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Automated quantum programming via reinforcement learning for combinatorial optimization
By design, the agents were constrained to 25 uncompiled instructions.
Through training, for all problems, the expected length of the trained agent programs is decreased compared to those of the untrained agent.
If too much decoherence is experienced by the later stages of the program, one could expect a strong degradation in performance, as evidenced by the decreased episode score between the QVM and QPU for the QAOA.
Read e-book Paradigms of Combinatorial Optimization: Problems and New Approaches, Volume 2 (Iste)
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