Calculating Square Root of a Matrix in Quantum Information Theory

In summary, the conversation discusses the definition of the absolute value of a matrix and the definition of the square root of a matrix. The participants also consider whether these definitions are basis independent and how to prove that the square root of a positive semidefinite Hermitian matrix is unique.
  • #1
aaaa202
1,169
2
I'm doing an online course in quantum information theory, but it seems to require some knowledge of linear algebra that I don't have.
A definition that popped up today was the definition of the absolute value of a matrix as:

lAl = √(A*A) , where * denotes conjugate transpose.

Now for a given matrix I can calculate the product A*A, but how is the square root of this defined? I have no idea, though I think it should be basis independent, so maybe square root of the trace.
A*A is clearly positive semidefinite but I don't know if I can use that for anything.
 
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  • #2
aaaa202 said:
I'm doing an online course in quantum information theory, but it seems to require some knowledge of linear algebra that I don't have.
A definition that popped up today was the definition of the absolute value of a matrix as:

lAl = √(A*A) , where * denotes conjugate transpose.

Now for a given matrix I can calculate the product A*A, but how is the square root of this defined? I have no idea, though I think it should be basis independent, so maybe square root of the trace.
A*A is clearly positive semidefinite but I don't know if I can use that for anything.
What's the definition of the square root of a number?
 
  • #3
Well I guess that the square root of a matrix A is then a matrix B such that B^2 = A. Well wouldn't it be better with a matrix B such that B*B = A. And is this definition basis independent.
 
  • #4
aaaa202 said:
Well I guess that the square root of a matrix A is then a matrix B such that B^2 = A. Well wouldn't it be better with a matrix B such that B*B = A. And is this definition basis independent.
That would be like requiring the square root of a complex number ##a## to satisfy ##b^*b = a##
 
  • #5
Ok well is the definition basis independent?
 
  • #6
aaaa202 said:
Ok well is the definition basis independent?

Let me rephrase that question. If a linear transformation T is represented by the matrix A in one basis and A' in another. Then is |A'| = |A|'?

What do you think?
 
  • #7
I am not sure. I need to show that:

(√(A))' = √(A')
B' = √(A')

Now if A' = UAU maybe I can use that somehow...
 
  • #8
aaaa202 said:
I am not sure. I need to show that:

(√(A))' = √(A')
B' = √(A')

Now if A' = UAU maybe I can use that somehow...

Or, think about diagonalizing A*A. It must be true if you want to accept that and get back to your quantum theory!
 
  • #9
Maybe something like this:

A' = UAU
B' = UBU

Now B2 = A

We want to show that:
√A' = (√A)' = B'

So is B'2 = A'?

Well B'2 = UBUUBU = UB2U = UAU = A'

But all this requires that A and A' and B and B' are related by a basis change with a unitary as above. When is this true?
 
  • #10
That's essentially the proof. The only technicality is that B' must be the right square root. That probably depends on showing that a positive semi-definite matrix has a unique positive semi-definite square root.
 
  • #11
The square root of a positive semidefinite Hermitian matrix ##A## is the unique positive semidefinite matrix ##B## such that ##B^2=A##. You can look at Ch. VI s.3 of "Linear algebra done wrong" for an explanation of why such matrix exists and why it is unique.
 
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FAQ: Calculating Square Root of a Matrix in Quantum Information Theory

1. What is the importance of calculating the square root of a matrix in quantum information theory?

The square root of a matrix is essential in quantum information theory because it allows for the computation of quantum states and operations. This is crucial for understanding and developing quantum algorithms and protocols, as well as for simulating quantum systems.

2. How is the square root of a matrix calculated in quantum information theory?

The square root of a matrix can be calculated using various methods, such as the singular value decomposition (SVD), diagonalization, or the Cayley-Hamilton theorem. These methods are based on linear algebra and involve finding the eigenvalues and eigenvectors of the matrix.

3. What are the applications of calculating the square root of a matrix in quantum information theory?

The square root of a matrix has various applications in quantum information theory, such as quantum error correction, quantum tomography, and quantum state estimation. It is also used in developing quantum algorithms for tasks such as data processing, optimization, and simulation.

4. Are there any challenges in calculating the square root of a matrix in quantum information theory?

Yes, there are challenges in calculating the square root of a matrix in quantum information theory. One major challenge is dealing with the exponential growth in the size of the matrix as the number of qubits increases. This requires efficient algorithms and techniques to handle large matrices in a timely manner.

5. How does the calculation of the square root of a matrix relate to quantum entanglement?

The calculation of the square root of a matrix is closely related to quantum entanglement as it allows for the manipulation and measurement of entangled states. Additionally, the square root of a matrix plays a crucial role in understanding and quantifying the entanglement present in a quantum system.

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