Quantum annealing processors naturally return low-energy solutions; some applications require the real minimum energy (optimization problems) and others require good low-energy samples (probabilistic sampling problems).

Optimization problems. In an optimization problem, you search for the best of many possible combinations. Optimization problems include scheduling challenges, such as “Should I ship this package on this truck or the next one or “What is the most efficient route a traveling salesperson should take to visit [1] different cities.Physics can help solve these sorts of problems because you can frame them as energy minimization problems. A fundamental rule of physics is that everything tends to seek a minimum energy state. Objects slide down hills; hot things cool down over time. This behavior is also true in the world of quantum physics. Quantum annealing simply uses quantum physics to find low-energy states of a problem and therefore the optimal or near-optimal combination of elements figure1 shows below.

Figure1: Quantum Annealing

Sampling problems. Sampling from many low-energy states and characterizing the shape of the energy landscape is useful for machine learning problems where you want to build a probabilistic model of reality. The samples give you [2] information about the model state for a given set of parameters, which can then be used to improve the model.

Quantum annealing (QA) is a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations (in other words, a meta-procedure for finding a procedure that finds an absolute minimum size/length/cost/distance from within a possibly very large, but nonetheless finite set of possible solutions using quantum fluctuation-based computation instead of classical computation).

Digital Annealer provides an alternative to quantum computing technology, which is at present both very expensive and difficult to run. Using a digital circuit design inspired by quantum phenomena, the Digital Annealer focuses on rapidly solving complex combinatorial optimization problems without [3] the added complications and costs typically associated with quantum computing methods.

The Digital Annealer computational architecture bridges the gap to the quantum world and paves the way for much faster, more efficient solving of today’s business problems. Our quantum-inspired computing solution is designed to solve large-scale combinatorial optimization problems which are unsolvable using today’s classical computers.

References:

1. https://docs.dwavesys.com/docs/latest/c_gs_2.html
2. https://www.fujitsu.com/global/services/business-services/digital-annealer
3. https://informaconnect.com/machine-learning-with-quantum-annealing

Cite this article:

S. Nandhinidwaraka (2021) Quantum Annealing-Uses of Computing, AnaTechmaz, pp.7