Article
Physics, Fluids & Plasmas
Tomas Notenson, Ignacio Garcia-Mata, Augusto J. Roncaglia, Diego A. Wisniacki
Summary: The OTOC is a measure of quantum information scrambling, commonly used to assess chaos in systems. This study extends this measure to systems with mixed dynamics, including the standard map, and demonstrates that the relaxation of the OTOC to equilibrium is governed by generalized classical resonances.
Article
Astronomy & Astrophysics
Tsutomu Ishikawa, Shoji Hashimoto
Summary: The proposed method utilizes lattice QCD to compute the Borel transform of the vacuum polarization function in the Shifman-Vainshtein-Zakharov QCD sum rule. By constructing the spectral sum corresponding to the Borel transform from two-point functions on the Euclidean lattice, the method is confirmed to be consistent with the operator product expansion in the large Borel mass region. This method provides a basis for direct comparison of OPE analyses with nonperturbative lattice computations.
Article
Astronomy & Astrophysics
Kishore Gopalakrishnan, Kandaswamy Subramanian
Summary: Fluxes of the magnetic helicity density are crucial for the growth of large-scale magnetic fields in turbulent dynamos. In this study, we analytically demonstrate the emergence of various types of magnetic helicity fluxes from triple correlators of fluctuating fields in the helicity density evolution equation. We also identify a new helicity flux contribution similar to Vishniac's proposal, which arises from the gradients of random magnetic and kinetic energies along the large-scale vorticity.
ASTROPHYSICAL JOURNAL
(2023)
Article
Mathematics
Francois Golse, Thierry Paul
Summary: This paper proves variants of the triangle inequality for the quantum analogues of the Wasserstein metric, introduces a different argument compared to the classical proof utilizing a Kantorovich duality analogy, and defines an analogue of the Brenier transport map.
JOURNAL OF FUNCTIONAL ANALYSIS
(2022)
Article
Multidisciplinary Sciences
Tianci Zhou, Brian Swingle
Summary: In chaotic many-body systems, scrambling or operator growth can be diagnosed by out-of-time-order correlators of local operators. We demonstrate that the growth of operators can also be observed in the out-of-time-order correlators of global operators. The characteristic spacetime shape of growing local operators can be accessed through global measurements without local control or readout. Based on a conjectured phase diagram for operator growth in chaotic systems with power-law interactions, we find that existing nuclear spin data for out-of-time-order correlators of global operators fit well with our theory. We also predict the observation of super-polynomial operator growth in dipolar systems in 3D and discuss its potential detection in future experiments with nuclear spins and ultra-cold polar molecules.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Michael V. Davidovich, Igor S. Nefedov, Olga E. Glukhova, Michael M. Slepchenkov, J. Miguel Rubi
Summary: We analyze the steady-state thermal regime of a one-dimensional triode resonant tunnelling structure. High currents generated by resonant tunnelling produce heat that may damage the structure, making it crucial to determine the optimal operating conditions. By calculating the current generated in the device and applying the principle of energy conservation in the electrodes, we obtain the temperature reached for different electrode materials and analyze the importance of heat conduction and thermal radiation. Our results demonstrate that conduction is the dominant factor and the electrode material plays a key role in determining the temperature variation.
SCIENTIFIC REPORTS
(2023)
Article
Physics, Multidisciplinary
Na-Na Zhang, Ming-Jie Tao, Wan-Ting He, Xin-Yu Chen, Xiang-Yu Kong, Fu-Guo Deng, Neill Lambert, Qing Ai
Summary: Simulation of open quantum dynamics for various photosynthetic systems shows that different spectral densities can impact energy transfer efficiency, and specific geometries and energy gaps can optimize energy transfer. The proposed approach proves to be universal for simulating the exact quantum dynamics of photosynthetic systems.
