Travis S. Humble

EDBT 2026 Demo / reviewers in the wild / expert

dblp:134/5278 · DBLP profile ↗

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24ranked-venue papers

2as first author

19since 2021 · last 2026

0000-0002-9449-0498ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 19 · 15 since 2021Software engineering, systems software and programming languages · 5 · 5 since 2021Theory of computation · 4 · 2 first-author · 3 since 2021

Year	Publication	Venue	Position
2026	iSwitch: QEC on Demand via In-Situ Encoding of Bare Qubits for Ion Trap Architectures abstract Recent advances in quantum hardware and error correction have paved the way for early fault-tolerant (EFT) quantum computing. We propose iSwitch, a hybrid system architecture for trapped-ion quantum computers (TIQC) that exploits ultra-high-fidelity single-qubit gates and efficient logical CNOTs enabled by ion shuttling. iSwitch employs bare qubits for single-qubit operations and QEC-encoded logical qubits for two-qubit gates, avoiding full logical encoding, gate synthesis, and magic state distillation. To enable this selective encoding, we develop a low-noise conversion protocol between bare and logical qubits, a hybrid instruction set tailored to 2D TIQC layouts, and a compiler that minimizes conversion overhead and optimizes scheduling. Evaluations on variational quantum algorithm benchmarks show that iSwitch achieves comparable fidelity to conventional QEC methods, while reducing qubit and operation counts by roughly 33–50%, offering a practical, resource-efficient path toward EFT quantum computing on trapped-ion platforms. Keyi Yin, Eneet Kaur, Reza Nejabati, Hartmut Haeffner, Wes Campbell, Eric R. Hudson, Jens Palsberg, Travis S. Humble, Yufei Ding 0001	ASPLOS (2)	11
2026	Scalability Analysis of Quantum Models for Stress and Emotion Detection abstract Stress and emotion detection from high-dimensional physiological signals is a challenging task, particularly when aiming for accurate classification across diverse behavioral states. Quantum machine learning (QML) is promising for modeling such high-dimensional data, but scalability is limited by qubit resources and the exponential cost of classical statevector simulation. This work studies the scalability of quantum support vector machines (QSVMs) for binary stress detection and three-class emotion recognition (Negative/Neutral/Positive) under varying qubit counts and angle-encoding strategies. We also present a comparison study with one-feature-per-qubit (1:1) and two-features-per-qubit (2:1) mappings. Experiments are executed on HPC infrastructure using NVIDIA CUDA-Q to evaluate performance, variance, and class-dependent separability at higher-qubit setups. Results show that larger Hilbert spaces can improve peak accuracy but may increase instability. At the same time, dense 2:1 encoding yields more consistent stress detection performance. For emotion recognition, scaling improves discrimination for classes like Negative and Positive more than Neutral. We find that effective QML scaling is task-dependent and benefits more from encoding design than simply increasing qubit count. Md. Saif Hassan Onim, Travis S. Humble, Himanshu Thapliyal	ACM Great Lakes Symposium on VLSI	2
2026	The role of quantum computing in advancing scientific high-performance computing: A perspective from the ADAC institute Gilles Buchs, Thomas L. Beck, Ryan S. Bennink, Daniel Claudino, Andrea Delgado 0002, Nur Aiman Fadel, Peter Groszkowski, Kathleen E. Hamilton, Travis S. Humble, Ang Li 0006, Phillip C. Lotshaw, Olli Mukkula, Ryousei Takano, In-Saeng Suh, Miwako Tsuji, Roel Van Beeumen, Ugo Varetto, Kazuya Yamazaki, Mikael P. Johansson	Future Gener. Comput. Syst.	9
2026	Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise abstract Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. In this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach. Jinyang Li 0001, Samudra Dasgupta, Yuhong Song, Lei Yang 0018, Travis S. Humble, Weiwen Jiang	IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.	5
