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arXiv:2210.08930

A modular quantum-classical framework for simulating chemical reaction pathways accurately

M R Nirmal, Sarkar, Shampa, Manoj Karunakaran Nambiar, Sriram Goverapet Srinivasan

Published Oct 17, 2022• 0 citations
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Abstract

A lot of progress has been made in recent times for simulating accurately the ground state energy of small molecules and their potential energy surface, using quantum-classical hybrid computing architecture. While these single point energy calculations are a significant milestone for quantum chemistry simulation on quantum hardware, a similarly important application is to trace accurately the reaction pathway of various chemical transformations. Such computations require accurate determination of the equilibrium or lowest energy molecular geometry, either by computing energy gradients with respect to the molecule's nuclear coordinates or perturbative distortion of the molecular configuration. In this work, we present a modular quantum-classical hybrid framework, to accurately simulate chemical reaction pathway of various kinds of molecular reactions. We demonstrate our framework by accurately tracing the isomerization pathway for small organic molecules. This framework can now be readily applied to study other 'active' molecules from the pharma and chemical industries.

DOI
10.48550/arxiv.2210.08930

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A lot of progress has been made in recent times for simulating accurately the ground state energy of small molecules and their potential energy surface, using quantum-classical hybrid computing architecture. While these single point energy calculations are a significant milestone for quantum chemistry simulation on quantum hardware, a similarly important application is to trace accurately the reaction pathway of various chemical transformations. Such computations require accurate determination of the equilibrium or lowest energy molecular geometry, either by computing energy gradients with respect to the molecule's nuclear coordinates or perturbative distortion of the molecular configuration. In this work, we present a modular quantum-classical hybrid framework, to accurately simulate chemical reaction pathway of various kinds of molecular reactions. We demonstrate our framework by accurately tracing the isomerization pathway for small organic molecules. This framework can now be readily applied to study other 'active' molecules from the pharma and chemical industries.
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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.
Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li2, Li3, and Li4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.
Quantum computers have been proved to be able to mimic quantum systems efficiently in polynomial time. Quantum chemistry problems, such as static molecular energy calculations and dynamical chemical reaction simulations, become very intractable on classical computers with scaling up of the system. Therefore, quantum simulation is a feasible and effective approach to tackle quantum chemistry problems. Proof-of-principle experiments have been implemented on the calculation of the hydrogen molecular energies and one-dimensional chemical isomerization reaction dynamics using nuclear magnetic resonance systems. We conclude that quantum simulation will surpass classical computers for quantum chemistry in the near future.
Abstract Modeling chemical reactions and complicated molecular systems has been proposed as the “killer application” of a future quantum computer. Accurate calculations of derivatives of molecular eigenenergies are essential toward this end, allowing for geometry optimization, transition state searches, predictions of the response to an applied electric or magnetic field, and molecular dynamics simulations. In this work, we survey methods to calculate energy derivatives, and present two new methods: one based on quantum phase estimation, the other on a low-order response approximation. We calculate asymptotic error bounds and approximate computational scalings for the methods presented. Implementing these methods, we perform geometry optimization on an experimental quantum processor, estimating the equilibrium bond length of the dihydrogen molecule to within 0.0140.0140.014 0.014 Å of the full configuration interaction value. Within the same experiment, we estimate the polarizability of the H 2{}_{2}2​ 2 molecule, finding agreement at the equilibrium bond length to within 0.060.060.06 0.06 a.u. ( 2%2 \%2% 2% relative error).
We develop a workflow within the ONIOM framework and demonstrate it on the hybrid computing system consisting of the supercomputer Fugaku and the Quantinuum Reimei trapped-ion quantum computer. This hybrid platform extends the layered approach for biomolecular chemical reactions to accurately treat the active site, such as a protein, and the large and often weakly correlated molecular environment. Our result marks a significant milestone in enabling scalable and accurate simulation of complex biomolecular reactions