TY - JOUR T1 - Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions Y1 - 2023 A1 - Yuri Alexeev A1 - Maximilian Amsler A1 - Paul Baity A1 - Marco Antonio Barroca A1 - Sanzio Bassini A1 - Torey Battelle A1 - Daan Camps A1 - David Casanova A1 - Young jai Choi A1 - Frederic T. Chong A1 - Charles Chung A1 - Chris Codella A1 - Antonio D. Corcoles A1 - James Cruise A1 - Alberto Di Meglio A1 - Jonathan Dubois A1 - Ivan Duran A1 - Thomas Eckl A1 - Sophia Economou A1 - Stephan Eidenbenz A1 - Bruce Elmegreen A1 - Clyde Fare A1 - Ismael Faro A1 - Cristina Sanz Fernández A1 - Rodrigo Neumann Barros Ferreira A1 - Keisuke Fuji A1 - Bryce Fuller A1 - Laura Gagliardi A1 - Giulia Galli A1 - Jennifer R. Glick A1 - Isacco Gobbi A1 - Pranav Gokhale A1 - Salvador de la Puente Gonzalez A1 - Johannes Greiner A1 - Bill Gropp A1 - Michele Grossi A1 - Emmanuel Gull A1 - Burns Healy A1 - Benchen Huang A1 - Travis S. Humble A1 - Nobuyasu Ito A1 - Artur F. Izmaylov A1 - Ali Javadi-Abhari A1 - Douglas Jennewein A1 - Shantenu Jha A1 - Liang Jiang A1 - Barbara Jones A1 - Wibe Albert de Jong A1 - Petar Jurcevic A1 - William Kirby A1 - Stefan Kister A1 - Masahiro Kitagawa A1 - Joel Klassen A1 - Katherine Klymko A1 - Kwangwon Koh A1 - Masaaki Kondo A1 - Doga Murat Kurkcuoglu A1 - Krzysztof Kurowski A1 - Teodoro Laino A1 - Ryan Landfield A1 - Matt Leininger A1 - Vicente Leyton-Ortega A1 - Ang Li A1 - Meifeng Lin A1 - Junyu Liu A1 - Nicolas Lorente A1 - Andre Luckow A1 - Simon Martiel A1 - Francisco Martin-Fernandez A1 - Margaret Martonosi A1 - Claire Marvinney A1 - Arcesio Castaneda Medina A1 - Dirk Merten A1 - Antonio Mezzacapo A1 - Kristel Michielsen A1 - Abhishek Mitra A1 - Tushar Mittal A1 - Kyungsun Moon A1 - Joel Moore A1 - Mario Motta A1 - Young-Hye Na A1 - Yunseong Nam A1 - Prineha Narang A1 - Yu-ya Ohnishi A1 - Daniele Ottaviani A1 - Matthew Otten A1 - Scott Pakin A1 - Vincent R. Pascuzzi A1 - Ed Penault A1 - Tomasz Piontek A1 - Jed Pitera A1 - Patrick Rall A1 - Gokul Subramanian Ravi A1 - Niall Robertson A1 - Matteo Rossi A1 - Piotr Rydlichowski A1 - Hoon Ryu A1 - Georgy Samsonidze A1 - Mitsuhisa Sato A1 - Nishant Saurabh A1 - Vidushi Sharma A1 - Kunal Sharma A1 - Soyoung Shin A1 - George Slessman A1 - Mathias Steiner A1 - Iskandar Sitdikov A1 - In-Saeng Suh A1 - Eric Switzer A1 - Wei Tang A1 - Joel Thompson A1 - Synge Todo A1 - Minh Tran A1 - Dimitar Trenev A1 - Christian Trott A1 - Huan-Hsin Tseng A1 - Esin Tureci A1 - David García Valinas A1 - Sofia Vallecorsa A1 - Christopher Wever A1 - Konrad Wojciechowski A1 - Xiaodi Wu A1 - Shinjae Yoo A1 - Nobuyuki Yoshioka A1 - Victor Wen-zhe Yu A1 - Seiji Yunoki A1 - Sergiy Zhuk A1 - Dmitry Zubarev AB -

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

UR - https://arxiv.org/abs/2312.09733 ER - TY - JOUR T1 - Quantum simulation with hybrid tensor networks Y1 - 2020 A1 - Xiao Yuan A1 - Jinzhao Sun A1 - Junyu Liu A1 - Qi Zhao A1 - You Zhou AB -

Tensor network theory and quantum simulation are respectively the key classical and quantum methods in understanding many-body quantum physics. Here we show hybridization of these two seemingly independent methods, inheriting both their distinct advantageous features of efficient representations of many-body wave functions. We introduce the framework of hybrid tensor networks with building blocks consisting of measurable quantum states and classically contractable tensors. As an example, we demonstrate efficient quantum simulation with hybrid tree tensor networks that use quantum hardware whose size is significantly smaller than the one of the target system. We numerically test our method for finding the ground state of 1D and 2D spin systems of up to 8×8 and 4×3 qubits with operations only acting on 8+1 and 4+1 qubits, respectively. Our approach paves the way to the near-term quantum simulation of large practical problems with intermediate size quantum hardware, with potential applications in quantum chemistry, quantum many-body physics, quantum field theory, and quantum gravity thought experiments.

UR - https://arxiv.org/abs/2007.00958 ER -