Thuật ngữ AI
Từ điển đầy đủ về Trí tuệ nhân tạo
Quantum Kernel
Similarity function computed using quantum circuits that measures the distance between data transformed into a quantum Hilbert space. Quantum kernels leverage entanglement and superposition to capture complex correlations inaccessible to classical kernels.
Quantum Feature Map
Non-linear transformation encoding classical data into quantum states via parameterized circuits. This map enables projecting data into high-dimensional spaces where linear separations become possible.
Quantum Gram Matrix
Similarity matrix computed by evaluating quantum inner products between all pairs of transformed data points. This matrix captures the geometric relationships of data in the quantum feature space.
Quantum Feature Space
High-dimensional space where classical data are projected via non-linear quantum transformations. This space allows revealing complex structures and patterns invisible in the original space.
Quantum Data Embedding
Specific encoding technique preserving the geometric structure of data in quantum space. Quantum embedding maintains distances and important relationships between data points.
Quantum Kernel Alignment
Optimization of quantum feature map parameters to maximize alignment with target labels. This technique improves quantum classifier performance by adapting kernels to the specific task.
Quantum Kernel Trick
Extension of the classical kernel trick using quantum circuits to implicitly compute high-dimensional features. This approach avoids the curse of dimensionality while capturing complex structures.