24 citations · 24 across the 4 of their papers we have counts for
6 papers
Quantum Fokker-Planck solvers as trainable generative models
Tariq Mahassen, Marcin Jastrzebski, Oleksandr Kyriienko
We develop and analyze generative models based on quantum solvers of stochastic differential equations. Unlike the common approach based on layered variational quantum circuits wit…
Adaptive Relational Learning on Multi-instance Quantum Data with Photonic Processors
Marcin Jastrzebski, Shang Yu, Raj B. Patel +1
Loading multiple quantum states in parallel into a quantum machine learning (QML) model can unlock learning tasks where key information resides in the \emph{relations} between stat…
Learning structural balance of graphs from quantum spectral features
Stefano Scali, Oleksandr Kyriienko
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs.…
Photonics-Enhanced Graph Convolutional Networks
Yuan Wang, Oleksandr Kyriienko
Photonics can offer a hardware-native route for machine learning (ML). However, efficient deployment of photonics-enhanced ML requires hybrid workflows that integrate optical proce…
Polaritonic Machine Learning for Graph-based Data Analysis
Yuan Wang, Stefano Scali, Oleksandr Kyriienko
Photonic and polaritonic systems offer a fast and efficient platform for accelerating machine learning (ML) through physics-based computing. To gain a computational advantage, howe…
Unsupervised quantum machine learning for fraud detection
Oleksandr Kyriienko, Einar B. Magnusson
We develop quantum protocols for anomaly detection and apply them to the task of credit card fraud detection (FD). First, we establish classical benchmarks based on supervised and…