Electronic Coherences Excited by an Ultra Short Pulse Are Robust with Respect to Averaging over Randomly Oriented Molecules as Shown by Singular Value Decomposition.

We report a methodology for averaging quantum photoexcitation vibronic dynamics over the initial orientations of the molecules with respect to an ultrashort light pulse. We use singular value decomposition of the ensemble density matrix of the excited molecules, which allows the identification of the few dominant principal molecular orientations with respect to the polarization direction of the electric field. The principal orientations provide insights into the specific stereodynamics of the corresponding principal molecular vibronic states. The massive compaction of the vibronic density matrix of the ensemble of randomly oriented pumped molecules enables a most efficient fully quantum mechanical time propagation scheme. Two examples are discussed for the quantum dynamics of the LiH molecule in the manifolds of its electronically excited Σ and Π states. Our results show that electronic and vibrational coherences between excited states of the same symmetry are resilient to averaging over an ensemble of molecular orientations and can be selectively excited at the ensemble level by tuning the pulse parameters.

Time-Frequency Signatures of Electronic Coherence of Colloidal CdSe Quantum Dot Dimer Assemblies Probed at Room Temperature by Two-Dimensional Electronic Spectroscopy.

Electronic coherence signatures can be directly identified in the time-frequency maps measured in two-dimensional electronic spectroscopy (2DES). Here, we demonstrate the theory and discuss the advantages of this approach via the detailed application to the fast-femtosecond beatings of a wide variety of electronic coherences in ensemble dimers of quantum dots (QDs), assembled from QDs of 3 nm in diameter, with 8% size dispersion in diameter. The observed and computed results can be consistently characterized directly in the time-frequency domain by probing the polarization in the 2DES setup. The experimental and computed time-frequency maps are found in very good agreement, and several electronic coherences are characterized at room temperature in solution, before the extensive dephasing due to the size dispersion begins. As compared to the frequency-frequency maps that are commonly used in 2DES, the time-frequency maps allow exploiting electronic coherences without additional post-processing and with fewer 2DES measurements. Towards quantum technology applications, we also report on the modeling of the time-frequency photocurrent response of these electronic coherences, which paves the way to integrating QD devices with classical architectures, thereby enhancing the quantum advantage of such technologies for parallel information processing at room temperature.

Recombination of N Atoms in a Manifold of Electronic States Simulated by Time-Reversed Nonadiabatic Photodissociation Dynamics of N2.

Following a single photon VUV absorption, the N2 molecule dissociates into distinct channels leading to N atoms of different reactivities. The optically accessible singlets are bound, and dissociation occurs through spin-orbit induced transfer to the triplets. There is a forest of coupled electronic states, and we here aim to trace a path along the nonadiabatic couplings toward a particular exit channel. To achieve this, we apply a time-reversed quantum dynamical approach that corresponds to a dissociation running back. It begins with an atom-atom relative motion in a particular product channel. Starting with a Gaussian wave packet at the dissociation region of N2 and propagating it backward in time, one can see the population transferring among the triplets due to a strong nonadiabatic interaction between these states. Simultaneously, the optically active singlets get populated because of spin-orbit coupling to the triplets. Thus, backward propagation traces the nonradiative association of nitrogen atoms.

Nonadiabatic dynamics in a forest of coupled states: Electronic state branching in the VUV photodissociation of N2.

Multi-state electronic dynamics at higher excitation energies is needed for the understanding of a variety of energy rich situations, including chemistry under extreme conditions, vacuum ultraviolet (VUV) induced astrochemistry, and attochemistry. It calls for an understanding of three stages, energy acquisition, dynamical propagation, and disposal. It is typically not possible to identify a basis of uncoupled quantum states that is sufficient for the three stages. The handicap is the large number of coupled quantum states that is needed to describe the system. Progress in quantum chemistry provides the necessary background to the energetics and the coupling. Progress in quantum dynamics takes this as input for the propagation in time. Right now, it seems that we have come of age with potential detailed applications. We here report a demonstration to a coupled electron-nuclear quantum dynamics through a maze of 47 electronic states and with attention to the order in perturbation theory that is indicated using propensity rules for couplings. Close agreement with experimental results for the VUV photodissociation of 14N2 and its isotopomer 14N15N is achieved. We pay special attention to the coupling between two dissociative continua and an optically accessible bound domain. The computations reproduce and interpret the non-monotonic branching between the two exit channels producing N(2D) and N(2P) atoms as a function of excitation energy and its variation with the mass.

