Variational quantum imaginary time evolution for matrix product state Ansatz with tests on transcorrelated Hamiltonians.

The matrix product state (MPS) Ansatz offers a promising approach for finding the ground state of molecular Hamiltonians and solving quantum chemistry problems. Building on this concept, the proposed technique of quantum circuit MPS (QCMPS) enables the simulation of chemical systems using a relatively small number of qubits. In this study, we enhance the optimization performance of the QCMPS Ansatz by employing the variational quantum imaginary time evolution (VarQITE) approach.

Improved optimization for the neural-network quantum states and tests on the chromium dimer.

The advent of Neural-network Quantum States (NQS) has significantly advanced wave function ansatz research, sparking a resurgence in orbital space variational Monte Carlo (VMC) exploration. This work introduces three algorithmic enhancements to reduce computational demands of VMC optimization using NQS: an adaptive learning rate algorithm, constrained optimization, and block optimization. We evaluate the refined algorithm on complex multireference bond stretches of H2O and N2 within the cc-pVDZ basis set and calculate the ground-state energy of the strongly correlated chromium dimer (Cr2) in the Ahlrichs SV basis set.

Up-regulation of BRAF activated non-coding RNA is associated with radiation therapy for lung cancer.

Radiation therapy has become more effective in treating primary tumors, such as lung cancer. Recent evidence suggested that BRAF activated non-coding RNAs (BANCR) play a critical role in cellular processes and are found to be dysregulated in a variety of cancers. The clinical significance of BANCR in radiation therapy, and its molecular mechanisms controlling tumor growth are unclear.