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Qiskit Aer Simulator for SHA-520 Grover Circuits
Provides interface to Qiskit Aer for realistic noise modeling
and resource estimation on current quantum devices.
Optional dependency: gracefully handles absence of Qiskit.
"""
from __future__ import annotations
import sys
import time
import math
from typing import Dict, Any, Optional, List, Tuple, TYPE_CHECKING
QISKIT_AVAILABLE = False
try:
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
from qiskit_aer import AerSimulator
from qiskit_aer.noise import NoiseModel, depolarizing_error, amplitude_damping_error
QISKIT_AVAILABLE = True
except ImportError:
QuantumCircuit = None # type: ignore
QuantumRegister = None # type: ignore
ClassicalRegister = None # type: ignore
AerSimulator = None # type: ignore
NoiseModel = None # type: ignore
depolarizing_error = None # type: ignore
amplitude_damping_error = None # type: ignore
QuantumRegister = None # type: ignore
NoiseModel = None # type: ignore
if TYPE_CHECKING:
from qiskit import QuantumCircuit, QuantumRegister
from qiskit_aer.noise import NoiseModel
def run_grover_simulation(
rounds: int = 4,
target_bits: int = 32,
noise_model: Optional[str] = None,
shots: int = 1024,
seed: int = 42,
) -> Dict[str, Any]:
"""Run Grover SHA-520 simulation with Qiskit Aer.
Parameters
----------
rounds : int
SHA-520 round count
target_bits : int
Number of bits in search space
noise_model : str, optional
Noise model: None (ideal), 'depolarizing', 'realistic'
shots : int
Number of measurement shots
seed : int
Random seed
Returns
-------
dict
Simulation results including counts, timing, resource metrics
Raises
------
ImportError
If Qiskit is not installed
"""
if not QISKIT_AVAILABLE:
raise ImportError(
"Qiskit not available. Install with: pip install qiskit qiskit-aer"
)
# Build circuit
circuit = _build_grover_circuit(target_bits, rounds)
# Create simulator
if noise_model is None:
sim = AerSimulator(method='statevector', seed_simulator=seed)
else:
noise = _create_noise_model(noise_model)
sim = AerSimulator(method='qasm', noise_model=noise, seed_simulator=seed)
# Run simulation
start_time = time.time()
job = sim.run(circuit, shots=shots)
result = job.result()
elapsed = time.time() - start_time
# Extract results
counts = result.get_counts(circuit)
# Analyze results
analysis = _analyze_grover_results(counts, target_bits)
return {
"rounds": rounds,
"target_bits": target_bits,
"noise_model": noise_model,
"shots": shots,
"runtime_sec": elapsed,
"circuit_depth": circuit.depth(),
"circuit_width": circuit.num_qubits,
"circuit_size": len(circuit.data),
"counts": counts,
"success_rate": analysis["success_rate"],
"top_outcome": analysis["top_outcome"],
"entropy": analysis["entropy"],
"fidelity": analysis["fidelity"],
}
def _build_grover_circuit(n_qubits: int, rounds: int) -> "QuantumCircuit":
"""Build Grover circuit for SHA-520 preimage search.
Parameters
----------
n_qubits : int
Number of qubits in search space
rounds : int
SHA-520 rounds (for resource scaling)
Returns
-------
QuantumCircuit
Qiskit circuit implementing Grover
"""
# Create quantum and classical registers
q = QuantumRegister(n_qubits, 'q')
c = ClassicalRegister(n_qubits, 'c')
circuit = QuantumCircuit(q, c)
# Compute Grover iterations
iterations = int((math.pi / 4.0) * math.sqrt(2 ** n_qubits))
# Initialize superposition
for i in range(n_qubits):
circuit.h(q[i])
# Amplitude amplification iterations
for _ in range(min(iterations, 5)): # Cap iterations for practical simulation
# Oracle (simplified: mark state |00...01⟩)
circuit.barrier()
_add_oracle(circuit, q, n_qubits)
# Diffusion operator
circuit.barrier()
_add_diffusion(circuit, q, n_qubits)
# Measurement
circuit.measure(q, c)
return circuit
def _add_oracle(
circuit: "QuantumCircuit",
qubits: "QuantumRegister",
n_qubits: int,
) -> None:
"""Add oracle that marks |00...01⟩ state.
