Hayata Morisaki

Abstract We introduce MNISQ, the first large-scale dataset for both quantum and classical machine learning during the NISQ era, containing 4.95 million circuits of 10 qubits constructed with up to 100 two-qubit gates. MNISQ serves as a foundational resource for developing natural language processing (NLP) models for quantum computing and deep learning models.

We propose a method for constructing PREPARE circuits for quantum phase estimation of a molecular Hamiltonian in quantum chemistry by using variational optimization of quantum circuits solely on classical computers. The PREPARE circuit generates a quantum state which encodes the coefficients of the terms in the Hamiltonian as probability amplitudes and plays a crucial role in the state-of-the-art efficient implementations of quantum phase estimation.