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Decrypt SHA256 cryogenic issues ( high altitude attempt tentativa de desincriptação em aviões) productos chip electrónica e integrado circuito quantum militar

 

Decryption Grover algorithm Sha256 part 4

 import numpy as np

from qiskit import QuantumCircuit, Aer, execute

from qiskit.providers.aer.noise import NoiseModel, depolarizing_error, thermal_relaxation_error

# ============================================================

# 1. Difusão (Grover)

# ============================================================

def diffusion(qc, qubits):

    qc.h(qubits)

    qc.x(qubits)

    qc.h(qubits[-1])

    qc.mcx(qubits[:-1], qubits[-1])

    qc.h(qubits[-1])

    qc.x(qubits)

    qc.h(qubits)

# ============================================================

# 2. Oracle para "decryption" (pré-imagem)

# ============================================================

def oracle_mark(qc, target):

    n = qc.num_qubits

    for i, bit in enumerate(target):

        if bit == "0":

            qc.x(i)

    qc.h(n-1)

    qc.mcx(list(range(n-1)), n-1)

    qc.h(n-1)

    for i, bit in enumerate(target):

        if bit == "0":

            qc.x(i)

# ============================================================

# 3. Grover automático

# ============================================================

def grover_circuit(n, target, iterations):

    qc = QuantumCircuit(n, n)

    qc.h(range(n))

    for _ in range(iterations):

        oracle_mark(qc, target)

        diffusion(qc, range(n))

    qc.measure(range(n), range(n))

    return qc

# ============================================================

# 4. Hardware realista com ruído escalável

# ============================================================

def hardware_noise(scale):

    noise = NoiseModel()

    # Parâmetros realistas (Sycamore/Aspen/Heron)

    T1 = 40e-6 / scale

    T2 = 60e-6 / scale

    gate_time = 50e-9

    dep1 = depolarizing_error(0.0003 * scale, 1)

    dep2 = depolarizing_error(0.008 * scale, 2)

    thermal = thermal_relaxation_error(T1, T2, gate_time)

    noise.add_all_qubit_quantum_error(thermal, ['x','h'])

    noise.add_all_qubit_quantum_error(dep1, ['x','h'])

    noise.add_all_qubit_quantum_error(dep2, ['cx'])

    return noise

# ============================================================

# 5. Execução automática com ruído escalado

# ============================================================

def run_scaled_noise(qc, scales=[0.5, 1, 2, 3]):

    backend = Aer.get_backend("qasm_simulator")

    results = []

    for s in scales:

        noise = hardware_noise(s)

        job = execute(qc, backend, noise_model=noise, shots=4096)

        counts = job.result().get_counts()

        results.append((s, counts))

    return results

# ============================================================

# 6. Decode automático

# ============================================================

def decode(counts):

    return max(counts, key=counts.get)

# ============================================================

# 7. ZNE automático (extrapolação linear)

# ============================================================

def ZNE(results):

    probs = []

    for scale, counts in results:

        total = sum(counts.values())

        marked = max(counts, key=counts.get)

        probs.append((scale, counts[marked] / total))

    # extrapolação linear simples

    (s1, p1), (s2, p2) = probs[0], probs[1]

    zne_estimate = p1 + (p1 - p2)

    return zne_estimate

# ============================================================

# 8. Pipeline completo

# ============================================================

def grover_decrypt(n=5, target="10101", iterations=2):

    qc = grover_circuit(n, target, iterations)

    results = run_scaled_noise(qc)

    

    print("\n--- Resultados por escala de ruído ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: {counts}")

    print("\n--- Decode automático por escala ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: estado marcado = {decode(counts)}")

    print("\n--- Estimativa ZNE (ruído zero) ---")

    print(ZNE(results))

    return results

# ============================================================

# 9. Execução

# ============================================================

grover_decrypt()

import numpy as np

from qiskit import QuantumCircuit, Aer, execute

from qiskit.providers.aer.noise import NoiseModel, depolarizing_error, thermal_relaxation_error

# ============================================================

# 1. SHA-256 reduzido (8 bits) para demonstração

# ============================================================

def sha256_reduced(x):

    return format((13*x + 7) % 256, "08b")

# ============================================================

# 2. Oracle SHA-256 (pré-imagem)

# ============================================================

def oracle_sha256(qc, x_qubits, h_qubits, target_hash):

    # Escreve target_hash nos qubits de hash

    for i, bit in enumerate(target_hash):

        if bit == "0":

            qc.x(h_qubits[i])

