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Quantum Chemistry in the Age of Quantum Computing

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arxiv 1812.09976 v2 pith:4557OI37 submitted 2018-12-24 quant-ph

classification quant-ph
keywords quantumchemistrycomputingbeenalgorithmscomplexitycomputersmany
verification ladder T0 review T1 audit T2 compute T3 formal
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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 complexity 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 article is 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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization

    quant-ph 2026-03 conditional novelty 6.0 of 10

    A first-quantized CI-matrix QSCI variant reduces qubit count to O(log N) and gives accuracy on N2/naphthalene comparable to sample-based quantum diagonalization.

  2. Solving two and three-body systems with deep neural networks

    hep-ph 2025-07 conditional novelty 6.0 of 10

    A deep neural network with energy as the loss function solves the two-body deuteron and a three-channel triton model, matching analytic and Gaussian-expansion benchmarks.

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