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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
publications
Accqoc: Accelerating quantum optimal control based pulse generation
Published in 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA), 2020
This paper proposes AccQOC, a comprehensive static/dynamic hybrid workflow to transform gate groups to pulses using Quantum Optimal Control (QOC) with a reasonable compilation time budget, addressing the challenge of large overhead due to long compilation time in quantum optimal control techniques.
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Magneto-Seebeck effect in magnetic tunnel junctions with perpendicular anisotropy
Published in AIP Advances, 2017
This paper analyzes the tunnel magneto-Seebeck effect in magnetic tunnel junctions with perpendicular anisotropy (p-MTJs) under various measurement temperatures, achieving a large tunnel magneto-Seebeck (TMS) ratio up to -838.8% at 200 K.
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CSE: Parallel finite state machines with convergence set enumeration
Published in 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2018
This paper proposes CSE, a Convergence Set based Enumeration based parallel Finite State Machine (FSM), to address the drawbacks of current software and hardware implementations in enumerative FSMs.
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Variational quantum pulse learning
Published in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022
This paper proposes variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks, inspired by the promising performance of variational quantum circuits (VQC).
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Improving quantum classifier performance in nisq computers by voting strategy from ensemble learning
Published in arXiv preprint arXiv:2210.01656, 2022
This study suggests optimizing ensemble quantum classifiers with plurality voting to address the unbalanced confidence distribution in variational quantum classifiers (VQCs) due to quantum noise in Noisy Intermediate-Scale Quantum (NISQ) computers.
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Topgen: Topology-aware bottom-up generator for variational quantum circuits
Published in arXiv preprint arXiv:2210.08190, 2022
This paper proposes a bottom-up approach to generate topology-specific ansatz for Variational Quantum Algorithms (VQA), considering circuit size and real device noise in the ansatz design process.
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Quest: Graph transformer for quantum circuit reliability estimation
Published in arXiv preprint arXiv:2210.16724, 2022
This paper proposes leveraging classical ML to predict noise impact on quantum circuit fidelity, using a graph transformer model to predict the noisy circuit fidelity by embedding each circuit into a graph with gate and noise properties as node features.
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Hybrid gate-pulse model for variational quantum algorithms
Published in 2023 Design Automation Conference (DAC), 2022
This paper presents a hybrid gate-pulse model that addresses the limitations of pure pulse-level frameworks in variational quantum algorithms, such as poor scalability due to large parameter space and lack of gate-level “knowledge”.
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Towards advantages of parameterized quantum pulses
Published in arXiv preprint arXiv:2304.09253, 2023
This study proposes a set of design spaces for parameterized pulses, evaluating these pulses based on metrics such as expressivity, entanglement capability, and effective parameter dimension, demonstrating the advantages of parameterized pulses over gate circuits in terms of duration and performance.
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Fidelity estimator, randomized benchmarking and ZNE for quantum pulses
Published in arXiv preprint arXiv:2305.12597, 2023
This paper proposes using reversed pulses to evaluate the performance of quantum pulses, enabling fidelity estimation without knowledge of the ideal results, and demonstrates the implementation of zero noise extrapolation (ZNE) on pulse programs for variational quantum eigensolver (VQE) tasks.
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Unleashing the potential of llms for quantum computing: A study in quantum architecture design
Published in arXiv preprint arXiv:2307.08191, 2023
This paper explores the opportunities and potentials of current and forthcoming generations of generative pre-trained transformers (GPTs) in assisting the development of noisy intermediate-scale quantum (NISQ) technologies and fault-tolerant quantum computing (FTQC) research.
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Robuststate: Boosting fidelity of quantum state preparation via noise-aware variational training
Published in arXiv preprint arXiv:2311.16035, 2023
This paper presents RobustState, a novel variational quantum state preparation (VQSP) training methodology that combines high robustness with high training efficiency by utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, incorporating real quantum noise into gradient calculations.
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SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE
Published in arXiv preprint arXiv:2311.17423, 2023
This paper explores the integration of parameterized quantum pulses with the contextual subspace method to improve the efficiency and practicality of the Variational Quantum Eigensolver (VQE) by minimizing quantum resource costs and enhancing the potential for processing larger molecular structures.
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NAPA: intermediate-level variational native-pulse ansatz for variational quantum algorithms
Published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024
This paper proposes NAPA, an intermediate-level variational native-pulse ansatz for variational quantum algorithms (VQAs), which exploits the full advantages of VQAs by targeting control pulses instead of gate ansatz, minimizing errors such as over-rotation and under-rotation.
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teaching
CS250 TA in Purdue
Undergraduate course, Purdue University, Department of Computer Science, 2023
I was responsible for conducting the lab sessions, grading assignments, and proctoring the exam. Additionally, I communicated with students to address their questions and concerns. Towards the end of the semester, I received positive feedback from the students for my teaching and support.
CS250 TA in Purdue
Undergraduate course, Purdue University, Department of Computer Science, 2024
I served as the head teaching assistant (TA) for this course, managing and distributing tasks to a team of over 30 graduate and undergraduate TAs. My responsibilities included conducting lab sessions, designing homework assignments, labs, and exams. I also actively engaged with students by answering their questions on the course discussion platform, EDSTEM. In recognition of my outstanding performance and dedication, I was awarded the ACM Graduate TA Award by Purdue University’s Department of Computer Science.