All of my publications can also be found on my google scholar page.
Counterdiabatic Optimised Local Driving
I. Čepaitė, A. J. Daley, A. Polkovnikov, C. W. Duncan
We outline a new method for speeding up adiabatic protocols which combines ideas from local counterdiabatic driving and optimal control methods, taking advantage of the strengths of each. We refer to it as Counterdiabatic Optimised Local Driving (COLD).
arxiv, arXiv:2203.01948, 2022
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.
Quantum Mach. Intell. 4, 6, 2022
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
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 