Scaling bottlenecks the making of digital quantum computers, posing challenges from both the quantum and the classical components. We present a classical architecture to cope with a comprehensive list of the latter challenges all at once , and implement it fully in an end-to-end system by integrating a multi-core RISC-V CPU with our in-house control electronics. Our architecture enables scalable, high-precision control of large quantum processors and accommodates evolving requirements of quantum hardware. A central feature is a microarchitecture executing quantum operations in parallel on arbitrary predefined qubit groups. Another key feature is a reconfigurable quantum instruction set that supports easy qubit re-grouping and instructions extensions. As a demonstration, we implement the widely-studied surface code quantum computing workflow, which is instructive for being demanding on both the controllers and the integrated classical computation. Our design, for the first time, reduces instruction issuing and transmission costs to constants, which do not scale with the number of qubits, without adding any overheads in decoding or dispatching. Our system uses a dedicated general-purpose CPU for both qubit control and classical computation, including syndrome decoding. Implementing recent theoretical proposals as decoding firmware that parallelizes general inner decoders, we can achieve unprecedented decoding capabilities of up to distances 47 and 67 with the currently available systems-on-chips for physical error rate p = 0.001 and p = 0.0001, respectively, all in just 1 μs.

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness hyperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.

In this paper, we study a new entity linking problem where both the entity mentions and the target entities are within a same social media platform.Compared with traditional entity linking problems that link mentions to a knowledge base, this new problem have less information about the target entities.However, if we can successfully link mentions to entities within a social media platform, we can improve a lot of applications such as comparative study in business intelligence and opinion leader finding.To study this problem, we constructed a dataset called Yelp-EL, where the business mentions in Yelp reviews are linked to their corresponding businesses on the platform.We conducted comprehensive experiments and analysis on this dataset with a learning to rank model that takes different types of features as input, as well as a few state-of-theart entity linking approaches.Our experimental results show that two types of features that are not available in traditional entity linking: social features and location features, can be very helpful for this task.

Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.

We propose a system that controls the spatial distribution of odors in an environment by generating electronically steerable ultrasound-driven narrow air flows. The proposed system is designed not only to remotely present a preset fragrance to a user, but also to provide applications that would be conventionally inconceivable, such as: 1) fetching the odor of a generic object placed at a location remote from the user and guiding it to his or her nostrils, or 2) nullifying the odor of an object near a user by carrying it away before it reaches his or her nostrils (Fig. 1). These are all accomplished with an ultrasound-driven air stream serving as an airborne carrier of fragrant substances. The flow originates from a point in midair located away from the ultrasound source and travels while accelerating and maintaining its narrow cross-sectional area. These properties differentiate the flow from conventional jet- or fan-driven flows and contribute to achieving a midair flow. In our system, we employed a phased array of ultrasound transducers so that the traveling direction of the flow could be electronically and instantaneously controlled. In this paper, we describe the physical principle of odor control, the system construction, and experiments conducted to evaluate remote fragrance presentation and fragrance tracking.