Abstract: Efficiently calculating the low-lying eigenvalues of Hamiltonians is a fundamental challenge in quantum computing. While various methods have been proposed to reduce the complexity of quantum circuits for this task, there remains room for further improvement. In this talk, I will introduce a framework for constructing shorter and more efficient Hamiltonian-based quantum circuits. Additionally, I will share results from various numerical simulations to demonstrate the effectiveness of our method in accurately determining the ground state energy of different quantum chemistry Hamiltonians.
Bio: Abhinav Anand is a postdoctoral researcher at the Duke quantum center working with Prof. Kenneth R. Brown. Previously, he completed his PhD at the University of Toronto under the supervision of Prof. Alán Aspuru-Guzik. His research interests lie at the intersection of quantum algorithms design, error correction, quantum chemistry, machine learning, and software development.
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