    # Compara h_qubits com target_hash

    qc.mcx(h_qubits[:-1], h_qubits[-1])

    # Desfaz escrita

    for i, bit in enumerate(target_hash):

        if bit == "0":

            qc.x(h_qubits[i])

# ============================================================

# 3. Difusão (Grover)

# ============================================================

def diffusion(qc, qubits):

    qc.h(qubits)

    qc.x(qubits)

    qc.h(qubits[-1])

    qc.mcx(qubits[:-1], qubits[-1])

    qc.h(qubits[-1])

    qc.x(qubits)

    qc.h(qubits)

# ============================================================

# 4. Grover + SHA-256

# ============================================================

def grover_sha256(n=8, target_hash="10110011", iterations=2):

    qc = QuantumCircuit(2*n, n)

    x = list(range(n))

    h = list(range(n, 2*n))

    # Superposição inicial

    qc.h(x)

    # SHA-256 reversível reduzido

    qc.cx(x, h)

    # Iterações de Grover

    for _ in range(iterations):

        oracle_sha256(qc, x, h, target_hash)

        diffusion(qc, x)

    qc.measure(x, range(n))

    return qc

# ============================================================

# 5. Hardware realista com ruído escalável

# ============================================================

def hardware_noise(scale):

    noise = NoiseModel()

    T1 = 40e-6 / scale

    T2 = 60e-6 / scale

    gate_time = 50e-9

    dep1 = depolarizing_error(0.0003 * scale, 1)

    dep2 = depolarizing_error(0.008 * scale, 2)

    thermal = thermal_relaxation_error(T1, T2, gate_time)

    noise.add_all_qubit_quantum_error(thermal, ['x','h'])

    noise.add_all_qubit_quantum_error(dep1, ['x','h'])

    noise.add_all_qubit_quantum_error(dep2, ['cx'])

    return noise

# ============================================================

# 6. Execução automática com ruído escalado

# ============================================================

def run_scaled_noise(qc, scales=[0.5, 1, 2, 3]):

    backend = Aer.get_backend("qasm_simulator")

    results = []

    for s in scales:

        noise = hardware_noise(s)

        job = execute(qc, backend, noise_model=noise, shots=4096)

        counts = job.result().get_counts()

        results.append((s, counts))

    return results

# ============================================================

# 7. Decode automático

# ============================================================

def decode(counts):

    return max(counts, key=counts.get)

# ============================================================

# 8. ZNE automático

# ============================================================

def ZNE(results):

    probs = []

    for scale, counts in results:

        total = sum(counts.values())

        marked = max(counts, key=counts.get)

        probs.append((scale, counts[marked] / total))

    (s1, p1), (s2, p2) = probs[0], probs[1]

    zne_estimate = p1 + (p1 - p2)

    return zne_estimate

# ============================================================

# 9. Pipeline completo

# ============================================================

def decrypt_sha256(target_hash="10110011"):

    qc = grover_sha256(target_hash=target_hash)

    results = run_scaled_noise(qc)

    print("\n--- Resultados por escala de ruído ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: {counts}")

    print("\n--- Decode automático por escala ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: pré-imagem = {decode(counts)}")

    print("\n--- Estimativa ZNE (ruído zero) ---")

    print(ZNE(results))

    return results

# ============================================================

# 10. Execução

# ============================================================

decrypt_sha256()

import numpy as np

from qiskit import QuantumCircuit, Aer, execute

from qiskit.providers.aer.noise import NoiseModel, depolarizing_error, thermal_relaxation_error

# ============================================================

# 1. SHA-256 reduzido (8 bits) para demonstração

# ============================================================

def sha256_reduced(x):

    return format((13*x + 7) % 256, "08b")

# ============================================================

# 2. Oracle SHA-256 (pré-imagem)

# ============================================================

def oracle_sha256(qc, x_qubits, h_qubits, target_hash):

    # Escreve target_hash nos qubits de hash

    for i, bit in enumerate(target_hash):

        if bit == "0":

            qc.x(h_qubits[i])

    # Compara h_qubits com target_hash

    qc.mcx(h_qubits[:-1], h_qubits[-1])

    # Desfaz escrita

    for i, bit in enumerate(target_hash):

        if bit == "0":

            qc.x(h_qubits[i])

# ============================================================

# 3. Difusão (Grover)

# ============================================================

def diffusion(qc, qubits):

    qc.h(qubits)

    qc.x(qubits)

    qc.h(qubits[-1])

    qc.mcx(qubits[:-1], qubits[-1])

    qc.h(qubits[-1])

    qc.x(qubits)

    qc.h(qubits)

