4.8 Review

Quantum Chemistry in the Age of Quantum Computing

期刊

CHEMICAL REVIEWS
卷 119, 期 19, 页码 10856-10915

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.chemrev.8b00803

关键词

-

资金

  1. Army Research Office [W911NF-15-1-0256]
  2. Vannevar Bush Faculty Fellowship program - Basic Research Office of the Assistant Secretary of Defense for Research and Engineering [ONR 00014-16-1-2008]
  3. Canada 150 Research Chair Program
  4. National Sciences and Engineering Research Council of Canada
  5. Czech Science Foundation [18-18940Y]
  6. DOE Computational Science Graduate Fellowship [DE-FG02-97ER25308]
  7. Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via U.S. Army Research Office [W911NF-17-C-0050]
  8. the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Quantum Algorithms Teams Program

向作者/读者索取更多资源

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century. Although many approximation methods have been introduced, the complexity of quantum mechanics remains hard to appease. The advent of quantum computation brings new pathways to navigate this challenging and complex landscape. By manipulating quantum states of matter and taking advantage of their unique features such as superposition and entanglement, quantum computers promise to efficiently deliver accurate results for many important problems in quantum chemistry, such as the electronic structure of molecules. In the past two decades, significant advances have been made in developing algorithms and physical hardware for quantum computing, heralding a revolution in simulation of quantum systems. This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Review Chemistry, Multidisciplinary

A quantum computing view on unitary coupled cluster theory

Abhinav Anand, Philipp Schleich, Sumner Alperin-Lea, Phillip W. K. Jensen, Sukin Sim, Manuel Diaz-Tinoco, Jakob S. Kottmann, Matthias Degroote, Artur F. Izmaylov, Alan Aspuru-Guzik

Summary: This article provides a review of the Unitary Coupled Cluster (UCC) ansatz and related methods used to solve electronic structure problems on quantum computers. It covers the history and formulation of the Coupled Cluster (CC) methods, including the application of UCC in quantum computing. The article discusses the implementation of UCC and its derived methods specific to quantum computing. It concludes with a summary of the reviewed methods and a reflection on open problems in the field.

CHEMICAL SOCIETY REVIEWS (2022)

Review Physics, Multidisciplinary

Noisy intermediate-scale quantum algorithms

Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, Alan Aspuru-Guzik

Summary: NISQ computers, composed of noisy qubits, are already being used in various fields. This review provides a comprehensive summary of NISQ computational paradigms and algorithms and introduces various benchmarking and software tools for programming and testing NISQ devices.

REVIEWS OF MODERN PHYSICS (2022)

Article Chemistry, Multidisciplinary

Towards the simulation of large scale protein-ligand interactions on NISQ-era quantum computers

Fionn D. Malone, Robert M. Parrish, Alicia R. Welden, Thomas Fox, Matthias Degroote, Elica Kyoseva, Nikolaj Moll, Raffaele Santagati, Michael Streif

Summary: We have developed a simple and efficient method for calculating interaction energies between large molecular systems using symmetry-adapted perturbation theory (SAPT) and the variational quantum eigensolver (VQE). The SAPT(VQE) method achieves high accuracy and reduces the quantum requirements.

CHEMICAL SCIENCE (2022)

Review Nanoscience & Nanotechnology

Machine learning for a sustainable energy future

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 Physics, Applied

Readout of a quantum processor with high dynamic range Josephson parametric amplifiers

