All of my publications can also be found on my google scholar page.
A Continuous Variable Born Machine
I. Čepaitė, B. Coyle, E. Kashefi
Generative Modelling has become a promising use case for near term quantum computers. In particular, due to the fundamentally probabilistic nature of quantum mechanics, quantum computers naturally model and learn probability distributions. The Born machine is an example of such a model. Here we present the Born machine within the framework of Continuous Variable Quantum Computing for the purpose of learning continuous probability distributions.
arxiv, arXiv:2011.00904, 2020
Singlet Pathway to the Ground State of Ultracold Polar Molecules
A. Yang, S. Botsi, S. Kumar, S. B. Pal, M. M. Lam, I. Čepaitė, A. Laugharn, and K. Dieckmann
We demonstrate a two-photon pathway to the ground state of 6Li40K molecules that involves only singlet-to-singlet optical transitions.
Phys. Rev. Lett. 124, 133203, 2020
Simulation of Networked Quantum Computing on Encrypted Data
I. Čepaitė
I adapt and simulate a Quantum Fully Homomorphic Encryption protocol based on the Measurement-Based Quantum Computing model on the recently released IBM 16-qubit cloud quantum processor.
Edinburgh University BSc Thesis, 2017
Ieva Čepaitė 