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The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle distribution functions (PDFs). Despite its success, solving…

Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkable adaptability in low-data regimes, making them promising…

Machine Learning · Computer Science 2025-10-10 Chih-Yu Chang , Milad Azvar , Chinedum Okwudire , Raed Al Kontar

Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over the bosonic degree of freedom…

Quantum Physics · Physics 2024-10-04 Jasvith Raj Basani , Murphy Yuezhen Niu , Edo Waks

Supersymmetric models are grounded in the intriguing concept of a hypothetical symmetry that relates bosonic and fermionic particles. This symmetry has profound implications, offering valuable extensions to the Standard Model of particle…

High Energy Physics - Lattice · Physics 2024-11-25 Emanuele Mendicelli , David Schaich

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding (LCE) framework addresses these challenges by embedding the…

Machine Learning · Computer Science 2020-03-03 Rui Shu , Tung Nguyen , Yinlam Chow , Tuan Pham , Khoat Than , Mohammad Ghavamzadeh , Stefano Ermon , Hung H. Bui

We introduce a novel quantum algorithm for the lattice Boltzmann method (LBM) based on the one-step simplified LBM. The structure of the algorithm allows for more flexibility in modelling different physics in contrast to earlier quantum…

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as…

Quantum Physics · Physics 2026-04-17 Austin Pechan , John Golden , Daniel O'Malley

We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transformers (ViTs), can be applied to graph-structured data. We find…

We obtain the complete operator bases at mass dimensions 5, 6, 7, 8, 9 for the low energy effective field theory (LEFT), which parametrize various physics effects between the QCD scale and the electroweak scale. The independence of the…

High Energy Physics - Phenomenology · Physics 2021-07-07 Hao-Lin Li , Zhe Ren , Ming-Lei Xiao , Jiang-Hao Yu , Yu-Hui Zheng

Atomic, molecular and optical (AMO) approaches to quantum computing are promising due to their increased connectivity, long coherence times and apparent scalability. However, they have a significantly reduced cadence of syndrome extraction…

Quantum Physics · Physics 2025-05-30 Mark L. Turner , Earl T. Campbell , Ophelia Crawford , Neil I. Gillespie , Joan Camps

Recently there has been much interest in performing search queries over encrypted data to enable functionality while protecting sensitive data. One particularly efficient mechanism for executing such queries is order-preserving…

Cryptography and Security · Computer Science 2016-10-14 Daniel S. Roche , Daniel Apon , Seung Geol Choi , Arkady Yerukhimovich

We construct the effective lowest-band Bose-Hubbard model incorporating interaction-induced on-site correlations. The model is based on ladder operators for local correlated states, which deviate from the usual Wannier creation and…

Quantum Gases · Physics 2016-11-25 U. Bissbort , F. Deuretzbacher , W. Hofstetter

Simulating molecular systems on quantum computers requires efficient mappings from Fermionic operators to qubit operators. Traditional mappings such as Jordan-Wigner or Bravyi-Kitaev often produce high-weight Pauli terms, increasing circuit…

Quantum Physics · Physics 2025-11-13 James Brown , Tarini S Hardikar , Kenny Heitritter , Kanav Setia

While quantum algorithms for solving large scale systems of linear equations offer potentially exponential speedups, their application has largely been confined to sparse matrices. This work extends the scope of these algorithms to a broad…

Quantum Physics · Physics 2026-02-27 Kun Tang , Jun Lai

We perform an extended numerical search for practical fermion-to-qubit encodings with error correcting properties. Ideally, encodings should strike a balance between a number of the seemingly incompatible attributes, such as having a high…

Quantum Physics · Physics 2024-05-29 Fedor Simkovic , Martin Leib , Francisco Revson F. Pereira

One of the most promising applications of quantum computers is solving partial differential equations (PDEs). By using the Schrodingerisation technique - which converts non-conservative PDEs into Schrodinger equations - the problem can be…

Quantum Physics · Physics 2025-08-01 Nikita Guseynov , Xiajie Huang , Nana Liu

Morse oscillator is one of the known solvable potentials which attracts many applications in quantum mechanics especially in quantum chemistry. One of the interesting results of this study is the generation of ladder operators for Morse…

Quantum Physics · Physics 2020-04-06 Nadhira A. H. , Nurisya M. S. , K. T. Chan

Positional encoding mechanisms enable Transformers to model sequential structure and long-range dependencies in text. While absolute positional encodings struggle with extrapolation to longer sequences due to fixed positional…

Computation and Language · Computer Science 2025-09-09 Chang Dai , Hongyu Shan , Mingyang Song , Di Liang

Quantum error correction is a key ingredient for large scale quantum computation, protecting logical information from physical noise by encoding it into many physical qubits. Topological stabilizer codes are particularly appealing due to…

Quantum Physics · Physics 2026-04-28 Hoang Viet Nguyen , Manh Hung Nguyen , Hoang Ta , Van Khu Vu , Yeow Meng Chee

In this paper, we aim to learn a low-dimensional Euclidean representation from a set of constraints of the form "item j is closer to item i than item k". Existing approaches for this "ordinal embedding" problem require expensive…

Machine Learning · Computer Science 2019-10-29 Nikhil Ghosh , Yuxin Chen , Yisong Yue