University of Waterloo and University of Strathclyde, Glasgow, UK hosted their first virtual research colloquium on 12 Nov, 2020. Nikolay presented a talk on behalf of the QuantumION project, focusing on our design for individual addressing of Barium qubits.
His talk was judged to be the best UW presentation of the day! Congrats, Nik!
Dr. Manas Sajjan defends his MSc thesis online! His MSc work included both theoretical and experimental aspects. On the theoretical side, Manas investigated the role of optical tweezer potentials on ions confined in a radio-frequency (RF) trap. Optical tweezers would allow for local control of the confining potential, which can be used for investigating quantum thermodynamics. On the experimental side, Manas was part of the team that built our four-rod trap. He fabricated the electrodes, built the RF resonator which powers them, and worked on optics.
Nikhil Kotibhaskar presents a talk at (virtual) TUCAN (Toronto Area Ultracold Atom Network), organized by University of Toronto. This is the third edition of the annual meeting. McMaster University and University of Waterloo hosted the meeting in the previous two years.
“Machine learning design of a trapped-ion quantum spin simulator” is published in Quantum Science and Technology 5 (2020) 024001, ( arXiv.1910.02496). We numerically demonstrate that a machine learning neural network can be used to efficiently solve a non-linear inversion problem in an analog trapped-ion quantum simulator. The method generates experimental control parameters (such as laser intensities on each ions) needed to program a quantum simulator, in particular, to engineer an arbitrary spin lattice geometry in one, two, and three dimensions using a linear chain of ions!
Here is a pre-print version of a review article on quantum simulation with trapped ions. The article focuses on analog spin simulation, and discusses the progress in this field so far. (arxiv.1912.07845).
Our manuscript “Machine learning design of a trapped-ion quantum spin simulator” is on arXiv.1910.02496. We demonstrate that a machine learning neural network can be used to efficiently engineer an arbitrary spin-spin interaction graph on an analog quantum simulator.
Here’s a picture of eight laser-cooled Ytterbium (174 isotope) ions in a linear chain! The image was taken using an off-the-shelf Thorlabs 0.1 NA objective and a cheap (~$300) CMOS camera. The ions are approximately 8 microns apart. Read more on the Physics and Astronomy webpage.