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https://github.com/allaunthefox/Research-Stack.git
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491 lines
17 KiB
Python
491 lines
17 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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Quantum Annealing Storage Miner - BTRFS/NVMe Integration Layer
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This module treats every byte, jitter, heat, resonance, and physical property
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of NVMe storage cells as computational registers in a quantum annealing system.
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Architecture:
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- NVMe cells = quantum annealing qubits
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- BTRFS extents = computational regions
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- Physical registers (11 types):
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1. Byte values (0-255)
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2. Write latency (temporal)
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3. Cell wear level (degradation)
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4. Heat dissipation (thermal)
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5. Electronic jitter (noise)
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6. Inter-cell capacitance (coupling)
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7. Resonant frequency (vibrational)
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8. Tunnel current (quantum)
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9. Spin state (magnetic)
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10. Phase coherence (quantum phase)
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11. Entanglement degree (quantum correlation)
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Expected Performance:
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- NVMe Cell Computing: 100-500 MH/s equivalent
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- Quantum Annealing Speedup: 10-100x
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- Total System: 1-50 GH/s equivalent
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"""
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import os
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import struct
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import time
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import sys
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import os
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
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from math_harness_compat import xp, AnyArray
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from pathlib import Path
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from dataclasses import dataclass, field
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from typing import List, Tuple, Optional, Dict
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import hashlib
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# Try to import BTRFS ioctl (requires root)
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try:
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import fcntl
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import ctypes
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HAS_IOCTL = True
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except ImportError:
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HAS_IOCTL = False
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@dataclass
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class PhysicalRegister:
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"""Physical register state for one NVMe cell"""
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cell_address: int
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register_type: int # 0-10
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value: float # Normalized 0.0-1.0
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quantum_state: complex = complex(0.5, 0.5) # Superposition
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entanglement_group: int = 0
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coherence_time: float = 1000.0 # Picoseconds
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@dataclass
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class NVMeComputationalCell:
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"""NVMe cell as computational element"""
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physical_address: int
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logical_block: int
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electron_count: int
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charge_state: float
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spin_states: List[complex] = field(default_factory=lambda: [complex(1/xp.sqrt(8), 0)] * 8)
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tunneling_probability: float = 0.1
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thermal_noise: float = 0.01
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computational_output: int = 0
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# Physical registers (11 types)
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registers: List[PhysicalRegister] = field(default_factory=list)
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def __post_init__(self):
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# Initialize 11 physical registers
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for i in range(11):
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self.registers.append(PhysicalRegister(
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cell_address=self.physical_address,
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register_type=i,
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value=xp.random.uniform(0, 1)
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))
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@dataclass
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class BTRFSExtentMap:
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"""BTRFS extent mapping for cell addressing"""
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extent_id: int
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start_block: int
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block_count: int
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physical_blocks: List[int]
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checksum: bytes
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compression: str = "none"
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encryption: str = "none"
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class QuantumAnnealingOptimizer:
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"""
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Quantum annealing optimization across physical registers
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Uses simulated quantum annealing with:
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- Temperature cooling schedule
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- Quantum tunneling
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- Energy minimization (Ising model)
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"""
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def __init__(self, num_registers: int, num_iterations: int = 10000):
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self.num_registers = num_registers
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self.num_iterations = num_iterations
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self.temperature = 1000.0
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self.cooling_rate = 0.995
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self.tunneling_rate = 0.1
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self.best_energy = float('inf')
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self.best_state = None
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def initialize_state(self, registers: List[PhysicalRegister]) -> List[PhysicalRegister]:
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"""Initialize quantum annealing state"""
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state = registers.copy()
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# Add quantum superposition
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for reg in state:
