8 papers
SAR and InSAR Change Detection with Quantum Generative Models
Samwel K. Sekwao, Shaunak De, Alexis Hocken +7
Change detection in synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) underpins disaster response, infrastructure monitoring and land-use enforcem…
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…
Mid-Circuit Measurements for Clifford Noise Reduction in Hamiltonian Simulations
James Brown, Jason Iaconis, Yuri Alexeev +6
Quantum simulation of fermionic Hamiltonians is a leading application of quantum computing, but accurate execution on present-day hardware is limited by error accumulation in deep…
Quantum Lattice Boltzmann Solutions for Transport under 3D Spatially Varying Advection on Trapped Ion Hardware
Sayonee Ray, Jezer Jojo, Jason Iaconis +5
The Quantum Lattice Boltzmann Method (QLBM) has emerged as one of the most promising quantum computing approaches for the numerical simulation of problems in computational fluid dy…
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…
Algorithmic Advances Towards a Realizable Quantum Lattice Boltzmann Method
Apurva Tiwari, Jason Iaconis, Jezer Jojo +4
The Quantum Lattice Boltzmann Method (QLBM) is one of the most promising approaches for realizing the potential of quantum computing in simulating computational fluid dynamics. Man…
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…