SpicyWei

Thank you @Yonv1943 for the help! The original limit of N is 1024, and the maximum maxpower is automatically set with max - 96 by max_tmp_power = int(mult_pow_timess.max().item() - 960),...

Ok, thanks for the advice!

We currently address the optimal contraction order solution for tensor networks in the form of tensor-train, where contraction is performed only between adjacent tensors, and we will subsequently implement large-scale...

@shixun404 Okay! ! I will test it on a tensor train containing 6, 8, and 10 as soon as possible!

I offer another version to try out

The reason for the error may be the use of open quantum bits in their method, which requires a reduction in the number of tensor from 381 to 345, possibly...

我补充一下发现问题的过程： 对于opt_einsum的random greedy的过程： 每次可以给出对应收缩的scaling（是正确的），按他给定的order计算结果应该为：log(224) = 2.350248018，而他的结果却为：2.65127，多了个log(2)，补充一个草图：  这一步表明我们不应该因为他多算了log(2)，而去给我们的代码增添这个误差。 为了确保正确性，我再次展示我们的结果，与log(224)相等 ：