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Towards Battery Simulations on a Quantum Computer using Quantum Embedding

Elias Walter, Birger Horstmann

Published Sep 4, 2024• 0 citations
OpenAlex

Abstract

Advances in quantum computing hold the potential to revolutionize the simulation of complex materials, including those critical for next-generation batteries. However, the complexity of typical battery materials is still prohibitive for exact simulations on current or near-term quantum hardware. One promising approach is the use of quantum embedding techniques, which enable the treatment of correlated electronic states in large systems by partitioning them into manageable subproblems. We explore the application of Density Matrix Embedding Theory (DMET) [1,2] as a quantum embedding method to bridge classical and quantum computational resources [3]. Within DMET, few correlated degrees of freedom can be solved on the quantum computer, while the remaining part is solved with classical methodology, self-consistently connected to the quantum part. This approach paves the way for real-world problems from battery simulation to profit from near-term quantum computers. Literature: [1] Knizia and Chan, Phys. Rev. Lett. 109, 186404 (2012) [2] Wouters et al., J. Chem. Theory Comput. 12, 2706 (2016) [3] Cao et al., npj Comput. Mater. 9, 78 (2023)

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Advances in quantum computing hold the potential to revolutionize the simulation of complex materials, including those critical for next-generation batteries. However, the complexity of typical battery materials is still prohibitive for exact simulations on current or near-term quantum hardware. One promising approach is the use of quantum embedding techniques, which enable the treatment of correlated electronic states in large systems by partitioning them into manageable subproblems. We explore the application of Density Matrix Embedding Theory (DMET) [1,2] as a quantum embedding method to bridge classical and quantum computational resources [3]. Within DMET, few correlated degrees of freedom can be solved on the quantum computer, while the remaining part is solved with classical methodology, self-consistently connected to the quantum part. This approach paves the way for real-world problems from battery simulation to profit from near-term quantum computers. Literature: [1] Knizia and Chan, Phys. Rev. Lett. 109, 186404 (2012) [2] Wouters et al., J. Chem. Theory Comput. 12, 2706 (2016) [3] Cao et al., npj Comput. Mater. 9, 78 (2023)
Abstract Quantum computing has shown great potential in various quantum chemical applications such as drug discovery, material design, and catalyst optimization. Although significant progress has been made in quantum simulation of simple molecules, ab initio simulation of solid-state materials on quantum computers is still in its early stage, mostly owing to the fact that the system size quickly becomes prohibitively large when approaching the thermodynamic limit. In this work, we introduce an orbital-based multi-fragment approach on top of the periodic density matrix embedding theory, resulting in a significantly smaller problem size for the current near-term quantum computer. We demonstrate the accuracy and efficiency of our method compared with the conventional methodologies and experiments on solid-state systems with complex electronic structures. These include spin polarized states of a hydrogen chain (1D-H), the equation of state of a boron nitride layer (h-BN) as well as the magnetic ordering in nickel oxide (NiO), a prototypical strongly correlated solid. Our results suggest that quantum embedding combined with a chemically intuitive fragmentation can greatly advance quantum simulation of realistic materials, thereby paving the way for solving important yet classically hard industrial problems on near-term quantum devices.
Quantum computers hold significant promise for the efficient and accurate simulation of strongly correlated materials, including those critical to next-generation battery technologies. However, current quantum hardware is constrained by limited qubit counts and susceptibility to decoherence, making exact simulations of complex materials infeasible. In response, the Noisy Intermediate-Scale Quantum (NISQ) era has fostered the development of hybrid quantum-classical algorithms that can operate effectively within these limitations. In this work, we apply NISQ-compatible methods to investigate fundamental processes occurring inside battery systems, especially in organic electrode materials. A key aspect of our approach is the decomposition of these systems into active spaces - reduced regions that capture the most chemically relevant electrons and interactions. Within these active spaces, quantum algorithms are employed to resolve the electronic structure with high precision. By strategically focusing quantum computational resources where they are most impactful, our approach highlights the potential of quantum computing in advancing the fundamental understanding of battery materials - marking an early but significant step toward practical quantum simulations in energy storage research.
Determining ground state energies of quantum systems by hybrid classical/quantum methods has emerged as a promising candidate application for near-term quantum computational resources. Short of large-scale fault-tolerant quantum computers, small-scale devices can be leveraged with current computational techniques to identify important subspaces of relatively large Hamiltonians. Inspired by the work that described the merging of dynamical mean-field theory (DMFT) with a small-scale quantum computational resource as an impurity solver [Bauer et al., arXiv:1510.03859v2], we describe an alternative embedding scheme, density matrix embedding theory (DMET), that naturally fits with the output from the variational quantum eigensolver and other hybrid approaches. This approach is validated using a quantum abstract machine simulator [Smith et al., arXiv:1608.03355] that reproduces the ground state energy of the Hubbard model converged to the infinite limit.
Abstract Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits. In order to harness the power of near-term quantum computers for simulations of larger systems, it is desirable to develop hybrid quantum-classical methods where the quantum computation is restricted to a small portion of the system. This is of particular relevance for molecules and solids where an active region requires a higher level of theoretical accuracy than its environment. Here, we present a quantum embedding theory for the calculation of strongly-correlated electronic states of active regions, with the rest of the system described within density functional theory. We demonstrate the accuracy and effectiveness of the approach by investigating several defect quantum bits in semiconductors that are of great interest for quantum information technologies. We perform calculations on quantum computers and show that they yield results in agreement with those obtained with exact diagonalization on classical architectures, paving the way to simulations of realistic materials on near-term quantum computers.
One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this Review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. By extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.
One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. By extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.