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In the current noisy intermediate-scale quantum (NISQ) Era, Quantum Computing faces significant challenges due to noise, which severely restricts the application of computing complex algorithms. Superconducting quantum chips, one of the pioneer quantum computation technologies, introduce additional noise when moving qubits to adjacent locations for operation on designated two-qubit gates. The current compilers rely on decision models that either count the swap gates or multiply the gate errors when choosing swap paths at the routing stage. Our research has unveiled the overlooked situations for error propagations through the circuit, leading to accumulations that may affect the final output. In this paper, we propose Error Propagation-Aware Routing (EPAR), designed to enhance the compilation performance by considering accumulated errors in routing. EPAR’s effectiveness is validated through benchmarks on a 27-qubit machine and two simulated systems with different topologies. The results indicate an average success rate improvement of 10% on both real and simulated heavy hex lattice topologies, along with a 16% enhancement in a mesh topology simulation. These findings underscore the potential of EPAR to advance quantum computing in the NISQ era substantially. 

Long Short-Term Memory (LSTM) deep neural networks are diverse in the tasks they can accomplish, such as image captioning and speech recognition. However, they remain susceptible to transient faults when deployed in environments with high-energy particles or radiation. It remains unknown how the potential transient faults will impact LSTM models. Therefore, we investigate the resilience of the weights and biases of these networks through four implementations of the original LSTM network. Based on the observations made through the fault injection of these networks, we propose an effective method of fault mitigation through Hamming encoding of selected weights and biases in a given network. 

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. The primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3× over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel[1], the state-of-the-art heterogeneity-aware scheduler, by 2.5× for the average JCT, and shortens the queuing delay by 13% and improve FTF (Finish-Time-Fairness) by 1.5%.