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Xian Wu

Publications and source records attributed to Xian Wu.

At least 199 records · Page 11Linked to original sources
arXiv2020

Amplitude and frequency sensing of microwave fields with a superconducting transmon qudit

Experiments with superconducting circuits require careful calibration of the applied pulses and fields over a large frequency range. This remains an ongoing challenge as commercial semiconductor electronics are not able to probe signals arriving at the chip due to its cryogenic environment. Here, we demonstrate how the on-chip amplitude and frequency of a microwave signal can be inferred from the ac Stark shifts of higher transmon levels. In our time-resolved measurements we employ Ramsey fringes, allowing us to detect the amplitude of the systems transfer function over a range of several hundreds of MHz with an energy sensitivity on the order of $10^{-4}$. Combined with similar measurements for the phase of the transfer function, our sensing method can facilitate pulse correction for high fidelity quantum gates in superconducting circuits. Additionally, the potential to characterize arbitrary microwave fields promotes applications in related areas of research, such as quantum optics or hybrid microwave systems including photonic, mechanical or magnonic subsystems.

quant-ph↗
arXiv2020

Automated Relational Meta-learning

In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.

cs.LG↗
arXiv2019

On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.

cs.AI↗
arXiv2019

Deep Portrait Image Completion and Extrapolation

General image completion and extrapolation methods often fail on portrait images where parts of the human body need to be recovered - a task that requires accurate human body structure and appearance synthesis. We present a two-stage deep learning framework for tacking this problem. In the first stage, given a portrait image with an incomplete human body, we extract a complete, coherent human body structure through a human parsing network, which focuses on structure recovery inside the unknown region with the help of pose estimation. In the second stage, we use an image completion network to fill the unknown region, guided by the structure map recovered in the first stage. For realistic synthesis the completion network is trained with both perceptual loss and conditional adversarial loss. We evaluate our method on public portrait image datasets, and show that it outperforms other state-of-art general image completion methods. Our method enables new portrait image editing applications such as occlusion removal and portrait extrapolation. We further show that the proposed general learning framework can be applied to other types of images, e.g. animal images.

cs.GR↗
arXiv2019

Chow quotients of Grassmannians by diagonal subtori

The literature on maximal torus orbits in the Grassmannian is vast; in this paper we initiate a program to extend this to diagonal subtori. Our main focus is generalizing portions of Kapranov's seminal work on Chow quotient compactifications of these orbit spaces. This leads naturally to discrete polymatroids, generalizing the matroidal framework underlying Kapranov's results. By generalizing the Gelfand-MacPherson isomorphism, these Chow quotients are seen to compactify spaces of arrangements of parameterized linear subspaces, and a generalized Gale duality holds here. A special case is birational to the Chen-Gibney-Krashen moduli space of pointed trees of projective spaces, and we show that the question of whether this birational map is an isomorphism is a specific instance of a much more general question that hasn't previously appeared in the literature, namely, whether the geometric Borel transfer principle in non-reductive GIT extends to an isomorphism of Chow quotients.

math.AG↗
arXiv2019

Optimal Control for the Quantum Simulation of Nuclear Dynamics

We propose a method for enacting the unitary time propagation of two interacting neutrons at leading order of chiral effective field theory by efficiently encoding the nuclear dynamics into a single multi-level quantum device. The emulated output of the quantum simulation shows that, by applying a single gate that draws on the underlying characteristics of the device, it is possible to observe multiple cycles of the nucleons' dynamics before the onset of decoherence. Owing to the signal's longevity, we can then extract spectroscopic properties of the simulated nuclear system. This allows us to validate the encoding of the nuclear Hamiltonian and the robustness of the simulation in the presence of quantum-hardware noise by comparing the extracted spectroscopic information to exact calculations. This work paves the way for transformative calculations of dynamical properties of nuclei on near-term quantum devices.

quant-ph↗
arXiv2019

Multi-Grained Named Entity Recognition

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

cs.CL↗
arXiv2019

Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model

In this paper we consider the problem of computing an $ε$-optimal policy of a discounted Markov Decision Process (DMDP) provided we can only access its transition function through a generative sampling model that given any state-action pair samples from the transition function in $O(1)$ time. Given such a DMDP with states $S$, actions $A$, discount factor $γ\in(0,1)$, and rewards in range $[0, 1]$ we provide an algorithm which computes an $ε$-optimal policy with probability $1 - δ$ where \emph{both} the time spent and number of sample taken are upper bounded by \[ O\left[\frac{|S||A|}{(1-γ)^3 ε^2} \log \left(\frac{|S||A|}{(1-γ)δε} \right) \log\left(\frac{1}{(1-γ)ε}\right)\right] ~. \] For fixed values of $ε$, this improves upon the previous best known bounds by a factor of $(1 - γ)^{-1}$ and matches the sample complexity lower bounds proved in Azar et al. (2013) up to logarithmic factors. We also extend our method to computing $ε$-optimal policies for finite-horizon MDP with a generative model and provide a nearly matching sample complexity lower bound.

math.OC↗
arXiv2018

Kinetic Inductance Traveling Wave Amplifiers For Multiplexed Qubit Readout

We describe a kinetic inductance traveling-wave (KIT) amplifier suitable for superconducting quantum information measurements and characterize its wideband scattering and noise properties. We use mechanical microwave switches to calibrate the four amplifier scattering parameters up to the device input and output connectors at the dilution refrigerator base temperature and a tunable temperature load to characterize the amplifier noise. Finally, we demonstrate the high fidelity simultaneous dispersive readout of two superconducting transmon qubits. The KIT amplifier provides low-noise amplification of both readout tones with readout fidelities of 83% and 89% and negligible effect on qubit lifetime and coherence.

