Prospects of Quantum Computing for Molecular Sciences

Abstract

Molecular science is governed by the dynamics of electrons, atomic nuclei, and their interaction with electromagnetic fields. A reliable physicochemical understanding of these processes is crucial for the design and synthesis of chemicals and materials of economic value. Although some problems in this field are adequately addressed by classical mechanics, many require an explicit quantum mechanical description. Such quantum problems represented by exponentially large wave function should naturally benefit from quantum computation on a number of logical qubits that scales only linearly with system size. In this perspective, we focus on the potential of quantum computing for solving relevant problems in the molecular sciences -- molecular physics, chemistry, biochemistry, and materials science.

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Cited by in corpus (23)

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• Molecule-specific Uncertainty Quantification in Quantum Chemical Studies
• Quantum Simulations of Chemistry in First Quantization with any Basis Set
• Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping
• Quantum Computing and Tensor Networks for Laminate Design: A Novel Approach to Stacking Sequence Retrieval
• VQE Method: A Short Survey and Recent Developments
• High ground state overlap via quantum embedding methods
• The variational quantum eigensolver self-consistent field method within a polarizable embedded framework
• Early Fault-Tolerant Quantum Algorithms in Practice: Application to Ground-State Energy Estimation
• Quantum-machine-assisted Drug Discovery
• Towards Efficient Quantum Computing for Quantum Chemistry: Reducing Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques
• Flexibility of the factorized form of the unitary coupled cluster ansatz
• Hierarchical quantum embedding by machine learning for large molecular assemblies
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• Chemically Motivated Simulation Problems are Efficiently Solvable by a Quantum Computer
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• Reducing the sampling complexity of energy estimation in quantum many-body systems using empirical variance information
• Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers
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