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Abstract: Variational quantum algorithms and quantum machine learning are considered to be promising candidates for achieving a quantum advantage on noisy intermediate-scale quantum (NISQ) computers, which are limited by decoherence and gate errors. In such a setting, the quality of the result of an algorithm strongly depends on the fidelity and duration of the applied quantum logic gates, but also on the so-called circuit depth, i.e., the number of subsequent gates required to execute the algorithm. A versatile hardware-native gate set, including, e.g., a continuous set of two-qubit gates, can thus improve the performance of noisy intermediate-scale quantum computing by reducing the circuit depth [1]. After revisiting some fundamentals of superconducting transmon qubits, this talk presents a hardware-efficient implementation of a continuous set of controlled-phase gates, parameterized by the conditional phase. The approach is an extension of the controlled-phase gate from [2] and is based on the resonant interaction between two flux-tunable transmons. In this implementation, an arbitrary conditional phase can be achieved by tuning a single pulse parameter, and the vanishing time integral of the employed net-zero control pulses [2] provides robustness against memory effects stemming from long-time distortions in flux control lines. Furthermore, by activating the gate via flux control of both qubits, we demonstrate that the gate can be performed between far-detuned qubits, strongly suppressing residual interactions when the gate is off. We characterize the continuous gate set with cross-entropy benchmarking for fixed values of the conditional phase and for phases randomly drawn from a uniform distribution, confirming a consistently high gate fidelity over the full range of conditional phases.
Ref.: [1] Lacroix et al., PRX Quantum 2020.
[2] Negirneac et al., PRL 2021.
