Thursday, July 9, 2026

Grover's algorithm break AES 256 laser satelite transmission quantic downlink (1)

 













COMPUTADOR CLÁSSICO (solo)

  - controla laser

  - controla telescópio

  - processa medições

        |

        | comandos clássicos

        v

MÓDULO ÓPTICO (750 m altitude)

  - laser pulsado (fotões únicos)

  - moduladores de polarização/fase

  - telescópio apontado ao satélite

        |

        | downlink quântico (fotões)

        v

SATÉLITE QUÂNTICO

  - telescópio de receção

  - detetores de fotões

  - correção de fase

  - cluster quântico com QEC

        |

        | processamento quântico

        v

RESULTADOS CLÁSSICOS

  - enviados de volta ao solo


at No comments:

Grover's algorithm AES 256 break decrypt trough satellites network

 













at No comments:

Grover's algorithm break decrypt possibility using large scale distance ocean


 













import numpy as np

import matplotlib.pyplot as plt


# --- Logical error rate model (surface code threshold approximation) ---

def logical_error_rate(physical_error, threshold=0.01, code_distance=5):

    if physical_error >= threshold:

        return 0.5  # logical qubit fails

    return (physical_error / threshold) ** ((code_distance + 1) / 2)


# --- Channel model ---

def channel_error(distance_km, loss_db_per_km=0.2):

    loss_db = distance_km * loss_db_per_km

    transmission = 10 ** (-loss_db / 10)

    return 1 - transmission


# --- Repeater chain with QEC ---

def repeater_chain_fidelity(num_repeaters, segment_length_km, code_distance):

    physical_error = channel_error(segment_length_km)

    logical_error = logical_error_rate(physical_error, code_distance=code_distance)

    return (1 - logical_error) ** num_repeaters


# --- Grover success probability ---

def grover_success(fidelity):

    return fidelity ** 2


# --- Simulation ---

segment_length = 100  # km

repeaters = np.arange(1, 51)

code_distance = 7  # moderate surface code


fidelities = [repeater_chain_fidelity(n, segment_length, code_distance) for n in repeaters]

success = [grover_success(f) for f in fidelities]


plt.plot(repeaters, success)

plt.xlabel("Number of Repeaters (100 km spacing)")

plt.ylabel("Grover Success Probability")

plt.title("Grover Algorithm With Quantum Error Correction (Surface Code)")

plt.grid(True)

plt.show()

at No comments:

Wednesday, July 8, 2026

Fiber optics propagation stimulation and distribution brute force cluster in our time AES 256 decryption attempt




 <!DOCTYPE html>

<html>

<head>

<meta charset="UTF-8">

<title>Fiber Optics & Distributed Brute-Force Simulation</title>

<style>

  body { font-family: Arial; padding: 20px; }

  .box { border: 1px solid #ccc; padding: 15px; margin-bottom: 20px; }

</style>

</head>

<body>


<h2>Fiber Optics Propagation & Distributed Brute-Force Simulation</h2>


<div class="box">

  <h3>Fiber Optic Parameters</h3>

  <label>Ocean Cable Length (km):</label>

  <input type="number" id="cableLength" value="8000"><br><br>


  <label>Cluster Nodes:</label>

  <input type="number" id="nodes" value="1000"><br><br>


  <label>Keys per Second per Node:</label>

  <input type="number" id="kps" value="1000000000"><br><br>


  <button onclick="simulate()">Simulate</button>

</div>


<div class="box">

  <h3>Results</h3>

  <p id="latency"></p>

  <p id="clusterSpeed"></p>

  <p id="aesTime"></p>

</div>


<script>

// Speed of light in fiber (approx)

const FIBER_SPEED = 200000; // km per second


// AES-256 keyspace

const AES256_KEYS = BigInt("115792089237316195423570985008687907853269984665640564039457584007913129639936");


// Format big numbers

function formatBig(n) {

    return n.toLocaleString("en-US");

}


function simulate() {

    let cableLength = parseFloat(document.getElementById("cableLength").value);

    let nodes = parseFloat(document.getElementById("nodes").value);

    let kps = parseFloat(document.getElementById("kps").value);


    // Fiber latency (one-way)

    let latencySeconds = cableLength / FIBER_SPEED;


    // Cluster brute-force speed

    let clusterSpeed = nodes * kps;


    // Time to brute-force AES-256 (worst-case)

    let clusterSpeedBig = BigInt(clusterSpeed);

    let secondsToCrack = AES256_KEYS / clusterSpeedBig;


