0
# Device Management and Local Simulation
1
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Comprehensive device interfaces, local quantum simulation, and emulation capabilities for quantum circuit development and testing.
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## Core Imports
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```python { .api }
7
from braket.devices import Device, Devices, LocalSimulator
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from braket.emulation import Emulator, PassManager, ValidationPass
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from braket.tasks import QuantumTask, QuantumTaskBatch
10
from braket.tasks import (
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GateModelQuantumTaskResult, AnalogHamiltonianSimulationQuantumTaskResult,
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AnnealingQuantumTaskResult, PhotonicModelQuantumTaskResult, ProgramSetQuantumTaskResult
13
)
14
```
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## Device Interface
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### Abstract Device Base
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```python { .api }
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from braket.devices import Device
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from abc import ABC, abstractmethod
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class Device(ABC):
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"""Abstract base class for quantum devices."""
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@abstractmethod
28
def run(
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self,
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task_specification,
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shots: int = 1000,
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**kwargs
33
) -> 'QuantumTask':
34
"""
35
Execute quantum task on device.
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Args:
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task_specification: Circuit, AHS program, or annealing problem
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shots: Number of measurement shots
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**kwargs: Additional device-specific parameters
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Returns:
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QuantumTask: Task execution handle
44
"""
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pass
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47
@abstractmethod
48
def run_batch(
49
self,
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task_specifications: list,
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shots: int = 1000,
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max_parallel: int = 10,
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**kwargs
54
) -> 'QuantumTaskBatch':
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"""
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Execute batch of quantum tasks.
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Args:
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task_specifications: List of task specifications
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shots: Number of measurement shots per task
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max_parallel: Maximum parallel executions
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**kwargs: Additional parameters
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Returns:
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QuantumTaskBatch: Batch execution handle
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"""
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pass
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@property
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@abstractmethod
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def name(self) -> str:
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"""Device name identifier."""
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pass
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@property
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@abstractmethod
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def status(self) -> str:
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"""Current device status."""
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pass
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@property
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@abstractmethod
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def type(self) -> str:
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"""Device type identifier."""
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pass
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@property
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@abstractmethod
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def provider_name(self) -> str:
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"""Device provider name."""
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pass
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@property
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@abstractmethod
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def properties(self) -> dict:
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"""Device capabilities and properties."""
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pass
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class Devices:
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"""Device enumeration utility."""
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@staticmethod
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def get_all() -> list[Device]:
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"""
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Get all available devices.
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Returns:
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list[Device]: All available quantum devices
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"""
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pass
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@staticmethod
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def get_by_type(device_type: str) -> list[Device]:
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"""
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Get devices by type.
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Args:
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device_type: Device type filter
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Returns:
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list[Device]: Devices matching type
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"""
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pass
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@staticmethod
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def get_by_provider(provider: str) -> list[Device]:
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"""
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Get devices by provider.
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Args:
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provider: Provider name filter
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Returns:
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list[Device]: Devices from provider
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"""
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pass
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```
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## Local Quantum Simulation
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### LocalSimulator Implementation
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```python { .api }
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from braket.devices import LocalSimulator
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import numpy as np
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class LocalSimulator(Device):
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"""Local quantum circuit simulator."""
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def __init__(
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self,
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backend: str = "braket_sv",
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max_shots: int = 100000,
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seed: int = None
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):
156
"""
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Initialize local simulator.
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Args:
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backend: Simulation backend ('braket_sv', 'braket_dm', 'braket_ahs')
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max_shots: Maximum number of shots per execution
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seed: Random seed for reproducible results
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"""
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self.backend = backend
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self.max_shots = max_shots
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self.seed = seed
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def run(
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self,
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task_specification,
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shots: int = 1000,
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inputs: dict = None,
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**kwargs
174
) -> 'LocalQuantumTask':
175
"""
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Execute circuit on local simulator.
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Args:
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task_specification: Quantum circuit to execute
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shots: Number of measurement shots
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inputs: Parameter inputs for parameterized circuits
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**kwargs: Additional simulation parameters
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Returns:
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LocalQuantumTask: Local task execution
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"""
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pass
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def run_batch(
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self,
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task_specifications: list,
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shots: int = 1000,
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max_parallel: int = None,
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inputs: list[dict] = None,
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**kwargs
196
) -> 'LocalQuantumTaskBatch':
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"""
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Execute batch of circuits locally.
