Demonstration of Geometric Landau-Zener Interferometry in a Superconducting Qubit

Geometric quantum manipulation and Landau-Zener interferometry have been separately explored in many quantum systems. In this Letter, we combine these two approaches to study the dynamics of a superconducting phase qubit. We experimentally demonstrate Landau-Zener interferometry based on the pure geometric phases in this solid-state qubit. We observe the interference caused by a pure geometric phase accumulated in the evolution between two consecutive Landau-Zener transitions, while the dynamical phase is canceled out by a spin-echo pulse. The full controllability of the qubit state as a function of the intrinsically robust geometric phase provides a promising approach for quantum state manipulation.Comment: 5 pages + 3 pages supplemental Materia

A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach

High dimensional hypothesis test deals with models in which the number of parameters is significantly larger than the sample size. Existing literature develops a variety of individual tests. Some of them are sensitive to the dense and small disturbance, and others are sensitive to the sparse and large disturbance. Hence, the powers of these tests depend on the assumption of the alternative scenario. This paper provides a unified framework for developing new tests which are adaptive to a large variety of alternative scenarios in high dimensions. In particular, our framework includes arbitrary hypotheses which can be tested using high dimensional $U$-statistic based vectors. Under this framework, we first develop a broad family of tests based on a novel variant of the $L_p$-norm with $p\in \{1,\dots,\infty\}$. We then combine these tests to construct a data-adaptive test that is simultaneously powerful under various alternative scenarios. To obtain the asymptotic distributions of these tests, we utilize the multiplier bootstrap for $U$-statistics. In addition, we consider the computational aspect of the bootstrap method and propose a novel low-cost scheme. We prove the optimality of the proposed tests. Thorough numerical results on simulated and real datasets are provided to support our theory

Rapid characterization of microscopic two-level systems using Landau-Zener transitions in a superconducting qubit

This is the published version. Copyright 2015 American Institute of PhysicsWe demonstrate a fast method to detect microscopic two-level systems in a superconducting phase qubit. By monitoring the population leak after sweeping the qubit bias flux, we are able to measure the two-level systems that are coupled with the qubit. Compared with the traditional method that detects two-level systems by energy spectroscopy, our method is faster and more sensitive. This method supplies a useful tool to investigate two-level systems in solid-state qubits

Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leverage the abundant unlabeled data. In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure. To accomplish this, we propose spatially adaptive networks (SPAD-Nets), a family of networks that dynamically adjust the structures to adapt to the spatial properties of input images, to build such a universal foundation model. We pre-train a spatial adaptive visual tokenizer (SPAD-VT) and then a spatial adaptive Vision Transformer (SPAD-ViT) via masked image modeling (MIM) on 55 public medical image datasets. The pre-training data comprises over 9 million image slices, representing the largest, most comprehensive, and most diverse dataset to our knowledge for pre-training universal foundation models for medical image analysis. The experimental results on downstream medical image classification and segmentation tasks demonstrate the superior performance and label efficiency of our model. Our code is available at https://github.com/function2-llx/PUMIT

Simulating the Kibble-Zurek mechanism of the Ising model with a superconducting qubit system

The Kibble-Zurek mechanism (KZM) predicts the density of topological defects produced in the dynamical processes of phase transitions in systems ranging from cosmology to condensed matter and quantum materials. The similarity between KZM and the Landau-Zener transition (LZT), which is a standard tool to describe the dynamics of some non-equilibrium physics in contemporary physics, is being extensively exploited. Here we demonstrate the equivalence between KZM in the Ising model and LZT in a superconducting qubit system. We develop a time-resolved approach to study quantum dynamics of LZT with nano-second resolution. By using this technique, we simulate the key features of KZM in the Ising model with LZT, e.g., the boundary between the adiabatic and impulse regions, the freeze-out phenomenon in the impulse region, especially, the scaling law of the excited state population as the square root of the quenching rate. Our results supply the experimental evidence of the close connection between KZM and LZT, two textbook paradigms to study the dynamics of the non-equilibrium phenomena.Comment: Title changed, authors added, and some experimental data update