This reference page just iterates the basics of multiplying Matrices along with some other matrix examples more related to quantum mechanics and, in particular, quantum computing.
Note: This is a work in progress!
[1×1] times [1×1]
The result we get, a 1×1 matrix, is the same as we’d get multiplying two scalars together:
But note that a 1×1 matrix is not a scalar. (The difference becomes obvious in the next two cases.)
[1×1] times [1×2]
Similar to the first case, here the result, a 1×2 matrix (a row vector), is the same as we’d get multiplying the 1×2 matrix by a scalar.
As in the first case, the result is the same as we’d get multiplying row vector by a scalar (another row vector):
But note that, unlike the scalar multiplication, the matrix multiplication cannot be reversed because [1×2][1×1] is an illegal operation. (The number of columns in the first matrix doesn’t match the number of rows in the second.)
Bottom line, a [1×1] matrix is not (always) the same as a scalar!
[2×1] times [1×1]
Here’s the legal version of putting the “scalar” matrix second. In this case, the single column of the 2×1 matrix (a column vector) matches the single row of the 1×1 matrix:
The result is the same as we’d get multiplying a column vector by a scalar (another column vector):
However, as in the second case above, the [2×1][1×1] operation cannot be reversed (due to column/row mismatch), whereas with the scalar operation it can.
[1×2] times [2×1] (inner product)
Multiplying a row vector by a column vector results in a 1×1 matrix usually treated as a scalar:
In Dirac Bra-Ket notation, generally speaking, a bra is row vector, and a ket is a column vector. Often the bra is the conjugate transpose of a ket. For example, given:
Where α is a complex number, then:
Where α* is the conjugate of α.
[2×1] times [1×2] (outer product)
Multiplying a column vector by a row vector results in a matrix with as many rows and columns as the vectors (in this case, a 2×2 matrix):
In Bra-Ket notation, this is (using the definition of |Ψ〉 above):
Which, among other things, allows the definition of quantum gates in terms of combinations of state vectors.
[2×2] times [2×2]
Multiplying two (same-sized) square matrices results in a new matrix of the same size (in this case, 2×2).
(Multiplying square matrices is what many think of as “matrix multiplication” but as the examples above show, it’s not the only form.)
Sticking to 2×2 matrices for a moment, in quantum computing the computational basis states, |0〉 and |1〉, are generally defined as:
Upon that, two common superposition states, |+〉 and |-〉, are defined as:
The tensor product of two states, a and b, is written:
Which expands to:
The Pauli sx gate:
Note this is also a NOT gate.
The Pauli sy gate:
The Pauli sz gate:
Note that these matrices are also the spin axes representations.
The Hadamard gate:
The Hadamard gate maps the |0〉 state to |+〉 and |1〉; state to |-〉. That is, it puts a computational basis state into a superposition of that basis.