Singlet Geminal Wavefunctions: Exploring Open-Shell Possibilities Beyond Closed-Shell Singlet Pairs
Singlet geminal wavefunctions provide a better starting point for strongly correlated systems compared to single Slater determinants. This work explores the more general electron-pair structures, particularly for open-shell singlets, to overcome the limitations of closed-shell singlet pairs.
Robust Amplitude Estimation for Estimating Ground State Energy of Hydrogen Molecule on Near-Term Quantum Devices
Robust Amplitude Estimation (RAE) can significantly reduce the error in estimating the ground state energy of the hydrogen molecule compared to direct measurement techniques, despite the inherent limitations of current quantum hardware.
Rigorous Analysis of Finite-Size Error Scaling in Periodic Coupled Cluster Theory
The finite-size error in periodic coupled cluster calculations for three-dimensional insulating systems exhibits an inverse volume scaling, even in the absence of any correction schemes. This is reconciled by showing that the Madelung constant correction can effectively reduce the finite-size errors in both orbital energies and electron repulsion integral contractions from the inverse length scaling to the inverse volume scaling.
Efficient Quantum Simulation of Chemically Relevant Dynamical Processes
Chemically relevant dynamical processes can be efficiently simulated on a quantum computer by preparing initial states through a hierarchical scattering process and then measuring dynamical quantities of interest.
Augmented Lagrangian Method for Coupled-Cluster Convergence Improvement
Improving convergence properties of the single-reference coupled cluster method through an augmented Lagrangian formalism.
QH9: Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
機械学習アプローチを使用して、量子ハミルトニアン行列の計算を加速化するための新しいデータセットとベンチマークが提供されました。
Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Machine learning models accelerate quantum chemistry computations by predicting Hamiltonian matrices accurately and efficiently.
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
The author proposes M-OFDFT, an approach using deep learning to solve molecular systems with orbital-free density functional theory, achieving accuracy comparable to Kohn-Sham DFT and enabling the study of large molecules efficiently.
Analyzing Electronic Wave Functions with Neural Networks
The author explores the use of sign equivariant neural networks to represent electronic wave functions, highlighting the limitations and challenges faced in achieving computational advantages. The empirical results suggest little evidence of benefits from non-linear combinations of Slater determinants.
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