im implementing the paper Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer but im using as a twist i want to implement on gpu
nice. as a developer i built till now the following python code:
from abc import ABC, abstractmethod
import numpy as np
import uuid
from typing import Dict, Any, Optional
from enum import Enum

class DistributionType(Enum):
    BIMODAL = "bimodal"
    GAUSSIAN = "gaussian"

class SpinGlassBase(ABC):
    def __init__(self, N: int, distribution: DistributionType = DistributionType.BIMODAL,
                 spins: Optional[np.ndarray] = None):
        self.N = N
        self.id = str(uuid.uuid4())
        self.distribution = distribution
        self.spins = spins if spins is not None else self.initialize_spins()
        self.couplings = self.generate_couplings()

    @abstractmethod
    def initialize_spins(self) -> np.ndarray:
        pass

    @abstractmethod
    def generate_couplings(self) -> np.ndarray:
        pass

    @abstractmethod
    def calculate_energy(self) -> float:
        pass

    @abstractmethod
    def to_dict(self) -> Dict[str, Any]:
        pass

    def _generate_distribution(self, size: tuple) -> np.ndarray:
        if self.distribution == DistributionType.BIMODAL:
            return np.random.choice([-1, 1], size=size)
        elif self.distribution == DistributionType.GAUSSIAN:
            return np.random.normal(0, 1, size=size)
        else:
            raise ValueError(f"Invalid distribution type: {self.distribution}")


from spin_glass_base import SpinGlassBase, DistributionType
import numpy as np
from typing import Dict, Any, Optional

class SKSpinGlass(SpinGlassBase):
    def __init__(self, N: int, distribution: DistributionType = DistributionType.BIMODAL,
                 spins: Optional[np.ndarray] = None, couplings: Optional[np.ndarray] = None):
        self.couplings = couplings
        super().__init__(N, distribution, spins)

    def initialize_spins(self) -> np.ndarray:
        return np.random.choice([-1, 1], size=self.N)

    def generate_couplings(self) -> np.ndarray:
        if self.couplings is not None:
            return self.couplings

        couplings = self._generate_distribution((self.N, self.N))
        couplings = np.triu(couplings, 1)
        couplings += couplings.T
        np.fill_diagonal(couplings, 0)
        return couplings

    def calculate_energy(self) -> float:
        return -np.sum(np.triu(self.couplings, 1) * np.outer(self.spins, self.spins))

    def to_dict(self) -> Dict[str, Any]:
        return {
            'N': self.N,
            'id': self.id,
            'problem_type': 'SK Spin-Glass',
            'distribution': self.distribution.value,
            'spins': self.spins.tolist(),
            'couplings': self.couplings.tolist()
        }

first i generate some instances of the problem such as sk-sping-glass with N for example 64.
and in the to find an optimal refrence solution i use Gurobi 

import time
import numpy as np
from gurobipy import Model, GRB, QuadExpr
from SpinGlassSolution import SpinGlassSolution
from SpinGlassSolver import SpinGlassSolver
from spin_glass_base import SpinGlassBase
from sk_spin_glass import SKSpinGlass
from two_d_spin_glass import TwoDSpinGlass

class GurobiSpinGlassSolver(SpinGlassSolver):
    def __init__(self, time_limit: int = 30):
        self.time_limit = time_limit
        
    def solve(self, instance: SpinGlassBase) -> SpinGlassSolution:
        if not isinstance(instance, (SKSpinGlass, TwoDSpinGlass)):
            raise ValueError("This solver only supports SK and 2D Lattice Spin Glass instances")
        
        start_time = time.time()

        # Create a new model
        model = Model("SpinGlass")
        model.setParam('OutputFlag', 0)  # Suppress Gurobi output
        model.setParam('TimeLimit', self.time_limit)

        # Create variables
        if isinstance(instance, SKSpinGlass):
            x = model.addVars(instance.N, vtype=GRB.BINARY, name="x")
        else:  # TwoDSpinGlass
            x = model.addVars(instance.N, instance.N, vtype=GRB.BINARY, name="x")
        
        model.update()

        # Set objective
        obj = QuadExpr()
        
        if isinstance(instance, SKSpinGlass):
            for i in range(instance.N):
                for j in range(i + 1, instance.N):
                    obj += -instance.couplings[i, j] * (2 * x[i] - 1) * (2 * x[j] - 1)
        else:  # TwoDSpinGlass
            for i in range(instance.N):
                for j in range(instance.N):
                    # Horizontal interactions
                    obj += -instance.horizontal_couplings[i, j] * (2 * x[i, j] - 1) * (2 * x[i, (j + 1) % instance.N] - 1)
                    # Vertical interactions
                    obj += -instance.vertical_couplings[i, j] * (2 * x[i, j] - 1) * (2 * x[(i + 1) % instance.N, j] - 1)
        
        model.setObjective(obj, GRB.MINIMIZE)
        
