Quantum machine learning for electronic structure calculations

Xia, Rongxin; Kais, Sabre

doi:10.1038/s41467-018-06598-z

Cited by 148 publications

(173 citation statements)

References 36 publications

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“…This was followed by several theoretical studies on VQE [7,[11][12][13][14][15][16][17] and demonstrations on other hardware such as superconducting qubits [7,16,18] and trapped ions [19,20]. Other approaches have been pursued as well, including methods for adiabatic quantum computation [21] and quantum machine learning [22].…”

Section: Introductionmentioning

confidence: 99%

An adaptive variational algorithm for exact molecular simulations on a quantum computer

et al. 2019

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Quantum simulation of chemical systems is one of the most promising near-term applications of quantum computers. The variational quantum eigensolver, a leading algorithm for molecular simulations on quantum hardware, has a serious limitation in that it typically relies on a pre-selected wavefunction ansatz that results in approximate wavefunctions and energies. Here we present an arbitrarily accurate variational algorithm that, instead of fixing an ansatz upfront, grows it systematically one operator at a time in a way dictated by the molecule being simulated. This generates an ansatz with a small number of parameters, leading to shallow-depth circuits. We present numerical simulations, including for a prototypical strongly correlated molecule, which show that our algorithm performs much better than a unitary coupled cluster approach, in terms of both circuit depth and chemical accuracy. Our results highlight the potential of our adaptive algorithm for exact simulations with present-day and near-term quantum hardware.

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Section: Introductionmentioning

confidence: 99%

An adaptive variational algorithm for exact molecular simulations on a quantum computer

et al. 2019

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“…Actually, with increasingly so-phisticated experiments, the limitation of existing semiclassical methods based on FPI for reproducing and explaining some quantum phenomena has been becoming increasingly evident due to the limited amount of paths, especially for the new attosecond measurements where a series of high-resolution photoelectron spectra with different pump-probe delays are needed to obtain attosecond time-resolved movies of electrons [25][26][27][28][29][30][31][32].Since the game Go was mastered by deep neural networks (DNNs), deep learning (DL) has received extensive attention [33,34]. Recently, this technique has powered many fields of science, including planning chemical syntheses [35], acceleration of super-resolution localization microscopy and nudged elastic band calculations [36][37][38][39], classifying scientific data [40,41], solving highdimensional problems in condensed matter systems [42][43][44][45][46][47][48], reconstructing the shape of ultrashort pulses [49], and so on. However, to our knowledge, its power in strong-field physics has not yet been excavated.…”

mentioning

confidence: 99%

“…Since the game Go was mastered by deep neural networks (DNNs), deep learning (DL) has received extensive attention [33,34]. Recently, this technique has powered many fields of science, including planning chemical syntheses [35], acceleration of super-resolution localization microscopy and nudged elastic band calculations [36][37][38][39], classifying scientific data [40,41], solving highdimensional problems in condensed matter systems [42][43][44][45][46][47][48], reconstructing the shape of ultrashort pulses [49], and so on. However, to our knowledge, its power in strong-field physics has not yet been excavated.…”

mentioning

confidence: 99%

Deep Learning for Feynman’s Path Integral in Strong-Field Time-Dependent Dynamics

et al. 2020

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Feynman's path integral approach is to sum over all possible spatio-temporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in classical view. However, the complete characterization of quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose deep-learning-performed strong-field Feynman's formulation with pre-classification scheme which can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build up a bridge between deep learning and strong-field physics through the Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science, and shed a new light on the quantum-classical correspondence.The wave function and the temporal evolution contain all information of quantum physics. However, they might be possibly the hardest to grasp in the classical world. Seventy years ago, Feynman proposed a path integral approach which has been viewed as the "sum over paths or histories" version of quantum mechanics, i.e. the wave function can be represented as a coherent superposition of contributions of all possible spatio-temporal paths [1,2]. Even though the Feynman's path integral (FPI) has been considered as the most fundamental way to interpret the quantum mechanics and answer what is the nature of measurements, the complete characterization of quantum wavepacket with all possible paths is formidable due to track ergodicity. Typically, only a very limited amount of paths could be accessed, and therefore only a reduced amount of information of quantum wavepacket could be obtained in different approximation methods so far.The development history of semiclassical methods based on FPI in strong-field physics, from the strong-field approximation (SFA) to the Coulomb corrected strongfield approximation (CCSFA) and quantum trajectory Monte Carlo methods, also proves that the more trajectories have been adopted, the more information could be extracted . As a result, despite of the notable success of these methods, there still exist a large number of unexplored regimes, including the open question about whether one could truly achieve the quantumclassical correspondence. Actually, with increasingly so-phisticated experiments, the limitation of existing semiclassical methods based on FPI for reproducing and explaining some quantum phenomena has been becoming increasingly evident due to the limited amount of paths, especially for the new attosecond measurements where a series of high-resolution photoelectron spectra with different pump-probe delays are needed to obtain attosecond time-resolved movies of electrons [25][26][27][...

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“…The machine learning methods proposed here are a significant departure from molecular dynamics (MD) simulations and recently advocated neural network architectures [36][37][38], for materials modeling. While we are typically interested in macroscopic phenomena, microscale dynamics must also be considered, as they play an important role in driving macroscopic behavior.…”

Section: Introductionmentioning

confidence: 99%

Methods for data-driven multiscale model discovery for materials

Brunton

Kutz

2019

J. Phys. Mater.

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Despite recent achievements in the design and manufacture of advanced materials, the contributions from first-principles modeling and simulation have remained limited, especially in regards to characterizing how macroscopic properties depend on the heterogeneous microstructure. An improved ability to model and understand these multiscale and anisotropic effects will be critical in designing future materials, especially given rapid improvements in the enabling technologies of additive manufacturing and active metamaterials. In this review, we discuss recent progress in the data-driven modeling of dynamical systems using machine learning and sparse optimization to generate parsimonious macroscopic models that are generalizable and interpretable. Such improvements in model discovery will facilitate the design and characterization of advanced materials by improving efforts in (1) molecular dynamics, (2) obtaining macroscopic constitutive equations, and (3) optimization and control of metamaterials.

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Quantum machine learning for electronic structure calculations

Cited by 148 publications

References 36 publications

An adaptive variational algorithm for exact molecular simulations on a quantum computer

An adaptive variational algorithm for exact molecular simulations on a quantum computer

Deep Learning for Feynman’s Path Integral in Strong-Field Time-Dependent Dynamics

Methods for data-driven multiscale model discovery for materials

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