2025	PowerMove: Optimizing Compilation for Neutral Atom Quantum Computers with Zoned Architecture abstract Neutral atom quantum computers (NAQCs) have emerged as promising candidates for scalable quantum computing, thanks to their advanced hardware capabilities, particularly qubit movement and the Zoned Architecture (ZA). However, fully harnessing these features presents significant compilation challenges, requiring careful coordination across gate scheduling, qubit positioning, atom movement, and inter-zone communication. In this paper, we propose PowerMove, an efficient compiler for NAQCs that unlocks new optimization opportunities, significantly improving qubit movement strategies while seamlessly integrating ZA. Our evaluation demonstrates orders-of-magnitude fidelity improvements over state-of-the-art methods, with execution time reduced by up to 3.76× and compilation time accelerated by up to 216.9× across various NISQ applications. Furthermore, PowerMove extends naturally to the fault-tolerant quantum computing (FTQC) setting, where physical qubits are replaced by logical qubits encoded in QEC codes, achieving a 4.78× reduction in execution time. These results highlight PowerMove's impact on both near-term NISQ applications and long-term FTQC implementations. We have open-sourced our codes at https://github.com/Scarlett0815/PowerMove to facilitate further research and collaboration within the community. Jixuan Ruan, Hezi Zhang, Ang Li 0006, Travis S. Humble, Yufei Ding 0001	ASPLOS (3)	5
2025	QECC-Synth: A Layout Synthesizer for Quantum Error Correction Codes on Sparse Architectures abstract Quantum Error Correction (QEC) codes are essential for achieving fault-tolerant quantum computing (FTQC). However, their implementation faces significant challenges due to disparity between required dense qubit connectivity and sparse hardware architectures. Current approaches often either underutilize QEC circuit features or focus on manual designs tailored to specific codes and architectures, limiting their capability and generality. In response, we introduce QECC-Synth, an automated compiler for QEC code implementation that addresses these challenges. We leverage the ancilla bridge technique tailored to the requirements of QEC circuits and introduces a systematic classification of its design space flexibilities. We then formalize this problem using the MaxSAT framework to optimize these flexibilities. Evaluation shows that our method significantly outperforms existing methods while demonstrating broader applicability across diverse QEC codes and hardware architectures. Keyi Yin, Hezi Zhang, Yunong Shi, Travis S. Humble, Ang Li 0006, Yufei Ding 0001	ASPLOS (1)	5
2025	ASDF: A Compiler for Qwerty, a Basis-Oriented Quantum Programming Language abstract Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers. Austin J. Adams, Sharjeel Khan, Arjun S. Bhamra, Ryan R. Abusaada, Anthony M. Cabrera, Cameron C. Hoechst, Travis S. Humble, Jeffrey Young 0001, Thomas M. Conte	CGO	7
2025	CaliQEC: In-situ Qubit Calibration for Surface Code Quantum Error Correction abstract Quantum Error Correction (QEC) is essential for fault-tolerant, large-scale quantum computation.However, error drift in qubits undermines QEC performance during long computations, necessitating frequent calibration.Conventional calibration methods disrupt quantum states, requiring system downtime and rendering in situ calibration impractical.To address this challenge, we propose QECali, a novel framework that enables in situ calibration for surface codes.Our evaluation demonstrates that QECali introduces modest qubit overhead and negligible increases in execution time, offering the first practical solution for in situ calibration in surface code based quantum computation. Keyi Yin, Jixuan Ruan, Dean Tullsen, Zhiding Liang, Andrew Sornborger, Ang Li 0006, Travis S. Humble, Yufei Ding 0001, Yunong Shi	ISCA	9