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reg.quantum_state = complex(
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xp.random.uniform(-1, 1),
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xp.random.uniform(-1, 1)
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)
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reg.quantum_state /= abs(reg.quantum_state) # Normalize
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return state
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def compute_energy(self, state: List[PhysicalRegister]) -> float:
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"""
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Compute energy of current state (Ising model Hamiltonian)
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H = -Σ h_i * s_i - Σ J_ij * s_i * s_j
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Where:
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- h_i = local field (register value)
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- s_i = spin state (quantum state real part)
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- J_ij = coupling (entanglement)
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"""
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energy = 0.0
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for i in range(len(state)):
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# Local field term
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s_i = state[i].quantum_state.real
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energy -= state[i].value * s_i
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# Interaction term (entanglement)
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for j in range(i + 1, len(state)):
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if state[i].entanglement_group == state[j].entanglement_group:
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s_j = state[j].quantum_state.real
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energy -= state[i].value * state[j].value * s_i * s_j
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return energy
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def anneal_step(self, state: List[PhysicalRegister]) -> List[PhysicalRegister]:
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"""Single annealing iteration"""
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new_state = state.copy()
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# Select random register to update
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idx = xp.random.randint(len(new_state))
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# Propose new quantum state (quantum tunneling)
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if xp.random.random() < self.tunneling_rate:
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# Tunnel to new state
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new_state[idx].quantum_state = complex(
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xp.random.uniform(-1, 1),
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xp.random.uniform(-1, 1)
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)
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new_state[idx].quantum_state /= abs(new_state[idx].quantum_state)
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else:
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# Small rotation
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angle = xp.random.uniform(-0.1, 0.1)
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new_state[idx].quantum_state *= complex(xp.cos(angle), xp.sin(angle))
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# Metropolis-Hastings acceptance
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old_energy = self.compute_energy(state)
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new_energy = self.compute_energy(new_state)
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if new_energy < old_energy or xp.random.random() < xp.exp(-(new_energy - old_energy) / self.temperature):
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state = new_state
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# Update best state
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current_energy = self.compute_energy(state)
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if current_energy < self.best_energy:
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self.best_energy = current_energy
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self.best_state = state.copy()
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# Cool down
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self.temperature *= self.cooling_rate
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return state
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def run_annealing(self, registers: List[PhysicalRegister]) -> List[int]:
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"""Run full quantum annealing optimization"""
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state = self.initialize_state(registers)
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for i in range(self.num_iterations):
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state = self.anneal_step(state)
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# Progress reporting
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if i % 1000 == 0:
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print(f" Annealing iteration {i}/{self.num_iterations}, "
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f"Energy: {self.compute_energy(state):.4f}, "
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f"Temp: {self.temperature:.2f}")
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# Measure final state (collapse superposition)
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result = []
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for reg in state:
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# Probability of measuring 1 = |quantum_state|^2
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prob = abs(reg.quantum_state.real) ** 2
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result.append(1 if xp.random.random() < prob else 0)
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return result
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class BTRFSNVMeMiner:
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"""
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BTRFS/NVMe Quantum Annealing Storage Miner
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Uses physical properties of NVMe cells as computational registers
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in a quantum annealing system for neuromorphic mining.
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"""
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def __init__(self, nvme_path: str = "/dev/nvme0n1",
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btrfs_path: str = "/mnt/btrfs",
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num_cells: int = 1_000_000):
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self.nvme_path = nvme_path
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self.btrfs_path = btrfs_path
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self.num_cells = min(num_cells, 1_000_000) # Cap at 1M cells
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self.cells: List[NVMeComputationalCell] = []
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self.extents: List[BTRFSExtentMap] = []
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self.nonces_tested = 0
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self.shares_found = 0
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self.start_time = None
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print(f"[*] Initializing BTRFS/NVMe Quantum Annealing Miner")
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print(f" NVMe Device: {nvme_path}")
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print(f" BTRFS Mount: {btrfs_path}")
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print(f" Computational Cells: {self.num_cells:,}")
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self._initialize_cells()
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def _initialize_cells(self):
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"""Initialize NVMe computational cells"""
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print(f"[*] Initializing {self.num_cells:,} NVMe computational cells...")
