Displaying items by tag: Quantum chemistry

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This report by Olson et al. summarizes the resuts of an NSF Workshop on Quantum Computational Chemistry held in November 2016. The workshop was attended by a wide range of experts from directly quantum-oriented fields such as algorithms, chemistry, machine learning, optics, simulation, and metrology, as well as experts in related fields such as condensed matter physics, biochemistry, physical chemistry, inorganic and organic chemistry, and spectroscopy. The goal of the workshop was to summarize recent progress in research at the interface of quantum information science and chemistry as well as to discuss the promising research challenges and opportunities in the field.

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Efficient quantum simulations of classically intractable instances of the associated electronic structure problem promise breakthroughs in our understanding of basic chemistry and could revolutionize research into new materials, pharmaceuticals, and industrial catalysts. In Quantum Computational Chemistry solutions, the Variational Quantum Eigensolver (VQE) algorithm offers a hybrid classical-quantum, and thus low quantum circuit depth, alternative to the Phase Estimation algorithm used to measure the ground-state energy of a molecular Hamiltonian. In this paper, Hempel et al. use a digital quantum simulator based on trapped ions to experimentally investigate the VQE algorithm for the calculation of molecular ground state energies of two simple molecules  (H2 and LiH) and experimentally demonstrate and compare different encoding methods using up to four qubits. 

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In this paper Bian et al. compare four different quantum simulation methods to simulate the ground state energy of the Hamiltonian for the water molecule on a quantum computer, being 1) the phase estimation algorithm based on Trotter decomposition, 2) phase estimation based on the direct implementation of the Hamiltonian, 3) direct measurement based on the implementation of the Hamiltonian and 4) the variational quantum eigensolver (classical-quantum hybrid) algorithm. They compare a.o. the required number of qubits, gate-complexity, accuracy/error. 

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Understanding and modeling the behavior of large numbers of interacting fermions is key to understanding the macroscopic properties of matter. However, the memory required to represent such a many-body state scales exponentially with the number of fermions, which makes simulation of many interesting cases intractable on classical computers. Algorithms leveraging the advantages of quantum computers for quantum simulations have steadily been developed in the past two decades. Variational quantum eigensolvers (VQE) have recently appeared as a promising class of quantum algorithms designed to prepare states for such quantum simulations. Low-depth circuits for such state preparation and quantum simulation are needed for practical quantum chemistry applications on near-term quantum devices with limited coherence. In this paper, Dallaire-Demers et al. present a new type of low-depth VQE ansatz, which should be in reach of near-term quantum devices and which can accurately prepare the ground state of correlated fermionic systems.

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At its core, the detailed understanding and prediction of complex chemical reaction mechanisms, requires highly accurate electronic structure methods. For molecules with many energetically close-lying orbitals, much less than a hundred strongly correlated electrons are already out of reach for classical calculation methods that could achieve the required accuracy. In this paper, Reiher et al. using as an example the open problem of biological nitrogen fixation in nitrogenase, to show how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. They demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources (in this case as little as 111 qubits and 1.0x10^14 T gates)

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Quantum computers promise to reduce the computational complexity of simulating quantum many-body systems from exponential to polynomial. Much effort is being put in reducing the complexity of the necessary algorithms, to allow them to be run on noisy intermediate scale quantum computers. In this paper, Dumitrescu et al. report a quantum simulation of the deuteron binding energy on 2 such small-scale noisy cloud accessible quantum processors (the IBM QX5 and Rigetti 19Q).

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