Overview of the Third Global Forum

Computer science continues to grow at a rapid pace,raising the issue of how universities can best adapt to this trend.At the third Global Forum on the Development of Computer Science(GFDCS),five heads of departments of computer science from Asia,Europe,and North America came together to exchange ideas under the theme Challenges and Opportunities of Computer Science in the New Era.Through the discussions,a number of new challenges were explored,including how to meet the growing demand for computer science education,how to manage increased teaching loads,how to foster collaboration between computer science and other disciplines,how to raise ethical awareness,and how to support new“transdisciplinary”modes of education and research.At the same time,there was a consensus on the need to strengthen the role of computer science in other departments,the importance of industrial collaboration,and the need for more scalable approaches to teaching.The evolving role of computer science within the context of broader science was also discussed.

Machine Learning−based Weather Support for the 2022 Winter Olympics

1.A key support for the 2022 Winter Olympics The XXIV Olympic Winter Games are scheduled to take place from 4 to 22 February 2022,followed by the Paralympic Games from 4 to 13 March,in Beijing and towns in the neighboring Hebei Province,China.Weather plays an extremely important role in the outcome of the games(Chen et al.,2018).It can not only cause a difference between a medal or not,but affect the safety of athletes.Success of the Winter Olympics will greatly depend on weather conditions at the outdoor competition venues,dealing with many weather elements including the snow surface temperature,apparent temperature,gust wind speed,snow,visibility,etc.To ensure that the scheduled games go smoothly,it is imperative to have hourly or even every 10-minutely forecasts as well as updated weather-related risk assessments at the venues for the next 240 hours.So far,the Beijing/Hebei Meteorological Observatory has already started intelligent weather forecasting at 3-km resolution based on the results of numerical weather prediction(NWP)models.However,these experiments have suggested that the current forecasting techniques are incapable of capturing the complex mountain weather variations around some venues.The forecasting capability of NWP is constrained partly by limited knowledge of the local weather mechanisms.

Panel Discussion on“Development Trends of Computer Science in the New Era”

At the panel session of the 3rd Global Forum on the Development of Computer Science,attendees had an opportunity to deliberate recent issues affecting computer science departments as a result of the recent growth in the field.6 heads of university computer science departments participated in the discussions,including the moderator,Professor Andrew Yao.The first issue was how universities are managing the growing number of applicants in addition to swelling class sizes.Several approaches were suggested,including increasing faculty hiring,implementing scalable teaching tools,and working closer with other departments through degree programs that integrate computer science with other fields.The second issue was about the position and role of computer science within broader science.Participants generally agreed that all fields are increasingly relying on computer science techniques,and that effectively disseminating these techniques to others is a key to unlocking broader scientific progress.

Healthcare Algorithms by Wearable Inertial Sensors: A Survey

Wearable smart devices, such as smart watch, wristband are becoming increasingly popular recently. They generally integrate the MEMS-designed inertial sensors, including accelerometer, gyroscope and compass, which provide a convenient and inexpensive way to collect motion data of users. Such rich, continuous motion data provide great potential for remote healthcare and decease diagnosis. Information processing algorithms play the critical role in these approaches, which is to extract the motion signatures and to access different kinds of judgements. This paper reviews key algorithms in these areas. In particular, we focus on three kinds of applications: 1) gait analysis; 2) fall detection and 3) sleep monitoring. They are the most popular healthcare applications based on the inertial data. By categorizing and introducing the key algorithms, this paper tries to build a clear map of how the inertial data are processed; how the inertial signatures are defined, extracted, and utilized in different kinds of applications. This will provide a valuable guidance for users to understand the methodologies and to select proper algorithm for specifi c application purpose.

