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SUMMARY:Synergies between quantum and classical computing for quantum chem
 istry and materials science
DTSTART;VALUE=DATE:20260928
DTSTAMP:20260922T141605Z
UID:bf1fbe58ce48d87a48f22739fa5abe8aa612705660ca0544b27ce328
CATEGORIES:Conferences - Seminars
DESCRIPTION:You can apply to participate and find all the relevant informa
 tion (speakers\, abstracts\, program\,...) on the event website: https://
 www.cecam.org/workshop-details/synergies-between-quantum-and-classical-com
 puting-for-quantum-chemistry-and-materials-science-1479.\n\nRegistration i
 s required to attend the full event\, take part in the social activities a
 nd present a poster at the poster session (if any).  However\, the EPFL 
 community is welcome to attend specific lectures without registration 
 if the topic is of interest to their research. Do not hesitate to contact 
 the CECAM Event Manager if you have any question.\n\nDescription\n\nComp
 utational chemistry and materials science have long relied on advanced com
 putational methods to model molecular systems and predict material propert
 ies. Techniques such as density functional theory (DFT) and post-Hartree-F
 ock approaches (e.g.\, MP2\, coupled-cluster) form the backbone of simulat
 ions in both academia and industry. These methods enable accurate modeling
  of reaction mechanisms\, catalyst design\, and materials discovery. Howev
 er\, they face intrinsic limitations when addressing systems with strong e
 lectronic correlations\, such as transition metal complexes or when comple
 x quantum dynamics are involved. The steep computational scaling of more a
 ccurate\, multi-configurational methods makes them impractical for many ch
 emically and technologically relevant systems. This bottleneck has sparked
  growing interest in quantum computing as a complementary approach.\n\nQua
 ntum computers promise to represent and process molecular quantum states m
 ore efficiently than classical machines. Despite notable progress in hardw
 are\, current quantum devices remain constrained by limited qubit counts a
 nd imperfect gate operations\, while fault-tolerant quantum computers are 
 not yet available. In the meantime\, error mitigation techniques seek to r
 ecover meaningful signals from ensembles of noisy circuit executions. Alth
 ough these methods have enabled experimental demonstrations of quantum alg
 orithms for chemistry\, they have yet to scale beyond systems accessible t
 o classical high-performance computing. This raises a critical question: c
 an quantum devices deliver a provable and practical advantage for chemistr
 y before full fault tolerance becomes a reality?\n\nOne promising approach
  to address this challenge is the integration of classical high-performanc
 e computing with quantum resources\, leveraging their respective strengths
  - a paradigm commonly referred to as quantum-centric supercomputing (QCSC
 ) [1]. For instance\, embedding schemes can treat strongly correlated regi
 ons of a molecule as an active space on a quantum processor while applying
  classical methods such as DFT to the remaining degrees of freedom [2]. An
 other recent development is sample-based quantum diagonalization (SQD)\, w
 hich employs samples from a quantum-prepared electronic wavefunction to pe
 rform selected configuration interaction (CI) calculations on a classical 
 supercomputer [3\, 4]. This hybrid strategy has already enabled quantum-po
 wered computations to scale beyond the limits of exact diagonalization. Ac
 hieving the next milestone\, quantum advantage in quantum chemistry\, has 
 become a central objective\, one that demands a broad\, multidisciplinary 
 effort.\n\nAdvancing QCSC requires close collaboration between classical a
 nd quantum communities [5]. Chemists and materials scientists contribute d
 eep domain expertise\, as well as indispensable reference classical state-
 of-the-art methods\, while quantum algorithms researchers design novel for
 mal techniques and implementations. Building bridges between these domains
 \, by identifying synergies\, defining benchmarks\, and pinpointing near-t
 erm use cases\, will shape the trajectory of this emerging field.\n\nRefer
 ences\n\n[1] J. Liu\, H. Ma\, H. Shang\, Z. Li\, J. Yang\, Phys. Chem. Che
 m. Phys.\, 26\, 15831-15843 (2024)\n[2] M. Rossmannek\, F. Pavošević\, 
 A. Rubio\, I. Tavernelli\, J. Phys. Chem. Lett.\, 14\, 3491-3497 (2023)\n
 [3] J. Robledo-Moreno\, et al. "Chemistry beyond exact solutions on a quan
 tum-centric supercomputer." arXiv preprint arXiv:2405.05068 (2024).\n[4] S
 . Piccinelli\, A. Baiardi\, M. Rossmannek et al\, Quantum chemistry with p
 rovable convergence via randomized sample-based quantum diagonalization\, 
 arXiv 2508.02578 (2025)\n[5] Y. Alexeev\, M. Amsler\, M. Barroca\, S. Bass
 ini\, T. Battelle\, D. Camps\, D. Casanova\, Y. Choi\, F. Chong\, C. Chung
 \, C. Codella\, A. Córcoles\, J. Cruise\, A. Di Meglio\, I. Duran\, T. Ec
 kl\, S. Economou\, S. Eidenbenz\, B. Elmegreen\, C. Fare\, I. Faro\, C. Fe
 rnández\, R. Ferreira\, K. Fuji\, B. Fuller\, L. Gagliardi\, G. Galli\, J
 . Glick\, I. Gobbi\, P. Gokhale\, S. de la Puente Gonzalez\, J. Greiner\, 
 B. Gropp\, M. Grossi\, E. Gull\, B. Healy\, M. Hermes\, B. Huang\, T. Humb
 le\, N. Ito\, A. Izmaylov\, A. Javadi-Abhari\, D. Jennewein\, S. Jha\, L. 
 Jiang\, B. Jones\, W. de Jong\, P. Jurcevic\, W. Kirby\, S. Kister\, M. Ki
 tagawa\, J. Klassen\, K. Klymko\, K. Koh\, M. Kondo\, D. Kürkçüog̃lu\,
  K. Kurowski\, T. Laino\, R. Landfield\, M. Leininger\, V. Leyton-Ortega\,
  A. Li\, M. Lin\, J. Liu\, N. Lorente\, A. Luckow\, S. Martiel\, F. Martin
 -Fernandez\, M. Martonosi\, C. Marvinney\, A. Medina\, D. Merten\, A. Mezz
 acapo\, K. Michielsen\, A. Mitra\, T. Mittal\, K. Moon\, J. Moore\, S. Mos
 tame\, M. Motta\, Y. Na\, Y. Nam\, P. Narang\, Y. Ohnishi\, D. Ottaviani\,
  M. Otten\, S. Pakin\, V. Pascuzzi\, E. Pednault\, T. Piontek\, J. Pitera\
 , P. Rall\, G. Ravi\, N. Robertson\, M. Rossi\, P. Rydlichowski\, H. Ryu\,
  G. Samsonidze\, M. Sato\, N. Saurabh\, V. Sharma\, K. Sharma\, S. Shin\, 
 G. Slessman\, M. Steiner\, I. Sitdikov\, I. Suh\, E. Switzer\, W. Tang\, J
 . Thompson\, S. Todo\, M. Tran\, D. Trenev\, C. Trott\, H. Tseng\, N. Tubm
 an\, E. Tureci\, D. Valiñas\, S. Vallecorsa\, C. Wever\, K. Wojciechowski
 \, X. Wu\, S. Yoo\, N. Yoshioka\, V. Yu\, S. Yunoki\, S. Zhuk\, D. Zubarev
 \, Future Generation Computer Systems\, 160\, 666-710 (2024)\n 
LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103
STATUS:CONFIRMED
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