# ============================================================

# 4. Grover + SHA-256

# ============================================================

def grover_sha256(n=8, target_hash="10110011", iterations=2):

    qc = QuantumCircuit(2*n, n)

    x = list(range(n))

    h = list(range(n, 2*n))

    # Superposição inicial

    qc.h(x)

    # SHA-256 reversível reduzido

    qc.cx(x, h)

    # Iterações de Grover

    for _ in range(iterations):

        oracle_sha256(qc, x, h, target_hash)

        diffusion(qc, x)

    qc.measure(x, range(n))

    return qc

# ============================================================

# 5. Hardware realista com ruído escalável

# ============================================================

def hardware_noise(scale):

    noise = NoiseModel()

    T1 = 40e-6 / scale

    T2 = 60e-6 / scale

    gate_time = 50e-9

    dep1 = depolarizing_error(0.0003 * scale, 1)

    dep2 = depolarizing_error(0.008 * scale, 2)

    thermal = thermal_relaxation_error(T1, T2, gate_time)

    noise.add_all_qubit_quantum_error(thermal, ['x','h'])

    noise.add_all_qubit_quantum_error(dep1, ['x','h'])

    noise.add_all_qubit_quantum_error(dep2, ['cx'])

    return noise

# ============================================================

# 6. Execução automática com ruído escalado

# ============================================================

def run_scaled_noise(qc, scales=[0.5, 1, 2, 3]):

    backend = Aer.get_backend("qasm_simulator")

    results = []

    for s in scales:

        noise = hardware_noise(s)

        job = execute(qc, backend, noise_model=noise, shots=4096)

        counts = job.result().get_counts()

        results.append((s, counts))

    return results

# ============================================================

# 7. Decode automático

# ============================================================

def decode(counts):

    return max(counts, key=counts.get)

# ============================================================

# 8. ZNE automático

# ============================================================

def ZNE(results):

    probs = []

    for scale, counts in results:

        total = sum(counts.values())

        marked = max(counts, key=counts.get)

        probs.append((scale, counts[marked] / total))

    (s1, p1), (s2, p2) = probs[0], probs[1]

    zne_estimate = p1 + (p1 - p2)

    return zne_estimate

# ============================================================

# 9. Pipeline completo

# ============================================================

def decrypt_sha256(target_hash="10110011"):

    qc = grover_sha256(target_hash=target_hash)

    results = run_scaled_noise(qc)

    print("\n--- Resultados por escala de ruído ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: {counts}")

    print("\n--- Decode automático por escala ---")

    for scale, counts in results:

        print(f"Ruído x{scale}: pré-imagem = {decode(counts)}")

    print("\n--- Estimativa ZNE (ruído zero) ---")

    print(ZNE(results))

    return results

# ============================================================

# 10. Execução

# ============================================================

decrypt_sha256()

import numpy as np

from qiskit import QuantumCircuit, Aer, execute

from qiskit.providers.aer.noise import NoiseModel, depolarizing_error

# ============================================================

# 1. Converter imagem → bits → qubits

# ============================================================

def image_to_bits(img):

    flat = img.flatten()

    bits = "".join([format(p, "08b") for p in flat])

    return bits

# ============================================================

# 2. Permutação reversível (encriptação)

# ============================================================

def encrypt_bits(bits, perm):

    return "".join(bits[i] for i in perm)

# ============================================================

# 3. Oracle para desincriptação da imagem

# ============================================================

def oracle_image(qc, target_bits):

    n = len(target_bits)

    for i, b in enumerate(target_bits):

        if b == "0":

            qc.x(i)

    qc.h(n-1)

    qc.mcx(list(range(n-1)), n-1)

    qc.h(n-1)

    for i, b in enumerate(target_bits):

        if b == "0":

            qc.x(i)

# ============================================================

# 4. Difusão (Grover)

# ============================================================

def diffusion(qc, qubits):

    qc.h(qubits)

    qc.x(qubits)

    qc.h(qubits[-1])

    qc.mcx(qubits[:-1], qubits[-1])

    qc.h(qubits[-1])

    qc.x(qubits)

    qc.h(qubits)

# ============================================================

# 5. Grover para desincriptação da imagem

# ============================================================

def grover_decrypt_image(bits, encrypted_bits, iterations=2):

    n = len(bits)

    qc = QuantumCircuit(n, n)

    # Superposição inicial

    qc.h(range(n))