Panel Discussion on“ The Role of Computer Science in University Development Strategy”

At the end of the keynotes during the second Global Forum of Development of Computer Science,a panel discussion was held to encourage further discussion on ways for universities to adapt to the rapidly changing computer science field.Five deans of top computer science departments participated,including the moderator.The discussions were guided along three topics,namely the role of computer science departments in universities today,the nature of computer science as a fundamental discipline or an applied one,and computer science education.Out of these topics,the panelists mainly focused on the interdisciplinary nature of computer science in teaching,research,and industry.The panelists agreed about ways to prepare for the interdisciplinary future,for example by establishing new research centers,introducing projectbased curricula,and collaborating with industry while keeping the campus vibrant.They also pointed out that universities may be under equipped for preparing future professionals to keep up with rapid new advances,especially in machine learning and artificial intelligence.

A survey of multi-modal learning theory

Deep multi-modal learning,a rapidly growing field with a wide range of practical applications,aims to effectively utilize and integrate information from multiple sources,known as modalities.Despite its impressive empirical performance,the theoretical foundations of deep multi-modal learning have yet to be fully explored.In this paper,we will undertake a comprehensive survey of recent developments in multi-modal learning theories,focusing on the fundamental properties that govern this field.Our goal is to provide a thorough collection of current theoretical tools for analyzing multi-modal learning,to clarify their implications for practitioners,and to suggest future directions for the establishment of a solid theoretical foundation for deep multi-modal learning.

Experimental Hamiltonian Learning of an 11-Qubit Solid-State Quantum Spin Register

Learning the Hamiltonian of a quantum system is indispensable for prediction of the system dynamics and realization of high fidelity quantum gates.However,it is a significant challenge to efficiently characterize the Hamiltonian which has a Hilbert space dimension exponentially growing with the system size.Here,we develop and implement an adaptive method to learn the effective Hamiltonian of an 11-qubit quantum system consisting of one electron spin and ten nuclear spins associated with a single nitrogen-vacancy center in a diamond.We validate the estimated Hamiltonian by designing universal quantum gates based on the learnt Hamiltonian and implementing these gates in the experiment.Our experimental result demonstrates a well-characterized 11-qubit quantum spin register with the ability to test quantum algorithms,and shows our Hamiltonian learning method as a useful tool for characterizing the Hamiltonian of the nodes in a quantum network with solid-state spin qubits.

Improved Superconducting Qubit State Readout by Path Interference

High fidelity single shot qubit state readout is essential for many quantum information processing protocols. In superconducting quantum circuit, the qubit state is usually determined by detecting the dispersive frequency shift of a microwave cavity from either transmission or reflection. We demonstrate the use of constructive interference between the transmitted and reflected signal to optimize the qubit state readout, with which we find a better resolved state discrimination and an improved qubit readout fidelity. As a simple and convenient approach, our scheme can be combined with other qubit readout methods based on the discrimination of cavity photon states to further improve the qubit state readout.

Probing a Dissipative Phase Transition with a Trapped Ion through Reservoir Engineering

Dissipation is often considered as a detrimental effect in quantum systems for unitary quantum operations.However,it has been shown that suitable dissipation can be useful resources in both quantum information and quantum simulation.Here,we propose and experimentally simulate a dissipative phase transition(DPT)model using a single trapped ion with an engineered reservoir.We show that the ion’s spatial oscillation mode reaches a steady state after the alternating application of unitary evolution under a quantum Rabi model Hamiltonian and sideband cooling of the oscillator.The average phonon number of the oscillation mode is used as the order parameter to provide evidence for the DPT.Our work highlights the suitability of trapped ions for simulating open quantum systems and shall facilitate further investigations of DPT with various dissipation terms.

Hybrid superconducting photonic-phononic chip for quantum information processing

The integration of qubits with long coherence times and functional quantum devices on a single chip,and thus the realization of an allsolidstate quantum computing chip,is an important goal in current experimental research on quantum information processing.Among various quantum platforms,a series of significant progresses have been made in photonic quantum chips and superconducting quantum chips,while both the number of qubits and the complexity of quantum circuits have been increasing.Although these two chip platforms have respective unique advantages and potentials,their shortcomings have been gradually revealed and need to be solved.By introducing phonon-integrated devices,it is possible to combine all unsuspended phononic,photonic,and superconducting quantum devices organi-cally on the same chip to achieve coherent coupling among them.Here,we provide a prospect and a short review on the integrated photonic,superconducting,and hybrid quantum chips for quantum information processing.

