Ionization of D2 launches a vibrational wave packet on the ground state of D+2. Removal of the second electron places a pair of D+ ions onto a Coulombic potential. Measuring the D+ kinetic energy determines the time delay between the first and the second ionization. Caught between a falling ionization and a rapidly rising intensity, the typical lifetime of the D+2 intermediate is less than 5 fs when an intense 8.6 fs laser pulse is used. We simulate Coulomb explosion imaging of the ground state wave function of D2 by a 4 fs optical pulse and compare with our experimental observations.
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http://dx.doi.org/10.1103/PhysRevLett.91.093002 | DOI Listing |
J Phys Chem A
January 2025
Department of Chemistry and Chemical Biology, Center for Computational Chemistry, University of New Mexico, Albuquerque, New Mexico 87131, United States.
The kinetics of electronically inelastic quenching of O(Δ) and O(Σ) by collisions with O(P) have been investigated using mixed quantum-classical trajectories governed by adiabatic potential energy surfaces and state couplings generated from a recently developed diabatic potential energy matrix (DPEM) for the 14 lowest-energy A' states of O. Using the coherent switching with decay of mixing (CSDM) method, dynamics calculations were performed both with 14 coupled electronic states and with 8 coupled electronical states, and similar results were obtained. The calculated thermal quenching rate coefficients are generally small, but they increase with temperature.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Flatiron Institute, Center for Computational Quantum Physics, New York, New York 10010, USA.
The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different Ansätze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
RIKEN, Condensed Matter Theory Laboratory, CPR, Wako, Saitama 351-0198, Japan.
We show that the ground-state expectation value of twisting operator is a topological order parameter for U(1)- and Z_{N}-symmetric symmetry-protected topological (SPT) phases in one-dimensional "spin" systems-it is quantized in the thermodynamic limit and can be used to identify different SPT phases and to diagnose phase transitions among them. We prove that this (nonlocal) order parameter must take values in Nth roots of unity, and its value can be changed by a generalized lattice translation acting as an N-ality transformation connecting distinct phases. This result also implies the Lieb-Schultz-Mattis (LSM) ingappability for SU(N) spins if we further impose a general translation symmetry.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
Quantinuum, 303 S. Technology Court, Broomfield, Colorado 80021, USA.
Although quantum mechanics underpins the microscopic behavior of all materials, its effects are often obscured at the macroscopic level by thermal fluctuations. A notable exception is a zero-temperature phase transition, where scaling laws emerge entirely due to quantum correlations over a diverging length scale. The accurate description of such transitions is challenging for classical simulation methods of quantum systems, and is a natural application space for quantum simulation.
View Article and Find Full Text PDFPhys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
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