In our quantum subgroup within the Matter Lab, we focus on advancing both NISQ and fault-tolerant quantum chemistry algorithms. We develop and implement NISQ algorithms, such as the Variational Quantum Eigensolver (VQE) and Generative Quantum Eigensolver (GQE), while also exploring fault-tolerant methods like Hamiltonian simulation and methods for solving partial differential equations (PDEs). Our mission is to maximize the utility of current quantum processors by optimizing compilation, minimizing noise, and running efficient, smart algorithms. We find novel ways of combining machine learning algorithms with quantum processes and apply these techniques to quantum chemistry, drug discovery, and more, pushing the current boundaries of quantum computing applications.
We work on developing computational tools to accelerate standard electronic structure methods. These vary from using machine learning for quantum monte carlo, hamiltonian learning, or automatic differentiation. Moreover, similar development happens in tandem for the development of more efficient quantum computing algorithms. Additionally, we routinely use density functional theory to supplement self-driving labs with in-silico chemical experiments.
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