Fan Chen

Noisy Intermediate-Scale Quantum (NISQ) devices have begun to exhibit early quantum advantages on classically intractable problems, spanning physics simulations to Gaussian boson sampling. Yet, realizing these benefits remains challenging for non-experts, primarily due to the complexities of programming in Open Quantum Assembly Language (OpenQASM). Although Large Language Model (LLM)-based agents have shown promise in automating classical programming workflows, their quantum counterparts have largely been restricted to specialized tasks such as quantum chemistry or error correction. In this paper, we present QAgent, an LLM-powered multi-agent system that fully automates OpenQASM programming. By integrating task planning, in-context few-shot learning, retrieval-augmented generation (RAG) for long-term context, predefined generation tools, and chain-of-thought (CoT) reasoning, the agents systematically improve both compilation and functional correctness. Our evaluations demonstrate substantial improvements: across multiple LLMs of varying sizes, QAgent enhances the accuracy of QASM code generation by 71.6\% compared to previous static LLM-based approaches. We envision this multi-agent system as a key enabler for democratizing quantum programming, bridging expertise gaps, and accelerating the practical adoption of quantum computing.

Variational Quantum Circuits (VQCs) have emerged as a powerful quantum computing paradigm, demonstrating a scaling advantage for problems intractable for classical computation. As VQCs require substantial resources and specialized expertise for their design, they represent significant intellectual properties (IPs). However, existing quantum circuit watermarking techniques suffer from two primary drawbacks: (1) watermarks can be removed during re-compilation of the circuits, and (2) these methods significantly increase task loss due to the extensive length of the inserted watermarks across multiple compilation stages. To address these challenges, we propose BVQC, a backdoor-based watermarking technique for VQCs that preserves the original loss in typical execution settings, while deliberately increasing the loss to a predefined level during watermark extraction. Additionally, BVQC employs a grouping algorithm to minimize the watermark task's interference with the base task, ensuring optimal accuracy for the base task. BVQC retains the original compilation workflow, ensuring robustness against re-compilation. Our evaluations show that BVQC greatly reduces Probabilistic Proof of Authorship (PPA) changes by 9.89e-3 and ground truth distance (GTD) by 0.089 compared to prior watermarking technologies.

The recent success of Quantum Neural Networks (QNNs) prompts model extraction attacks on cloud platforms, even under black-box constraints. These attacks repeatedly query the victim QNN with malicious inputs. However, existing extraction attacks tailored for classical models yield local substitute QNNs with limited performance due to NISQ computer noise. Drawing from bagging-based ensemble learning, which uses independent weak learners to learn from noisy data, we introduce a novel QNN extraction approach. Our experimental results show this quantum ensemble learning approach improves local QNN accuracy by up to 15.09% compared to previous techniques.

Modern data centers suffer from a growing carbon footprint due to insufficient support for environmental sustainability. While hardware accelerators and renewable energy have been utilized to enhance sustainability, addressing Quality of Service (QoS) degradation caused by renewable energy supply and hardware recycling remains challenging: (1) prior accelerators exhibit significant carbon footprints due to limited reconfigurability and inability to adapt to renewable energy fluctuations; (2) integrating recycled NAND flash chips in data centers poses challenges due to their short lifetime, increasing energy consumption; (3) the absence of a sustainability estimator impedes data centers and users in evaluating and improving their environmental impact. This study aims to improve system support for environmentally sustainable data centers by proposing a reconfigurable hardware accelerator for intensive computing primitives and developing a fractional NAND flash cell to extend the lifetime of recycled flash chips while supporting graceful capacity degradation. We also introduce a sustainability estimator to evaluate user task energy consumption and promote sustainable practices. We present our preliminary results and recognize this as an ongoing initiative with significant potential to advance environmentally sustainable computing in data centers and stimulate further exploration in this critical research domain.

Quantum computing (QC) demonstrates substantial theoretical promise in addressing classically intractable problems. Recent investments and advancements across QC system stacks, including hardware, software, and algorithms, underscore a pivotal shift from theoretical exploration to the practical realization of applications. Despite this progress, the prevailing emphasis has predominantly centered on performance enhancement, often overlooking security considerations. In response to this gap, our article presents a comprehensive tutorial and survey aimed at identifying and categorizing vulnerabilities inherent in quantum computing systems. Beginning with an overview encompassing essential principles, ecosystem components, and unique attributes in the quantum computing system stack, we also provide a summary of development resources to facilitate efficient initiation in this domain. Building on this foundational knowledge, we introduce a taxonomy of QC security organized by victim layer and security attack objectives. Utilizing this taxonomy as a guiding framework, the article delivers an extensive survey of the latest advancements in QC security, with the overarching goal of equipping the reader with a comprehensive understanding of quantum computing system principles and an informed awareness of diverse and dynamic QC security threats. The intention is to benefit both industry stakeholders and research communities, ultimately aiming to proactively identify and mitigate security concerns within QC systems, thereby establishing a robust foundation for secure quantum computing environments.

In October 2023, Fan Chen, an Associate Editor at IEEE Circuits and Systems Magazine , had the privilege of interviewing Dr. Nicky Lu, an icon in IC design and the semiconductor industry. This engaging interview covers Dr. Lu’s educational journey, illustrious career, perspectives on research, innovation, entrepreneurship, and his forward-looking vision, along with invaluable advice for the younger generation. We’re confident our CAS readers will find this conversation both enlightening and enjoyable!

Resistive random access memory (ReRAM) is a promising technology that can perform low-cost and in-situ matrix-vector multiplication (MVM) in analog domain. Scientific computing requires high-precision floating-point (FP) processing. However, performing floating-point computation in ReRAM is challenging because of high hardware cost and execution time due to the large FP value range. In this work we present ReFloat, a data format and an accelerator architecture, for low-cost and high-performance floating-point processing in ReRAM for iterative linear solvers. ReFloat matches the ReRAM crossbar hardware and represents a block of FP values with reduced bits and an optimized exponent base for a high range of dynamic representation. Thus, ReFloat achieves less ReRAM crossbar consumption and fewer processing cycles and overcomes the noncovergence issue in a prior work. The evaluation on the SuiteSparse matrices shows ReFloat achieves 5.02× to 84.28× improvement in terms of solver time compared to a state-of-the-art ReRAM based accelerator.