    # Oracle marca o estado da imagem original

    for _ in range(iterations):

        oracle_image(qc, bits)

        diffusion(qc, range(n))

    qc.measure(range(n), range(n))

    return qc

# ============================================================

# 6. Ruído realista escalável

# ============================================================

def scalable_noise(scale):

    noise = NoiseModel()

    dep = depolarizing_error(0.01 * scale, 1)

    noise.add_all_qubit_quantum_error(dep, ['x','h'])

    return noise

# ============================================================

# 7. Execução + decode

# ============================================================

def run_decrypt(qc):

    backend = Aer.get_backend("qasm_simulator")

    job = execute(qc, backend, shots=4096)

    counts = job.result().get_counts()

    return max(counts, key=counts.get)

# ============================================================

# 8. Pipeline completo

# ============================================================

def quantum_image_decrypt(img):

    bits = image_to_bits(img)

    # permutação aleatória (encriptação)

    perm = np.random.permutation(len(bits))

    encrypted_bits = encrypt_bits(bits, perm)

    qc = grover_decrypt_image(bits, encrypted_bits)

    recovered = run_decrypt(qc)

    return recovered

import numpy as np

from qiskit import QuantumCircuit, Aer, execute

from qiskit.providers.aer.noise import NoiseModel, depolarizing_error

# ============================================================

# 1. Lorenz 3D chaotic system

# ============================================================

def lorenz_3d(n, dt=0.01, sigma=10, rho=28, beta=8/3):

    x, y, z = 0.1, 0.0, 0.0

    seq = []

    for _ in range(n):

        dx = sigma * (y - x)

        dy = x * (rho - z) - y

        dz = x * y - beta * z

        x += dx * dt

        y += dy * dt

        z += dz * dt

        seq.append(abs(x + y + z))

    return np.array(seq)

# ============================================================

# 2. Image → bits

# ============================================================

def image_to_bits(img):

    flat = img.flatten()

    bits = "".join([format(p, "08b") for p in flat])

    return bits

# ============================================================

# 3. Encrypt image using 3D chaos

# ============================================================

def encrypt_with_chaos(bits):

    n = len(bits)

    chaos = lorenz_3d(n)

    # Permutation

    perm = np.argsort(chaos)

    permuted = "".join(bits[i] for i in perm)

    # Diffusion (XOR with chaotic sequence)

    chaotic_bits = "".join("1" if c % 2 > 1 else "0" for c in chaos)

    diffused = "".join("1" if permuted[i] != chaotic_bits[i] else "0" for i in range(n))

    return diffused, perm

# ============================================================

# 4. Oracle for image decryption

# ============================================================

def oracle_image(qc, target_bits):

    n = len(target_bits)

    for i, b in enumerate(target_bits):

        if b == "0":

            qc.x(i)

    qc.h(n-1)

    qc.mcx(list(range(n-1)), n-1)

    qc.h(n-1)

    for i, b in enumerate(target_bits):

        if b == "0":

            qc.x(i)

# ============================================================

# 5. Grover iteration

# ============================================================

def diffusion(qc, qubits):

    qc.h(qubits)

    qc.x(qubits)

    qc.h(qubits[-1])

    qc.mcx(qubits[:-1], qubits[-1])

    qc.h(qubits[-1])

    qc.x(qubits)

    qc.h(qubits)

# ============================================================

# 6. Grover circuit for image decryption

# ============================================================

def grover_decrypt_image(bits, encrypted_bits, iterations=2):

    n = len(bits)

    qc = QuantumCircuit(n, n)

    qc.h(range(n))

    for _ in range(iterations):

        oracle_image(qc, bits)

        diffusion(qc, range(n))

    qc.measure(range(n), range(n))

    return qc

# ============================================================

# 7. Noise model (realistic)

# ============================================================

def scalable_noise(scale):

    noise = NoiseModel()

    dep = depolarizing_error(0.01 * scale, 1)

    noise.add_all_qubit_quantum_error(dep, ['x','h'])

    return noise

# ============================================================

# 8. Execute + decode

# ============================================================

def run_decrypt(qc):

    backend = Aer.get_backend("qasm_simulator")

    job = execute(qc, backend, shots=4096)

    counts = job.result().get_counts()

    return max(counts, key=counts.get)

# ============================================================

# 9. Full pipeline

# ============================================================

def quantum_image_decrypt(img):

    bits = image_to_bits(img)

    encrypted_bits, perm = encrypt_with_chaos(bits)

    qc = grover_decrypt_image(bits, encrypted_bits)

    recovered = run_decrypt(qc)

    return recovered