    // Convert to years

    let years = Number(secondsToCrack) / (60 * 60 * 24 * 365);


    document.getElementById("latency").innerHTML =

        "Fiber Latency (one-way): " + latencySeconds.toFixed(4) + " seconds";


    document.getElementById("clusterSpeed").innerHTML =

        "Cluster Speed: " + formatBig(clusterSpeed) + " keys/second";


    document.getElementById("aesTime").innerHTML =

        "Estimated Time to Brute-Force AES-256: " + years.toExponential(4) + " years";

}

</script>


</body>

</html>

at No comments:

AES 256 ( Grover's algorithms) decryption assumptions quantum machine need more 15.248 days in the future speed












import math

# -----------------------------
# 1. Core parameters
# -----------------------------

AES256_LOGICAL_QUBITS = 6500          # literature midpoint
GROVER_ITERATIONS = 2**128            # Grover complexity for AES-256
T_GATE_DEPTH_PER_AES = 20000          # approximate reversible AES depth
SURFACE_CODE_OVERHEAD = 1000          # physical/logical qubit ratio
TARGET_RUNTIME_SECONDS = 365 * 24 * 3600  # 1 year

# -----------------------------
# 2. Physical qubit model
# -----------------------------

def physical_qubits(logical_qubits=AES256_LOGICAL_QUBITS,
                    overhead=SURFACE_CODE_OVERHEAD):
    return logical_qubits * overhead

# -----------------------------
# 3. Parallel Grover model
# -----------------------------

def required_parallel_instances(target_runtime_seconds=TARGET_RUNTIME_SECONDS,
                                grover_iterations=GROVER_ITERATIONS,
                                gate_time=1e-6):
    """
    gate_time = logical gate time (1 microsecond default)
    """
    iterations_per_second = 1 / gate_time
    required = grover_iterations / (iterations_per_second * target_runtime_seconds)
    return math.ceil(required)

# -----------------------------
# 4. Total physical qubits
# -----------------------------

def total_physical_qubits(parallel_instances,
                          logical_qubits=AES256_LOGICAL_QUBITS,
                          overhead=SURFACE_CODE_OVERHEAD):
    return parallel_instances * physical_qubits(logical_qubits, overhead)

# -----------------------------
# 5. Full simulation wrapper
# -----------------------------

def simulate(target_runtime_seconds=TARGET_RUNTIME_SECONDS,
             logical_qubits=AES256_LOGICAL_QUBITS,
             overhead=SURFACE_CODE_OVERHEAD,
             gate_time=1e-6):

    P = required_parallel_instances(target_runtime_seconds,
                                    GROVER_ITERATIONS,
                                    gate_time)

    total = total_physical_qubits(P, logical_qubits, overhead)

    return {
        "logical_qubits_per_instance": logical_qubits,
        "physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),
        "parallel_instances_required": P,
        "total_physical_qubits_required": total
    }

# -----------------------------
# 6. Run the simulation
# -----------------------------

result = simulate()

for k, v in result.items():
    print(f"{k}: {v}")



# ============================================================
# AES‑256 Grover Resource Estimation Notebook
# ============================================================

import math
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, FloatSlider, IntSlider, Dropdown

# ------------------------------------------------------------
# 1. Core constants from literature
# ------------------------------------------------------------

AES256_LOGICAL_QUBITS = 6500          # midpoint from Grassl et al.
GROVER_ITERATIONS = 2**128            # Grover complexity for AES-256
DEFAULT_SURFACE_CODE_OVERHEAD = 1000  # physical/logical qubit ratio
DEFAULT_GATE_TIME = 1e-6              # 1 microsecond logical gate
SECONDS_PER_YEAR = 365 * 24 * 3600

# ------------------------------------------------------------
# 2. Physical qubit model
# ------------------------------------------------------------

def physical_qubits(logical_qubits, overhead):
    return logical_qubits * overhead

# ------------------------------------------------------------
# 3. Parallel Grover model
# ------------------------------------------------------------

def required_parallel_instances(target_runtime_seconds,
                                grover_iterations,
                                gate_time):
    iterations_per_second = 1 / gate_time
    required = grover_iterations / (iterations_per_second * target_runtime_seconds)
    return math.ceil(required)

# ------------------------------------------------------------
# 4. Total physical qubits
# ------------------------------------------------------------

def total_physical_qubits(parallel_instances,
                          logical_qubits,
                          overhead):
    return parallel_instances * physical_qubits(logical_qubits, overhead)

# ------------------------------------------------------------
# 5. Simulation wrapper
# ------------------------------------------------------------

def simulate(target_runtime_seconds,
             logical_qubits,
             overhead,
             gate_time):