FRONTIERS OF PHYSICS
(2021)
Article
Multidisciplinary Sciences
Linhu Li, Sen Mu, Ching Hua Lee, Jiangbin Gong
Summary: The authors predict the emergence of quantized classical response in the steady-state response of a non-Hermitian system, based on the fundamental mathematical properties of the Green's function, without invoking linear response theory. This new paradigm is classical in nature, applies to non-Hermitian settings, and is characterized by fascinating plateaus with quantized values based on spectral winding numbers as topological invariants.
NATURE COMMUNICATIONS
(2021)
Article
Physics, Multidisciplinary
Akram Touil, Baris Cakmak, Sebastian Deffner
Summary: This study investigates the potential of utilizing quantum entanglement to extract thermodynamic work by analyzing bipartite quantum systems with locally thermal states. The results demonstrate that the quantum mutual information contributes to the extractable work, providing insights into the thermodynamic value of quantum correlations.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2022)
Article
Materials Science, Multidisciplinary
Thomas Bilitewski, Subhro Bhattacharjee, Roderich Moessner
Summary: The research focuses on correlations, transport, and chaos in a Heisenberg magnet as a classical many-body system. By exploring the effects of temperature and dimensionality, various physical phenomena and their correlations have been revealed, providing insights into the complex behavior of such systems.
Article
Chemistry, Physical
Nastasia Mauger, Thomas Ple, Louis Lagardere, Sara Bonella, Etienne Mangaud, Jean-Philip Piquemal, Simon Huppert
Summary: The study demonstrates the accuracy and efficiency of a method called adQTB for accounting for nuclear quantum effects in molecular simulations. A refined algorithm is proposed with improved accuracy, and simulations of liquid water using adQTB are reported. Comparisons with reference calculations show excellent accuracy in a wide range of structural and thermodynamic observables, as well as infrared vibrational spectra.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Materials Science, Multidisciplinary
Tobias Dornheim, Damar C. Wicaksono, Juan E. Suarez-Cardona, Panagiotis Tolias, Maximilian P. Boehme, Zhandos A. Moldabekov, Michael Hecht, Jan Vorberger
Summary: We propose an accurate framework for computing positive frequency moments M(alpha)(q) = (omega alpha) of different dynamic properties from imaginary-time quantum Monte Carlo data. As an application, we calculate the first five moments of the dynamic structure factor S(q, omega) of the uniform electron gas at the electronic Fermi temperature using ab initio path integral Monte Carlo simulations. We demonstrate excellent agreement with known sum rules for alpha = 1, 3, and provide results for alpha = 2, 4, 5. Our approach can be extended to other dynamic properties such as the single-particle spectral function A(q, omega), and has wide-ranging applications in studying ultracold atoms, exotic warm dense matter, and condensed matter systems.
Review
Chemistry, Multidisciplinary
Zhe Liu, Alessandro Sergi, Gabriel Hanna
Summary: Mixed quantum-classical dynamics is an efficient method for simulating the dynamics of quantum subsystems coupled to many-body environments. The recently developed DECIDE method has shown high accuracy and low computational cost, but has mainly been applied using subsystem and adiabatic energy bases. This review provides a step-by-step derivation of the DECIDE approach in a quantum harmonic oscillator position basis for a hydrogen bond model, demonstrating energy conservation and calculating various quantities of interest. Limitations of incomplete basis representation are also discussed.
APPLIED SCIENCES-BASEL
(2022)
Article
Physics, Multidisciplinary
Francois Gay-Balmaz, Cesare Tronci
Summary: A fully Hamiltonian theory of quantum-classical spin dynamics is proposed, which ensures a series of consistency properties and satisfies Heisenberg's uncertainty principle. The theory models the interaction between a classical Bloch vector and a quantum spin observable, and can be extended to systems with multiple spins and orbital degrees of freedom.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2023)
Article
Physics, Fluids & Plasmas
Gustavo Montes, Soham Biswas, Thomas Gorin
Summary: In this paper, we construct quantum analogs by replacing random decisions in classical stochastic processes with superpositions of all paths. These analogs generate and destroy coherences, which can change the scaling behavior of classical observables. Using zero temperature Glauber dynamics in a linear Ising spin chain, we find quantum analogs with different domain growth exponents.