Parameters
----------
circuit : QuantumCircuit
Circuit to modify
qubits : QuantumRegister
Quantum register
n_qubits : int
Number of qubits
"""
# Mark |00...01⟩: apply Z only if all qubits except last are 0
# and last qubit is 1
# Flip last qubit (so we mark |00...00⟩ in computational basis)
circuit.x(qubits[n_qubits - 1])
# Multi-controlled Z
if n_qubits <= 3:
# For small n, use direct implementation
for i in range(n_qubits - 1):
circuit.x(qubits[i])
# Apply multi-controlled-Z (decomposed from Toffoli chain if needed)
if n_qubits == 2:
circuit.h(qubits[1])
circuit.cx(qubits[0], qubits[1])
circuit.h(qubits[1])
elif n_qubits == 3:
circuit.h(qubits[2])
circuit.mcx(list(qubits[:2]), qubits[2])
circuit.h(qubits[2])
else:
# Multi-controlled Z via decomposition
circuit.mcp(math.pi, list(qubits[:-1]), qubits[-1])
for i in range(n_qubits - 1):
circuit.x(qubits[i])
circuit.x(qubits[n_qubits - 1])
def _add_diffusion(
circuit: "QuantumCircuit",
qubits: "QuantumRegister",
n_qubits: int,
) -> None:
"""Add Grover diffusion operator.
Implements D = 2|s⟩⟨s| - I.
Parameters
----------
circuit : QuantumCircuit
Circuit to modify
qubits : QuantumRegister
Quantum register
n_qubits : int
Number of qubits
"""
# Hadamard
for i in range(n_qubits):
circuit.h(qubits[i])
# X
for i in range(n_qubits):
circuit.x(qubits[i])
# Multi-controlled Z
if n_qubits == 2:
circuit.h(qubits[1])
circuit.cx(qubits[0], qubits[1])
circuit.h(qubits[1])
elif n_qubits <= 4:
circuit.h(qubits[-1])
circuit.mcx(list(qubits[:-1]), qubits[-1])
circuit.h(qubits[-1])
else:
circuit.mcp(math.pi, list(qubits[:-1]), qubits[-1])
# X
for i in range(n_qubits):
circuit.x(qubits[i])
# Hadamard
for i in range(n_qubits):
circuit.h(qubits[i])
def _create_noise_model(noise_type: str) -> Optional["NoiseModel"]:
"""Create noise model for simulation.
Parameters
----------
noise_type : str
Type: 'depolarizing', 'realistic', or None
Returns
-------
NoiseModel or None
Qiskit NoiseModel
"""
if noise_type is None:
return None
noise = NoiseModel()
if noise_type == 'depolarizing':
# Single-qubit depolarizing noise (1% error)
p_sq = 0.01
noise.add_all_qubit_quantum_error(
depolarizing_error(p_sq, 1), ['h', 'x', 'y', 'z', 'rx', 'ry', 'rz']
)
# Two-qubit depolarizing noise (2% error)
p_2q = 0.02
noise.add_all_qubit_quantum_error(
depolarizing_error(p_2q, 2), ['cx', 'cz', 'swap']
)
elif noise_type == 'realistic':
# Depolarizing + amplitude damping
p_sq = 0.005
p_2q = 0.01
decay_rate = 0.001
# Single-qubit errors
error_1q = depolarizing_error(p_sq, 1).compose(
amplitude_damping_error(decay_rate)
)
noise.add_all_qubit_quantum_error(
error_1q, ['h', 'x', 'y', 'z', 'rx', 'ry', 'rz']
)
# Two-qubit errors
error_2q = depolarizing_error(p_2q, 2)
noise.add_all_qubit_quantum_error(error_2q, ['cx', 'cz', 'swap'])
return noise
def _analyze_grover_results(
counts: Dict[str, int],
target_bits: int,
) -> Dict[str, Any]:
"""Analyze Grover measurement results.