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Args:
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task_specifications: List of quantum circuits
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shots: Number of shots per circuit
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max_parallel: Parallel execution limit (uses CPU cores)
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inputs: Parameter inputs for each circuit
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**kwargs: Additional parameters
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Returns:
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LocalQuantumTaskBatch: Batch execution handle
209
"""
210
pass
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@property
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def name(self) -> str:
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"""Simulator name."""
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return f"LocalSimulator({self.backend})"
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@property
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def status(self) -> str:
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"""Simulator status (always ONLINE for local)."""
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return "ONLINE"
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@property
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def type(self) -> str:
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"""Device type."""
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return "SIMULATOR"
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@property
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def provider_name(self) -> str:
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"""Provider name."""
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return "Local"
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@property
233
def properties(self) -> dict:
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"""Simulator capabilities."""
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return {
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"backend": self.backend,
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"max_shots": self.max_shots,
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"max_qubits": 25 if self.backend == "braket_sv" else 12,
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"supported_operations": self._get_supported_operations(),
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"supports_noise": self.backend == "braket_dm"
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}
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def _get_supported_operations(self) -> list[str]:
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"""Get list of supported quantum operations."""
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if self.backend == "braket_sv":
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return ["H", "X", "Y", "Z", "S", "T", "Rx", "Ry", "Rz", "CNot", "CCNot", "Unitary"]
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elif self.backend == "braket_dm":
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return ["H", "X", "Y", "Z", "S", "T", "Rx", "Ry", "Rz", "CNot", "Noise"]
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else:
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return []
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# Local simulation examples
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def simulate_circuit_locally(circuit, shots: int = 1000, backend: str = "braket_sv") -> dict:
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"""
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Simulate quantum circuit using local resources.
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Args:
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circuit: Quantum circuit to simulate
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shots: Number of measurement shots
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backend: Local simulation backend
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Returns:
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dict: Simulation results and performance metrics
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"""
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import time
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simulator = LocalSimulator(backend=backend)
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start_time = time.time()
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task = simulator.run(circuit, shots=shots)
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result = task.result()
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end_time = time.time()
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return {
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'measurement_counts': result.measurement_counts,
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'measurement_probabilities': result.measurement_probabilities,
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'execution_time': end_time - start_time,
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'simulator_info': {
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'backend': backend,
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'shots': shots,
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'qubit_count': circuit.qubit_count,
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'circuit_depth': circuit.depth
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}
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}
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def compare_simulation_backends(circuit) -> dict:
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"""
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Compare different local simulation backends.
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Args:
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circuit: Circuit to simulate across backends
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Returns:
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dict: Performance comparison across backends
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"""
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backends = ["braket_sv", "braket_dm"]
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comparison = {}
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for backend in backends:
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try:
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result = simulate_circuit_locally(circuit, shots=1000, backend=backend)
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comparison[backend] = {
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'execution_time': result['execution_time'],
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'memory_usage': 'Unknown', # Would need profiling
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'accuracy': 'Exact' if backend == "braket_sv" else 'Approximate',
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'noise_support': backend == "braket_dm",
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'results': result['measurement_counts']
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}
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except Exception as e:
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comparison[backend] = {'error': str(e)}
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return comparison
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def optimize_local_simulation(circuit, target_accuracy: float = 0.01) -> dict:
315
"""
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Optimize local simulation for best performance/accuracy tradeoff.
