Article
Multidisciplinary Sciences
Joshua J. Goings, Alec White, Joonho Lee, Christofer S. Tautermann, Matthias Degroote, Craig Gidney, Toru Shiozaki, Ryan Babbush, Nicholas C. Rubin
Summary: An accurate assessment of the potential computational advantages of quantum computers in chemical simulation is crucial for their deployment. This study explores the resources required for assessing the electronic structure of cytochrome P450 enzymes using quantum and classical computations, defining a boundary for classical-quantum advantage. The results show that simulation of large-scale CYP models has the potential to be a quantum advantage problem, emphasizing the interplay between classical computations and quantum algorithms in chemical simulation.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Quantum Science & Technology
Ilya G. Ryabinkin, Artur F. Izmaylov, Scott N. Genin
Summary: The iterative qubit coupled cluster (iQCC) method is a systematic variational approach for solving electronic structure problems on universal quantum computers. It reduces the number of iterations needed to achieve desired accuracy, and introduces corrections based on perturbation theory series for efficient evaluation on classical computers. Additionally, the method introduces the concept of qubit active-space to further reduce quantum resource requirements.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Article
Quantum Science & Technology
Saad Yalouz, Bruno Senjean, Jakob Gunther, Francesco Buda, Thomas E. O'Brien, Lucas Visscher
Summary: In the NISQ era, solving the electronic structure problem in chemistry through a combination of different algorithms is crucial. Research on active spaces and conical intersections is essential for improving the accuracy of quantum computers.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Article
Chemistry, Multidisciplinary
Karol Kowalski, Nicholas P. Bauman
Summary: This paper presents an extension of many-body downfolding methods to reduce the resources required in the quantum phase estimation (QPE) algorithm. By employing Fock-space variants of the SW transformation (or RRST), the qubit-mapped similarity-transformed Hamiltonians can have significantly increased locality, simplifying quantum circuit simulations of quantum dynamics.
APPLIED SCIENCES-BASEL
(2023)
Article
Physics, Multidisciplinary
Oliver G. Maupin, Andrew D. Baczewski, Peter J. Love, Andrew J. Landahl
Summary: This article presents example quantum chemistry programs written with JaqalPaq, intended to be run on the QSCOUT platform, using the VQE algorithm to compute ground state energies of H2, HeH+, and LiH molecules. The second-quantized Hamiltonians are calculated with the PySCF package, and fermion-to-qubit mappings are obtained from the OpenFermion package. Emulation using JaqalPaq is used to compare simulated bond-dissociation curves with exact forms.
Article
Quantum Science & Technology
Zi-Jian Zhang, Thi Ha Kyaw, Jakob S. Kottmann, Matthias Degroote, Alan Aspuru-Guzik
Summary: This work presents a method for constructing reduced-size entangler pools leveraging classical algorithms, which ranks and screens entanglers based on mutual information between qubits in classically approximated ground state. Numerical experiments demonstrate that a reduced entangler pool can achieve the same numerical accuracy as the original pool, paving a new way for adaptive construction of ansatz circuits in variational quantum algorithms.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Article
Physics, Multidisciplinary
Dan-Bo Zhang, Bin-Lin Chen, Zhan-Hao Yuan, Tao Yin
Summary: This paper proposes a new variational quantum eigensolver (VQE) based on minimizing energy variance, called variance-VQE. Unlike traditional VQE methods, variance-VQE treats ground and excited states equally, and finds low-energy excited states by optimizing a combination of energy and variance, which is more efficient than minimizing energy or variance alone. The optimization can be boosted with stochastic gradient descent by Hamiltonian sampling, reducing the need for quantum resources.
Article
Physics, Multidisciplinary
Cica Gustiani, Richard Meister, Simon C. Benjamin
Summary: Variational methods are a promising approach for quantum computers to solve chemistry problems. In this study, we use adaptive evolving quantum circuits described in a related paper to solve problems. The results show that this approach can outperform human-designed circuits and we compare them for larger instances up to 14 qubits. Additionally, we propose a novel approach to improve the performance and compactness of circuits by constraining the circuit evolution in the physically relevant subspace. We consider both static and dynamic properties of molecular systems. The emulation environment used is QuESTlink and all resources are open source and linked in this paper.