2025	OneAdapt: Resource-Adaptive Compilation of Measurement-Based Quantum Computing for Photonic Hardware Hezi Zhang, Jixuan Ruan, Dean Tullsen, Yufei Ding 0001, Ang Li 0006, Travis S. Humble	MICRO	6
2025	A Digital Twin of Scalable Quantum Clouds Waylon Luo, Betis Baheri, Travis S. Humble, Jiapeng Zhao, Tong Zhan, Rajan Maharjan, Qiang Guan	SIGSIM-PADS	3
2025	A cross-platform execution engine for the quantum intermediate representation abstract Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution. Vicente Leyton-Ortega, Daniel Claudino, Seth R. Johnson, Austin J. Adams, Sharmin Afrose, Meenambika Gowrishankar, Anthony M. Cabrera, Travis S. Humble	J. Supercomput.	9
2024	Surf-Deformer: Mitigating Dynamic Defects on Surface Code via Adaptive Deformation abstract In this paper, we introduce Surf-Deformer, a code deformation framework that seamlessly integrates adaptive defect mitigation functionality into the current surface code workflow. It crafts several basic deformation instructions based on fundamental gauge transformations, which can be combined to explore a larger design space than previous methods. This enables more optimized deformation processes tailored to specific defect situations, restoring the QEC capability of deformed codes more efficiently with minimal qubit resources. Additionally, we design an adaptive code layout that accommodates our defect mitigation strategy while ensuring efficient execution of logical operations. Our evaluation shows that Surf-Deformer outperforms previous methods by significantly reducing the end-to-end failure rate of various quantum programs by 35× to 70×, while requiring only about 50% of the qubit resources compared to the previous method to achieve the same level of failure rate. Ablation studies show that Surf-Deformer surpasses previous defect removal methods in preserving QEC capability and facilitates surface code communication by achieving nearly optimal throughnut. Keyi Yin, Travis S. Humble, Ang Li 0006, Yunong Shi, Yufei Ding 0001	MICRO	3
2024	Quantum-centric supercomputing for materials science: A perspective on challenges and future directions Yuri Alexeev, Maximilian Amsler, Marco Antonio Barroca, Sanzio Bassini, Torey Battelle, Daan Camps, David Casanova, Young Jay Choi, Fred Chong, Charles Chung, Christopher Codella, Antonio D. Córcoles, James Cruise, Alberto Di Meglio, Ivan Duran, Thomas Eckl, Sophia E. Economou, Stephan J. Eidenbenz, Bruce Elmegreen, Clyde Fare, Ismael Faro, Cristina Sanz Fernández, Rodrigo Neumann Barros Ferreira, Keisuke Fuji, Bryce Fuller, Laura Gagliardi, Giulia Galli, Jennifer R. Glick, Isacco Gobbi, Pranav Gokhale, Salvador de la Puente Gonzalez, Johannes Greiner, William Gropp, Michele Grossi, Emanuel Gull, Burns Healy, Matthew R. Hermes, Benchen Huang, Travis S. Humble, Nobuyasu Ito, Artur F. Izmaylov, Ali Javadi-Abhari, Douglas M. Jennewein, Shantenu Jha, Bert de Jong, Petar Jurcevic, William M. Kirby, Stefan Kister, Masahiro Kitagawa, Joel Klassen, Katherine Klymko, Kwangwon Koh, Masaaki Kondo, Doga Murat Kürkçüoglu, Krzysztof Kurowski, Teodoro Laino, Ryan Landfield, Matthew L. Leininger, Vicente Leyton-Ortega, Ang Li 0006, Meifeng Lin, Junyu Liu, Nicolás Lorente, André Luckow, Simon Martiel, Francisco Martín-Fernández, Margaret Martonosi, Claire Marvinney, Arcesio Castañeda Medina, Dirk Merten, Antonio Mezzacapo, Kristel Michielsen, Abhishek Mitra, Tushar Mittal, Kyungsun Moon, Joel Moore, Sarah Mostame, Mario Motta, Young-Hye Na, Yunseong Nam, Prineha Narang, Yu-ya Ohnishi, Daniele Ottaviani, Matthew Otten, Scott Pakin, Vincent R. Pascuzzi, Edwin Pednault, Tomasz Piontek, Jed W. Pitera, Patrick Rall, Gokul Subramanian Ravi, Niall Robertson, Matteo A. C. Rossi, Piotr Rydlichowski, Hoon Ryu, Georgy Samsonidze, Mitsuhisa Sato, Nishant Saurabh, Kunal Sharma, Soyoung Shin, George Slessman, Mathias Steiner, Iskandar Sitdikov, In-Saeng Suh, Eric D. Switzer, Joel Thompson, Synge Todo, Minh C. Tran, Dimitar Trenev, Christian Trott, Huan-Hsin Tseng, Norm M. Tubman, Esin Tureci, David García Valiñas, Sofia Vallecorsa, Christopher Wever, Konrad W. Wojciechowski, Xiaodi Wu 0001, Shinjae Yoo, Nobuyuki Yoshioka, Victor Wen-zhe Yu, Seiji Yunoki, Sergiy Zhuk, Dmitry Zubarev	Future Gener. Comput. Syst.	39
