Generalized fast quasi-adiabatic population transfer for improved qubit readout, shuttling, and noise mitigation

TL;DR

This paper introduces fast quasi-adiabatic conversion strategies that improve quantum state transfer fidelity by accounting for noise and pulse shaping, surpassing traditional adiabatic methods.

Contribution

It develops explicit pulse-shaping techniques for fast quasi-adiabatic conversion that incorporate noise mitigation, extending beyond the adiabatic approximation.

Findings

Enhanced state transfer fidelity with noise-aware pulse shaping

Framework for tailored noise mitigation strategies

Applicable to various quantum systems and noise models

Abstract

Population-transfer schemes are commonly used to convert information robustly stored in some quantum system for manipulation and memory into more macroscopic degrees of freedom for measurement. These schemes may include, e.g., spin-to-charge conversion for spins in quantum dots, detuning of charge qubits between a noise-insensitive operating point and a measurement point, spatial shuttling of qubits encoded in spins or ions, and parity-to-charge conversion schemes for qubits based on Majorana zero modes. A common strategy is to use a slow (adiabatic) conversion. However, in an adiabatic scheme, the adiabaticity conditions, on the one hand, and accumulation of errors through dephasing, leakage, and energy relaxation processes on the other hand, limit the fidelity that can be achieved. Here, we give explicit fast quasiadiabatic (fast-QUAD) conversion strategies (pulse shapes) beyond the adiabatic approximation that allow for optimal state conversion. In contrast with many other approaches, here we account for noise in combination with pulse shaping. Although we restrict to noise sources that can be modeled by a classical fluctuating parameter, we allow generally for anisotropic nonGaussian noise that is nevertheless sufficiently weak to lead to a small error. Inspired by analytic methods that have been developed for dynamical decoupling theory, we provide a general framework for unique noise mitigation strategies that can be tailored to the system and environment of interest.