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DTSTART;TZID=America/Los_Angeles:20250624T133000
DTEND;TZID=America/Los_Angeles:20250624T150000
UID:dac_DAC 2025_sess126@linklings.com
SUMMARY:Quantum Breakthroughs Creating Game-Changing Applications
DESCRIPTION:The session discusses game-changing applications of quantum co
 mputing in finance, community detection, computational advantages of quant
 um machine learning over classical models, quantum cryptanalysis, and deve
 loping distributed quantum computing for real-world applications. The firs
 t paper solves portfolio optimization problems in finance with the help of
  a novel design of an HHL quantum algorithm. Community detection is an imp
 ortant problem in network analysis; the second paper demonstrates the pote
 ntial of hybrid quantum-inspired solutions for advancing community detecti
 on in large-scale graph data. The third paper shows that hybrid quantum ne
 ural networks provide a more scalable and resource-efficient solution over
  purely classical models, positioning them as a promising alternative for 
 tackling complex computational problems. The fourth and fifth papers discu
 ss novel quantum arithmetic circuits for quantum cryptanalysis with the he
 lp of measurement-based uncomputation and windowed arithmetic. Distributed
  quantum computing (DQC) offers a promising pathway for scaling up quantum
  computing; therefore, the sixth paper discusses hardware-software co-desi
 gn for DQC systems, paving the way for more practical and efficient implem
 entations for real-world applications.\n\nScalable Community Detection Usi
 ng QHD and QUBO Formulation\n\nWe present a quantum-inspired algorithm tha
 t utilizes Quantum Hamiltonian Descent (QHD) for efficient community detec
 tion. Our approach reformulates the community detection task as a Quadrati
 c Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to
  identify optimal community structu...\n\n\nJinglei Cheng (University of P
 ittsburgh); Ruilin Zhou, Yuhang Gan, and Chen Qian (University of Californ
 ia, Santa Cruz); and Junyu Liu (University of Pittsburgh)\n---------------
 ------\nHardware-Software Co-design for Distributed Quantum Computing\n\nD
 istributed quantum computing (DQC) offers a promising pathway for scaling 
 up quantum computing. Entanglement is indispensable for implementing non-l
 ocal operations in DQC, especially the teleportation of quantum states and
  gates. Practical remote entanglement generation is probabilistic, whose d
 ura...\n\n\nJi Liu (Argonne National Laboratory), Allen Zang (University o
 f Chicago), Martin Suchara (Microsoft), Tian Zhong (University of Chicago)
 , and Paul Hovland (Argonne National Laboratory)\n---------------------\nS
 APO: Improving the Scalability and Accuracy of Quantum Linear Solver for P
 ortfolio Optimization\n\nPortfolio optimization problem stands as one of t
 he most important financial service, which suffers from huge computational
  pressure due to arithmetic complexity. While, quantum computing has devel
 oped rapidly over the last few decades, offering polynomial or even expone
 ntial speedup that turns out ...\n\n\nTianze Zhu, Liqiang Lu, and Jiajun C
 hen (Zhejiang University); Yuhang Chen (Peking University); Hengrui Chen, 
 Meng Xi, and Jinshan Zhang (Zhejiang University); Xiaoming SUN (Institute 
 of Computing Technology, Chinese Academy of Sciences); and Jianwei Yin (Zh
 ejiang University)\n---------------------\nComputational Advantage in Hybr
 id Quantum Neural Networks: Myth or Reality?\n\nHybrid Quantum Neural Netw
 orks (HQNNs), under the umbrella of Quantum Machine Learning (QML), have g
 arnered significant attention due to their potential to enhance computatio
 nal performance by integrating quantum layers within traditional neural ne
 twork (NN) architectures. \nDespite numerous state-of...\n\n\nMuhammad Kas
 hif, Alberto Marchisio, and Muhammad Shafique (New York University, Abu Dh
 abi)\n---------------------\nOptimizing windowed arithmetic for quantum at
 tacks against RSA2048\n\nWindowed arithmetic [Gid19] uses precomputed look
 up tables (LUTs) to reduce quantum-classical multiplication costs, achievi
 ng state-of-the-art resource estimates for integer factoring [GE21]. We in
 troduce four optimizations to enhance this approach, focusing on efficient
  uncomputation-of-lookups (un...\n\n\nAlessandro Luongo (Center for Quantu
 m Technologies (CQT)); Varun Narasimhachar (Agency for Science, Technology
  and Research (A*STAR)); and Adithya Sireesh (University of Edinburgh)\n--
 -------------------\nMeasurement-based uncomputation of quantum circuits f
 or modular arithmetic\n\nMeasurement-based uncomputation (MBU) is a techni
 que used to perform probabilistic uncomputation of quantum circuits. We fo
 rmalize this technique  for the case of single-qubit registers, and we sho
 w applications to modular arithmetic. Using MBU, we reduce Toffoli count a
 nd depth by 10% to 15% for mo...\n\n\nAlessandro Luongo (Center for Quantu
 m Technologies (CQT)); Antonio Michele Miti (Sapienza Universit`a di Roma)
 ; Varun Narasimhachar (Agency for Science, Technology and Research (A*STAR
 )); and Adithya Sireesh (University of Edinburgh)\n\nTopics: Design\n\nTra
 cks: DES6: Quantum Computing\n\nSession Chairs: Saurabh Kotiyal (Altera) a
 nd Sanjaya Lohani (Southern Methodist University)
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