2 citations · 3 across the 14 of their papers we have counts for
18 papers
Quantum Enhanced Deep Reinforcement Learning for Distribution Network Reconfiguration
Daniel Germain, Mason Blanchard, Audrey Versteegen +5
Modern smart grids generate increasingly rich operational data and deploy greater numbers of remotely controlled switching assets than ever before, creating new opportunities for d…
Two-Stage Quantum-Classical Distribution Network Reconfiguration via Cycle-Edge Encoding
Nowar Alashkar, Cade Kennedy, Phillip C. Lotshaw +6
Distribution network reconfiguration (DNR) is a combinatorial optimization problem that seeks a low-loss network topology subject to topological and electrical operating constraint…
Warm-Start Iterative QITE for Distribution Network Reconfiguration via Branch-Exchange Encoding
Noah Crum, Pete Pritchard, Tanner Rase +5
We present a hybrid quantum-classical algorithm for distribution network reconfiguration, a combinatorial optimization problem on power distribution networks, that combines a radia…
Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification
Sang Hyub Kim, Julien Baglio, Rajiv Krishnakumar +7
Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We ad…
Practical Quantum Topological Data Analysis with Applications to High-Dimensional Feature Extraction and Time Series Analysis
Jason Iaconis, Sayonee Ray, Samwel Sekwao +2
Topological data analysis (TDA) provides a powerful framework for extracting information about the shape of complex, unstructured data, but the classical cost of computing high dim…
Quantum Parity Representations: Learnable Basis Discovery, Encoders, and Shadow Deployment
Sang Hyub Kim, Oliver Knitter, Jonathan Mei +4
We study parity features as representations that can be evaluated entirely classically once the binary or quantized input representation and parity words are fixed, particularly wh…
Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models
Oliver Knitter, Sang Hyub Kim, Maximilian Wurzer +7
We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power consumption of a Forte Enterprise…
Protein folding on a 64 qubit trapped-ion hardware via counterdiabatic quantum optimization
Alejandro Gomez Cadavid, Pavle Nikačević, Pranav Chandarana +11
We report the largest trapped-ion hardware demonstration of lattice protein-folding optimization to date, using bias-field digitized counterdiabatic quantum optimization (BF-DCQO)…
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
Carlos Flores-Garrigós, Anton Simen, Qi Zhang +6
We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorporates multivariate dependencies…
Measuring what matters: A scalable framework for application-level quantum benchmarking
Willie Aboumrad, Claudio Girotto, Joshua Goings +16
As quantum computing systems continue to mature, there is an increasing need for benchmarking methodologies that capture performance in terms of meaningful, application-level metri…
Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Miguel Angel Lopez-Ruiz, Daiwei Zhu, Jonas Hatzenbuhler +8
We present a quantum optimization framework for the Shipment Selection Problem (SSP) in electric freight logistics, developed jointly by IonQ and Einride. Idle gaps arising from st…
End-to-end performance of quantum-accelerated large-scale linear algebra workflows
Daiwei Zhu, Miguel Angel Lopez-Ruiz, François-Henry Rouet +5
Solving large-scale sparse linear systems is a challenging computational task due to the introduction of non-zero elements, or "fill-in". The Graph Partitioning Problem (GPP) arise…
Large-scale portfolio optimization on a trapped-ion quantum computer
Alejandro Gomez Cadavid, Ananth Kaushik, Pranav Chandarana +10
We present an end-to-end pipeline for large-scale portfolio selection with cardinality constraints and experimentally demonstrate it on trapped-ion quantum processors using hardwar…
Protein folding with an all-to-all trapped-ion quantum computer
Sebastián V. Romero, Alejandro Gomez Cadavid, Pavle Nikačević +8
We experimentally demonstrate that the bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm, implemented on IonQ's fully connected trapped-ion quantum proc…
Pathfinding Quantum Simulations of Neutrinoless Double-Beta Decay
Ivan A. Chernyshev, Roland C. Farrell, Marc Illa +10
We present results from co-designed quantum simulations of the neutrinoless double-beta decay of a simple nucleus in 1+1D quantum chromodynamics using IonQ's Forte-generation trapp…
Accelerating large-scale linear algebra using variational quantum imaginary time evolution
Willie Aboumrad, Daiwei Zhu, Claudio Girotto +6
The solution of large sparse linear systems via factorization methods such as LU or Cholesky decomposition, can be computationally expensive due to the introduction of non-zero ele…
Quantum Large Language Model Fine-Tuning
Sang Hyub Kim, Jonathan Mei, Claudio Girotto +2
We introduce a hybrid quantum-classical deep learning architecture for large language model fine-tuning. The classical portion of the architecture is a sentence transformer that is…
End-to-End Demonstration of Quantum Generative Adversarial Networks for Steel Microstructure Image Augmentation on a Trapped-Ion Quantum Computer
Samwel Sekwao, Jason Iaconis, Claudio Girotto +6
Generative adversarial networks (GANs) are a machine learning technique capable of producing high-quality synthetic images. In the field of materials science, when a crystallograph…