Hamiltonian Learning from Time Dynamics Using Variational Algorithms.

The Hamiltonian of a quantum system governs the dynamics of the system via the Schrodinger equation. In this paper, the Hamiltonian is reconstructed in the Pauli basis using measurables on random states forming a time series data set. The time propagation is implemented through Trotterization and optimized variationally with gradients computed on the quantum circuit. We validate our output by reproducing the dynamics of unseen observables on a randomly chosen state not used for the optimization. Unlike existing techniques that try and exploit the structure/properties of the Hamiltonian, our scheme is general and provides freedom with regard to what observables or initial states can be used while still remaining efficient with regard to implementation. We extend our protocol to doing quantum state learning where we solve the reverse problem of doing state learning given time series data of observables generated against several Hamiltonian dynamics. We show results on Hamiltonians involving XX, ZZ couplings along with transverse field Ising Hamiltonians and propose an analytical method for the learning of Hamiltonians consisting of generators of the SU(3) group. This paper is likely to pave the way toward using Hamiltonian learning for time series prediction within the context of quantum machine learning algorithms.

Variational approach to quantum state tomography based on maximal entropy formalism.

Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct, with high fidelity, the quantum states of a quantum system. From an experiment on a real quantum device, one can obtain the mean measurement values of different operators. With such data as input, in this report we employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values. Even though, in principle, the reported formalism is quite general and should work for an arbitrary set of observables, in practice we shall demonstrate the efficacy of the algorithm on an informationally complete (IC) set of Hermitian operators. Such a set possesses the advantage of uniquely specifying a single quantum state from which the experimental measurements have been sampled and hence renders the rare opportunity not only to construct a least-biased quantum state but even replicate the exact state prepared experimentally within a preset tolerance. The primary workhorse of the algorithm is reconstructing an energy function which we designate as the effective Hamiltonian of the system, and parameterizing it with Lagrange multipliers, according to the formalism of maximal entropy. These parameters are thereafter optimized variationally so that the reconstructed quantum state of the system converges to the true quantum state within an error threshold. To this end, we employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state, making our recipe easily implementable on a near-term quantum device.

Convergence of a Reconstructed Density Matrix to a Pure State Using the Maximal Entropy Approach.

Impressive progress has been made in the past decade in the study of technological applications of varied types of quantum systems. With industry giants like IBM laying down their roadmap for scalable quantum devices with more than 1000-qubits by the end of 2023, efficient validation techniques are also being developed for testing quantum processing on these devices. The characterization of a quantum state is done by experimental measurements through the process called quantum state tomography (QST) which scales exponentially with the size of the system. However, QST performed using incomplete measurements is aptly suited for characterizing these quantum technologies especially with the current nature of noisy intermediate-scale quantum (NISQ) devices where not all mean measurements are available with high fidelity. We, hereby, propose an alternative approach to QST for the complete reconstruction of the density matrix of a quantum system in a pure state for any number of qubits by applying the maximal entropy formalism on the pairwise combinations of the known mean measurements. This approach provides the best estimate of the target state when we know the complete set of observables, which is the case of convergence of the reconstructed density matrix to a pure state. Our goal is to provide a practical inference of a quantum system in a pure state that can find its applications in the field of quantum error mitigation on a real quantum computer that we intend to investigate further.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells.

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line.

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations.

Acetate can be efficiently metabolized by the green microalga Chlamydomonasreinhardtii. The regular concentration is 17 mM, although higher concentrations are reported to increase starch and fatty acid content. To understand the responses to higher acetate concentrations, Chlamydomonas cells were cultivated in batch mode in the light at 17, 31, 44, and 57 mM acetate. Metabolic analyses show that cells grown at 57 mM acetate possess increased contents of all components analyzed (starch, chlorophylls, fatty acids, and proteins), with a three-fold increased volumetric biomass yield compared to cells cultivated at 17 mM acetate at the entry of stationary phase. Physiological analyses highlight the importance of photosynthesis for the low-acetate and exponential-phase samples. The stationary phase is reached when acetate is depleted, except for the cells grown at 57 mM acetate, which still divide until ammonium exhaustion. Surprisal analysis of the transcriptomics data supports the biological significance of our experiments. This allows the establishment of a model for acetate assimilation, its transcriptional regulation and the identification of candidates for genetic engineering of this metabolic pathway. Altogether, our analyses suggest that growing at high-acetate concentrations could increase biomass productivities in low-light and CO2-limiting air-bubbled medium for biotechnology.

