Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.
We report 1.6 ± 1 µm exciton transport in self-assembled supramolecular light-harvesting nanotubes (LHNs) assembled from amphiphillic cyanine dyes. We stabilize LHNs in a sucrose glass matrix, greatly reducing light and oxidative damage and allowing the observation of exciton-exciton annihilation signatures under weak excitation flux. Fitting to a one-dimensional diffusion model, we find an average exciton diffusion constant of 55 ± 20 cm2/s, among the highest measured for an organic system. We develop a simple model that uses cryogenic measurements of static and dynamic energetic disorder to estimate a diffusion constant of 32 cm2/s, in agreement with experiment. We ascribe large exciton diffusion lengths to low static and dynamic energetic disorder in LHNs. We argue that matrix-stabilized LHNS represent an excellent model system to study coherent excitonic transport.
One of the challenges for achieving efficient exciton transport in solar energy conversion systems is precise structural control of the light-harvesting building blocks. Here, we create a tunable material consisting of a connected chromophore network on an ordered biological virus template. Using genetic engineering, we establish a link between the inter-chromophoric distances and emerging transport properties. The combination of spectroscopy measurements and dynamic modelling enables us to elucidate quantum coherent and classical incoherent energy transport at room temperature. Through genetic modifications, we obtain a significant enhancement of exciton diffusion length of about 68% in an intermediate quantum-classical regime.
Energy Transfer , Genetic Engineering , Computer Simulation , Electrochemistry , Materials Testing , Models, Theoretical , Spectrum Analysis , Temperature
Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.
Nature's highly efficient light-harvesting antennae, such as those found in green sulfur bacteria, consist of supramolecular building blocks that self-assemble into a hierarchy of close-packed structures. In an effort to mimic the fundamental processes that govern nature's efficient systems, it is important to elucidate the role of each level of hierarchy: from molecule, to supramolecular building block, to close-packed building blocks. Here, we study the impact of hierarchical structure. We present a model system that mirrors nature's complexity: cylinders self-assembled from cyanine-dye molecules. Our work reveals that even though close-packing may alter the cylinders' soft mesoscopic structure, robust delocalized excitons are retained: Internal order and strong excitation-transfer interactions--prerequisites for efficient energy transport--are both maintained. Our results suggest that the cylindrical geometry strongly favors robust excitons; it presents a rational design that is potentially key to nature's high efficiency, allowing construction of efficient light-harvesting devices even from soft, supramolecular materials.
Nanotubes , Carbocyanines/chemistry , Coloring Agents/chemistry , Models, Theoretical
A remarkable amount of theoretical research has been carried out to elucidate the physical origins of the recently observed long-lived quantum coherence in the electronic energy transfer process in biological photosynthetic systems. Although successful in many respects, several widely used descriptions only include an effective treatment of the protein-chromophore interactions. In this work, by combining an all-atom molecular dynamics simulation, time-dependent density functional theory, and open quantum system approaches, we successfully simulate the dynamics of the electronic energy transfer of the Fenna-Matthews-Olson pigment-protein complex. The resulting characteristic beating of populations and quantum coherences is in good agreement with the experimental results and the hierarchy equation of motion approach. The experimental absorption, linear, and circular dichroism spectra and dephasing rates are recovered at two different temperatures. In addition, we provide an extension of our method to include zero-point fluctuations of the vibrational environment. This work thus presents, to our knowledge, one of the first steps to explain the role of excitonic quantum coherence in photosynthetic light-harvesting complexes based on their atomistic and molecular description.
Bacterial Proteins/chemistry , Light-Harvesting Protein Complexes/chemistry , Molecular Dynamics Simulation , Quantum Theory , Electrons , Energy Transfer , Pigments, Biological/chemistry , Temperature , Time Factors
Non-Markovian and nonequilibrium phonon effects are believed to be key ingredients in the energy transfer in photosynthetic complexes, especially in complexes which exhibit a regime of intermediate exciton-phonon coupling. In this work, we utilize a recently developed measure for non-Markovianity to elucidate the exciton-phonon dynamics in terms of the information flow between electronic and vibrational degrees of freedom. We study the measure in the hierarchical equation of motion approach which captures strong coupling effects and nonequilibrium molecular reorganization. We propose an additional trace distance measure for the information flow that could be extended to other master equations. We find that for a model dimer system and for the Fenna-Matthews-Olson complex the non-Markovianity is significant under physiological conditions.