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Args:
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circuit: Circuit to optimize simulation for
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target_accuracy: Target measurement accuracy
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Returns:
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dict: Optimized simulation configuration
324
"""
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optimization = {
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'recommended_backend': 'braket_sv',
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'recommended_shots': 1000,
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'expected_time': 0.0,
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'expected_memory': 0,
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'rationale': []
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}
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# Analyze circuit characteristics
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qubit_count = circuit.qubit_count
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circuit_depth = circuit.depth
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has_noise = any(hasattr(inst.operator, 'noise') for inst in circuit.instructions)
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# Backend recommendation
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if has_noise:
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optimization['recommended_backend'] = 'braket_dm'
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optimization['rationale'].append("Circuit contains noise - using density matrix simulator")
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elif qubit_count > 20:
343
optimization['recommended_backend'] = 'braket_dm'
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optimization['rationale'].append(f"{qubit_count} qubits - density matrix more memory efficient")
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else:
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optimization['recommended_backend'] = 'braket_sv'
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optimization['rationale'].append("State vector simulator for exact results")
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# Shot count optimization
350
if target_accuracy <= 0.001:
351
optimization['recommended_shots'] = 100000
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elif target_accuracy <= 0.01:
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optimization['recommended_shots'] = 10000
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else:
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optimization['recommended_shots'] = 1000
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optimization['rationale'].append(f"Shots: {optimization['recommended_shots']} for {target_accuracy} accuracy")
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# Performance estimates
360
if optimization['recommended_backend'] == 'braket_sv':
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optimization['expected_memory'] = 2 ** (qubit_count + 3) # 8 bytes per amplitude
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optimization['expected_time'] = circuit_depth * 0.001 * (2 ** qubit_count)
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else:
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optimization['expected_memory'] = 2 ** (2 * qubit_count + 4) # Density matrix
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optimization['expected_time'] = circuit_depth * 0.002 * (2 ** (2 * qubit_count))
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return optimization
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```
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### Local Task Management
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```python { .api }
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from braket.tasks import QuantumTask
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class LocalQuantumTask(QuantumTask):
376
"""Local quantum task execution."""
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def __init__(self, task_specification, shots: int, simulator: LocalSimulator):
379
"""
380
Initialize local quantum task.
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Args:
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task_specification: Circuit or program to execute
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shots: Number of measurement shots
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simulator: Local simulator instance
386
"""
387
self.task_specification = task_specification
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self.shots = shots
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self.simulator = simulator
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self._result = None
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self._state = "CREATED"
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def result(self) -> 'GateModelQuantumTaskResult':
394
"""
395
Get task execution results.
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Returns:
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GateModelQuantumTaskResult: Task results
399
"""
400
if self._result is None:
401
self._execute()
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return self._result
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404
def state(self) -> str:
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"""
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Get current task state.
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Returns:
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str: Task state (CREATED, RUNNING, COMPLETED, FAILED)
410
"""
411
return self._state
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def _execute(self) -> None:
414
"""Execute the quantum task locally."""
415
import time
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import numpy as np
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self._state = "RUNNING"
419
start_time = time.time()
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421
try:
422
# Simulate circuit execution
423
if hasattr(self.task_specification, 'instructions'):
424
# Quantum circuit
425
circuit = self.task_specification
426
427
# Basic state vector simulation (simplified)
428
n_qubits = circuit.qubit_count
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state_vector = np.zeros(2**n_qubits, dtype=complex)
430
state_vector[0] = 1.0 # |00...0⟩ initial state
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# Apply gates (simplified implementation)
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for instruction in circuit.instructions:
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gate = instruction.operator
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targets = instruction.target
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# Gate application logic would go here
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# Sample measurements
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probabilities = np.abs(state_vector) ** 2
440
samples = np.random.choice(
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len(probabilities),
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size=self.shots,
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p=probabilities
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)
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# Count measurements
447
measurement_counts = {}
448
for sample in samples:
449
bitstring = format(sample, f'0{n_qubits}b')
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measurement_counts[bitstring] = measurement_counts.get(bitstring, 0) + 1
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452
self._result = GateModelQuantumTaskResult(
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measurement_counts=measurement_counts,
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measurement_probabilities={
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k: v/self.shots for k, v in measurement_counts.items()
456
},
457
task_metadata={
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'shots': self.shots,
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'execution_duration': time.time() - start_time,
460
'simulator_backend': self.simulator.backend
461
}
462
)
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self._state = "COMPLETED"
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except Exception as e:
467
self._state = "FAILED"
468
self._result = {'error': str(e)}
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class LocalQuantumTaskBatch:
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"""Batch execution of local quantum tasks."""
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def __init__(self, tasks: list[LocalQuantumTask]):
474
"""
475
Initialize local task batch.
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477
Args:
478
tasks: List of local quantum tasks
479
"""
480
self.tasks = tasks
481
482
def results(self) -> list:
483
"""
484
Get results from all tasks in batch.