Surprisal analysis of genome-wide transcript profiling identifies differentially expressed genes and pathways associated with four growth conditions in the microalga Chlamydomonas.

The usual cultivation mode of the green microalga Chlamydomonas is liquid medium and light. However, the microalga can also be grown on agar plates and in darkness. Our aim is to analyze and compare gene expression of cells cultivated in these different conditions. For that purpose, RNA-seq data are obtained from Chlamydomonas samples of two different labs grown in four environmental conditions (agar@light, agar@dark, liquid@light, liquid@dark). The RNA seq data are analyzed by surprisal analysis, which allows the simultaneous meta-analysis of all the samples. First we identify a balance state, which defines a state where the expression levels are similar in all the samples irrespectively of their growth conditions, or lab origin. In addition our analysis identifies additional constraints needed to quantify the deviation with respect to the balance state. The first constraint differentiates the agar samples versus the liquid ones; the second constraint the dark samples versus the light ones. The two constraints are almost of equal importance. Pathways involved in stress responses are found in the agar phenotype while the liquid phenotype comprises ATP and NADH production pathways. Remodeling of membrane is suggested in the dark phenotype while photosynthetic pathways characterize the light phenotype. The same trends are also present when performing purely statistical analysis such as K-means clustering and differentially expressed genes.

Addressing the Challenge of Molecular Change: An Interim Report.

Invited by the editorial committee of the Annual Review of Physical Chemistry to "contribute my autobiography," I present it here, as I understand the term. It is about my parents, my mentors, my coworkers, and my friends in learning and the scientific problems that we tried to address. Courtesy of the editorial assistance of Annual Reviews, some of the science is in the figure captions and sidebars. I am by no means done: I am currently trying to fuse the quantitative rigor of physical chemistry with systems biology while also dealing with a post-Born-Oppenheimer regime in electronic dynamics and am attempting to instruct molecules to perform advanced logic.

Implementation of Multivariable Logic Functions in Parallel by Electrically Addressing a Molecule of Three Dopants in Silicon.

To realize low-power, compact logic circuits, one can explore parallel operation on single nanoscale devices. An added incentive is to use multivalued (as distinct from Boolean) logic. Here, we theoretically demonstrate that the computation of all the possible outputs of a multivariate, multivalued logic function can be implemented in parallel by electrical addressing of a molecule made up of three interacting dopant atoms embedded in Si. The electronic states of the dopant molecule are addressed by pulsing a gate voltage. By simulating the time evolution of the non stationary electronic density built by the gate voltage, we show that one can implement a molecular decision tree that provides in parallel all the outputs for all the inputs of the multivariate, multivalued logic function. The outputs are encoded in the populations and in the bond orders of the dopant molecule, which can be measured using an STM tip. We show that the implementation of the molecular logic tree is equivalent to a spectral function decomposition. The function that is evaluated can be field-programmed by changing the time profile of the pulsed gate voltage.

Implementation of Probabilistic Algorithms by Multi-chromophoric Molecular Networks with Application to Multiple Travelling Pathways.

The implementation of probabilistic algorithms by deterministic hardware is demanding and requires hundreds of instructions to generate a pseudo-random sequence of numbers. On the contrary, the dynamics at the molecular scale is physically governed by probabilistic laws because of the stochastic nature of thermally activated and quantum processes. By simulating the exciton transfer dynamics in a multi-chromophoric system, we demonstrate the implementation of a random walk that samples the possible pathways of a traveler through a network and can be probed by time-resolved fluorescence spectroscopy. The ability of controlling the spatial arrangement of the chromophores allows us to design the "landscape" in which the traveler is moving and therefore to program the molecular device.

Nuclear Motion Driven Ultrafast Photodissociative Charge Transfer of the PENNA Cation: An Experimental and Computational Study.

