I recently started work at Riverlane, a quantum engineering company. We work on making quantum computing useful sooner by developing an operating system Deltaflow.OS to help quantum hardware companies scale faster, reduce errors and implement practical quantum error correction. Quantum Computers are very sensitive systems and it is unavoidable that they constantly make errors. Therefore, to make them practically useful, we need to have benchmarking systems which characterise the errors in the system, and systems that correct those errors.

During 2019, I was freelancing for the company Dreams AI as a data scientist working on machine learning projects involving Optical Character Recognition (OCR), Natural Language Processing (NLP), and low dimensional data embedding. The projects I was working on proved successful and I was invited to come to Hong Kong to take them further. During my time in Hong Kong (from 15th March to 26th May 2020), I worked on two projects.

I completed my PhD in the field of 3D nanomagnetism in Thin Film Magnetism group. I was exploring how the structure geometry affects the magnetic properties of nanostructures. These structures and effects have a potential to be the platform for the future data storage and processing devices. I worked on fabrication of these devices, computational simulations and experimental measurements of their properties. My thesis can be accessed here. FEBID Focused Electron Induced Deposition (FEBID) is a technique I used in my research for 3D printing with a resolution of ~50nm, about 2,000 times better than conventional 3D printers.

My Masters Project for BSci in Physics was on “Electrically contacting nanocrystals using a selectively etched nanogap”. I measured the electrical conductivity of a device where a nanoparticles served as the contact between conductors. Due to the small size of nanoparticles, we get an effect known as Coulomb blockade. This results in the device no longer following Ohm’s resistance law and instead has staricase-like resistance (see Figure below). The Coulomb blockade could be used to build single-electron transistors (SET) for low-power computation and highly sensitive sensors.

I spent the summer of 2015 doing a research internship in Oxford with Simon Benjamin’s group under Dr Stefan Zohren. I learned about and worked on projects in quantum and classical machine learning and optimization. I programmed neural networks and implemented optimization algorithms to test theoretical predictions. The first part of my project was investigating the potential of quantum computers for deep learning, trying to understand and improve on the recent paper by Microsoft Research [1].

During the summer of 2014, I worked for HrPro, one of the leading Human Resourse software companies in Croatia. As an intern, I helped build a graphical interface for representing the organizational structure of companies. Additionally, I worked on a method for optimizing the placement of about 10,000 workers in stores throught the country for Konzum, the Croatia’s biggest chains of supermarkets. I implemented the Hungarian method in C# to minimize the overall transport costs of employees to the stores they work in.