quant-ph↗
arXiv2018

Active protection of a superconducting qubit with an interferometric Josephson isolator

Nonreciprocal microwave devices play several critical roles in high-fidelity, quantum-nondemolition (QND) measurement schemes. They separate input from output, impose unidirectional routing of readout signals, and protect the quantum systems from unwanted noise originated by the output chain. However, state-of-the-art, cryogenic circulators and isolators are disadvantageous in scalable superconducting quantum processors because they use magnetic materials and strong magnetic fields. Here, we realize an active isolator formed by coupling two nondegenerate Josephson mixers in an interferometric scheme. Nonreciprocity is generated by applying a phase gradient between the same-frequency pumps feeding the Josephson mixers, which play the role of the magnetic field in a Faraday medium. To demonstrate the applicability of this Josephson-based isolator for quantum measurements, we incorporate it into the output line of a superconducting qubit, coupled to a fast resonator and a Purcell filter. We also utilize a wideband, superconducting directional coupler for coupling the readout signals into and out of the qubit-resonator system and a quantum-limited Josephson amplifier for boosting the readout fidelity. By using this novel quantum setup, we demonstrate fast, high-fidelity, QND measurements of the qubit while providing more than 20 dB of protection against amplified noise reflected off the Josephson amplifier.

quant-ph↗
arXiv2018

Local Density Estimation in High Dimensions

An important question that arises in the study of high dimensional vector representations learned from data is: given a set $\mathcal{D}$ of vectors and a query $q$, estimate the number of points within a specified distance threshold of $q$. We develop two estimators, LSH Count and Multi-Probe Count that use locality sensitive hashing to preprocess the data to accurately and efficiently estimate the answers to such questions via importance sampling. A key innovation is the ability to maintain a small number of hash tables via preprocessing data structures and algorithms that sample from multiple buckets in each hash table. We give bounds on the space requirements and sample complexity of our schemes, and demonstrate their effectiveness in experiments on a standard word embedding dataset.

cs.DS↗
arXiv2018

Learning to Segment Object Candidates via Recursive Neural Networks

To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for detecting object candidates over scales. Unlike traditional methods that mainly adopt fixed similarity measures for merging regions or finding object proposals, our approach adaptively learns the region merging similarity and the objectness measure during the process of hierarchical region grouping. Specifically, guided by a structured loss, the ReNN model jointly optimizes the cross-region similarity metric with the region merging process as well as the objectness prediction. During inference of the object proposal generation, we introduce randomness into the greedy search to cope with the ambiguity of grouping regions. Extensive experiments on standard benchmarks, e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of producing object proposals with high recall while well preserving the object boundaries and outperforms other existing methods in both accuracy and efficiency.

cs.CV↗
arXiv2018

Enhanced Superconducting Transition Temperature in Electroplated Rhenium

We show that electroplated Re films in multilayers with noble metals such as Cu, Au, and Pd have an enhanced superconducting critical temperature relative to previous methods of preparing Re. The dc resistance and magnetic susceptibility indicate a critical temperature of approximately 6 K. Magnetic response as a function of field at 1.8 K demonstrates type-II superconductivity, with an upper critical field on the order of 2.5 T. Critical current densities greater than 10^7 A/m^2 were measured above liquid-helium temperature. Low-loss at radio frequency was obtained below the critical temperature for multilayers deposited onto resonators made with Cu traces on commercial circuit boards. These electroplated superconducting films can be integrated into a wide range of standard components for low-temperature electronics.

cond-mat.supr-con↗
arXiv2018

Electromagnetically induced transparency in circuit QED with nested polariton states

Electromagnetically induced transparency (EIT) is a signature of quantum interference in an atomic three-level system. By driving the dressed cavity-qubit states of a two-dimensional circuit QED system, we generate a set of polariton states in the nesting regime. The lowest three energy levels are utilized to form the $Λ$-type system. EIT is observed and verified by Akaike's information criterion based testing. Negative group velocities up to $-0.52\pm0.09$ km/s are obtained based on the dispersion relation in the EIT transmission spectrum.

quant-ph↗
arXiv2018

High Coherence Plane Breaking Packaging for Superconducting Qubits

We demonstrate a pogo pin package for a superconducting quantum processor specifically designed with a nontrivial layout topology (e.g., a center qubit that cannot be accessed from the sides of the chip). Two experiments on two nominally identical superconducting quantum processors in pogo packages, which use commercially available parts and require modest machining tolerances, are performed at low temperature (10 mK) in a dilution refrigerator and both found to behave comparably to processors in standard planar packages with wirebonds where control and readout signals come in from the edges. Single- and two-qubit gate errors are also characterized via randomized benchmarking. More detailed crosstalk measurements indicate levels of crosstalk less than -40 dB at the qubit frequencies, opening the possibility of integration with extensible qubit architectures.

quant-ph↗
arXiv2018

Neural Tensor Factorization

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data. However, they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users' preference over time as well as changes in the underlying factors that drive the change in user-item relationship over time. We address these limitations by proposing a Neural Tensor Factorization (NTF) model for predictive tasks on dynamic relational data. The NTF model generalizes conventional tensor factorization from two perspectives: First, it leverages the long short-term memory architecture to characterize the multi-dimensional temporal interactions on relational data. Second, it incorporates the multi-layer perceptron structure for learning the non-linearities between different latent factors. Our extensive experiments demonstrate the significant improvement in rating prediction and link prediction on dynamic relational data by our NTF model over both neural network based factorization models and other traditional methods.

cs.LG↗
arXiv2018

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.

cs.CY↗