NEW JOURNAL OF PHYSICS
(2023)
Review
Chemistry, Physical
Daniel Claudino
Summary: The rapid progress in quantum chemistry over the past few decades has been largely due to the synergy between theoretical and computational advancements. However, the current computer architecture is reaching a state of stagnant development. Quantum computing has emerged as a promising avenue for the further advancement of quantum chemistry, but it presents several challenges. This review discusses the basic aspects of quantum information and their relation to quantum computing, specifically in the context of enabling simulations of quantum chemistry.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
(2022)
Review
Chemistry, Multidisciplinary
Mohammad Haidar, Marko J. J. Rancic, Thomas Ayral, Yvon Maday, Jean-Philip Piquemal
Summary: Quantum chemistry is a promising application of quantum computing, but the current quantum processing units still have significant errors. However, the variational quantum eigensolver algorithms can potentially overcome these issues. This article introduces the OpenVQE open-source QC package, which provides tools for using and developing chemically-inspired adaptive methods and can utilize the Atos Quantum Learning Machine (QLM).
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2023)
Article
Quantum Science & Technology
Kubra Yeter-Aydeniz, Bryan T. Gard, Jacek Jakowski, Swarnadeep Majumder, George S. Barron, George Siopsis, Travis S. Humble, Raphael C. Pooser
Summary: Quantum chemistry serves as a key benchmark for current and future quantum computer performance, with state-of-the-art methods outlined for achieving chemical accuracy on NISQ devices. These methods include extending variational eigensolvers with symmetry preserving Ansatze and using quantum imaginary time evolution and Lanczos as complementary methods. A new error mitigation method is also highlighted, demonstrating rapid advancements in electronic structure calculations.
ADVANCED QUANTUM TECHNOLOGIES
(2021)
Article
Biochemistry & Molecular Biology
Ivana Mihalikova, Martin Friak, Matej Pivoluska, Martin Plesch, Martin Saip, Mojmir Sob
Summary: Quantum computers are making significant progress, particularly in quantum chemistry. This study utilizes simulations and experiments to analyze the impact of various computational techniques on the performance of quantum computers.
Review
Physics, Multidisciplinary
Jules Tilly, Hongxiang Chen, Shuxiang Cao, Dario Picozzi, Kanav Setia, Ying Li, Edward Grant, Leonard Wossnig, Ivan Rungger, George H. Booth, Jonathan Tennyson
Summary: The variational quantum eigensolver (VQE) is a method used to compute the ground state energy of a Hamiltonian, which is important in quantum chemistry and condensed matter physics. It has the advantage of being able to model complex wavefunctions in polynomial time, making it a promising application for quantum computing. However, there are still many open questions regarding optimization, quantum noise, and other challenges, which require further research and exploration.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2022)
Article
Automation & Control Systems
B. D. Deebak, Fida Hussain Memon, Sunder Ali Khowaja, Kapal Dev, Weizheng Wang, Nawab Muhammad Faseeh Qureshi
Summary: Cognitive-inspired Internet of Medical Things (CI-IoMT) combines cognitive science and artificial intelligence to analyze data generated by IoT devices and design smart communication systems for ubiquitous services. However, conventional protocols used in IoMT are vulnerable to quantum-computer attacks, necessitating an efficient CI-IoMT scheme for access privacy, preservation, and trust guarantee. This article proposes an identity-based seamless privacy preservation (IB-SPP) scheme that uses fast user authentication to shorten access timing. Simulation analysis shows that the proposed IB-SPP scheme has shorter response time and requires less data volume compared to existing schemes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Review
Computer Science, Interdisciplinary Applications
Jaiteg Singh, Kamalpreet Singh Bhangu
Summary: This study aims to develop a clear understanding of the promises and limitations of the current state-of-the-art quantum computing use cases and to define directions for future research. It bridges the gap between computer professionals and non-physicists by offering conceptual and notational information and surveys existing applications, technological advancements, and contemporary challenges associated with quantum computing.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Chemistry, Multidisciplinary
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
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
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.
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
Physics, Applied
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
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
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
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
Chemistry, Physical
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
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)