HHL Algorithm for Solving Linear Equations

In summary: However, it seems that some papers have not properly explained this step, leading to confusion for readers. Overall, more clarity and explanation is needed in order to fully understand the HHL algorithm and its implementation. In summary, the HHL algorithm for solving linear equations involves applying the momentum operator and Fourier transform, followed by using the phase estimation algorithm and uncomputing it in the last step. However, some papers have not properly explained this step, leading to confusion for readers. More clarity and explanation is needed in order to fully understand the algorithm and its implementation.
  • #1
dakshina gandikota
2
0
I have a question about HHL algorithm https://arxiv.org/pdf/0811.3171.pdf for solving linear equations of the form:

A x = b

Where A, x and b are matrices

Take for example

4x1 + 2x2 =14
5x1 + 3x2 = 19

HHL apply the momentum operator eiAτto/T on the state, do a Fourier Transform on |b> and *uncompute phase estimation*. I don't follow the last step. How can you uncompute, when you haven't computed the phase estimation?

Earlier in the paper they mention that let |u> be the eigen vector of |b> using phase estimation. Is this what they are referring to? There is no circuit to back their algorithm in that pdf.

I also checked a particular implementation of HHL https://arxiv.org/pdf/1110.2232v2.pdf where they mention

"Apply the quantum inverse Fourier transform to the register C. Denote the basis states after quantum Fourier transform as |k>"

It seems they didn't edit the sentence properly. So not much help.

Does anyone think these are not properly described in the papers? By the way, there has been a thread https://quantumcomputing.stackexcha...systems-of-equations-hhl09-step-2-preparation to discuss the paper but not address the specific issues.

Thanks
 
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  • #2
. Yes, it does seem that the papers are not properly describing the steps of the HHL algorithm. Specifically, the last step of the algorithm involves using the phase estimation algorithm to estimate the eigenvalues of A and then applying the inverse Fourier transform on the register C. This step is often referred to as "uncomputing the phase estimation" because it essentially "undoes" the phase estimation. It is important to note that this step is necessary in order to obtain the correct solution for the linear system of equations.
 

1. What is the HHL algorithm?

The HHL algorithm, or the Harrow-Hassidim-Lloyd algorithm, is a quantum algorithm used to solve linear systems of equations. It was developed in 2009 by Aram Harrow, Avinatan Hassidim, and Seth Lloyd.

2. How does the HHL algorithm work?

The HHL algorithm works by encoding the solution to a linear system of equations into a quantum state, and then performing quantum operations on this state to retrieve the solution. It uses quantum phase estimation and the quantum inverse Fourier transform to achieve this.

3. What are the advantages of using the HHL algorithm?

The HHL algorithm has several advantages over classical algorithms for solving linear systems of equations. It has a much faster runtime, as it can solve a linear system in polynomial time compared to the exponential time required by classical algorithms. It also has the potential to solve larger and more complex systems of equations.

4. What are the limitations of the HHL algorithm?

While the HHL algorithm has many advantages, it also has some limitations. It requires a large number of qubits to encode the system of equations, which can be difficult to achieve in practice. It also requires precise control over the quantum operations, which can be challenging to implement.

5. How is the HHL algorithm being used in real-world applications?

The HHL algorithm is still a relatively new and developing concept, but it has already been used in various real-world applications. These include solving systems of linear equations in finance, machine learning, and quantum chemistry. It is also being researched for potential use in optimization problems and data analysis.

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