        # Optimize model
        model.optimize()
        
        # Extract solution
        if isinstance(instance, SKSpinGlass):
            best_spins = np.array([2 * int(x[i].X) - 1 for i in range(instance.N)], dtype=int)
        else:  # TwoDSpinGlass
            best_spins = np.array([[2 * int(x[i, j].X) - 1 for j in range(instance.N)] for i in range(instance.N)], dtype=int)
        
        best_energy = model.objVal
        solving_time = time.time() - start_time
        
        # Check optimization status
        status = model.status
        if status == GRB.OPTIMAL:
            print("Optimal solution found within time limit.")
        elif status == GRB.TIME_LIMIT:
            print("Time limit reached. Returning best solution found.")
        else:
            print(f"Optimization ended with status {status}")
        
        return SpinGlassSolution(best_spins, best_energy, solving_time)

then what i wanted to do is to implement the DA using GPU this is my research focus.
till now i developer the following code:

import time
import torch
from SpinGlassSolution import SpinGlassSolution
from SpinGlassSolver import SpinGlassSolver
from spin_glass_base import SpinGlassBase

class DigitalAnnealerSpinGlassSolver(SpinGlassSolver):
    def solve(self, instance: SpinGlassBase) -> SpinGlassSolution:
        start_time = time.time()
        initial_temperature = 100.0
        final_temperature = 0.01
        alpha = 0.95
        num_iterations = 250
        num_runs = 500
        offset_increase_rate = 1
        N = instance.N
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        # Keep the large matrix and spins on the GPU
        J = torch.tensor(instance.couplings, dtype=torch.float32, device=device)  # coupling matrix on GPU
        best_energy = float('inf')
        best_spins = None  # Initialize best spins as None
        
        # Keep other variables on CPU to avoid frequent transfers
        initial_state = torch.tensor(instance.spins, dtype=torch.float32)  # Keep initial_state on CPU

        # Main loop for multiple runs
        for _ in range(num_runs):
            spins = initial_state.clone()  # Clone initial state on CPU for each run
            temperature = initial_temperature
            Eoffset = 0.0  # Offset initialized on CPU
            
            # Monte Carlo steps
            for step in range(num_iterations):
                if temperature > final_temperature:
                    temperature *= alpha  # Update temperature on CPU

                # Calculate energy changes ΔE for flipping each spin
                spins_gpu = spins.to(device)  # Transfer spins to GPU for matrix operations
                delta_Es = 2 * spins_gpu * torch.matmul(J, spins_gpu)  # Compute energy changes on GPU
                delta_Es = delta_Es.cpu()  # Move result back to CPU
                
                # Adjust ΔE with the offset and decide on flips (on CPU)
                accept_probabilities = torch.exp(-(delta_Es - Eoffset) / temperature)
                accepted_flips = (delta_Es < Eoffset) | (torch.rand(N) < accept_probabilities)

                if torch.any(accepted_flips):
                    # If at least one flip is accepted, randomly select one and apply it
                    accepted_indices = torch.nonzero(accepted_flips).squeeze()
                    if accepted_indices.dim() == 0:
                        # Only one accepted flip, directly use it
                        selected_flip = accepted_indices.item()
                    else:
                        # Multiple accepted flips, choose one randomly
                        selected_flip = accepted_indices[torch.randint(len(accepted_indices), (1,)).item()]
                    spins[selected_flip] *= -1  # Apply the selected flip on CPU
                    Eoffset = 0.0  # Reset the offset on CPU
                else:
                    # Increase the offset if no flip is accepted
                    Eoffset += offset_increase_rate  # Offset updated on CPU

            # Calculate the final energy for this run
            spins_gpu = spins.to(device)  # Transfer spins to GPU for energy calculation
            final_energy = -0.5 * torch.sum(spins_gpu * torch.matmul(J, spins_gpu)).item()  # Fully GPU computation

            if final_energy < best_energy:
                best_energy = final_energy  # Update best energy on CPU
                best_spins = spins.clone()  # Update best spins on CPU

        solving_time = time.time() - start_time

        # Return final result
        final_spins = best_spins.numpy()  # Final spins are already on CPU
        return SpinGlassSolution(final_spins, best_energy, solving_time)

based on the algo in the paper do you think my digital annealer is an implementation of the DA in a gpu. 
can you review my solution as a phd expert