485
486
Returns:
487
list: Results from all tasks
488
"""
489
return [task.result() for task in self.tasks]
490
491
def unsuccessful(self) -> list[LocalQuantumTask]:
492
"""
493
Get unsuccessful tasks.
494
495
Returns:
496
list[LocalQuantumTask]: Failed tasks
497
"""
498
return [task for task in self.tasks if task.state() == "FAILED"]
499
500
def execute_local_batch_efficiently(circuits: list, shots: int = 1000) -> dict:
501
"""
502
Execute batch of circuits with optimal local resource utilization.
503
504
Args:
505
circuits: List of quantum circuits
506
shots: Number of shots per circuit
507
508
Returns:
509
dict: Batch execution results with performance analytics
510
"""
511
import multiprocessing
512
import time
513
from concurrent.futures import ThreadPoolExecutor
514
515
simulator = LocalSimulator()
516
start_time = time.time()
517
518
# Determine optimal parallelization
519
cpu_count = multiprocessing.cpu_count()
520
max_parallel = min(len(circuits), cpu_count)
521
522
batch_results = {
523
'results': [],
524
'execution_stats': {
525
'total_circuits': len(circuits),
526
'parallel_workers': max_parallel,
527
'total_execution_time': 0,
528
'average_time_per_circuit': 0,
529
'throughput_circuits_per_second': 0
530
}
531
}
532
533
# Execute circuits in parallel
534
with ThreadPoolExecutor(max_workers=max_parallel) as executor:
535
tasks = [
536
executor.submit(simulator.run, circuit, shots)
537
for circuit in circuits
538
]
539
540
for i, task in enumerate(tasks):
541
result = task.result().result()
542
batch_results['results'].append({
543
'circuit_index': i,
544
'measurement_counts': result.measurement_counts,
545
'measurement_probabilities': result.measurement_probabilities
546
})
547
548
# Calculate performance statistics
549
end_time = time.time()
550
total_time = end_time - start_time
551
552
batch_results['execution_stats']['total_execution_time'] = total_time
553
batch_results['execution_stats']['average_time_per_circuit'] = total_time / len(circuits)
554
batch_results['execution_stats']['throughput_circuits_per_second'] = len(circuits) / total_time
555
556
return batch_results
557
```
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559
## Quantum Circuit Emulation
560
561
### Emulator Infrastructure
562
563
```python { .api }
564
from braket.emulation import Emulator, PassManager, ValidationPass
565
566
class Emulator:
567
"""Quantum circuit emulator with compilation passes."""
568
569
def __init__(
570
self,
571
pass_manager: PassManager = None,
572
target_device: Device = None
573
):
574
"""
575
Initialize quantum circuit emulator.
576
577
Args:
578
pass_manager: Compilation pass manager
579
target_device: Target device for emulation properties
580
"""
581
self.pass_manager = pass_manager or PassManager()
582
self.target_device = target_device
583
584
def emulate(
585
self,
586
circuit,
587
shots: int = 1000,
588
inputs: dict = None
589
) -> 'EmulationResult':
590
"""
591
Emulate quantum circuit with compilation.
592
593
Args:
594
circuit: Quantum circuit to emulate
595
shots: Number of measurement shots
596
inputs: Input parameters for parameterized circuits
597
598
Returns:
599
EmulationResult: Emulation results with compilation info
600
"""
601
pass
602
603
def add_pass(self, pass_instance) -> 'Emulator':
604
"""
605
Add compilation pass to emulator.
606
607
Args:
608
pass_instance: Compilation pass to add
609
610
Returns:
611
Emulator: Self for method chaining
612
"""
613
self.pass_manager.add_pass(pass_instance)
614
return self
615
616
class PassManager:
617
"""Compilation pass management for quantum circuits."""
618
619
def __init__(self):
620
"""Initialize empty pass manager."""
621
self.passes = []
622
623
def add_pass(self, pass_instance) -> 'PassManager':
624
"""
625
Add compilation pass.
626
627
Args:
628
pass_instance: Pass to add
629
630
Returns:
631
PassManager: Self for method chaining
632
"""
633
self.passes.append(pass_instance)
634
return self
635
636
def run(self, circuit) -> 'Circuit':
637
"""
638
Run all passes on circuit.
