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Register a new userBypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning
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A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.
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The emergent properties of quantum materials, such as symmetry-broken phases and associated spectral gaps, can be effectively manipulated by ultrashort photon pulses. Impulsive optical excitation generally results in a complex non-equilibrium electron and lattice dynamics that involves multiple processes on distinct timescales, and a common conception is that for times shorter than about 100 fs the gap in the electronic spectrum is not seriously affected by lattice vibrations. Here, we directly monitor the photo-induced collapse of the spectral gap in a canonical charge-density-wave material, blue bronze Rb0.3MoO3. We find that ultra-fast (about 60 fs) vibrational disordering due to efficient hot-electron energy dissipation quenches the gap significantly faster than the typical structural bottleneck time corresponding to one half-cycle oscillation (about 315 fs) of the coherent charge-density-wave amplitude mode. This result not only demonstrates the importance of incoherent lattice motion in the photo-induced quenching of electronic order, but also resolves the perennial debate about the nature of the spectral gap in a coupled electron-lattice system.
Do electrons become ferromagnetic just because of their repulisve Coulomb interaction? Our calculations on the three-dimensional electron gas imply that itinerant ferromagnetim of delocalized electrons without lattice and band structure, the most basic model considered by Stoner, is suppressed due to many-body correlations as speculated already by Wigner, and a possible ferromagnetic transition lowering the density is precluded by the formation of the Wigner crystal.
The cost of the exact solution of the many-electron problem is believed to be exponential in the number of degrees of freedom, necessitating approximations that are controlled and accurate but numerically tractable. In this paper, we show that one of these approximations, the self-energy embedding theory (SEET), is derivable from a universal functional and therefore implicitly satisfies conservation laws and thermodynamic consistency. We also show how other approximations, such as the dynamical mean field theory (DMFT) and its combinations with many-body perturbation theory, can be understood as a special case of SEET and discuss how the additional freedom present in SEET can be used to obtain systematic convergence of results.
We investigate the normal state of the 11 iron-based superconductor FeSe0.42Te0.58 by angle resolved photoemission. Our data reveal a highly renormalized quasiparticle dispersion characteristic of a strongly correlated metal. We find sheet dependent effective carrier masses between ~ 3 - 16 m_e corresponding to a mass enhancement over band structure values of m*/m_band ~ 6 - 20. This is nearly an order of magnitude higher than the renormalization reported previously for iron-arsenide superconductors of the 1111 and 122 families but fully consistent with the bulk specific heat.
The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, we implement these ideas by engineering two different experimental platforms, based on quantum optics and ultra-cold atoms respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. We perform also a similar analysis on the Rigetti superconducting quantum computer. Therefore, we find that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.
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