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Two-dimensional electronic spectroscopy (2DES) is a powerful tool for exploring quantum effects in energy transport within photosynthetic systems and investigating novel material properties. However, simulating the dynamics of these…

Quantum computers have the potential to simulate chemical systems beyond the capability of classical computers. Recent developments in hybrid quantum-classical approaches enable the determinations of the ground or low energy states of…

Quantum Physics · Physics 2021-12-06 Chee-Kong Lee , Jonathan Wei Zhong Lau , Liang Shi , Leong Chuan Kwek

Quantum computers hold great promise for arriving at exact simulations of nuclear dynamical processes (e.g., scattering and reactions) that are paramount to the study of nuclear matter at the limit of stability and to explaining the…

Resolving quantum many-body problems represents one of the greatest challenges in physics and physical chemistry, due to the prohibitively large computational resources that would be required by using classical computers. A solution has…

Quantum computation is a promising emerging technology which, compared to conventional computation, allows for substantial speed-ups e.g. for integer factorization or database search. However, since physical realizations of quantum…

Quantum Physics · Physics 2018-06-07 Alwin Zulehner , Robert Wille

Quantum mechanical problems are among the hardest to simulate and, in some cases, remain intractable even for the most powerful computers. Quantum computing has emerged as a new technological platform to address such challenges, with rapid…

Quantum Physics · Physics 2025-09-01 Alexander Miessen

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data…

We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum…

Simulating quantum algorithms with classical resources generally requires exponential resources. However, heuristic classical approaches are often very efficient in approximately simulating special circuit structures, for example with…

Quantum Physics · Physics 2018-08-17 Bjarni Jónsson , Bela Bauer , Giuseppe Carleo

Large-scale quantum networks with thousands of nodes require scalable network protocols and physical hardware to realize. In this work, we introduce packet switching as a new paradigm for quantum data transmission in both future and…

Quantum Physics · Physics 2022-05-17 Stephen DiAdamo , Bing Qi , Glen Miller , Ramana Kompella , Alireza Shabani

Classical simulators play a major role in the development and benchmark of quantum algorithms and practically any software framework for quantum computation provides the option of running the algorithms on simulators. However, the…

Quantum Physics · Physics 2022-05-04 Gian Giacomo Guerreschi

Analog quantum simulators emulate complex many-body dynamics through native continuous-time evolution under hardware-defined interactions. Yet once a platform is specified, its interaction structure is largely fixed by the underlying…

Quantum Physics · Physics 2026-05-08 Yiming Huang , Jiaxing Song , Xiaoxia Cai , Xiao Yuan

Quantum Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scalable and robust, existing QGNNs often lack…

Quantum Physics · Physics 2026-01-13 Arthur M. Faria , Ignacio F. Graña , Savvas Varsamopoulos

Quantum neuromorphic computing (QNC) is a sub-field of quantum machine learning (QML) that capitalizes on inherent system dynamics. As a result, QNC can run on contemporary, noisy quantum hardware and is poised to realize challenging…

Quantum Physics · Physics 2024-02-22 Rodrigo Araiza Bravo , Khadijeh Najafi , Taylor L. Patti , Xun Gao , Susanne F. Yelin

Quantum convolutional neural networks (QCNNs) offer a promising architecture for near-term quantum machine learning by combining hierarchical feature extraction with modest parameter growth. However, any QCNN operating on classical data…

Quantum Physics · Physics 2025-12-16 Xingyun Feng

We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By…

Signal Processing · Electrical Eng. & Systems 2025-08-06 Xingyu Huang , Ruining Fan , Mouli Chakraborty , Avishek Nag , Anshu Mukherjee

Although a universal quantum computer is still far from reach, the tremendous advances in controllable quantum devices, in particular with solid-state systems, make it possible to physically implement "quantum simulators". Quantum…

Quantum Physics · Physics 2016-06-08 Zhixin Wang , Xiu Gu , Lian-Ao Wu , Yu-xi Liu

Owing to its outstanding parallel computing capabilities, quantum computing (QC) has been a subject of continuous attention. With the gradual maturation of QC platforms, it has increasingly played a significant role in various fields such…

Networking and Internet Architecture · Computer Science 2024-11-20 Wei Zhao , Tangjie Weng , Yue Ruan , Zhi Liu , Xuangou Wu , Xiao Zheng , Nei Kato

Quantum simulators are essential tools for developing and testing quantum algorithms. However, the high-frequency traversal characteristic of quantum simulators represents an unprecedented demand in the history of IT, and existing…

Emerging Technologies · Computer Science 2025-08-22 Mingyang Yu , Haorui Yang , Donglin Wang , Desheng Kong , Ji Du , Yulong Fu , Wei Wang , Jing Xu

Quantum computing is advancing rapidly in hardware and algorithms, but broad accessibility demands a comprehensive, efficient, unified software stack. Such a stack must flexibly span diverse hardware and evolving algorithms, expose usable…