Quantum Control Noise Spectroscopy with Optimal Suppression of Dephasing
arXiv:2204.10894 · doi:10.1103/PhysRevA.106.022425
Abstract
We extend quantum noise spectroscopy (QNS) of amplitude control noise to settings where dephasing noise or detuning errors make significant contributions to qubit dynamics. Previous approaches to characterize amplitude noise are limited by their vulnerability to low-frequency dephasing noise and static detuning errors, which can overwhelm the target control noise signal and introduce bias into estimates of the amplitude noise spectrum. To overcome this problem, we leverage optimal control to identify a family of amplitude control waveforms that optimally suppress low-frequency dephasing noise and detuning errors, while maintaining the spectral concentration in the amplitude filter essential for spectral estimation. The waveforms found via numerical optimization have surprisingly simple analytic forms, consisting of oscillating sine waves obeying particular amplitude and frequency constraints. In numerically simulated QNS experiments, these waveforms demonstrate superior robustness, enabling accurate estimation of the amplitude noise spectrum in regimes where existing approaches are biased by low-frequency dephasing noise and detuning errors.
14 pages + appendices, 7 figures
References in corpus (7)
- The Magnus expansion and some of its applications
- Dynamical decoupling and noise spectroscopy with a superconducting flux qubit
- How to Enhance Dephasing Time in Superconducting Qubits
- Dynamically Error-Corrected Gates for Universal Quantum Computation
- Experimental noise filtering by quantum control
- Multiqubit Spectroscopy of Gaussian Quantum Noise
- The SMART protocol -- Pulse engineering of a global field for robust and universal quantum computation
Cited by in corpus (8)
- Resource-efficient digital characterization and control of classical non-Gaussian noise
- SPAM-Robust Multi-axis Quantum Noise Spectroscopy in Temporally Correlated Environments
- Completely Positive Map for Noisy Driven Quantum Systems Derived by Keldysh Expansion
- Machine learning non-Markovian two-level quantum noise spectroscopy
- Sparse Non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations
- Binary Quantum Control Optimization with Uncertain Hamiltonians
- Efficient learning and optimizing non-Gaussian correlated noise in digitally controlled qubit systems
- Universally Robust Control of Open Quantum Systems