    P = required_parallel_instances(target_runtime_seconds,
                                    GROVER_ITERATIONS,
                                    gate_time)

    total = total_physical_qubits(P, logical_qubits, overhead)

    return {
        "logical_qubits_per_instance": logical_qubits,
        "physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),
        "parallel_instances_required": P,
        "total_physical_qubits_required": total
    }

# ------------------------------------------------------------
# 6. Interactive exploration
# ------------------------------------------------------------

def interactive_simulation(logical_qubits=AES256_LOGICAL_QUBITS,
                           overhead=DEFAULT_SURFACE_CODE_OVERHEAD,
                           gate_time=DEFAULT_GATE_TIME,
                           runtime_years=1):

    target_runtime_seconds = runtime_years * SECONDS_PER_YEAR
    result = simulate(target_runtime_seconds, logical_qubits, overhead, gate_time)

    print("=== AES‑256 Grover Resource Estimate ===")
    print(f"Logical qubits per instance: {result['logical_qubits_per_instance']:,}")
    print(f"Physical qubits per instance: {result['physical_qubits_per_instance']:,}")
    print(f"Parallel Grover instances required: {result['parallel_instances_required']:,}")
    print(f"Total physical qubits required: {result['total_physical_qubits_required']:,}")

    # Plot scaling
    plt.figure(figsize=(10,6))
    plt.title("Total Physical Qubits vs Runtime Target")
    runtimes = np.logspace(2, 7, 100)  # seconds
    totals = [
        total_physical_qubits(
            required_parallel_instances(rt, GROVER_ITERATIONS, gate_time),
            logical_qubits,
            overhead
        )
        for rt in runtimes
    ]
    plt.loglog(runtimes, totals)
    plt.xlabel("Runtime target (seconds)")
    plt.ylabel("Total physical qubits")
    plt.grid(True, which="both")
    plt.show()


# ------------------------------------------------------------
# 7. Launch interactive widget
# ------------------------------------------------------------

interact(
    interactive_simulation,
    logical_qubits=IntSlider(value=AES256_LOGICAL_QUBITS, min=2000, max=12000, step=500),
    overhead=IntSlider(value=DEFAULT_SURFACE_CODE_OVERHEAD, min=100, max=5000, step=100),
    gate_time=FloatSlider(value=DEFAULT_GATE_TIME, min=1e-9, max=1e-5, step=1e-9),
    runtime_years=FloatSlider(value=1, min=0.01, max=10, step=0.01)
)




# ============================================================
# AES‑256 Grover Resource Estimation Notebook
# ============================================================

import math
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, FloatSlider, IntSlider, Dropdown

# ------------------------------------------------------------
# 1. Core constants from literature
# ------------------------------------------------------------

AES256_LOGICAL_QUBITS = 6500          # midpoint from Grassl et al.
GROVER_ITERATIONS = 2**128            # Grover complexity for AES-256
DEFAULT_SURFACE_CODE_OVERHEAD = 1000  # physical/logical qubit ratio
DEFAULT_GATE_TIME = 1e-6              # 1 microsecond logical gate
SECONDS_PER_YEAR = 365 * 24 * 3600

# ------------------------------------------------------------
# 2. Physical qubit model
# ------------------------------------------------------------

def physical_qubits(logical_qubits, overhead):
    return logical_qubits * overhead

# ------------------------------------------------------------
# 3. Parallel Grover model
# ------------------------------------------------------------

def required_parallel_instances(target_runtime_seconds,
                                grover_iterations,
                                gate_time):
    iterations_per_second = 1 / gate_time
    required = grover_iterations / (iterations_per_second * target_runtime_seconds)
    return math.ceil(required)

# ------------------------------------------------------------
# 4. Total physical qubits
# ------------------------------------------------------------

def total_physical_qubits(parallel_instances,
                          logical_qubits,
                          overhead):
    return parallel_instances * physical_qubits(logical_qubits, overhead)

# ------------------------------------------------------------
# 5. Simulation wrapper
# ------------------------------------------------------------

def simulate(target_runtime_seconds,
             logical_qubits,
             overhead,
             gate_time):

    P = required_parallel_instances(target_runtime_seconds,
                                    GROVER_ITERATIONS,
                                    gate_time)

    total = total_physical_qubits(P, logical_qubits, overhead)

    return {
        "logical_qubits_per_instance": logical_qubits,
        "physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),
        "parallel_instances_required": P,
        "total_physical_qubits_required": total
    }

