27 citations · 42 across the 20 of their papers we have counts for
22 papers
Demonstration of Parallel Multi-QPU Execution for Fragment-Based Quantum Chemistry Using On-Premises Hardware
Nils Herrmann, Mariam Akhtar, Luke W. Bertels +21
Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subprob…
Predictive Structure Behind Rare Outcomes in Random Quantum Circuits
Myeongsu Kim, Travis Humble, Sabre Kais
Local random quantum circuits selected for unusually large output peaks reveal intermediate-state structure that predicts new peak formation and guides circuit construction. In one…
Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
Seongmin Kim, In-Saeng Suh, Travis S. Humble +3
Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods h…
Scaling Sample-Based Quantum Diagonalization on GPU-Accelerated Systems using OpenMP Offload
Robert Walkup, Juha Jäykkä, Igor Pasichnyk +14
Hybrid quantum-HPC algorithms advance research by delegating complex tasks to quantum processors and using HPC systems to orchestrate workflows and complementary computations. Samp…
Q-IRIS: The Evolution of the IRIS Task-Based Runtime to Enable Classical-Quantum Workflows
Narasinga Rao Miniskar, Mohammad Alaul Haque Monil, Elaine Wong +4
Extreme heterogeneity in emerging HPC systems are starting to include quantum accelerators, motivating runtimes that can coordinate between classical and quantum workloads. We pres…
Certified randomness amplification by dynamically probing remote random quantum states
Minzhao Liu, Pradeep Niroula, Matthew DeCross +49
Cryptography depends on truly unpredictable numbers, but physical sources emit biased or correlated bits. Quantum mechanics enables the amplification of imperfect randomness into n…
Quantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks
Sounak Bhowmik, Travis S. Humble, Himanshu Thapliyal
Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work,…
Emotion Recognition in Older Adults with Quantum Machine Learning and Wearable Sensors
Md. Saif Hassan Onim, Travis S. Humble, Himanshu Thapliyal
We investigate the feasibility of inferring emotional states exclusively from physiological signals, thereby presenting a privacy-preserving alternative to conventional facial reco…
ASDF: A Compiler for Qwerty, a Basis-Oriented Quantum Programming Language
Austin J. Adams, Sharjeel Khan, Arjun S. Bhamra +6
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 synthesizi…
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults
Md Saif Hassan Onim, Travis S. Humble, Himanshu Thapliyal
Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable prevent…
Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization
Jeffery Yu, Javier Robledo Moreno, Joseph T. Iosue +17
Approximating the ground state of many-body systems is a key computational bottleneck underlying important applications in physics and chemistry. The most widely known quantum algo…
GTQCP: Greedy Topology-Aware Quantum Circuit Partitioning
Joseph Clark, Travis S. Humble, Himanshu Thapliyal
We propose Greedy Topology-Aware Quantum Circuit Partitioning (GTQCP), a novel quantum gate circuit partitioning method which partitions circuits by applying a greedy heuristic to…
Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization
Junghoon Chae, Chad A. Steed, Travis S. Humble
Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum c…
Ancillary entangling Floquet kicks for accelerating quantum algorithms
C. -C. Joseph Wang, Phillip C. Lotshaw, Titus Morris +3
Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the sys…
Residue Number System (RNS) based Distributed Quantum Addition
Bhaskar Gaur, Travis S. Humble, Himanshu Thapliyal
Quantum Arithmetic faces limitations such as noise and resource constraints in the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers. We propose using Distribut…
Rethinking Programming Paradigms in the QC-HPC Context
Silvina Caino-Lores, Daniel Claudino, Eugene Dumitrescu +3
Programming for today's quantum computers is making significant strides toward modern workflows compatible with high performance computing (HPC), but fundamental challenges still r…
Logical Error Rates for a [[4,2,2]]-Encoded Variational Quantum Eigensolver Ansatz
Meenambika Gowrishankar, Daniel Claudino, Jerimiah Wright +1
Quantum computing offers a potential for algorithmic speedups for applications, such as large-scale simulations in chemistry and physics. However, these speedups must yield results…
A Cross-Platform Execution Engine for the Quantum Intermediate Representation
Elaine Wong, Vicente Leyton-Ortega, Daniel Claudino +6
Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of the…
Improving probabilistic error cancellation in the presence of non-stationary noise
Samudra Dasgupta, Travis S. Humble
We investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of non-stationary noise, which is an obstacle to achieving accurate observable estim…
Adiabatic Quantum Support Vector Machines
Prasanna Date, Dong Jun Woun, Kathleen Hamilton +7
Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine le…
Toward Consistent High-fidelity Quantum Learning on Unstable Devices via Efficient In-situ Calibration
Zhirui Hu, Robert Wolle, Mingzhen Tian +3
In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. Besides, the noise on quantum devices is not s…
Improving the accuracy and efficiency of quantum connected moments expansions
Daniel Claudino, Bo Peng, Nicholas P. Bauman +2
The still-maturing noisy intermediate-scale quantum (NISQ) technology faces strict limitations on the algorithms that can be implemented efficiently. In quantum chemistry, the vari…