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In this work, we report on a novel quantum gate approximation algorithm basedon the application of parametric two-qubit gates in the synthesis process. Theutilization of these parametric two-qubit gates in the circuit design allows usto transform the discrete combinatorial problem of circuit synthesis into anoptimization problem over continuous variables. The circuit is then compressedby a sequential removal of two-qubit gates from the design, while the remainingbuilding blocks are continuously adapted to the reduced gate structure byiterated learning cycles. We implemented the developed algorithm in theSQUANDER software package and benchmarked it against several state-of-the-artquantum gate synthesis tools. Our numerical experiments revealed outstandingcircuit compression capabilities of our compilation algorithm providing themost optimal gate count in the majority of the addressed quantum circuits.

Efficient quantum gate decomposition via adaptive circuit compression