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Reinforcement learning of quantum circuit architectures for molecular potential energy curves
Maureen Krumtünger, Alissa Wilms, Paul K. Faehrmann +4
Quantum chemistry and optimization are two of the most prominent applications of quantum computers. Variational quantum algorithms have been proposed for solving problems in these…
A Transferable Machine Learning Approach to Predict Quantum Circuit Parameters for Electronic Structure Problems
Davide Bincoletto, Korbinian Stein, Jonas Motyl +1
The individual optimization of quantum circuit parameters is currently one of the main practical bottlenecks in variational quantum eigensolvers for electronic systems. To this end…
Swap Network Augmented Ansätze on Arbitrary Connectivity
Teodor Parella-Dilmé, Jakob S. Kottmann, Antonio Acín
Efficient parametrizations of quantum states are essential for trainable hybrid classical-quantum algorithms. A key challenge in their design consists in adapting to the available…
State Specific Measurement Protocols for the Variational Quantum Eigensolver
Davide Bincoletto, Jakob S. Kottmann
A central roadblock in the realization of variational quantum eigensolvers on quantum hardware is the high overhead associated with measurement repetitions, which hampers the compu…