School of Engineering Department of Electronic and Computer Engineering 145 EDA for Superconducting Quantum Computing Supervisor: SHAO Qiming / ECE Student: NGUYEN Viet Phong / PHYS Course: UROP 1000, Summer Quantum computing is a kind of computer that leverage properties of quantum mechanics in storing information and computation. In quantum computing, the basic unit of information is qubit, which can simultaneously exist in both state of 0 and 1, due to a property known as superposition of quantum mechanics. In addition, another important concept in quantum computing is entanglement. When qubits become entangled, their states become interconnected, and hence it is able to manipulate qubits by only a single operation, instead of manipulating each bit individually as in classical computer. Because of these unique features, quantum computing demonstrates advantages in specialized tasks that require complex computations over classical computing. Some fields that quantum computing can have significant impacts are cryptography, machine learning, drug design, and beyond. At this moment, superconducting qubit devices are one of the most promising platforms for implementation of quantum computing because they are macroscopic in size and lithography defined. To scale up the system, electronic design automation (EDA) tools are essential to reduce cycle time and improve yields. In this project, I explore the state of art of the EDA for superconducting quantum computing and using IBM Qiskit Metal, an open-source project for quantum computing EDA, to design simple quantum circuits and do analysis by different methods. The organization of this report is as follow: section II gives an overview of circuit quantization methods, the section III is about design and anlysis of quantum circuits with Qiskit Metal and section IV is the references. Embedded Systems for AI Hardware Supervisor: SHAO Qiming / ECE Student: JIANG Yicheng / CPEG Course: UROP 3200, Fall In this UROP3200 project, I engaged in research related to AI hardware and explored algorithms aimed at reducing computational costs in the context of limited computing power. Additionally, I participated in the 2023 ACM/IEEE TinyML Design Contest at ICCAD, focusing on the algorithmic aspect. The contest challenged participants to design and implement an open-source algorithm capable of automatically distinguishing lifethreatening ventricular arrhythmias (VAs) from intracardiac electrogram (IEGM) recordings using a resourceconstrained microcontroller unit (MCU) platform. The evaluation criteria included detection precision, memory utilization, and inference latency. In this progress report, I present the methodology and results of my algorithm design, its limitations, and future direction related to AI hardware.
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