639
640
Args:
641
circuit: Input quantum circuit
642
643
Returns:
644
Circuit: Compiled quantum circuit
645
"""
646
compiled_circuit = circuit
647
for pass_instance in self.passes:
648
compiled_circuit = pass_instance.run(compiled_circuit)
649
return compiled_circuit
650
651
class ValidationPass:
652
"""Circuit validation compilation pass."""
653
654
def __init__(self, target_device: Device = None):
655
"""
656
Initialize validation pass.
657
658
Args:
659
target_device: Target device for validation
660
"""
661
self.target_device = target_device
662
663
def run(self, circuit) -> 'Circuit':
664
"""
665
Validate circuit against device constraints.
666
667
Args:
668
circuit: Circuit to validate
669
670
Returns:
671
Circuit: Validated circuit (may raise ValidationError)
672
"""
673
pass
674
675
# Emulation examples
676
def create_realistic_emulator(target_device_properties: dict) -> Emulator:
677
"""
678
Create emulator that mimics real device behavior.
679
680
Args:
681
target_device_properties: Properties of target quantum device
682
683
Returns:
684
Emulator: Configured emulator with realistic behavior
685
"""
686
pass_manager = PassManager()
687
688
# Add validation pass for device constraints
689
validation_pass = ValidationPass()
690
pass_manager.add_pass(validation_pass)
691
692
# Add device-specific compilation passes
693
if 'connectivity' in target_device_properties:
694
# Add SWAP insertion pass for limited connectivity
695
# swap_pass = SwapInsertionPass(target_device_properties['connectivity'])
696
# pass_manager.add_pass(swap_pass)
697
pass
698
699
if 'native_gates' in target_device_properties:
700
# Add gate decomposition pass
701
# decomposition_pass = GateDecompositionPass(target_device_properties['native_gates'])
702
# pass_manager.add_pass(decomposition_pass)
703
pass
704
705
emulator = Emulator(pass_manager=pass_manager)
706
return emulator
707
708
def benchmark_emulation_vs_simulation(circuit) -> dict:
709
"""
710
Benchmark emulation vs direct simulation performance.
711
712
Args:
713
circuit: Circuit to benchmark
714
715
Returns:
716
dict: Performance comparison results
717
"""
718
import time
719
720
benchmark_results = {
721
'circuit_info': {
722
'qubit_count': circuit.qubit_count,
723
'depth': circuit.depth,
724
'gate_count': len(circuit.instructions)
725
},
726
'simulation': {},
727
'emulation': {}
728
}
729
730
# Direct simulation
731
simulator = LocalSimulator()
732
start_time = time.time()
733
sim_task = simulator.run(circuit, shots=1000)
734
sim_result = sim_task.result()
735
sim_time = time.time() - start_time
736
737
benchmark_results['simulation'] = {
738
'execution_time': sim_time,
739
'compilation_time': 0,
740
'result_counts': sim_result.measurement_counts
741
}
742
743
# Emulation with compilation
744
emulator = Emulator()
745
start_time = time.time()
746
compile_start = time.time()
747
compiled_circuit = emulator.pass_manager.run(circuit)
748
compile_time = time.time() - compile_start
749
750
emu_result = emulator.emulate(compiled_circuit, shots=1000)
751
total_emu_time = time.time() - start_time
752
753
benchmark_results['emulation'] = {
754
'execution_time': total_emu_time,
755
'compilation_time': compile_time,
756
'optimizations_applied': len(emulator.pass_manager.passes),
757
'compiled_circuit_depth': compiled_circuit.depth
758
}
759
760
# Performance analysis
761
benchmark_results['analysis'] = {
762
'speedup_factor': sim_time / total_emu_time if total_emu_time > 0 else float('inf'),
763
'compilation_overhead': compile_time / total_emu_time if total_emu_time > 0 else 0,
764
'depth_reduction': (circuit.depth - compiled_circuit.depth) / circuit.depth if circuit.depth > 0 else 0
765
}
766
767
return benchmark_results
768
```
769
770
## Task Result Types
771
772
### Result Type Classes
773
774
```python { .api }
775
from braket.tasks import (
776
GateModelQuantumTaskResult, AnalogHamiltonianSimulationQuantumTaskResult,
777
AnnealingQuantumTaskResult, PhotonicModelQuantumTaskResult, ProgramSetQuantumTaskResult
778
)
779
780
class GateModelQuantumTaskResult:
781
"""Results from gate-model quantum circuit execution."""