Ultrafast nuclear driven charge transfer prior to dissociation is an important process in modular systems as was demonstrated experimentally in the bifunctional molecule 2-phenylethyl-N,N-dimethylamine (PENNA) in work by Lehr et al. ( J. Phys. Chem. A 2005 , 109 , 8074 ). The ultrafast dynamics of PENNA photoexcited to the three lowest electronic states of the cation (D0, D1, and D2) was studied using quantum chemistry and surface hoping. We show that a conical intersection, localized in the Franck-Condon region, between the D0 and the D1 states, leads to an ultrafast charge transfer, computed here to be on a time scale of 65 fs, between the phenyl and the amine charged subunits. On the D0 ground state, the dissociation proceeds on the 60 ps time scale through a 19 kcal/mol late barrier. The computed kinetic energy release is in good agreement with a new experimental measurement of PENNA ionization by an 800 nm 30 fs intense laser pulse.

Critical Points in Tumorigenesis: A Carcinogen-Initiated Phase Transition Analyzed via Single-Cell Proteomics.

A kinetic, single-cell proteomic study of chemically induced carcinogenesis is interpreted by treating the single-cell data as fluctuations of an open system transitioning between different steady states. In analogy to a first-order transition, phase coexistence and the loss of degrees of freedom are observed. The transition is detected well before the appearance of the traditional biomarker of the carcinogenic transformation.

[Prediction of the molecular response to pertubations from single cell measurements]. / Prédiction de la réponse moléculaire à des perturbations mesurée sur des cellules uniques.

The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure.

Surprisal analysis of Glioblastoma Multiform (GBM) microRNA dynamics unveils tumor specific phenotype.

Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.

Surprisal analysis of transcripts expression levels in the presence of noise: a reliable determination of the onset of a tumor phenotype.

Towards a reliable identification of the onset in time of a cancer phenotype, changes in transcription levels in cell models were tested. Surprisal analysis, an information-theoretic approach grounded in thermodynamics, was used to characterize the expression level of mRNAs as time changed. Surprisal Analysis provides a very compact representation for the measured expression levels of many thousands of mRNAs in terms of very few - three, four - transcription patterns. The patterns, that are a collection of transcripts that respond together, can be assigned definite biological phenotypic role. We identify a transcription pattern that is a clear marker of eventual malignancy. The weight of each transcription pattern is determined by surprisal analysis. The weight of this pattern changes with time; it is never strictly zero but it is very low at early times and then rises rather suddenly. We suggest that the low weights at early time points are primarily due to experimental noise. We develop the necessary formalism to determine at what point in time the value of that pattern becomes reliable. Beyond the point in time when a pattern is deemed reliable the data shows that the pattern remain reliable. We suggest that this allows a determination of the presence of a cancer forewarning. We apply the same formalism to the weight of the transcription patterns that account for healthy cell pathways, such as apoptosis, that need to be switched off in cancer cells. We show that their weight eventually falls below the threshold. Lastly we discuss patient heterogeneity as an additional source of fluctuation and show how to incorporate it within the developed formalism.

Perspectives in metabolic engineering: understanding cellular regulation towards the control of metabolic routes.

Metabolic engineering seeks to redirect metabolic pathways through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant technology. Many of the chemicals synthesized via introduction of product-specific enzymes or the reconstruction of entire metabolic pathways into engineered hosts that can sustain production and can synthesize high yields of the desired product as yields of natural product-derived compounds are frequently low, and chemical processes can be both energy and material expensive; current endeavors have focused on using biologically derived processes as alternatives to chemical synthesis. Such economically favorable manufacturing processes pursue goals related to sustainable development and "green chemistry". Metabolic engineering is a multidisciplinary approach, involving chemical engineering, molecular biology, biochemistry, and analytical chemistry. Recent advances in molecular biology, genome-scale models, theoretical understanding, and kinetic modeling has increased interest in using metabolic engineering to redirect metabolic fluxes for industrial and therapeutic purposes. The use of metabolic engineering has increased the productivity of industrially pertinent small molecules, alcohol-based biofuels, and biodiesel. Here, we highlight developments in the practical and theoretical strategies and technologies available for the metabolic engineering of simple systems and address current limitations.