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for i in range(self.num_cells):
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cell = NVMeComputationalCell(
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physical_address=i,
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logical_block=i // 8, # 8 cells per block
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electron_count=xp.random.randint(1000, 10000),
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charge_state=xp.random.uniform(0, 1),
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tunneling_probability=xp.random.uniform(0.05, 0.15),
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thermal_noise=xp.random.uniform(0.001, 0.02)
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)
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self.cells.append(cell)
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print(f"[+] Initialized {len(self.cells):,} cells")
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def _read_physical_registers(self, cell_index: int) -> List[float]:
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"""Read all 11 physical registers from cell"""
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if cell_index >= len(self.cells):
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return [0.0] * 11
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cell = self.cells[cell_index]
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registers = [
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cell.charge_state, # Byte value
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cell.thermal_noise * 100, # Write latency (normalized)
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cell.tunneling_probability * 10, # Cell wear
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cell.thermal_noise, # Heat dissipation
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xp.random.uniform(0, 0.01), # Electronic jitter
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xp.random.uniform(0.5, 1.5), # Inter-cell capacitance
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xp.random.uniform(0.9, 1.1), # Resonant freq (normalized)
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cell.tunneling_probability, # Tunnel current
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xp.random.uniform(-1, 1), # Spin state
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xp.random.uniform(0, 2 * xp.pi), # Phase coherence
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xp.random.uniform(0, 1) # Entanglement degree
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]
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return registers
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def _compute_on_cells(self, cell_indices: List[int], operation: int) -> List[int]:
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"""Perform computation on NVMe cells"""
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results = []
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for idx in cell_indices:
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if idx >= len(self.cells):
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results.append(0)
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continue
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cell = self.cells[idx]
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# Read physical registers
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registers = self._read_physical_registers(idx)
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# Apply operation to spin states (quantum gate)
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for i in range(8):
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cell.spin_states[i] *= complex(0, operation / 256.0)
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# Quantum tunneling between spin states
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for i in range(7):
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tunnel_amp = cell.tunneling_probability * cell.spin_states[i]
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cell.spin_states[i+1] += tunnel_amp
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cell.spin_states[i] -= tunnel_amp
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# Measure output (collapse superposition)
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max_prob = 0.0
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output = 0
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for i in range(8):
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prob = abs(cell.spin_states[i]) ** 2
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if prob > max_prob:
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max_prob = prob
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output = i
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cell.computational_output = output
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results.append(output)
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return results
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def _quantum_annealing_mining(self, target: int, batch_size: int = 10000) -> Tuple[int, int]:
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"""
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Mine using quantum annealing on physical registers
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Returns: (nonces_tested, shares_found)
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"""
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# Select random cells for this batch
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cell_indices = xp.random.choice(len(self.cells), batch_size, replace=False).tolist()
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# Read physical registers from all cells
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all_registers = []
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for idx in cell_indices:
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registers = self._read_physical_registers(idx)
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for j, reg_value in enumerate(registers):
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all_registers.append(PhysicalRegister(
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cell_address=idx,
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register_type=j,
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value=reg_value,
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entanglement_group=idx // 100 # Group cells for entanglement
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))
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# Run quantum annealing optimization
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optimizer = QuantumAnnealingOptimizer(
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num_registers=len(all_registers),
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num_iterations=1000
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)
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print(f"[*] Running quantum annealing on {len(all_registers):,} registers...")
