Towards Solving Optimization Problems With A Quantum Computer

We need to follow another approach

Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.

Optimization is not only an essential part of machine learning algorithms. It is also the solution to many industrial problems in itself. There are ubiquitous examples of applications that require finding an optimal object from a finite set of things.

Quantum computing beckons with exponential acceleration in solving problems. There’s a problem, though. We can’t just put a problem into a quantum algorithm, and everything is OK.

To use a quantum algorithm, we have to formulate our problem so the quantum algorithm can work. We have to encode the problem we want to solve into qubits.

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But, how do we do this? How do we encode an optimization problem into qubits?

Of course, there are myriads of ways to do this. In volume 1, we already got to know a popular method — the quantum oracle.

The quantum oracle is a placeholder for a yet unknown quantum transformation gate representing something you want to identify. First, you define an instantiation of the oracle for each possible something. Then, you craft a quantum circuit…