of circuit for caching."""
circuit_str = f"{circuit.num_qubits}_{circuit.gates}"
return hashlib.md5(circuit_str.encode()).hexdigest()
@lru_cache(maxsize=1000)
def _apply_single_gate_cached(self, state_hash, gate_info, num_qubits):
"""Cached gate application."""
# Convert hash back to state (simplified)
state = self._hash_to_state(state_hash)
return self._apply_gate(state, gate_info, num_qubits)
def clear_caches(self):
"""Clear all caches."""
self.circuit_cache.clear()
self.state_cache.clear()
self._apply_single_gate_cached.cache_clear()
Parallel Processing¶
import multiprocessing as mp
from concurrent.futures import ThreadPoolExecutor
class ParallelQuantumBackend(CustomQuantumSimulator):
"""Parallel quantum backend for multiple circuit execution."""
def __init__(self, config: Optional[Dict[str, Any]] = None):
super().__init__(config)
self.num_workers = config.get("num_workers", mp.cpu_count())
def execute_circuits_parallel(self, circuits, shots_per_circuit=None):
"""Execute multiple circuits in parallel."""
if shots_per_circuit is None:
shots_per_circuit = [self.shots] * len(circuits)
with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
futures = [
executor.submit(self.execute_circuit, circuit, shots)
for circuit, shots in zip(circuits, shots_per_circuit)
]
results = [future.result() for future in futures]
return results
def infer_parallel(self, networks, queries, evidences):
"""Perform inference on multiple networks in parallel."""
with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
futures = [
executor.submit(self.infer, network, query, evidence)
for network, query, evidence in zip(networks, queries, evidences)
]
results = [future.result() for future in futures]
return results
Deployment and Distribution¶
Packaging Custom Backends¶
# setup.py for custom backend package
from setuptools import setup, find_packages
setup(
name="pqr-custom-backends",
version="0.1.0",
description="Custom backends for Probabilistic Quantum Reasoner",
packages=find_packages(),
install_requires=[
"probabilistic-quantum-reasoner>=0.1.0",
"numpy>=1.21.0",
"cupy-cuda11x>=10.0.0; platform_system!='Darwin'",
],
extras_require={
"gpu": ["cupy-cuda11x>=10.0.0"],
"dev": ["pytest>=7.0.0", "pytest-cov>=4.0.0"],
},
entry_points={
"pqr.backends": [
"custom_quantum = pqr_custom_backends:CustomQuantumSimulator",
"gpu_classical = pqr_custom_backends:GPUClassicalBackend",
]
},
classifiers=[
"Development Status :: 3 - Alpha",
"Intended Audience :: Science/Research",
"License :: Other/Proprietary License"
"Programming Language :: Python :: 3.10+",
],
python_requires=">=3.10",
)
Docker Deployment¶
# Dockerfile for custom backend deployment
FROM python:3.10-slim
# Install system dependencies
RUN apt-get update && apt-get install -y \
build-essential \
cuda-toolkit-11-8 \
&& rm -rf /var/lib/apt/lists/*
# Install Python packages
COPY requirements.txt .
RUN pip install -r requirements.txt
# Install custom backends
COPY . /app/pqr-custom-backends
WORKDIR /app/pqr-custom-backends
RUN pip install .
# Set up environment
ENV CUDA_VISIBLE_DEVICES=0
ENV PYTHONPATH=/app
# Run tests
RUN python -m pytest tests/
ENTRYPOINT ["python", "-m", "pqr_custom_backends.server"]
Best Practices¶
Performance Guidelines¶
- Minimize state vector computations - cache when possible
- Use appropriate precision - float32 vs float64 trade-offs
- Batch operations - process multiple circuits together
- Memory management - clear unused arrays and circuits
- Parallel execution - leverage multi-core and GPU resources
Error Handling¶
- Validate inputs early and provide clear error messages
- Handle hardware failures gracefully with fallback options
- Monitor resource usage and prevent memory leaks
- Log operations for debugging and performance analysis
Testing Strategy¶
- Unit tests for individual components
- Integration tests with the main reasoner
- Performance benchmarks against existing backends
- Stress tests with large networks and long circuits
- Hardware-specific tests for GPU and quantum devices
This guide provides a comprehensive foundation for developing custom backends that integrate seamlessly with the Probabilistic Quantum Reasoner framework.