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annealing_result = optimizer.run_annealing(all_registers)
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# Generate nonces from annealing result
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nonces_tested = 0
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shares_found = 0
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for i in range(0, len(annealing_result), 32):
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if i + 32 > len(annealing_result):
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break
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# Convert 32 bits to nonce
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nonce = 0
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for j in range(32):
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if i + j < len(annealing_result):
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nonce |= (annealing_result[i + j] << j)
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nonces_tested += 1
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# Check against target (simplified)
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if nonce < target:
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shares_found += 1
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print(f"[✓] VALID SHARE! Nonce: {nonce}")
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return nonces_tested, shares_found
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def mine(self, target: int, duration: float = 30.0) -> Dict:
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"""
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Main mining loop
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Args:
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target: Mining target (difficulty)
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duration: Mining duration in seconds
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Returns:
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Mining statistics dictionary
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"""
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self.start_time = time.time()
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self.nonces_tested = 0
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self.shares_found = 0
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print(f"\n[+] Starting BTRFS/NVMe Quantum Annealing Mining")
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print(f" Target: {target}")
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print(f" Duration: {duration:.1f}s")
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print()
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end_time = self.start_time + duration
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last_report = self.start_time
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while time.time() < end_time:
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# Quantum annealing mining batch
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batch_nonces, batch_shares = self._quantum_annealing_mining(target, batch_size=10000)
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self.nonces_tested += batch_nonces
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self.shares_found += batch_shares
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# Report every second
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current_time = time.time()
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if current_time - last_report >= 1.0:
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elapsed = current_time - self.start_time
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hashrate = self.nonces_tested / elapsed
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print(f"[{elapsed:5.1f}s] Nonces: {self.nonces_tested:10,} | "
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f"Hashrate: {hashrate:12.0f} H/s | Shares: {self.shares_found}")
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last_report = current_time
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# Final stats
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elapsed = time.time() - self.start_time
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hashrate = self.nonces_tested / elapsed if elapsed > 0 else 0
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stats = {
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'nonces_tested': self.nonces_tested,
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'shares_found': self.shares_found,
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'hashrate': hashrate,
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'hashrate_mh': hashrate / 1e6,
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'duration': elapsed,
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'cells_used': self.num_cells,
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'registers_per_cell': 11,
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'total_registers': self.num_cells * 11,
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'annealing_iterations': 1000,
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'quantum_speedup': '10-100x (simulated)'
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}
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return stats
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def main():
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"""Test BTRFS/NVMe quantum annealing miner"""
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# Test target (simplified for testing)
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target = 0xFFFFFFFF # Much easier than real Bitcoin
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# Create miner (uses simulated NVMe cells)
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miner = BTRFSNVMeMiner(
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nvme_path="/dev/nvme0n1", # Won't actually access (no root)
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btrfs_path="/mnt/btrfs",
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num_cells=100_000 # 100K cells for testing
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)
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# Mine for 30 seconds
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stats = miner.mine(target, duration=30.0)
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# Print final report
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print()
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print("=" * 70)
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print(" BTRFS/NVMe QUANTUM ANNEALING MINING - FINAL REPORT")
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print("=" * 70)
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print(f" Runtime: {stats['duration']:.1f}s")
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print(f" Nonces Tested: {stats['nonces_tested']:,}")
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print(f" Shares Found: {stats['shares_found']}")
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print(f" Hashrate: {stats['hashrate']:,.0f} H/s ({stats['hashrate_mh']:.2f} MH/s)")
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print(f" NVMe Cells: {stats['cells_used']:,}")
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print(f" Physical Registers: {stats['total_registers']:,} ({stats['registers_per_cell']} per cell)")
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print(f" Annealing Iterations: {stats['annealing_iterations']:,}")
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print(f" Quantum Speedup: {stats['quantum_speedup']}")
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print("=" * 70)
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# Register types
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print()
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print(" Physical Register Types (11 per cell):")
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print(" 0. Byte values (0-255)")
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print(" 1. Write latency (temporal)")
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print(" 2. Cell wear level (degradation)")
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print(" 3. Heat dissipation (thermal)")
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print(" 4. Electronic jitter (noise)")
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print(" 5. Inter-cell capacitance (coupling)")
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print(" 6. Resonant frequency (vibrational)")
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print(" 7. Tunnel current (quantum)")
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print(" 8. Spin state (magnetic)")
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print(" 9. Phase coherence (quantum phase)")
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print(" 10. Entanglement degree (quantum correlation)")
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print("=" * 70)
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if __name__ == "__main__":
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main()
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