1 citations · 1 across the 8 of their papers we have counts for
9 papers
Quantum computational advantage in random-circuit sampling on IBM superconducting quantum computers
Tigran Sedrakyan, Yuxuan Zhang, Hovnatan Karapetyan +3
We report forward random-circuit sampling (RCS) on the 120-qubit Nighthawk r2 superconducting processor (ibm_phoenix) with square-lattice connectivity, using 61 qubits, native CZ g…
Resolving Structure in Prethermal Floquet Dynamics with Precision Quantum Computation
Eyal Leviatan, Tasneem Watad, Roy Perry +38
Periodically driven interacting quantum many-body systems can exhibit long-lived prethermal dynamics, where local observables retain coherent structure even as entanglement and ope…
A Scalable Approach to Solve the Carleman Linearized Burgers' Equation on a Quantum Computer
Reuben Demirdjian, Yvan Quinn, Vincent P. Su +2
Efficiently solving nonlinear ordinary and partial differential equations using a quantum computer is a major challenge due its inherent linearity. To circumvent this challenge, th…
Benchmarking Zero-Setup Quantum Circuit Simulators
Arul Rhik Mazumder, Hovnatan Karapetyan, Mohammed Zuhair Mullath +3
Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU…
Heuristic Quantum Advantage with Peaked Circuits
Hrant Gharibyan, Mohammed Zuhair Mullath, Nicholas E. Sherman +3
We design and demonstrate heuristic quantum advantage with peaked circuits (HQAP circuits) on Quantinuum's System Model H2 quantum processor. Through extensive experimentation with…
Quantum Image Classification: Experiments on Utility-Scale Quantum Computers
Hrant Gharibyan, Hovnatan Karapetyan, Tigran Sedrakyan +4
We perform image classification on the Honda Scenes Dataset on Quantinuum's H-2 and IBM's Heron chips utilizing up to 72 qubits and thousands of two-qubit gates. For data loading,…
Quantum Image Loading: Hierarchical Learning and Block-Amplitude Encoding
Hrant Gharibyan, Hovnatan Karapetyan, Tigran Sedrakyan +4
Given the excitement for the potential of quantum computing for machine learning methods, a natural subproblem is how to load classical data into a quantum state. Leveraging insigh…
Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits
Hrant Gharibyan, Vincent Su, Hayk Tepanyan
We present hierarchical learning, a novel variational architecture for efficient training of large-scale variational quantum circuits. We test and benchmark our technique for distr…
Interval edge-colorings of composition of graphs
Petros A. Petrosyan, Hayk H. Tepanyan
An edge-coloring of a graph with consecutive integers is called an \emph{interval -coloring} if all colors are used, and the colors of edges incident to…