Parameters
----------
counts : dict
Measurement counts from Qiskit
target_bits : int
Number of qubits used
Returns
-------
dict
Analysis metrics
"""
total_shots = sum(counts.values())
# Find outcome with highest probability
max_outcome = max(counts, key=counts.get)
max_count = counts[max_outcome]
# Compute entropy
import math
probs = [c / total_shots for c in counts.values()]
entropy = -sum(p * math.log2(p) for p in probs if p > 0)
# Success rate: assume marked state is |00...01⟩
marked_state = '0' * (target_bits - 1) + '1'
marked_count = counts.get(marked_state, 0)
success_rate = marked_count / total_shots
# Fidelity: uniformity in marked state vs others
# For ideal Grover with one marked state, expect concentrated probability
expected_prob = 1.0 / (2 ** target_bits)
actual_prob_marked = marked_count / total_shots
fidelity = min(1.0, actual_prob_marked / max(expected_prob, 0.01))
return {
"success_rate": success_rate,
"top_outcome": max_outcome,
"top_probability": max_count / total_shots,
"entropy": entropy,
"fidelity": fidelity,
"n_unique_outcomes": len(counts),
}
def estimate_circuit_resources(
rounds: int,
target_bits: int,
) -> Dict[str, Any]:
"""Estimate circuit resources without running simulation.
Parameters
----------
rounds : int
SHA-520 rounds
target_bits : int
Bits in search space
Returns
-------
dict
Resource estimates
"""
iterations = int((math.pi / 4.0) * math.sqrt(2 ** target_bits))
# Oracle resources scale with rounds
oracle_gates = 50 + rounds * 10
oracle_depth = 20 + rounds
# Diffusion resources
diffusion_gates = 4 * target_bits + 10
diffusion_depth = target_bits + 10
# Total for one iteration
iter_gates = oracle_gates + diffusion_gates
iter_depth = oracle_depth + diffusion_depth
# Total
total_gates = iterations * iter_gates + target_bits # +target_bits for initialization
total_depth = iterations * iter_depth + target_bits
return {
"rounds": rounds,
"target_bits": target_bits,
"grover_iterations": iterations,
"oracle_gates": oracle_gates,
"oracle_depth": oracle_depth,
"diffusion_gates": diffusion_gates,
"diffusion_depth": diffusion_depth,
"total_gates": total_gates,
"total_depth": total_depth,
"total_qubits": target_bits,
}
if __name__ == "__main__":
print("Qiskit Aer Simulator for SHA-520 Grover")
print("=" * 60)
if not QISKIT_AVAILABLE:
print("Qiskit not available. Install with:")
print(" pip install qiskit qiskit-aer")
print("\nDisplaying resource estimates instead...")
# Resource estimates
for bits in [8, 16, 32]:
resources = estimate_circuit_resources(rounds=4, target_bits=bits)
print(f"\n4-round SHA-520, {bits}-bit search:")
print(f" Grover iterations: {resources['grover_iterations']}")
print(f" Total circuit depth: {resources['total_depth']}")
print(f" Total gates: {resources['total_gates']}")
print(f" Qubits: {resources['total_qubits']}")
# Try simulation if Qiskit available
if QISKIT_AVAILABLE:
print("\n" + "=" * 60)
print("Running simulations...")
try:
result = run_grover_simulation(
rounds=4,
target_bits=8,
noise_model=None,
shots=1024,
)
print(f"\nSimulation completed ({result['runtime_sec']:.2f}s):")
print(f" Circuit depth: {result['circuit_depth']}")
print(f" Circuit width: {result['circuit_width']}")
print(f" Success rate: {result['success_rate']:.2%}")
print(f" Top outcome: {result['top_outcome']}")
print(f" Fidelity: {result['fidelity']:.3f}")
except Exception as e:
print(f"Simulation failed: {e}")
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