2024	Integrating quantum computing resources into scientific HPC ecosystems Thomas L. Beck, Alessandro Baroni 0003, Ryan S. Bennink, Gilles Buchs, Eduardo Antonio Coello Pérez, Markus Eisenbach 0002, Rafael Ferreira da Silva, Muralikrishnan Gopalakrishnan Meena, Kalyana C. Gottiparthi, Peter Groszkowski, Travis S. Humble, Ryan Landfield, Ketan Maheshwari, Sarp Oral, Michael A. Sandoval, Amir Shehata, In-Saeng Suh, Christopher Zimmer 0001	Future Gener. Comput. Syst.	11
2024	Impact of Unreliable Devices on Stability of Quantum Computations abstract Noisy intermediate-scale quantum (NISQ) devices are valuable platforms for testing the tenets of quantum computing, but these devices are susceptible to errors arising from de-coherence, leakage, cross-talk, and other sources of noise. This raises concerns regarding the stability of results when using NISQ devices since strategies for mitigating errors generally require well-characterized and stationary error models. Here, we quantify the reliability of NISQ devices by assessing the necessary conditions for generating stable results within a given tolerance. We use similarity metrics derived from device characterization data to derive and validate bounds on the stability of a 5-qubit implementation of the Bernstein-Vazirani algorithm. Simulation experiments conducted with noise data from IBM washington, spanning January 2022 to April 2023, revealed that the reliability metric fluctuated between 41% and 92%. This variation significantly surpasses the maximum allowable threshold of 2.2% needed for stable outcomes. Consequently, the device proved unreliable for consistently reproducing the statistical mean in the context of the Bernstein-Vazirani circuit. Samudra Dasgupta, Travis S. Humble	ACM Trans. Quantum Comput.	2
2023	TDAG: Tree-based Directed Acyclic Graph Partitioning for Quantum Circuits abstract We propose the Tree-based Directed Acyclic Graph (TDAG) partitioning for quantum circuits, a novel quantum circuit partitioning method which partitions circuits by viewing them as a series of binary trees and selecting the tree containing the most gates. TDAG produces results of comparable quality (number of partitions) to an existing method called ScanPartitioner (an exhaustive search algorithm) with an 95% average reduction in execution time. Furthermore, TDAG improves compared to a faster partitioning method called QuickPartitioner by 38% in terms of quality of the results with minimal overhead in execution time. Joseph Clark, Travis S. Humble, Himanshu Thapliyal	ACM Great Lakes Symposium on VLSI	2
2023	Noise-Resilient and Reduced Depth Approximate Adders for NISQ Quantum Computing abstract The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics. Bhaskar Gaur, Travis S. Humble, Himanshu Thapliyal	ACM Great Lakes Symposium on VLSI	2
2023	Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut abstract Finding high-quality parameters is a central obstacle to using the quantum approximate optimization algorithm (QAOA). Previous work partially addresses this issue for QAOA on unweighted MaxCut problems by leveraging similarities in the objective landscape among different problem instances. However, we show that the more general weighted MaxCut problem has significantly modified objective landscapes, with a proliferation of poor local optima. Our main contribution is a simple rescaling scheme that overcomes these deleterious effects of weights. We show that for a given QAOA depth, a single “typical” vector of QAOA parameters can be successfully transferred to weighted MaxCut instances. This transfer leads to a median decrease in the approximation ratio of only 2.0 percentage points relative to a considerably more expensive direct optimization on a dataset of 34,701 instances with up to 20 nodes and multiple weight distributions. This decrease can be reduced to 1.2 percentage points at the cost of only 10 additional QAOA circuit evaluations with parameters sampled from a pretrained metadistribution, or the transferred parameters can be used as a starting point for a single local optimization run to obtain approximation ratios equivalent to those achieved by exhaustive optimization in 96.35% of our cases. Ruslan Shaydulin, Phillip C. Lotshaw, Jeffrey Larson 0001, James Ostrowski 0001, Travis S. Humble	ACM Trans. Quantum Comput.	5
