Linear Algebra: Quantum Mechanics

Related lectures (28)

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Covers fundamental concepts in quantum mechanics, including vector spaces, superposition, observables, and inner product.

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Covers linear independence, bases, and coordinate systems with examples and theorems.

Covers Dirac's notation in linear algebra and the tensor product concept in Hilbert spaces.

Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.

Covers determining vector spaces, calculating kernels and images, defining bases, and discussing subspaces and vector spaces.

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Explores matrices, change of bases, eigenvectors, eigenvalues, and vector spaces.

Covers electronic structure calculations using quantum mechanics and introduces fundamental concepts in computational quantum chemistry.

Explains linear independence, bases, and dimension in vector spaces, including the importance of the order of vectors in a basis.