Observation of Topological Links Associated with Hopf Insulators in a Solid-State Quantum Simulator

Hopf insulators are intriguing three-dimensional topological insulators characterized by an integer topological invariant. They originate from the mathematical theory of Hopf fibration and epitomize the deep connection between knot theory and topological phases of matter, which distinguishes them from other classes of topological insulators. Here, we implement a model Hamiltonian for Hopf insulators in a solid-state quantum simulator and report the first experimental observation of their topological properties, including nontrivial topological links associated with the Hopf fibration and the integer-valued topological invariant obtained from a direct tomographic measurement. Our observation of topological links and Hopf fibration in a quantum simulator opens the door to probe rich topological properties of Hopf insulators in experiments. The quantum simulation and probing methods are also applicable to the study of other intricate three-dimensional topological model Hamiltonians.

DeepCPI:A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions(CPIs)in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development.Conventional similarity-or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets.In the present study,we propose Deep CPI,a novel general and scalable computational framework that combines effective feature embedding(a technique of representation learning)with powerful deep learning methods to accurately predict CPIs at a large scale.Deep CPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data.Evaluations of the measured CPIs in large-scale databases,such as Ch EMBL and Binding DB,as well as of the known drug–target interactions from Drug Bank,demonstrated the superior predictive performance of Deep CPI.Furthermore,several interactions among smallmolecule compounds and three G protein-coupled receptor targets(glucagon-like peptide-1 receptor,glucagon receptor,and vasoactive intestinal peptide receptor)predicted using Deep CPI were experimentally validated.The present study suggests that Deep CPI is a useful and powerful tool for drug discovery and repositioning.The source code of Deep CPI can be downloaded from https://github.com/Fangping Wan/Deep CPI.

Experimental study of Forrelation in nuclear spins

Correlation functions are often employed to quantify the relationships among interdependent variables or sets of data.Recently,a new class of correlation functions,called FORRELATION,has been introduced by Aaronson and Ambainis for studying the query complexity of quantum devices.It was found that there exists a quantum query algorithm solving 2-fold FORRELATION problems with an exponential quantum speedup over all possible classical means,which represents essentially the largest possible separation between quantum and classical query complexities.Here we report an experimental study probing the2-fold and 3-fold FORRELATIONS encoded in nuclear spins.The major experimental challenge is to control the spin fluctuation to within a threshold value,which is achieved by developing a set of optimized GRAPE pulse sequences.Overall,our small-scale implementation indicates that the quantum query algorithm is capable of determining the values of FORRELATIONS within an acceptable accuracy required for demonstrating quantum supremacy,given the current technology and in the presence of experimental noise.

Low-carbon technology calls for comprehensive electricitymarket redesign

The energy transition also calls for electricitymarket redesign. Low-carbon technologies will fundamentally reshape the electricity sector. The electricity generation and demand will be significantly unpredictable and uncontrollable thus require for a more sophisticated system operation to guarantee the grid stability and reliability. The higher difficulty induced by the green-technology penetration expose the electricity-market to a higher marketfailure risk. Thus, the future low-carbon electricity-market and associated regulation scheme require a comprehensive new design.

Recent advances for quantum classifiers

Machine learning has achieved dramatic success in a broad spectrum of applications.Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications,giving rise to an emergent research frontier of quantum machine learning.Along this line,quantum classifiers,which are quantum devices that aim to solve classification problems in machine learning,have attracted tremendous attention recently.In this review,we give a relatively comprehensive overview for the studies of quantum classifiers,with a focus on recent advances.First,we will review a number of quantum classification algorithms,including quantum support vector machines,quantum kernel methods,quantum decision tree classifiers,quantum nearest neighbor algorithms,and quantum annealing based classifiers.Then,we move on to introduce the variational quantum classifiers,which are essentially variational quantum circuits for classifications.We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem,where the training of quantum classifiers might be hindered by the exponentially vanishing gradient.In addition,the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