2021	Editorial on Celebrating Quantum Computing with ACM abstract No abstract available. Travis S. Humble, Mingsheng Ying	ACM Trans. Quantum Comput.	1
2020	Inaugural Issue Editorial for ACM Transactions on Quantum Computing abstract No abstract available. Travis S. Humble, Mingsheng Ying	ACM Trans. Quantum Comput.	1
2018	Sparse Hardware Embedding of Spiking Neuron Systems for Community Detection abstract We study the applicability of spiking neural networks and neuromorphic hardware for solving general opti- mization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems to construct a fully connected spiking neural system to generate synchronous spike responses indicative of the underlying community structure in an undirected, unweighted graph. Mapping this fully connected system to current generation neuromorphic hardware is done by embedding sparse tree graphs to generate only the leading-order spiking dynamics. We demonstrate that for a chosen set of benchmark graphs, the spike responses generated on a current generation neuromorphic processor can improve the stability of graph partitions and non-overlapping communities can be identified even with the loss of higher-order spiking behavior if the graphs are sufficiently dense. For sparse graphs, the loss of higher-order spiking behavior improves the stability of certain graph partitions but does not retrieve the known community memberships. Kathleen E. Hamilton, Neena Imam, Travis S. Humble	ACM J. Emerg. Technol. Comput. Syst.	3
2017	High-Performance Computing with Quantum Processing Units abstract The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for functional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that assumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. We also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance. Keith A. Britt, Travis S. Humble	ACM J. Emerg. Technol. Comput. Syst.	2
2012	Concurrent FFT computing on multicore processors abstract SUMMARY The emergence of streaming multicore processors with multi‐SIMD (single‐instruction multiple‐data) architectures and ultra‐low power operation combined with real‐time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional Fast Fourier Transform (FFT) implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing and GFLOP throughput) the fastest FFT results reported to date for the Cell in the open literature. We also provide the first results for multi‐FFT implementation and application on the novel, ultra‐low power Coherent Logix HyperX processor. Copyright © 2011 John Wiley & Sons, Ltd. Jacob Barhen, Travis S. Humble, Pramita Mitra, Neena Imam, Bryan Schleck, Charlotte Kotas, Michael Traweek	Concurr. Comput. Pract. Exp.	2
2010	Multi-FFT Vectorization for the Cell Multicore Processor abstract The emergence of streaming multicore processors with multi-SIMD architectures and ultra-low power operation combined with real-time compute and I/O reconfigurability opens unprecedented opportunities for executing sophisticated signal processing algorithms faster and within a much lower energy budget. Here, we present an unconventional FFT implementation scheme for the IBM Cell, named transverse vectorization. It is shown to outperform (both in terms of timing or GFLOP throughput) the fastest FFT results reported to date in the open literature. Jacob Barhen, Travis S. Humble, Pramita Mitra, Michael Traweek	CCGRID	2