782
783
def __init__(
784
self,
785
measurement_counts: dict = None,
786
measurement_probabilities: dict = None,
787
values: list = None,
788
measured_qubits: list = None,
789
task_metadata: dict = None
790
):
791
"""
792
Initialize gate model task result.
793
794
Args:
795
measurement_counts: Raw measurement count data
796
measurement_probabilities: Measurement probabilities
797
values: Observable expectation values
798
measured_qubits: List of measured qubit indices
799
task_metadata: Task execution metadata
800
"""
801
self.measurement_counts = measurement_counts or {}
802
self.measurement_probabilities = measurement_probabilities or {}
803
self.values = values or []
804
self.measured_qubits = measured_qubits or []
805
self.task_metadata = task_metadata or {}
806
807
def get_counts(self) -> dict:
808
"""
809
Get measurement counts.
810
811
Returns:
812
dict: Measurement counts by bitstring
813
"""
814
return self.measurement_counts
815
816
def get_probabilities(self) -> dict:
817
"""
818
Get measurement probabilities.
819
820
Returns:
821
dict: Measurement probabilities by bitstring
822
"""
823
return self.measurement_probabilities
824
825
class AnalogHamiltonianSimulationQuantumTaskResult:
826
"""Results from analog Hamiltonian simulation tasks."""
827
828
def __init__(
829
self,
830
pre_sequence: list = None,
831
post_sequence: list = None,
832
task_metadata: dict = None
833
):
834
"""
835
Initialize AHS task result.
836
837
Args:
838
pre_sequence: Pre-evolution measurements
839
post_sequence: Post-evolution measurements
840
task_metadata: Task execution metadata
841
"""
842
self.pre_sequence = pre_sequence or []
843
self.post_sequence = post_sequence or []
844
self.task_metadata = task_metadata or {}
845
846
class AnnealingQuantumTaskResult:
847
"""Results from quantum annealing tasks."""
848
849
def __init__(
850
self,
851
record_array: list = None,
852
variable_count: int = 0,
853
task_metadata: dict = None
854
):
855
"""
856
Initialize annealing task result.
857
858
Args:
859
record_array: Annealing solution records
860
variable_count: Number of problem variables
861
task_metadata: Task execution metadata
862
"""
863
self.record_array = record_array or []
864
self.variable_count = variable_count
865
self.task_metadata = task_metadata or {}
866
867
class PhotonicModelQuantumTaskResult:
868
"""Results from photonic quantum computing tasks."""
869
870
def __init__(
871
self,
872
measurements: list = None,
873
task_metadata: dict = None
874
):
875
"""
876
Initialize photonic task result.
877
878
Args:
879
measurements: Photonic measurement data
880
task_metadata: Task execution metadata
881
"""
882
self.measurements = measurements or []
883
self.task_metadata = task_metadata or {}
884
885
class ProgramSetQuantumTaskResult:
886
"""Results from program set execution tasks."""
887
888
def __init__(
889
self,
890
results: list = None,
891
task_metadata: dict = None
892
):
893
"""
894
Initialize program set task result.
895
896
Args:
897
results: List of individual program results
898
task_metadata: Task execution metadata
899
"""
900
self.results = results or []
901
self.task_metadata = task_metadata or {}
902
903
def get_result(self, index: int):
904
"""
905
Get result for specific program in set.
906
907
Args:
908
index: Program index in set
909
910
Returns:
911
Individual program result
912
"""
913
if 0 <= index < len(self.results):
914
return self.results[index]
915
raise IndexError(f"Program index {index} out of range")
916
917
def get_all_results(self) -> list:
918
"""
919
Get all program results.
920
921
Returns:
922
list: All program execution results
923
"""
924
return self.results
925
926
def analyze_task_results(result) -> dict:
927
"""
928
Analyze quantum task results for insights.
