Separating the dynamics of variables that evolve on different timescales is a common assumption in exploring complex systems, and a great deal of progress has been made in understanding chemical systems by treating independently the fast processes of an activated chemical species from the slower processes that proceed activation. Protein motion underlies all biocatalytic reactions, and understanding the nature of this motion is central to understanding how enzymes catalyze reactions with such specificity and such rate enhancement. This understanding is challenged by evidence of breakdowns in the separability of timescales of dynamics in the active site form motions of the solvating protein. Quantum simulation methods that bridge these timescales by simultaneously evolving quantum and classical degrees of freedom provide an important method on which to explore this breakdown. In the following dissertation, three problems of enzyme catalysis are explored through quantum simulation.
Separating the dynamics of variables that evolve on different timescales is a common assumption in exploring complex systems, and a great deal of progress has been made in understanding chemical systems by treating independently the fast processes of an activated chemical species from the slower processes that proceed activation. Protein motion underlies all biocatalytic reactions, and understanding the nature of this motion is central to understanding how enzymes catalyze reactions with such specificity and such rate enhancement. This understanding is challenged by evidence of breakdowns in the separability of timescales of dynamics in the active site form motions of the solvating protein. Quantum simulation methods that bridge these timescales by simultaneously evolving quantum and classical degrees of freedom provide an important method on which to explore this breakdown. In the following dissertation, three problems of enzyme catalysis are explored through quantum simulation.
Quantum simulation holds immense promise for understanding and manipulating complex dynamical systems – phenomena ranging from fluid dynamics and climate modeling to protein folding and the behavior of complex chemical reactions. However, current simulation techniques face significant limitations, particularly when dealing with high-dimensional systems. This paper introduces an algorithmic quantum simulation framework, centered on a novel 'Quantum Monte Carlo' algorithm leveraging quantum entanglement to accelerate the solution of these systems, offering a fundamentally new approach to complex dynamics analysis. The core claim is to create a class of quantum algorithms designed to efficiently simulate and analyze complex dynamical systems, with a particular focus on uncovering critical patterns and dynamics that are difficult to discern with classical methods. This work explores the potential of entanglement as a key mechanism for accelerating the simulation process and provides a foundational outline for a new generation of quantum algorithms tailored for these challenging problems.
In this introductory paper we overview existing approaches to enzyme – catalysed reactions. From the Michaelis – Menten kinetics to quantum scatterings several models are considered. We try to introduce our ”information – driven measurement engines” (an other model of enzyme action) in relation to existent theories. We conclude that our approach is a special type of quantum mechanical treatment of enzyme catalysed reactions, in its nascent form. It is, we suppose, stands more in relation to a hypothetical gas – phase scattering, maybe even resonance scattering, as a one – dimensional vacuum energy – dependent motion along the reaction path. We are aware that at this time, no realistic gas – phase scattering is available in the area, and our approach is thus similarly hypothetical. Still, it might provide a fresh view on enzyme – catalysed reactions, utilising a somewhat unusual Hamiltonian second – order tensor operator formalism. During arriving at our goal, we give some more extensive review devoted to current catalyzed and uncatalized reaction investigations, of a pure chemical and biochemical nature, exploring both experimental and theoretical procedures.
Accurate simulation of dynamic processes in molecules and reactions is among the most challenging problems in quantum chemistry. Quantum computers promise efficient chemical simulation, but the existing quantum algorithms require many logical qubits and gates, placing practical applications beyond existing technology. Here, we carry out the first quantum simulations of chemical dynamics by employing a more hardware-efficient encoding scheme that uses both qubits and bosonic degrees of freedom. Our trapped-ion device accurately simulates the dynamics of nonadiabatic chemical processes, which are among the most difficult problems in computational chemistry because they involve strong coupling between electronic and nuclear motions. We demonstrate the programmability and versatility of our approach by simulating the dynamics of three different molecules, as well as open-system dynamics in the condensed phase, all with the same quantum resources. Our approach requires orders of magnitude fewer resources than equivalent qubit-only quantum simulations, demonstrating the potential of using hybrid encoding schemes to accelerate quantum simulations of complex chemical processes, which could have applications in fields ranging from energy conversion and storage to biology and drug design.
My research has three main foci, one which is of interest to the fields of quantum information and quantum optics is to characterize the effects of coupling between quantum systems. Advances in technology made since the conception of quantum mechanics spurred the development of quantum technologies like quantum optics and quantum computing. These applications require a good understanding of the quantum systems that compose them, and the clever ways in which the information in these systems is stored and manipulated. By understanding the ways which quantum systems interact with their environment, their usefulness can be further utilized. The second is to develop methods of calculation which simplify the calculational and computational side of quantum dynamics. Understanding how quantum systems evolve in time is very important, but traditional methods of solving for the dynamics of quantum systems struggle to deal with time dependent parameters. Using results from the theory of Lie algebras, I will show how quantum dynamics problems can be made less computationally intensive by avoiding the partial differential equations typically associated with such problems and reducing the problem to a system of coupled ordinary differential equations. The third focus is to develop methods of visualizing properties of quantum systems. I think this is important as an educational tool and as a way to develop intuition about how quantum systems evolve considering how few opportunities there are to visualize quantum effects on a macroscopic scale. This problem is particularly challenging because quantum mechanics is non-deterministic, meaning that the only nontrivial definitive statements that can be made regarding quantum systems deal with probabilities. This coupled with the fact that many of the systems of interest depend on upwards of six variables and also evolve in time make creating useful visual representations challenging. Despite the challenges, I feel that useful insights can be gained from the visualizations, and will illustrate a few examples.
Quantum-enhanced computational catalysis offers a transformative framework for modeling complex reaction pathways with unprecedented accuracy and scalability. As catalytic processes often involve strongly correlated electrons, multidimensional potential energy surfaces, and dynamic coupling between nuclear and electronic degrees of freedom, classical simulation methods face steep computational barriers. This study explores a hybrid quantum–classical workflow that integrates quantum algorithms for electronic structure with advanced tensor decomposition techniques to compress, analyze, and optimize high-dimensional reaction data. By leveraging tensor-network representations—such as matrix product states, tensor-train formats, and hierarchical Tucker decompositions—the approach systematically reduces computational cost while maintaining chemically relevant accuracy. The research demonstrates how tensor-optimized quantum simulations can accelerate transition-state discovery, enhance reaction pathway resolution, and improve predictions of catalytic efficiency across heterogeneous, homogeneous, and photocatalytic systems. The synergy between quantum solvers and tensor decomposition establishes a powerful platform for next-generation catalytic modeling and materials discovery.
In this introductory paper we overview existing approaches to enzyme – catalysed reactions. From the Michaelis – Menten kinetics to quantum scatterings several models are considered. We try to introduce our ”information – d riven measurement engines” (an other model of enzyme action) in relation to existent theories. We conclude that our approach is a special type of quantum mechanical treatment of enzyme catalysed reactions, in its nascent form. It is, we suppose, stands mor e in relation to a hypothetical gas – phase scattering, maybe even resonance scattering, as a one – dimensional vacuum energy – dependent motion along the reaction path. We are aware that at this time, no realistic gas – phase scattering is available in th e area, and our approach is thus similarly hypothetical. Still, it might provide a fresh view on enzyme – catalysed reactions, utilising a somewhat unusual Hamiltonian second – order tensor operator formalism. During arriving at our goal, we give some mor e extensive review devoted to current cataly s ed and uncatal y s ed reaction investigations, of a pure chemical and biochemical nature, exploring both experimental and theoretical procedures.