8 citations · 14 across the 7 of their papers we have counts for
8 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…
Neural Flux Attachment: From Bose Condensates to Chiral Topological Matter
Rudik Badalyan, Khachatur G. Nazaryan, Tigran A. Sedrakyan
Can one neural wave function describe both a Bose condensate and a chiral topological liquid? We introduce ChernFormer, which combines a fermionic transformer with a fixed Chern-Si…
Private training in quantum machine learning
Tigran Sedrakyan, Frédéric Grosshans, Elham Kashefi
With the emergence of machine learning (ML) models trained on large datasets containing potentially sensitive data, a major question in AI safety is how to make learning private wi…
Quantization and quantum oscillations of the sublattice charge order in Dirac insulators
Arindam Tarafdar, Tigran A. Sedrakyan
We report the quantization, quantum oscillations, and singular behavior of sublattice symmetry-breaking sublattice charge order (SCO) in two-dimensional Dirac insulators at charge…
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…
Photonic quantum generative adversarial networks for classical data
Tigran Sedrakyan, Alexia Salavrakos
In generative learning, models are trained to produce new samples that follow the distribution of the target data. These models were historically difficult to train, until proposal…
Error-mitigated photonic quantum circuit Born machine
Alexia Salavrakos, Tigran Sedrakyan, James Mills +2
In this article, we study quantum circuit Born machines (QCBMs) in the context of photonic quantum computing. QCBMs are a popular choice of quantum generative machine learning mode…