arXiv subjects

Valentina Gualtieri

Publications and source records attributed to Valentina Gualtieri.

3 recordsLinked to original sources

arXiv2025

Unified evolutionary optimization for high-fidelity spin qubit operations

Developing optimal strategies to calibrate quantum processors for high-fidelity operation is one of the outstanding challenges in quantum computing today. Here, we demonstrate multiple examples of high-fidelity operations achieved using a unified global optimization-driven automated calibration routine on a six dot semiconductor quantum processor. Within the same algorithmic framework we optimize readout, shuttling and single-qubit quantum gates by tailoring task-specific cost functions and tuning parameters based on the underlying physics of each operation. Our approach reaches systematically $99\%$ readout fidelity, $>99\%$ shuttling fidelity over an effective distance of 10$μ$m, and $>99.5\%$ single-qubit gate fidelity on timescales similar or shorter compared to those of expert human operators. The flexibility of our gradient-free closed loop algorithmic procedure allows for seamless application across diverse qubit functionalities while providing a systematic framework to tune-up semiconductor quantum devices and enabling interpretability of the identified optimal operation points.

quant-ph↗

arXiv2024

QDsim: A user-friendly toolbox for simulating large-scale quantum dot devices

We introduce QDsim, a python package tailored for the rapid generation of charge stability diagrams in large-scale quantum dot devices, extending beyond traditional double or triple dots. QDsim is founded on the constant interaction model from which we rephrase the task of finding the lowest energy charge configuration as a convex optimization problem. Therefore, we can leverage the existing package CVXPY, in combination with an appropriate powerful solver, for the convex optimization which streamlines the creation of stability diagrams and polytopes. Through multiple examples, we demonstrate how QDsim enables the generation of large-scale dataset that can serve a basis for the training of machine-learning models for automated tuning algorithms. While the package currently does not support quantum effects beyond the constant interaction model, QDsim is a tool that directly addresses the critical need for cost-effective and expeditious data acquisition for better tuning algorithms in order to accelerate the development of semiconductor quantum devices.

cond-mat.mes-hall↗

arXiv2020

Quantum-classical dynamical distance and quantumness of quantum walks

We introduce a fidelity-based measure $\text{D}_{\text{CQ}}(t)$ to quantify the differences between the dynamics of classical (CW) and quantum (QW) walks over a graph. We provide universal, graph-independent, analytic expressions of this quantum-classical dynamical distance, showing that at short times $\text{D}_{\text{CQ}}(t)$ is proportional to the coherence of the walker, i.e. a genuine quantum feature, whereas for long times it depends only on the size of the graph. At intermediate times, $\text{D}_{\text{CQ}}(t)$ does depend on the graph topology through its algebraic connectivity. Our results show that the difference in the dynamical behaviour of classical and quantum walks is entirely due to the emergence of quantum features at short times. In the long time limit, quantumness and the different nature of the generators of the dynamics, e.g. the open system nature of CW and the unitary nature of QW, are instead contributing equally.

quant-ph↗