929
930
Args:
931
result: Quantum task result object
932
933
Returns:
934
dict: Comprehensive result analysis
935
"""
936
analysis = {
937
'result_type': type(result).__name__,
938
'execution_info': result.task_metadata,
939
'statistics': {}
940
}
941
942
if isinstance(result, GateModelQuantumTaskResult):
943
# Gate model analysis
944
counts = result.get_counts()
945
total_shots = sum(counts.values())
946
947
analysis['statistics'] = {
948
'total_shots': total_shots,
949
'unique_outcomes': len(counts),
950
'most_probable': max(counts.items(), key=lambda x: x[1]),
951
'entropy': calculate_shannon_entropy(counts),
952
'uniformity': len(counts) / (2 ** len(list(counts.keys())[0]))
953
}
954
955
elif isinstance(result, AnnealingQuantumTaskResult):
956
# Annealing analysis
957
analysis['statistics'] = {
958
'solution_count': len(result.record_array),
959
'variable_count': result.variable_count,
960
'unique_solutions': len(set(tuple(record) for record in result.record_array))
961
}
962
963
elif isinstance(result, ProgramSetQuantumTaskResult):
964
# Program set analysis
965
analysis['statistics'] = {
966
'program_count': len(result.results),
967
'total_executions': len(result.results),
968
'batch_execution': True,
969
'individual_results_available': True
970
}
971
972
return analysis
973
974
def calculate_shannon_entropy(counts: dict) -> float:
975
"""
976
Calculate Shannon entropy of measurement distribution.
977
978
Args:
979
counts: Measurement counts dictionary
980
981
Returns:
982
float: Shannon entropy in bits
983
"""
984
import math
985
986
total_counts = sum(counts.values())
987
if total_counts == 0:
988
return 0.0
989
990
entropy = 0.0
991
for count in counts.values():
992
if count > 0:
993
prob = count / total_counts
994
entropy -= prob * math.log2(prob)
995
996
return entropy
997
```
998
999
## Performance Optimization
1000
1001
### Simulation Optimization Strategies
1002
1003
```python { .api }
1004
def optimize_simulation_performance(circuits: list) -> dict:
1005
"""
1006
Optimize quantum simulation performance for circuit collection.
1007
1008
Args:
1009
circuits: List of quantum circuits to optimize
1010
1011
Returns:
1012
dict: Optimization recommendations and performance estimates
1013
"""
1014
optimization_report = {
1015
'circuit_analysis': [],
1016
'recommendations': [],
1017
'performance_estimates': {},
1018
'resource_requirements': {}
1019
}
1020
1021
total_qubits = 0
1022
max_qubits = 0
1023
total_depth = 0
1024
1025
for i, circuit in enumerate(circuits):
1026
circuit_info = {
1027
'index': i,
1028
'qubit_count': circuit.qubit_count,
1029
'depth': circuit.depth,
1030
'gate_count': len(circuit.instructions),
1031
'recommended_backend': 'braket_sv'
1032
}
1033
1034
# Analyze circuit characteristics
1035
if circuit.qubit_count > 15:
1036
circuit_info['recommended_backend'] = 'braket_dm'
1037
optimization_report['recommendations'].append(
1038
f"Circuit {i}: Use density matrix simulator for {circuit.qubit_count} qubits"
1039
)
1040
1041
# Check for noise
1042
has_noise = any(hasattr(inst.operator, 'noise') for inst in circuit.instructions)
1043
if has_noise:
1044
circuit_info['recommended_backend'] = 'braket_dm'
1045
optimization_report['recommendations'].append(
1046
f"Circuit {i}: Noise detected, using density matrix simulator"
1047
)
1048
1049
optimization_report['circuit_analysis'].append(circuit_info)
1050
1051
total_qubits += circuit.qubit_count
1052
max_qubits = max(max_qubits, circuit.qubit_count)
1053
total_depth += circuit.depth
1054
1055
# Resource requirement estimates
1056
optimization_report['resource_requirements'] = {
1057
'max_memory_gb': estimate_memory_requirements(max_qubits, 'braket_sv'),
1058
'estimated_total_time_seconds': estimate_execution_time(circuits),
1059
'recommended_parallel_jobs': min(len(circuits), 8),
1060
'storage_requirements_mb': estimate_storage_requirements(circuits)
1061
}
1062
1063
# Performance optimization recommendations
1064
optimization_report['recommendations'].extend([
1065
"Use parallel execution for independent circuits",
1066
"Consider circuit compression for repeated patterns",
1067
"Implement result caching for identical circuits",
1068
f"Batch similar circuits (max {max_qubits} qubits detected)"
1069
])
1070
1071
return optimization_report
1072
1073
def estimate_memory_requirements(qubit_count: int, backend: str) -> float:
1074
"""
1075
Estimate memory requirements for quantum simulation.