Theodore White, Alex Opremcak, George Sterling, Alexander Korotkov, Daniel Sank, Rajeev Acharya, Markus Ansmann, Frank Arute, Kunal Arya, Joseph C. Bardin, Andreas Bengtsson, Alexandre Bourassa, Jenna Bovaird, Leon Brill, Bob B. Buckley, David A. Buell, Tim Burger, Brian Burkett, Nicholas Bushnell, Zijun Chen, Ben Chiaro, Josh Cogan, Roberto Collins, Alexander L. Crook, Ben Curtin, Sean Demura, Andrew Dunsworth, Catherine Erickson, Reza Fatemi, Leslie Flores Burgos, Ebrahim Forati, Brooks Foxen, William Giang, Marissa Giustina, Alejandro Grajales Dau, Michael C. Hamilton, Sean D. Harrington, Jeremy Hilton, Markus Hoffmann, Sabrina Hong, Trent Huang, Ashley Huff, Justin Iveland, Evan Jeffrey, Maria Kieferova, Seon Kim, Paul V. Klimov, Fedor Kostritsa, John Mark Kreikebaum, David Landhuis, Pavel Laptev, Lily Laws, Kenny Lee, Brian J. Lester, Alexander Lill, Wayne Liu, Aditya Locharla, Erik Lucero, Trevor McCourt, Matt McEwen, Xiao Mi, Kevin C. Miao, Shirin Montazeri, Alexis Morvan, Matthew Neeley, Charles Neill, Ani Nersisyan, Jiun How Ng, Anthony Nguyen, Murray Nguyen, Rebecca Potter, Chris Quintana, Pedram Roushan, Kannan Sankaragomathi, Kevin J. Satzinger, Christopher Schuster, Michael J. Shearn, Aaron Shorter, Vladimir Shvarts, Jindra Skruzny, W. Clarke Smith, Marco Szalay, Alfredo Torres, Bryan W. K. Woo, Z. Jamie Yao, Ping Yeh, Juhwan Yoo, Grayson Young, Ningfeng Zhu, Nicholas Zobrist, Yu Chen, Anthony Megrant, Julian Kelly, Ofer Naaman

Summary: We present a high dynamic range Josephson parametric amplifier (JPA) that utilizes an array of rf-SQUIDs as the active nonlinear element. The amplifier is matched to the 50-ohm environment and achieves a bandwidth of 250-300 MHz with input saturation powers up to -95 dBm at 20 dB gain. A 54-qubit Sycamore processor is utilized for benchmarking these devices, providing calibration for readout power, estimation of amplifier added noise, and comparison against standard impedance matched parametric amplifiers with a single dc-SQUID. The results demonstrate that the high power rf-SQUID array design has no adverse effects on system noise, readout fidelity, or qubit dephasing, with an estimated upper bound of amplifier added noise at 1.6 times the quantum limit. Additionally, this design shows no degradation in readout fidelity due to gain compression that can occur in multi-tone multiplexed readout with traditional JPAs.

APPLIED PHYSICS LETTERS (2023)

Article Chemistry, Physical

Toward more accurate adiabatic connection approach for multireference wavefunctions

Mikulas Matousek, Michal Hapka, Libor Veis, Katarzyna Pernal

Summary: A multiconfigurational adiabatic connection (AC) formalism is an attractive approach to compute the dynamic correlation within DMRG models. The study investigates the effect of removing the fixed-RDM approximation in AC and finds that lifting this approximation is a viable way toward improving the accuracy of existing AC approximations.

JOURNAL OF CHEMICAL PHYSICS (2023)

Article Chemistry, Physical

Projection-Based Density Matrix Renormalization Group in Density Functional Theory Embedding

Pavel Beran, Katarzyna Pernal, Fabijan Pavosevic, Libor Veis

Summary: The density matrix renormalization group (DMRG) method is an efficient and accurate computational method for treating strong correlation in large active spaces. However, its application on larger molecules is limited by computational scaling and the treatment of missing dynamical electron correlation. In this work, we present a first step towards combining DMRG with density functional theory (DFT) through projection-based wave function (WF)-in-DFT embedding. On proof-of-concept molecular examples, we demonstrate that the developed DMRG-in-DFT approach accurately describes molecules with strongly correlated fragments.

JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2023)

Article Multidisciplinary Sciences

Machine learning models to accelerate the design of polymeric long-acting injectables

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

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

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

Augmenting Polymer Datasets by Iterative Rearrangement

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

Partitioning Quantum Chemistry Simulations with Clifford Circuits

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 Chemistry, Physical

Role of Spin Polarization and Dynamic Correlation in Singlet-Triplet Gap Inversion of Heptazine Derivatives

Daria Drwal, Mikulas Matousek, Pavlo Golub, Aleksandra Tucholska, Michal Hapka, Jiri Brabec, Libor Veis, Katarzyna Pernal

Summary: The new generation of proposed light-emitting molecules for OLEDs with a negative ST gap has attracted research interest. Spin polarization plays a role in the inversion mechanism of the ST gap. A descriptor for screening candidate molecules with negative ST gaps is proposed. Numerical results show that the effect of spin polarization decreases linearly with increasing HOMO-LUMO exchange integral.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2023)

Article Computer Science, Interdisciplinary Applications

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks

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

Digital pipette: open hardware for liquid transfer in self-driving laboratories

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.

DIGITAL DISCOVERY (2023)

Review Chemistry, Multidisciplinary

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

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)

暂无数据