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7 Most Common Types of Quantum Annealing Algorithms

Quantum annealing is a type of quantum computing that involves finding the minimum energy state of a given problem. It works by slowly changing a Hamiltonian from one that is easy to solve to one that encodes the problem. The idea is that as the Hamiltonian changes, the system will evolve into a state that is the solution to the problem. In this article, we will explore the seven most common types of quantum annealing algorithms.

1. Simulated Annealing

Simulated annealing is a classical optimization algorithm that is often used as a benchmark for quantum annealing algorithms. It works by randomly moving through the search space, accepting moves that improve the solution and occasionally accepting moves that worsen the solution to avoid getting stuck in local optima.

2. Quantum Monte Carlo

Quantum Monte Carlo is a classical algorithm that uses statistical sampling to approximate the ground state of a quantum system. It works by sampling the system at different temperatures and then extrapolating to zero temperature, where the ground state is found.

3. Quantum Approximate Optimization Algorithm (QAOA)

QAOA is a variational quantum algorithm that uses a sequence of unitary operations to approximate the ground state of a given problem. It works by choosing a parameterized unitary operator that depends on a set of parameters, and then optimizing those parameters to minimize the energy of the system.

4. Quantum Adiabatic Evolution

Quantum adiabatic evolution is the original quantum annealing algorithm. It works by starting with a Hamiltonian that is easy to solve and gradually changing it to one that encodes the problem. The idea is to evolve the system slowly enough that it remains in the ground state throughout the process.

5. Quantum Annealing with Transverse Fields

Quantum annealing with transverse fields is a modification of quantum adiabatic evolution that involves adding a transverse magnetic field to the Hamiltonian. This helps to prevent