Article
Chemistry, Medicinal
Devon P. Holst, Pascal Friederich, Alan Aspuru-Guzik, Timothy P. Bender
Summary: This study used various computational methods to calibrate and compare the frontier orbital energies and optical gaps of novel boron subphthalocyanine derivatives and related compounds. The results showed that computationally inexpensive semiempirical methods outperformed most density functional theory methods for calibration. By using free software and a standard laptop, researchers can confidently determine the physical properties of these materials before the synthesis and purification process.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Chemistry, Physical
Evan Komp, Nida Janulaitis, Stephanie Valleau
Summary: Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features, overcoming the infeasibility caused by the curse of dimensionality. The research discusses various kinetic datasets, input feature representations, and the use and design of machine learning algorithms for predicting reaction rate constants. Areas for further exploration to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants are also identified.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2022)
Article
Chemistry, Multidisciplinary
Martin Seifrid, Riley J. Hickman, Andres Aguilar-Granda, Cyrille Lavigne, Jenya Vestfrid, Tony C. Wu, Theophile Gaudin, Emily J. Hopkins, Alan Aspuru-Guzik
Summary: Self-driving laboratories, in the form of automated experimentation platforms guided by machine learning algorithms, have emerged as a potential solution to the need for accelerated science. While automated synthesis remains a bottleneck, combining automated and manual synthesis efforts significantly expands the explorable chemical space. Quantifying the cost and considering the capabilities of both automated and manual synthesis can help determine the most efficient synthetic route.
ACS CENTRAL SCIENCE
(2022)
Article
Chemistry, Physical
Phillip W. K. Jensen, Lasse Bjorn Kristensen, Cyrille Lavigne, Alan Aspuru-Guzik
Summary: This study explores the application of molecules and molecular electronics in quantum computing, constructing one-qubit gates using scattering in molecules and two-qubit controlled-phase gates using electron-electron scattering along metallic leads. Furthermore, a class of circuit implementations is proposed, and the framework is demonstrated by illustrating one-qubit gates using the molecular electronic structure of molecular hydrogen as a baseline model.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Correction
Chemistry, Multidisciplinary
Sungwon Kim, Juhwan Noh, Geun Ho Gu, Alan Aspuru-Guzik, Yousung Jung
ACS CENTRAL SCIENCE
(2022)
Review
Chemistry, Multidisciplinary
Martin Seifrid, Robert Pollice, Andres Aguilar-Granda, Zamyla Morgan Chan, Kazuhiro Hotta, Cher Tian Ser, Jenya Vestfrid, Tony C. Wu, Alan Aspuru-Guzik
Summary: Self-driving laboratories have great potential for development, but there are still many challenges to be overcome. Cognitive challenges include optimization with constraints and unexpected outcomes, for which general algorithmic solutions have not yet been developed. A more practical challenge is software control and integration, as few instrument manufacturers design products with self-driving laboratories in mind. Motor function challenges mainly involve handling heterogeneous systems, such as dispensing solids or performing extractions. Therefore, it is important to carefully reconsider the translation of manual experimental protocols for self-driving laboratories.
ACCOUNTS OF CHEMICAL RESEARCH
(2022)
Editorial Material
Chemistry, Physical
John M. Herbert, Martin Head-Gordon, Hrant P. Hratchian, Teresa Head-Gordon, Rommie E. Amaro, Alan Aspuru-Guzik, Roald Hoffmann, Carol A. Parish, Christina M. Payne, Troy Van Voorhis
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2022)
Review
Nanoscience & Nanotechnology
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, Luis Martin Mejia-Mendoza, Xin Zhou, Yonggang Wen, Alan Aspuru-Guzik, Edward H. Sargent, Zhi Wei Seh
Summary: This Perspective highlights the recent advances in machine learning-driven energy research and proposes a set of key performance indicators to compare the benefits of different ML-accelerated workflows in the field of renewable energy.
