Nothing but Being There Matters: Expectancy-Value Motivation Between U.S. and Chinese Middle School Students

Current literature theorizes that culture-induced expectancy beliefs and values in learning may engage learners of varied cultures in differentiated motivational processes. The purpose of the study was to determine the extent to which U.S. and Chinese middle school students differed in expectancy-value motivation in physical education. Middle school students from the U.S. (n = 813, 14 schools) and China (n = 806, 8 schools) provided data on expectancy-value motivation in physical education. A MANOVA with country as the independent factor and grade level as covariate revealed that the U.S. students held higher expectancy beliefs (p =.001, η2=.62), while the Chinese students showed stronger appreciation for the attainment (p =.001, η2=.33) and utility values (p =.001, η2=.35). The students from both countries equally appreciated the intrinsic value (p =.45). A canonical correlation analysis demonstrated that the expectancy-value motivation declined with age/grade increase at the same pace regardless of culture. These findings clarify for us the cultural influence or non-cultural influence on the expectancy-value motivation in middle school students. They inform us about the potential to develop intrinsic-value based across-cultural motivation strategies as well as the cultural sensitivity of applying motivation strategies focusing on expectancy of success, attainment value, and utility value

Multi-mode Cavity Centric Architectures for Quantum Simulation

Near-term quantum computing technologies grapple with huge complexity overheads, hindering their ability to induce algorithms, necessitating engineering and scientific innovations. One class of problems of interest is Quantum Simulation, whereby quantum systems are simulated using a quantum computer. However, current devices are yet to surpass classical tensor network techniques. For problems of interest, where classical simulation techniques fail, large degrees of entanglement are required. Another challenge of implementing quantum simulation problems is that qubits sit idle whilst alternating simulation terms are implemented, exposing the system to decoherence. In the near term, 2D planar superconducting lattices of circuit-QED elements such as the transmon continue to draw substantial attention, but they are hindered by their nearest neighbor topology and relatively short lifespan, two problems that are problematic for quantum simulation. One technology of particular interest is the multi-mode superconducting resonator capable of storing multiple qubits in one device. We observe that these cavities have a natural virtual topology that aligns particularly well with quantum simulation problems, and exhibit much longer lifespans in comparison to other planar superconducting hardware. In this paper we present MUCIC, we discuss the simple integration of these devices into the current landscape and their implications to quantum simulation, motivated by their alignment to the quantum simulation problem, and potential as a quantum memory candidate. We report the development of MUCICs transpiler, leading to reductions of up to 82% in quantum simulation circuit depths. Additionally, our investigation demonstrates improvements of up to 19.4% in converged results from Variational Quantum Algorithms

Quantum Memory: A Missing Piece in Quantum Computing Units

Memory is an indispensable component in classical computing systems. While the development of quantum computing is still in its early stages, current quantum processing units mainly function as quantum registers. Consequently, the actual role of quantum memory in future advanced quantum computing architectures remains unclear. With the rapid scaling of qubits, it is opportune to explore the potential and feasibility of quantum memory across different substrate device technologies and application scenarios. In this paper, we provide a full design stack view of quantum memory. We start from the elementary component of a quantum memory device, quantum memory cells. We provide an abstraction to a quantum memory cell and define metrics to measure the performance of physical platforms. Combined with addressing functionality, we then review two types of quantum memory devices: random access quantum memory (RAQM) and quantum random access memory (QRAM). Building on top of these devices, quantum memory units in the computing architecture, including building a quantum memory unit, quantum cache, quantum buffer, and using QRAM for the quantum input-output module, are discussed. We further propose the programming model for the quantum memory units and discuss their possible applications. By presenting this work, we aim to attract more researchers from both the Quantum Information Science (QIS) and classical memory communities to enter this emerging and exciting area.Comment: 41 pages, 11 figures, 7 table

Enabling Full-Stack Quantum Computing with Changeable Error-Corrected Qubits

Executing quantum applications with quantum error correction (QEC) faces the gate non-universality problem imposed by the Eastin-Knill theorem. As one resource-time-efficient solution, code switching changes the encoding of logical qubits to implement universal logical gates. Unfortunately, it is still unclear how to perform full-stack fault-tolerant quantum computing (FTQC) based on the changeable logical qubit. Specifically, three critical problems remain unsolved: a) how to implement the dynamic logical qubit on hardware; b) how to determine the appropriate timing for logical qubit varying; c) how to improve the overall system performance for programs of different features. To overcome those design problems, We propose CECQ, to explore the large design space for FTQC based on changeable logical qubits. Experiments on various quantum programs demonstrate the effectiveness of CECQ

Optimal Synthesis of Stabilizer Codes via MaxSAT

Quantum Error Correction (QEC) codes are crucial for achieving fault-tolerant quantum computing in the long term. However, efficiently implementing these codes on hardware poses significant challenges, including hardware connectivity matching, efficient circuit scheduling, and fault-tolerance enforcement. In this study, we present an optimal synthesizer that stitches generic stabilizer codes onto diverse hardware structures via MaxSAT. Our evaluation demonstrates (1) the capability of our approach to be applied for various codes and devices and (2) the consistently better efficiency than the best prior heuristic approaches that only target specific QEC codes. By bridging the gap between high-level QEC code design and low-level hardware constraints, this work paves the way toward achieving long-term fault-tolerant quantum computing goals

MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms

The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN systems optimize the computation and communication individually based on the conventional practice of scaling dense DNNs. For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery. To this end, we propose MGG, a novel system design to accelerate full-graph GNNs on multi-GPU platforms. The core of MGG is its novel dynamic software pipeline to facilitate fine-grained computation-communication overlapping within a GPU kernel. Specifically, MGG introduces GNN-tailored pipeline construction and GPU-aware pipeline mapping to facilitate workload balancing and operation overlapping. MGG also incorporates an intelligent runtime design with analytical modeling and optimization heuristics to dynamically improve the execution performance. Extensive evaluation reveals that MGG outperforms state-of-the-art full-graph GNN systems across various settings: on average 4.41X, 4.81X, and 10.83X faster than DGL, MGG-UVM, and ROC, respectively