Approximation Algorithms for Stochastic Combinatorial Optimization Problems

Stochastic optimization has established itself as a major method to handle uncertainty in various optimization problems by modeling the uncertainty by a probability distribution over possible realizations.Traditionally,the main focus in stochastic optimization has been various stochastic mathematical programming(such as linear programming,convex programming).In recent years,there has been a surge of interest in stochastic combinatorial optimization problems from the theoretical computer science community.In this article,we survey some of the recent results on various stochastic versions of classical combinatorial optimization problems.Since most problems in this domain are NP-hard(or#P-hard,or even PSPACE-hard),we focus on the results which provide polynomial time approximation algorithms with provable approximation guarantees.Our discussions are centered around a few representative problems,such as stochastic knapsack,stochastic matching,multi-armed bandit etc.We use these examples to introduce several popular stochastic models,such as the fixed-set model,2-stage stochastic optimization model,stochastic adaptive probing model etc,as well as some useful techniques for designing approximation algorithms for stochastic combinatorial optimization problems,including the linear programming relaxation approach,boosted sampling,content resolution schemes,Poisson approximation etc.We also provide some open research questions along the way.Our purpose is to provide readers a quick glimpse to the models,problems,and techniques in this area,and hopefully inspire new contributions.

Greenberger-Horne-Zeilinger test for multi-dimension and arbitrary time nodes entangled histories

Quantum entanglement,since proposed by Einstein,Podolsky and Rosen(EPR)[1]and further explored by Schr?dinger[2]in1935,has always been the focus of quantum physics realm.The EPR paradox revealed the conflict between quantum theory and local realism.Almost 30 years later,in 1964,Bell first came up with the prototype of a family of inequalities,which was later called Bell inequality[3,4],to express certain limitation that every local clas-

Quantum enhanced convolutional neural networks for NISQ computers

The interplay between quantum physics and machine learning may lead to unprecedented perspectives for both fields [1]. On the one hand, ideas and techniques from machine learning, or more broadly artificial intelligence, can be exploited to tackle challenging problems in the quantum domain.

Scalable fast benchmarking for individual quantum gates with local twirling

With the development of controllable quantum systems,fast and practical characterization of multi-qubit gates has become essential for building high-fidelity quantum computing devices.The usual way to fulfill this requirement via randomized benchmarking demands complicated implementation of numerous multi-qubit twirling gates.How to efficiently and reliably estimate the fidelity of a quantum process remains an open problem.This work thus proposes a character-cycle benchmarking protocol and a character-average benchmarking protocol using only local twirling gates to estimate the process fidelity of an individual multi-qubit operation.Our protocols were able to characterize a large class of quantum gates including and beyond the Clifford group via the local gauge transformation,which forms a universal gate set for quantum computing.We demonstrated numerically our protocols for a non-Clifford gate—controlled-(T X)and a Clifford gate—five-qubit quantum errorcorrecting encoding circuit.The numerical results show that our protocols can efficiently and reliably characterize the gate process fidelities.Compared with the cross-entropy benchmarking,the simulation results show that the character-average benchmarking achieves three orders of magnitude improvements in terms of sampling complexity.

Clinically applicable Gleason grading(GD)system for prostate cancer based on deep learning

To the Editor:Prostate cancer is one of the most common malignant tumors of the male genital system,with approximately 1.1 million new cases in 2012.[1]The accurate diagnosis of prostate cancer leads to a better chance of successful treatment when it is still confined to the prostate gland.The Gleason grading(GD)system was first established by Donald Gleason during 1966 to 1974.[2,3]The Gleason pattern ranges from 1 to 5.A higher score corresponds to poorer differentiation,which indicates a worse prognosis and higher metastasis possibility.The total score is calculated with the first half of the dominant Gleason pattern and the second half based on the non-dominant one.