# 检查ROCm是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 创建一个10000x10000的上三角矩阵
matrix = torch.triu(torch.rand((10000, 10000), dtype=torch.float32)).to(device)

# 创建一个长度为10000的二进制向量
binary_vector = torch.randint(0, 2, (10000,), dtype=torch.float32).to(device)

...

# 显示结果
print(result)

tensor(6344590.5000, device='cuda:0')

速度非常快。

print(qubo)
print(x)

tensor([[0.6717, 0.2371, 0.3101,  ..., 0.5782, 0.7420, 0.1201],
        [0.0000, 0.1776, 0.7430,  ..., 0.5994, 0.1445, 0.1131],
        [0.0000, 0.0000, 0.4777,  ..., 0.9339, 0.5034, 0.7254],
        ...,
        [0.0000, 0.0000, 0.0000,  ..., 0.1156, 0.8715, 0.9204],
        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.5723, 0.5254],
        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.6098]],
       device='cuda:0')

tensor([0., 0., 0.,  ..., 0., 0., 1.], device='cuda:0')

看起来它运行成功了。

print(len(qubo))
print(len(x))

10000
10000

我们成功解决了一个包含10,000个量子比特的问题。

同样地，我尝试解决一个HOBO问题，虽然由于其复杂性需要更多时间，但它也成功运行了：

tensor([[[3.4074e-01, 2.3801e-01, 7.2555e-01,  ..., 5.7089e-01,
          9.3300e-01, 9.4946e-01],
         [4.5312e-01, 6.0727e-02, 6.4917e-01,  ..., 9.2022e-01,
          4.8904e-01, 7.7863e-01],
         [4.8968e-01, 1.3415e-01, 1.3029e-01,  ..., 1.3355e-01,
          3.1534e-01, 1.4187e-01],
         ...,
         [8.1578e-01, 4.4324e-01, 6.7385e-01,  ..., 4.7309e-01,
          8.9954e-01, 1.3278e-01],
         [3.1411e-01, 6.2912e-01, 3.0486e-01,  ..., 7.0183e-01,
          2.1883e-01, 4.6304e-01],
         [2.8279e-01, 2.7991e-01, 3.7780e-01,  ..., 8.6366e-01,
          1.0008e-01, 4.9714e-01]],

        [[3.6964e-02, 4.8730e-01, 3.2427e-01,  ..., 7.9467e-01,
          7.8272e-01, 7.5983e-01],
         [7.0970e-01, 2.3678e-01, 2.4753e-02,  ..., 7.0028e-01,
          2.4440e-01, 1.1518e-02],
         [6.3244e-01, 8.4304e-01, 9.5919e-01,  ..., 6.2282e-01,
          4.0956e-01, 4.5244e-01],
         ...,
         [4.4514e-01, 4.7023e-01, 5.3170e-02,  ..., 5.9058e-02,
          6.1356e-01, 9.7442e-01],
         [1.7204e-02, 6.4554e-01, 4.0960e-01,  ..., 4.3088e-01,
          6.7305e-01, 7.0740e-01],
         [5.8251e-01, 4.6846e-01, 7.5960e-01,  ..., 7.6140e-01,
          8.1435e-01, 1.9587e-01]],

        [[1.1972e-01, 1.0914e-01, 4.9694e-01,  ..., 9.5165e-01,
          4.4203e-01, 3.6698e-01],
         [3.1464e-01, 6.1380e-01, 1.2407e-01,  ..., 3.7007e-01,
          7.6723e-02, 3.5921e-01],
         [1.1962e-01, 5.5726e-01, 1.7458e-01,  ..., 4.3072e-01,
          7.2724e-01, 3.4609e-01],
         ...,
         [7.7374e-01, 7.4722e-02, 3.7408e-01,  ..., 2.1651e-01,
          4.6314e-01, 7.4453e-02],
         [7.3573e-01, 6.9235e-01, 4.4228e-01,  ..., 5.4889e-01,
          6.1121e-01, 9.5530e-01],
         [3.2142e-01, 5.7895e-02, 3.3993e-01,  ..., 4.1517e-02,
          8.8431e-01, 9.4296e-01]],

        ...,

        [[4.0457e-01, 2.8757e-01, 5.4801e-01,  ..., 1.0961e-01,
          7.2955e-01, 1.9745e-01],
         [8.9825e-01, 1.0816e-01, 6.0176e-01,  ..., 3.0971e-01,
          7.8467e-01, 4.8715e-01],
         [9.9333e-01, 1.5634e-01, 9.9381e-01,  ..., 8.3018e-01,
          6.3215e-01, 1.2996e-01],
         ...,
         [6.1138e-01, 7.5022e-01, 5.3412e-01,  ..., 7.2379e-01,
          1.9942e-01, 7.2978e-02],
         [6.0287e-01, 7.0418e-01, 2.6854e-01,  ..., 7.5977e-02,
          1.3940e-01, 2.4227e-01],
         [3.3239e-01, 4.3019e-01, 6.400

8e-01,  ..., 5.2265e-01,
          1.2314e-01, 9.3854e-01]],

        [[9.4510e-01, 5.6508e-01, 3.4954e-01,  ..., 8.2149e-01,
          9.1106e-01, 2.4986e-04],
         [9.9170e-01, 2.9942e-01, 5.7906e-01,  ..., 4.9604e-01,
          2.4864e-01, 2.2348e-01],
         [3.3026e-01, 4.4755e-01, 2.5998e-01,  ..., 4.6217e-01,
          5.0340e-01, 4.0357e-01],
         ...,
         [1.9716e-01, 2.9640e-02, 5.8540e-01,  ..., 4.8116e-01,
          2.9869e-01, 6.4445e-01],
         [3.0518e-01, 2.6747e-01, 6.4220e-02,  ..., 9.0268e-01,
          7.4376e-02, 8.2438e-01],
         [1.4391e-01, 2.9811e-01, 8.7411e-01,  ..., 9.6410e-01,
          5.4904e-01, 3.1477e-01]],

        [[4.7378e-01, 5.8130e-01, 5.5962e-01,  ..., 7.8212e-02,
          2.1484e-01, 1.6522e-01],
         [9.0710e-01, 2.7738e-01, 9.7132e-01,  ..., 3.2095e-01,
          2.7935e-01, 3.4490e-01],
         [6.5868e-02, 3.0124e-01, 3.8631e-01,  ..., 8.2379e-01,
          8.5930e-01, 9.4988e-01],
         ...,
         [9.0615e-01, 6.0957e-02, 8.0838e-02,  ..., 1.1742e-01,
          9.3179e-02, 3.6622e-01],
         [7.0447e-01, 1.2479e-01, 6.1637e-01,  ..., 8.9149e-01,
          2.5499e-01, 4.4584e-01],
         [7.3772e-01, 3.1814e-01, 6.3537e-01,  ..., 1.2058e-01,
          1.6863e-01, 1.1854e-01]]], device='cuda:0')

使用AMD没有遇到任何问题。以上就是全部内容。