We present an implementation of alchemical free energy simulations at the quantum mechanical level by directly interpolating the electronic Hamiltonian. The method is compatible with any level of electronic structure theory and requires only one quantum calculation for each molecular dynamics step in contrast to multiple energy evaluations that would be needed when interpolating the ground-state energies. We demonstrate the correctness and applicability of the technique by computing alchemical free energy changes of gas-phase molecules, with both nuclear and electron creation/annihilation. We also show an initial application to first-principles pKa calculation for solvated molecules where we quantum mechanically annihilate a bonded proton.
We introduce a computational framework for simulating nonadiabatic vibronic dynamics on circuit quantum electrodynamics (cQED) platforms. Our approach leverages hybrid oscillator-qubit quantum hardware with midcircuit measurements and resets, enabling the incorporation of environmental effects such as dissipation and dephasing. To demonstrate its capabilities, we simulate energy transfer dynamics in a triad model of photosynthetic chromophores inspired by natural antenna systems. We specifically investigate the role of dissipation during the relaxation dynamics following photoexcitation, where electronic transitions are coupled to the evolution of quantum vibrational modes. Our results indicate that hybrid oscillator-qubit devices, operating with noise levels below the intrinsic dissipation rates of typical molecular antenna systems, can achieve the simulation fidelity required for practical computations on near-term and early fault-tolerant quantum computing platforms.
Achieving fast and accurate reaction prediction is central to a suite of chemical applications. Nevertheless, classic approaches based on templates or simple models are typically fast but with limited scope or accuracy, while the emerging machine learning-based models are limited in their transferability due to the lack of large reaction databases. Here, we address these limitations by formalizing the model reaction concept based on fixed-depth condensed reaction graphs that are shown to achieve a cost and accuracy balance that is applicable to many problems. The model reaction concept can be utilized to provide reliable predictions of activation energies and transition state geometries for a large range of organic reactions. In addition, using an alkane pyrolysis system as a benchmarking example, we show that the accuracy of the activation energy prediction can be further improved by adding correction terms based on the empirical Brønsted-Evans-Polanyi (BEP) relationship. These successful applications demonstrate that the model reaction can serve as a general tool to reduce the cost associated with ab initio transition state searches.