What are quantum-inspired algorithms?

There are many types of quantum-inspired algorithms. One commonly used quantum-inspired algorithm is based on a computational model called adiabatic quantum computing. This approach uses a concept from quantum physics known as the adiabatic theorem. When you apply that theorem to solve a problem, you:

  • First prepare a system and initialize it to its lowest energy state. For a simple system, one which we completely understand, this is easy to do.
  • Next, slowly transform that system into a more complex one that describes the problem you are trying to solve. The adiabatic theorem states that, as long as this transformation happens slowly enough, the system has time to adapt and will stay in that lowest energy configuration. When we're done with our transformations, we've solved our problem.

A good analogy of this is to imagine you have a glass of water. If you move that glass slowly across a table, the contents won't spill because the system has time to adapt to its new configuration. If you were to move the glass quickly however, the system has been forced to change too quickly, and we have water everywhere.

Adiabatic quantum computation is an area of active research that's already being used in the industry. A number of techniques have been developed to simulate this type of physics. These kinds of classical algorithms, which we can run on classical computers today, are also known as quantum-inspired optimization.

What is quantum-inspired optimization (QIO)?

Optimization problems are found in every industry, such as manufacturing, finance, and transportation. In fact, industries such as logistics are dedicated entirely to solving optimization problems. To solve these problems, we search through feasible solutions. The best solution is the one with the lowest cost. Adiabatic quantum algorithms are well-suited to solving many optimization problems.

Today, we can emulate adiabatic quantum algorithms by using quantum-inspired techniques on classical hardware, an approach which is known as quantum-inspired optimization (QIO). These techniques often perform better than state-of-the-art classical optimization techniques.

Applying QIO to real-world problems may offer businesses new insights or help lower costs by making their processes more efficient. QIO gives us the opportunity to:

  • Find a solution faster than other optimization techniques for a fixed use case and fixed quality of solution.
  • Find a higher quality solution than other optimization techniques for a fixed problem and fixed amount of time.
  • Use a more realistic model than other optimization techniques by extending the problem to consider more variables.

Since QIO methods are heuristics, they're not guaranteed to find the optimal solution. Also, they don't always outperform other optimization techniques. In reality, it depends on the problem, and discovering what makes QIO perform better than other methods in some situations and not others is still an active area of research.