Introduction
Quantum annealing and simulated annealing are two optimization methods used to solve complex problems. They are used in various fields such as physics, chemistry, and computer science. These two methods are often compared, and this blog post aims to provide a factual comparison between them.
Quantum Annealing
Quantum annealing is a technique used to solve optimization problems by taking advantage of quantum mechanics. This technique uses a quantum computer to find the minimum energy state of a given problem. The most famous quantum annealing device is the D-Wave system, which has been used in fields such as machine learning, finance, and bioinformatics.
Simulated Annealing
Simulated annealing is a classical optimization technique that is based on the physical process of annealing. It is used to solve optimization problems that are difficult to solve using traditional methods. The simulated annealing algorithm works by probabilistically accepting worse solutions in the hope of finding a global minimum.
Comparison
Both quantum annealing and simulated annealing are used to solve optimization problems. However, they have some differences that make them suitable for solving different kinds of problems.
Quantum annealing is exceptionally fast when solving certain types of optimization problems, such as discrete optimization problems, but it falls short when solving problems that have too many variables. On the other hand, Simulated annealing has the advantage of being a classical optimization algorithm, which means it can run on any computer architecture.
In terms of performance, the D-Wave system, which is a quantum annealing system, can solve problems exponentially faster than a classical computer, but only under the right circumstances. If the problem has too many variables or if it is not explicitly designed for quantum annealing, then the hardware is not expected to run faster than a classical solver running on a high-performance computing cluster.
Simulated annealing is capable of providing an excellent estimate of the global minimum for any given set of optimization problems. However, it takes a longer time to achieve the optimum.
Conclusion
In summary, quantum annealing and simulated annealing are two optimization techniques that are employed to solve optimization problems. The choice between the two techniques depends on various factors, including the size and complexity of the problem, resources required, and the required level of accuracy.
Although both methods differ in some ways, they both have an important role to play in optimization, and the choice depends on the specific problem and requirements at hand.
References
- Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220 (4598), 671-680.
- Kadowaki, T., & Nishimori, H. (1998). Quantum annealing in the transverse Ising model. Physical Review E, 58 (5), 5355.
- Farhi, E., Goldstone, J., & Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.