Lead Researcher – Quantum Machine Learning

The Role The Lead Researcher in Quantum Machine Learning will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Lead Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Lead Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms. Reporting directly to the Director of AI, the Lead Researcher in Quantum Machine Learning plays a pivotal role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally. The Responsibilities The Lead Applied Researcher should expect to work in the following Quantum Machine Learning Team activities. Fundamental research in quantum machine learning Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing Keeping up with state-of-the-art approaches in quantum machine learning Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks Researching and understanding where the quantum brings the benefit to machine learning Developing data encoding and data processing techniques for different types of quantum computers Assisting in writing patent draft applications Writing research papers for scientific journals Efficient implementation of quantum machine learning algorithms Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q Adjusting and improving implementation of hybrid quantum neural networks for different QPUs Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane) Assisting in the development of our quantum machine learning SDK Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution Supporting industry projects implementation Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems Assisting in writing applied industry research papers for scientific journals The Requirements The Lead Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning. PhD in computer science, physics, mathematics, electrical engineering, or equivalent subject is required Publication record in quantum machine learning, or variational quantum algorithms is required Advanced expertise in one or more quantum