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Existing quantum discrete adiabatic approaches are hindered by circuit depth that increases linearly with the number of evolution steps, a significant challenge for current quantum hardware with limited coherence times. To address this, we…

Quantum Physics · Physics 2025-06-02 Boxuan Ai , Shuo He , Xiang Zhao , Lin Yang , Guozhen Liu , Pengfei Gao , Hongbao Liu , Tao Tang , Jiecheng Yang , Jie Wu

Computing using a continuous-time evolution, based on the natural interaction Hamiltonian of the quantum computer hardware, is a promising route to building useful quantum computers in the near-term. Adiabatic quantum computing, quantum…

Quantum Physics · Physics 2019-03-06 James G. Morley , Nicholas Chancellor , Sougato Bose , Viv Kendon

The challenge in building high-fidelity quantum gates lies in overcoming control errors and decoherence effects caused by the coupling between the quantum system and the external environment. Nonadiabatic holonomic quantum computation uses…

Quantum Physics · Physics 2025-11-04 Yue Heng Liu , Qi Li

The NP-complete problem of the travelling salesman (TSP) is considered in the framework of quantum adiabatic computation (QAC). We first derive a remarkable lower bound for the computation time for adiabatic algorithms in general as a…

Quantum Physics · Physics 2007-05-23 Tien D. Kieu

Adiabatic $U(2)$ geometric phases are studied for arbitrary quantum systems with a three-dimensional Hilbert space. Necessary and sufficient conditions for the occurrence of the non-Abelian geometrical phases are obtained without actually…

Quantum Physics · Physics 2008-11-26 Ali Mostafazadeh

A convenient framework is developed to generalize Berry's investigation of the adiabatic geometrical phase for a classical relativistic charged scalar field in a curved background spacetime which is minimally coupled to electromagnetism and…

High Energy Physics - Theory · Physics 2009-09-25 Ali Mostafazadeh

We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians, enhancing machine learning performance. Classical feature vectors are embedded…

The fast forward scheme of adiabatic quantum dynamics is applied to finite regular spin clusters with various geometries and the nature of driving interactions is elucidated. The fast forward is the quasi-adiabatic dynamics guaranteed by…

Quantum Physics · Physics 2019-06-26 Iwan Setiawan , Bobby Eka Gunara , Sanat Avazbaev , Katsuhiro Nakamura

We derive a version of the adiabatic theorem that is especially suited for applications in adiabatic quantum computation, where it is reasonable to assume that the adiabatic interpolation between the initial and final Hamiltonians is…

Quantum Physics · Physics 2009-10-21 D. A. Lidar , A. T. Rezakhani , A. Hamma

Concepts from non-Hermitian quantum mechanics have proven useful in understanding and manipulating a variety of classical systems, such as those encountered in optics, classical mechanics, and metamaterial design. Recently, the…

Quantum Physics · Physics 2025-03-20 Tomoki Ozawa , Henning Schomerus

A new family of methods involving complex coefficients for the numerical integration of differential equations is presented and analyzed. They are constructed as linear combinations of symmetric-conjugate compositions obtained from a basic…

Numerical Analysis · Mathematics 2021-10-14 Fernando Casas , Alejandro Escorihuela-Tomàs

We propose applying the adiabatic algorithm to prepare high-energy eigenstates of integrable models on a quantum computer. We first review the standard adiabatic algorithm to prepare ground states in each magnetization sector of the…

Quantum Physics · Physics 2026-03-18 Maximilian Lutz , Lorenzo Piroli , Georgios Styliaris , J. Ignacio Cirac

We analyze the complexity of the quantum optimization algorithm based on adiabatic evolution for the set partition problem. We introduce a cost function defined on a logarithmic scale of the partition residues so that the total number of…

Quantum Physics · Physics 2007-05-23 V. N. Smelyanskiy , U. V. Toussaint , D. A. Timucin

The quantum mechanical motion of the atomic nuclei is considered over a single- or a multi-dimensional subspace of electronic states which is separated by a gap from the rest of the electronic spectrum over the relevant range of nuclear…

Chemical Physics · Physics 2019-07-24 Edit Matyus , Stefan Teufel

Quantum operations by utilizing the underlying geometric phases produced in physical systems are favoured due to its potential robustness. When a system in a non-degenerate eigenstate undergoes an adiabatically cyclic evolution dominated by…

Quantum Physics · Physics 2024-05-22 Hao-Long Zhang , Yi-Hao Kang , Fan Wu , Zhen-Biao Yang , Shi-Biao Zheng

We propose a novel non-Hermitian adiabatic quantum optimization algorithm. One of the new ideas is to use a non-Hermitian auxiliary "initial'' Hamiltonian that provides an effective level repulsion for the main Hamiltonian. This effect…

Quantum Physics · Physics 2012-11-15 Gennady P. Berman , Alexander I. Nesterov

The parareal in time algorithm allows to efficiently use parallel computing for the simulation of time-dependent problems. It is based on a decomposition of the time interval into subintervals, and on a predictor-corrector strategy, where…

Numerical Analysis · Mathematics 2010-11-30 X. Dai , C. Le Bris , F. Legoll , Y. Maday

We propose a hybrid classical-quantum digitized-counterdiabatic algorithm to tackle the protein folding problem on a tetrahedral lattice. Digitized-counterdiabatic quantum computing is a paradigm developed to compress quantum algorithms via…

Quantum Physics · Physics 2023-07-13 Pranav Chandarana , Narendra N. Hegade , Iraitz Montalban , Enrique Solano , Xi Chen

In previous implementations of adiabatic quantum algorithms using spin systems, the average Hamiltonian method with Trotter's formula was conventionally adopted to generate an effective instantaneous Hamiltonian that simulates an adiabatic…

Recently, continuous-time dynamical systems have proved useful in providing conceptual and quantitative insights into gradient-based optimization, widely used in modern machine learning and statistics. An important question that arises in…

Optimization and Control · Mathematics 2021-04-29 Guilherme França , Michael I. Jordan , René Vidal
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