# ------------------------------------------------------------
# 6. Interactive exploration
# ------------------------------------------------------------

def interactive_simulation(logical_qubits=AES256_LOGICAL_QUBITS,
                           overhead=DEFAULT_SURFACE_CODE_OVERHEAD,
                           gate_time=DEFAULT_GATE_TIME,
                           runtime_years=1):

    target_runtime_seconds = runtime_years * SECONDS_PER_YEAR
    result = simulate(target_runtime_seconds, logical_qubits, overhead, gate_time)

    print("=== AES‑256 Grover Resource Estimate ===")
    print(f"Logical qubits per instance: {result['logical_qubits_per_instance']:,}")
    print(f"Physical qubits per instance: {result['physical_qubits_per_instance']:,}")
    print(f"Parallel Grover instances required: {result['parallel_instances_required']:,}")
    print(f"Total physical qubits required: {result['total_physical_qubits_required']:,}")

    # Plot scaling
    plt.figure(figsize=(10,6))
    plt.title("Total Physical Qubits vs Runtime Target")
    runtimes = np.logspace(2, 7, 100)  # seconds
    totals = [
        total_physical_qubits(
            required_parallel_instances(rt, GROVER_ITERATIONS, gate_time),
            logical_qubits,
            overhead
        )
        for rt in runtimes
    ]
    plt.loglog(runtimes, totals)
    plt.xlabel("Runtime target (seconds)")
    plt.ylabel("Total physical qubits")
    plt.grid(True, which="both")
    plt.show()


# ------------------------------------------------------------
# 7. Launch interactive widget
# ------------------------------------------------------------

interact(
    interactive_simulation,
    logical_qubits=IntSlider(value=AES256_LOGICAL_QUBITS, min=2000, max=12000, step=500),
    overhead=IntSlider(value=DEFAULT_SURFACE_CODE_OVERHEAD, min=100, max=5000, step=100),
    gate_time=FloatSlider(value=DEFAULT_GATE_TIME, min=1e-9, max=1e-5, step=1e-9),
    runtime_years=FloatSlider(value=1, min=0.01, max=10, step=0.01)
)








import math

# ============================================================
# MACHINE PARAMETERS
# ============================================================

PHYSICAL_QUBITS_TOTAL = 100_000_000_000   # 100 billion
SURFACE_CODE_OVERHEAD = 1000              # physical/logical qubits
LOGICAL_QUBITS = PHYSICAL_QUBITS_TOTAL // SURFACE_CODE_OVERHEAD

# AES-256 Grover parameters
AES256_LOGICAL_QUBITS_REQUIRED = 6500
GROVER_ITERATIONS = 2**128
GATE_TIME = 1e-6  # 1 microsecond logical gate

# ============================================================
# CALCULATE PARALLEL GROVER INSTANCES
# ============================================================

def parallel_instances_available(total_logical, per_instance):
    return total_logical // per_instance

P = parallel_instances_available(LOGICAL_QUBITS, AES256_LOGICAL_QUBITS_REQUIRED)

# ============================================================
# RUNTIME ESTIMATION
# ============================================================

def grover_runtime_seconds(iterations, gate_time, parallel_instances):
    return iterations * gate_time / parallel_instances

runtime_seconds = grover_runtime_seconds(GROVER_ITERATIONS, GATE_TIME, P)

# ============================================================
# OUTPUT RESULTS
# ============================================================

print("=== 100 BILLION PHYSICAL QUBIT MACHINE SIMULATION ===")
print(f"Total physical qubits: {PHYSICAL_QUBITS_TOTAL:,}")
print(f"Surface-code overhead: {SURFACE_CODE_OVERHEAD}")
print(f"Logical qubits available: {LOGICAL_QUBITS:,}")
print(f"Logical qubits needed per AES-256 Grover instance: {AES256_LOGICAL_QUBITS_REQUIRED:,}")
print(f"Parallel Grover instances possible: {P:,}")
print()
print(f"Grover iterations required for AES-256: 2^128 ≈ {GROVER_ITERATIONS:.3e}")
print(f"Logical gate time: {GATE_TIME} seconds")
print()
print(f"Estimated runtime with 100 billion physical qubits: {runtime_seconds:.3e} seconds")

years = runtime_seconds / (365*24*3600)
print(f"Runtime in years: {years:.3e} years")


at No comments:
Subscribe to: Posts (Atom)

Quantum attack vs Grover ( decryption AES 256 CODE !!!)( best machines and codes)

  from qiskit import QuantumCircuit, Aer, execute import numpy as np n = 8  # toy key size (safe) oracle_key = "10100110" def aes_...