NATURE REVIEWS MATERIALS
(2023)
Article
Multidisciplinary Sciences
Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Hase, Alan Aspuru-Guzik, Christine Allen
Summary: Long-acting injectables are considered promising for chronic disease treatment, and this study demonstrates the use of machine learning to predict drug release and guide the design of new formulations. The data-driven approach has the potential to reduce development time and cost.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Medicinal
Po-Yu Kao, Ya-Chu Yang, Wei-Yin Chiang, Jen-Yueh Hsiao, Yudong Cao, Alex Aliper, Feng Ren, Alan Aspuru-Guzik, Alex Zhavoronkov, Min-Hsiu Hsieh, Yen-Chu Lin
Summary: This article explores the application of hybrid quantum-classical generative adversarial networks (GAN) in drug discovery. By substituting each element of GAN with a variational quantum circuit (VQC), small molecule discovering is achieved. Applying VQC in both the noise generator and discriminator, it can generate small molecules with better physicochemical properties and performance while having fewer trainable parameters. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Medicinal
Stanley Lo, Martin Seifrid, Theeophile Gaudin, Alaan Aspuru-Guzik
Summary: One of the biggest challenges in polymer property prediction is finding an effective representation that accurately captures the sequence of repeat units. Inspired by data augmentation techniques in computer vision and natural language processing, we explore rearranging the molecular representation iteratively while preserving connectivity to augment polymer data and reveal additional substructural information. We evaluate the impact of this technique on machine learning models trained on three polymer datasets and compare it to common molecular representations. Data augmentation does not significantly improve machine learning property prediction performance compared to non-augmented representations, except in datasets where the target property is primarily influenced by the polymer sequence.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Chemistry, Physical
Philipp Schleich, Joseph Boen, Lukasz Cincio, Abhinav Anand, Jakob S. Kottmann, Sergei Tretiak, Pavel A. Dub, Alan Aspuru-Guzik
Summary: The limited availability of noisy qubits in current quantum computing hardware restricts the investigation of larger, more complex molecules in quantum chemistry calculations. In this study, a classical and near-classical treatment within the framework of quantum circuits is explored. A product ansatz for the parametrized wavefunction is used, along with post-treatment to account for interactions between subsystems. The circuit structure is molecule-dependent and is constructed using simulated annealing and genetic algorithms.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Sergio Pablo-Garcia, Santiago Morandi, Rodrigo A. Vargas-Hernandez, Kjell Jorner, Zarko Ivkovic, Nuria Lopez, Alan Aspuru-Guzik
Summary: GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory.
NATURE COMPUTATIONAL SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Naruki Yoshikawa, Kourosh Darvish, Mohammad Ghazi Vakili, Animesh Garg, Alan Aspuru-Guzik
Summary: Self-driving laboratories require robotic liquid handling and transfer, and we propose a 3D-printed digital pipette design that overcomes the limitations of current robot grippers. It is cost-effective and easy to assemble, and performance evaluation shows comparable precision to commercial devices.
Review
Chemistry, Multidisciplinary
Martin Seifrid, Robert Pollice, Andres Aguilar-Granda, Zamyla Morgan Chan, Kazuhiro Hotta, Cher Tian Ser, Jenya Vestfrid, Tony C. Wu, Alan Aspuru-Guzik
Summary: To address climate change and disease risks, it is crucial to accelerate technological advancements through better integration between hypothesis generation, design, experimentation, and data analysis. Automated laboratories can significantly speed up molecular and materials discovery by generating information-rich data. Open high-quality datasets will enhance the accessibility and reproducibility of science. This paper presents successful efforts in building self-driving laboratories for the development of new materials.
ACCOUNTS OF CHEMICAL RESEARCH
(2022)