We present a detailed theoretical study of the transfer of electronic excitation energy through the Fenna-Matthews-Olson (FMO) pigment-protein complex, using the newly developed modified scaled hierarchical approach (Shi, Q.; et al. J. Chem. Phys. 2009, 130, 084105). We show that this approach is computationally more efficient than the original hierarchical approach. The modified approach reduces the truncation levels of the auxiliary density operators and the correlation function. We provide a systematic study of how the number of auxiliary density operators and the higher-order correlation functions affect the exciton dynamics. The time scales of the coherent beating are consistent with experimental observations. Furthermore, our theoretical results exhibit population beating at physiological temperature. Additionally, the method does not require a low-temperature correction to obtain the correct thermal equilibrium at long times.
Photosynthetic Reaction Center Complex Proteins/chemistry , Energy Transfer , Models, Theoretical , Quantum Theory , Temperature
Exciton transport in semiconductor nanoparticles underlies recent experiments on electrically controlled nanostructures and proposals for new artificial light-harvesting systems. In this work, we develop a novel method for the numerical evaluation of the Forster matrix element, based on a three-dimensional real space grid and the self-consistent solution of the mesoscopic exciton in a macroscopic dielectric environment. This method enables the study of the role of the nanoparticle shape, spatially varying dielectric environments, and externally applied electric fields. Depending on the orientation of the transition dipole, the Forster coupling is shown to be either increased or decreased as a function of the nanoparticle shape and of the properties of the dielectric environment. In the presence of an electric field, we investigate the relation between excitonic binding and confinement effects. We also study a type II core-shell quantum dot where electron and hole are spatially separated due to a particular configuration of the bandstructure.
We utilize the novel non-Markovian quantum jump (NMQJ) approach to stochastically simulate exciton dynamics derived from a time-convolutionless master equation. For relevant parameters and time scales, the time-dependent, oscillatory decoherence rates can have negative regions, a signature of non-Markovian behavior and of the revival of coherences. This can lead to non-Markovian population beatings for a dimer system at room temperature. We show that strong exciton-phonon coupling to low frequency modes can considerably modify transport properties. We observe increased exciton transport, which can be seen as an extension of recent environment-assisted quantum transport concepts to the non-Markovian regime. Within the NMQJ method, the Fenna-Matthew-Olson protein is investigated as a prototype for larger photosynthetic complexes.
The role of quantum coherence in the dynamics of photosynthetic energy transfer in chromophoric complexes is not fully understood. In this work, we quantify the biological importance of fundamental physical processes, such as the excitonic Hamiltonian evolution and phonon-induced decoherence, by their contribution to the efficiency of the primary photosynthetic event. We study the effect of spatial correlations in the phonon bath and slow protein scaffold movements on the efficiency and the contributing processes. To this end, we develop two theoretical approaches based on a Green's function method and energy transfer susceptibilities. We investigate the Fenna-Matthews-Olson protein complex, in which we find a contribution of coherent dynamics of about 10% in the presence of uncorrelated phonons and about 30% in the presence of realistically correlated ones.
Photosynthetic Reaction Center Complex Proteins/chemistry , Quantum Theory , Energy Transfer , Photosynthesis , Photosynthetic Reaction Center Complex Proteins/metabolism , Temperature
Energy transfer within photosynthetic systems can display quantum effects such as delocalized excitonic transport. Recently, direct evidence of long-lived coherence has been experimentally demonstrated for the dynamics of the Fenna-Matthews-Olson (FMO) protein complex [Engel et al., Nature (London) 446, 782 (2007)]. However, the relevance of quantum dynamical processes to the exciton transfer efficiency is to a large extent unknown. Here, we develop a theoretical framework for studying the role of quantum interference effects in energy transfer dynamics of molecular arrays interacting with a thermal bath within the Lindblad formalism. To this end, we generalize continuous-time quantum walks to nonunitary and temperature-dependent dynamics in Liouville space derived from a microscopic Hamiltonian. Different physical effects of coherence and decoherence processes are explored via a universal measure for the energy transfer efficiency and its susceptibility. In particular, we demonstrate that for the FMO complex, an effective interplay between the free Hamiltonian evolution and the thermal fluctuations in the environment leads to a substantial increase in energy transfer efficiency from about 70% to 99%.
Energy Transfer , Photosynthesis , Quantum Theory , Markov Chains , Models, Chemical , Proteins/chemistry , Proteins/metabolism , Temperature
