Quantum Chemistry in the Age of Quantum Computing.

By Yudong Cao, Jonathan Romero, Jonathan Olson, M. Degroote, Peter D. Johnson, Mária Kieferová, I. Kivlichan, Tim Menke, B. Peropadre, Nicolas P. D. Sawaya and 3 more. Chemical Reviews, 2018

1,725 citations recorded by the source corpus.

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Physical and Theoretical Chemistry

View Paper: https://www.semanticscholar.org/paper/1eaab9b33f1261744567455a14830e8a92796cf5

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Citations in the corpus

Engineered dissipation for quantum information science (2022, background): “Quantum simulation has myriad near-term applications for in physics, engineering, chemistry 168–170 , and biology 171 , for which quantum devices can be tailor-made to emulate a problem of interest.”
Variational Quantum Linear Solver (2019, neutral): “[21] Y.”
Error mitigation with Clifford quantum-circuit data (2020, background): “However, candidate applications such as quantum chemistry require chemical accuracy to beat classical methods [8, 33].”
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, background): “It has been shown to have exponential or polynomial advantage in various domains such as cryptography [57], database search [21], chemistry [13], [30], [52] and machine learning [7], [18], [24], [38], [55], etc.”
Noise-induced barren plateaus in variational quantum algorithms (2020, methodology): “The action of a UCC ansatz with single ( T 1 ) and double ( T 2 ) excitations is given by | ψ ⟩ = exp( T − T † ) ψ 0 ⟩ , where T = T 1 + T 2 , and where Here the i and j indices range over “occupied” orbitals whereas the a and b indices range over “virtual” or-bitals [50, 69].”
On scientific understanding with artificial intelligence (2022, background): “[35] Yudong Cao, Jonathan Romero, Jonathan P Olson, Matthias Degroote, Peter D Johnson, Mária Kieferová, Ian D Kivlichan, Tim Menke, Borja Peropadre, Nicolas PD Sawaya, et al.”