The method is feasible because it only changes inner product operation and the complexity of algorithm doesn't increase.
因为升维后只是改变了内积运算，并没有使算法复杂性随着维数的增加而增加，因此这种方法才是可行的。
Based on this connection, one method for constructing quantum constacyclic codes is presented by finding self-orthogonal classical constacyclic codes over the field GF(4) under a trace inner product.
利用这一联系，提出了GF（4）上的经典常数循环码满足迹内积自正交的充要条件，从而构造出了对应的量子常数循环码。
A new adaptive variable neural network is developed according to the inner product feature of sample space. This method does not need to iterate learning.
根据样本空间的内积特性，提出一种无需迭代学习的自适应变结构神经网络。
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