There are only two main types of algorithms
It is a generic quantum algorithm and a quantum-classical hybrid algorithm.
Generic quantum algorithm
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What is generic quantum algorithm?
All of the algorithms are designed to be computed using only quantum computers.
So the advantage is that we can take full advantage of the strengths of quantum computation. Shor's algorithmis a typical example. -
How to learn generic quantum algorithm?
Generic quantum algorithms can also be divided into two main categories.Quantum phase estimation type
Given a matrix and eigenvectors**, this is an algorithm** to find the eigenvalues of the matrix.
Incidentally, eigenvalues are "the rate of change in the magnitude of a vector after matrix transformation."
It consists of an algorithm county full of algorithms; Shor's algorithm belongs here.
What is it used for?
- Quantum chemistry calculations
- Materials calculations
- Combinatorial optimization problems
- Some machine learning
- Cryptanalysis
Quantum Amplitude Amplification and Estimation Type
This type is like the quantum phase estimation type.
Instead of focusing on one particular phase, we focus on the amplitude. Grover's algorithm belongs here.
What is it used for?
- Search
- Finance
- Fluid Simulation
Quantum classical hybrid algorithm
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What is the quantum-classical hybrid algorithm?
It is an algorithm designed to alternate between a quantum computer and an existing computer.
So the advantage is that it can complement the quantum computer, which is still under research and development and has many errors.
Also, since it is a combination of the two, it is naturally subject to existing computers' processing limitations and communication speed. This is a weakness. -
How to learn quantum classical hybrid algorithm?
Quantum-classical hybrid algorithms can also be divided into two main categories.Quantum variational algorithm
It was recently created due to the difficulty of performing the quantum phase estimation type of general-purpose quantum algorithms. Although the purpose of finding eigenvalues is the same, the principle is different. The phase estimation type obtains eigenvalues probabilistically, whereas the variational algorithm obtains them deterministically. How it is obtained is based on the quantum variational principle, minimizing a quantity called the expected value. The quantum variational algorithm is currently the mainstream, so if in doubt, start here.
What is it used for?
- Quantum chemistry calculations
- Combinatorial optimization problems
Quantum Machine Learning Algorithm
The quantum machine learning algorithm is derived from the quantum variational algorithm and is intended to learn based on data.
If you want to prioritize data utilization, you can learn from here.
What is used for?
- Machine learning
Which one should I learn after all?
| People who study general-purpose quantum algorithms | People who study quantum-classical hybrid algorithms |
|---|---|
| serious person | People who are just "happy if they can use it." |
| People who want to investigate in the long term | People who want to get results in the short/medium term |
| People who want to do science | People who want to do business |
Summary
- Generic Quantum Algorithm
- Quantum phase estimation type
- Quantum chemical calculations
- Materials calculations
- Combinatorial optimization problems
- Some machine learning
- Cryptanalysis
- Quantum Amplitude Amplification and Estimation Type
- Search
- Finance
- Fluid simulation
- Quantum phase estimation type
- Quantum-classical hybrid algorithm
- Quantum Variational Algorithm
- Quantum chemical calculations
- Combinatorial optimization problems
- Quantum machine learning algorithms
- Machine Learning
- Quantum Variational Algorithm