The QPU and Hybrid Classic/Quantum Models

In a few years, when we’ll have quantum computers with numerous qubits, there will still be tasks that will be better performed with classical computers. That’s why even today when quantum computers are far from perfect, many companies are exploring hybrid classic/quantum algorithms

Why are hybrid models attractive today?

One reason is that classical computers might be better for certain tasks, such as reading data from external storage, communicating over existing networks, or just running the endless variety of programs that run well enough on classical computers. In those cases, one could build on the CPU/GPU analogy. The GPU — a graphical processing unit — is a very useful co-processor to a general CPU. Similarly, a QPU — the quantum processing unit — could be a fantastic processor to a classical CPU.

Another reason is a practical reason and is related to the stability of today’s quantum computers. Whether because of temperature changes, vibrations or external interference, quantum computers are limited in how long they can sustain a calculation. As a result, quantum information scientists modify the algorithms — sometimes quite extensively — to work in a hybrid fashion. We see this often in VQE or QAOA or other variational algorithm implementations, in what we often term a ‘generate/solve’ loop, which goes something like the following:

The reason that a quantum circuit needs to be generated at every cycle is primarily the ‘state preparation’ block, as the initial values of the quantum circuit will change each cycle.

At Classiq, we believe that the limiting factors in creating quantum circuits should only be ingenuity and imagination, not the difficulties in gate-level design. That’s why our platform offers a high-level language to express the algorithm, and then automatically synthesizes it to meet the desired constraints. As part of this platform, we make it easy to integrate classical and quantum algorithms into a single hybrid solution.

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