1076
1077
Args:
1078
qubit_count: Number of qubits in circuit
1079
backend: Simulation backend
1080
1081
Returns:
1082
float: Estimated memory requirement in GB
1083
"""
1084
if backend == 'braket_sv':
1085
# State vector: 2^n complex numbers, 16 bytes each
1086
return (2 ** qubit_count) * 16 / (1024 ** 3)
1087
elif backend == 'braket_dm':
1088
# Density matrix: 2^(2n) complex numbers, 16 bytes each
1089
return (2 ** (2 * qubit_count)) * 16 / (1024 ** 3)
1090
else:
1091
return 0.1 # Default estimate
1092
1093
def estimate_execution_time(circuits: list) -> float:
1094
"""
1095
Estimate total execution time for circuit collection.
1096
1097
Args:
1098
circuits: List of circuits to analyze
1099
1100
Returns:
1101
float: Estimated execution time in seconds
1102
"""
1103
total_time = 0.0
1104
1105
for circuit in circuits:
1106
# Time estimation based on circuit complexity
1107
qubit_factor = 2 ** min(circuit.qubit_count, 20)
1108
depth_factor = circuit.depth
1109
gate_factor = len(circuit.instructions)
1110
1111
# Empirical formula (would need benchmarking to calibrate)
1112
circuit_time = (qubit_factor * depth_factor * 0.001) + (gate_factor * 0.0001)
1113
total_time += circuit_time
1114
1115
return total_time
1116
1117
def estimate_storage_requirements(circuits: list) -> float:
1118
"""
1119
Estimate storage requirements for simulation results.
1120
1121
Args:
1122
circuits: List of circuits
1123
1124
Returns:
1125
float: Estimated storage in MB
1126
"""
1127
storage_mb = 0.0
1128
1129
for circuit in circuits:
1130
# Estimate result size based on measurement outcomes
1131
max_outcomes = 2 ** circuit.qubit_count
1132
# Assume JSON storage with reasonable overhead
1133
circuit_storage = max_outcomes * 50 / 1024 / 1024 # 50 bytes per outcome
1134
storage_mb += circuit_storage
1135
1136
return storage_mb
1137
1138
def create_performance_optimized_simulator(circuit_characteristics: dict) -> LocalSimulator:
1139
"""
1140
Create optimally configured simulator based on circuit characteristics.
1141
1142
Args:
1143
circuit_characteristics: Analysis of circuits to be executed
1144
1145
Returns:
1146
LocalSimulator: Optimally configured simulator
1147
"""
1148
max_qubits = circuit_characteristics.get('max_qubits', 10)
1149
has_noise = circuit_characteristics.get('has_noise', False)
1150
total_circuits = circuit_characteristics.get('circuit_count', 1)
1151
1152
# Select optimal backend
1153
if has_noise:
1154
backend = 'braket_dm'
1155
elif max_qubits > 18:
1156
backend = 'braket_dm' # Memory efficiency for large systems
1157
else:
1158
backend = 'braket_sv' # Accuracy for smaller systems
1159
1160
# Configure shot limits based on workload
1161
if total_circuits > 100:
1162
max_shots = 10000 # Lower per-circuit limit for large batches
1163
else:
1164
max_shots = 100000 # Higher limit for smaller batches
1165
1166
return LocalSimulator(
1167
backend=backend,
1168
max_shots=max_shots,
1169
seed=42 # Reproducible results
1170
)
1171
```
1172
1173
This comprehensive device management and simulation documentation covers all aspects of quantum device interfaces, local simulation capabilities, emulation infrastructure, and performance optimization strategies provided by the Amazon Braket SDK.