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Deep neural quantum states have recently achieved remarkable performance in solving challenging quantum many-body problems. While transformer networks appear particularly promising due to their success in computer science, we show that…

Disordered Systems and Neural Networks · Physics 2025-03-14 Ao Chen , Vighnesh Dattatraya Naik , Markus Heyl

Quantum algorithms have been identified as a potential means to accelerate computational fluid dynamics (CFD) simulations, with the lattice Boltzmann method (LBM) being a promising candidate for realizing quantum speedups. Here, we extend…

We investigate optimized quantum state preparation for quantum metrology applications in noisy environments. Using the QFI-Opt package, we simulate a low-depth variational quantum circuit (VQC) composed of a sequence of global rotations and…

We develop a system of non-linear stochastic evolution equations that describes the continuous measurements of quantum systems with mixed initial state. We address quantum systems with unbounded Hamiltonians and unbounded interaction…

Mathematical Physics · Physics 2025-03-20 Carlos M. Mora

This article presents the derivation of a comprehensive formula for the Clebsch-Gordan coefficients in a quantum system. The formula is derived by employing the iterative application of angular momentum ladder operators on each defined…

Quantum Physics · Physics 2025-09-05 Everardo Rivera-Oliva

A Quantified Model-Competition (QMC) mechanism for multi-scale flows is extracted from the integral (analytical) solution of the Boltzmann-BGK model equation. In the QMC mechanism, the weight of the rarefied model and the weight of the…

Computational Physics · Physics 2020-07-15 Sha Liu , Chengwen Zhong , Ming Fang

{Many-body quantum states at thermal equilibrium are ubiquitous in nature. Investigating their dynamical properties is a formidable task due to the complexity of the Hilbert space they live in. Quantum computers may have the potential to…

Quantum Physics · Physics 2024-07-25 Mirko Consiglio , Tony J. G. Apollaro

Quantum entanglement is essential for modern quantum information processing. Entanglement gates convert initially non-entangled states into entangled ones by applying time-dependent parametric pulses. While Bell state preparation has been…

Quantum Physics · Physics 2026-05-27 Jia-Nan Wu , Bingsuo Zou , Guojun Jin , Yongyou Zhang

Recently, there has been a renewed interest in the machine learning community for variants of a sparse greedy approximation procedure for concave optimization known as {the Frank-Wolfe (FW) method}. In particular, this procedure has been…

Computer Vision and Pattern Recognition · Computer Science 2015-10-27 Hector Allende , Emanuele Frandi , Ricardo Nanculef , Claudio Sartori

Computational fluid dynamics (CFD) is a cornerstone of classical scientific computing, and there is growing interest in whether quantum computers can accelerate such simulations. To date, the existing proposals for fault-tolerant quantum…

Quantum machine learning (QML) seeks to exploit the intrinsic properties of quantum mechanical systems, including superposition, coherence, and quantum entanglement for classical data processing. However, due to the exponential growth of…

Quantum Physics · Physics 2025-10-09 Timothy Heightman , Edward Jiang , Ruth Mora-Soto , Maciej Lewenstein , Marcin Płodzień

Wegner's method of flow equations offers a useful tool for diagonalizing a given Hamiltonian and is widely used in various branches of quantum physics. Here, generalizing this method, a condition is derived, under which the corresponding…

Quantum Physics · Physics 2015-05-13 Yuichi Itto , Sumiyoshi Abe

In this study, we utilized the quantum flow (QFlow) method to perform quantum simulations of correlated systems. The QFlow approach allows for sampling large sub-spaces of the Hilbert space by solving coupled variational problems in reduced…

Quantum Physics · Physics 2024-10-17 Karol Kowalski , Nicholas P. Bauman

Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood training. We introduce the generalized conditional flow matching…

Control strategies for dissipative preparation of target quantum states, both pure and mixed, and subspaces are obtained by switching between a set of available semigroup generators. We show that the class of problems of interest can be…

Quantum Physics · Physics 2015-06-17 Pierre Scaramuzza , Francesco Ticozzi

We propose an approximate solution of the time-dependent Schr\"odinger equation using the method of stationary states combined with a variational matrix method for finding the energies and eigenstates. We illustrate the effectiveness of the…

Quantum Physics · Physics 2009-11-11 Paolo Amore , Alfredo Aranda , Francisco M. Fernandez , Hugh Jones

Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the…

Quantum Physics · Physics 2018-06-07 Chengzhi Wu , Bo Qi , Chunlin Chen , Daoyi Dong

Although the Smoothed Particle Hydrodynamics (SPH) method has been demonstrated as a promising numerical solver for multiphase flow problems due to its Lagrangian nature, its application to complex channel flow may encounter additional…

Fluid Dynamics · Physics 2025-06-18 Zi-Yang Zhan , Zhen Chen

Continuous-variable cluster states offer a potentially promising method of implementing a quantum computer. This paper extends and further refines theoretical foundations and protocols for experimental implementation. We give a…

Quantum Physics · Physics 2015-05-13 Mile Gu , Christian Weedbrook , Nicolas C. Menicucci , Timothy C. Ralph , Peter van Loock

Despite the significant role of turbomachinery in fluid-based energy transfer, precise simulation of rotating solid objects with complex geometry is a challenging task. In the present study, the volume penalization method (VPM) is combined…

Fluid Dynamics · Physics 2026-01-16 Ming Liu , Yosuke Hasegawa