Filtering variational quantum algorithms for combinatorial optimization

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Deep Learning F-VQE
Overview

F-VQEs

See https://arxiv.org/pdf/2106.10055.pdf.

How to Use

Just run the main code (calls _test_circuit_training). This will train the F-VQE on the simple MaxCut problem provided.

The solution to the default problem (defined in _APPLY_PROBLEM_COST_FUNCTION) is simply (0,4),(1,2,3), with a total score of 20. This corresponds to a target score of 1.5 once the MaxCut score is translated into a minimization problem. Therefore, you should see the F-VQE's iteration score (printed as the circuit trains) decrease from its initial value (around 11 or 12, typically) to a final result near 1.5.

How to Use 2

To define other problems, simply convert the problem into a minimization problem, where the cost/success score is always positive. Simply code the cost function into APPLY_PROBLEM_COST_FUNCTION and run the training.

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