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Many quantum algorithms can be seen as a transition from a well-defined initial quantum state of a complex quantum system, to an unknown target quantum state, corresponding to a certain eigenvalue either of the Hamiltonian or of a…

Quantum Physics · Physics 2022-07-19 Aikaterini Mandilara , Daniil Fedotov , Vladimir M. Akulin

Hamiltonian simulations are key subroutines in adiabatic quantum computation, quantum control, and quantum many-body physics, where quantum dynamics often happen in the low-energy sector. In contrast to time-independent Hamiltonian…

Quantum Physics · Physics 2026-01-06 Shuo Zhou , Zhaokai Pan , Weiyuan Gong , Tongyang Li

Quantum coherence is one of the fundamental aspects distinguishing classical and quantum theories. Coherence between different energy eigenstates is particularly important, as it serves as a valuable resource under the law of energy…

Quantum Physics · Physics 2024-05-07 Naoto Shiraishi , Ryuji Takagi

Reliable preparation of many-body ground states is an essential task in quantum computing, with applications spanning areas from chemistry and materials modeling to quantum optimization and benchmarking. A variety of approaches have been…

Quantum Physics · Physics 2026-02-09 Ricard Puig , Berta Casas , Alba Cervera-Lierta , Zoë Holmes , Adrián Pérez-Salinas

We present a novel method for improving the quantum simulation of the ground state energy of molecules. We perform a pre-processing step classically, which reduces the dimensionality of the problem by generating a custom mapping which…

Quantum Physics · Physics 2023-03-03 Kaur Kristjuhan , Mark Nicholas Jones

Advantages of using a low-energy effective theory to study bound state properties are briefly discussed, and a nonperturbative implementation of such an effective theory is described within the context of nonrelativistic quantum mechanics.…

High Energy Physics - Phenomenology · Physics 2009-10-31 Richard Hill

A quantum thermodynamic system is described by a Hamiltonian and a list of conserved, non-commuting charges, and a fundamental goal is to determine the minimum energy of the system subject to constraints on the charges. Recently, [Liu et…

Despite its simplicity and strong theoretical guarantees, adiabatic state preparation has received considerably less interest than variational approaches for the preparation of low-energy electronic structure states. Two major reasons for…

Quantum Physics · Physics 2025-02-19 Etienne Granet , Khaldoon Ghanem , Henrik Dreyer

Quantum algorithms for estimating the ground state energy of a quantum system often operate by preparing a classically accessible quantum state and then applying quantum phase estimation. Whether this approach yields quantum advantage…

Quantum Physics · Physics 2026-02-25 Daochen Wang

Quantum annealers are an alternative approach to quantum computing which make use of the adiabatic theorem to efficiently find the ground state of a physically realizable Hamiltonian. Such devices are currently commercially available and…

Quantum Physics · Physics 2021-02-24 Justin Copenhaver , Adam Wasserman , Birgit Wehefritz-Kaufmann

We investigate quantum many-body systems where all low-energy states are entangled. As a tool for quantifying such systems, we introduce the concept of the entanglement gap, which is the difference in energy between the ground-state energy…

Quantum Physics · Physics 2007-05-23 Mark R. Dowling , Andrew C. Doherty , Stephen D. Bartlett

We develop a quantum-inspired numerical procedure for searching low-energy states of a classical Hamiltonian composed of two-body fully-connected random Ising interactions and a random local longitudinal magnetic field. In this method, we…

Statistical Mechanics · Physics 2022-03-21 Hiroshi Ueda , Yuichi Otsuka , Seiji Yunoki

A powerful method for calculating the eigenvalues of a Hamiltonian operator consists of converting the energy eigenvalue equation into a matrix equation by means of an appropriate basis set of functions. The convergence of the method can be…

Quantum Physics · Physics 2007-05-23 Paolo Amore , Alfredo Aranda , Francisco Fernandez , Hugh Jones

We study the problem of simulating the dynamics of spin systems when the initial state is supported on a subspace of low energy of a Hamiltonian $H$. This is a central problem in physics with vast applications in many-body systems and…

Quantum Physics · Physics 2021-09-10 Burak Şahinoğlu , Rolando D. Somma

Ground-state preparation for a given Hamiltonian is a common quantum-computing task of great importance and has relevant applications in quantum chemistry, computational material modeling, and combinatorial optimization. We consider an…

Quantum Physics · Physics 2022-10-18 Yu-Qin Chen , Shi-Xin Zhang , Chang-Yu Hsieh , Shengyu Zhang

Computer-aided engineering techniques are indispensable in modern engineering developments. In particular, partial differential equations are commonly used to simulate the dynamics of physical phenomena, but very large systems are often…

Quantum Physics · Physics 2022-04-26 Yuki Sato , Ruho Kondo , Satoshi Koide , Hideki Takamatsu , Nobuyuki Imoto

Variational quantum eigensolver(VQE) typically minimizes energy with hybrid quantum-classical optimization, which aims to find the ground state. Here, we propose a VQE by minimizing energy variance, which is called as variance-VQE(VVQE).…

Quantum Physics · Physics 2020-06-30 Dan-Bo Zhang , Zhan-Hao Yuan , Tao Yin

In recent years, energy correlators have emerged as powerful observables for probing the fragmentation dynamics of high-energy collisions. We introduce the first numerical strategy for calculating energy correlators using the Hamiltonian…

High Energy Physics - Phenomenology · Physics 2025-05-07 Kyle Lee , Francesco Turro , Xiaojun Yao

Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground state preparation scheme with shallow variational warm-start…

Quantum Physics · Physics 2023-03-21 Youle Wang , Chenghong Zhu , Mingrui Jing , Xin Wang

Taking uncertainty into account is crucial when making strategic decisions. To guard against the risk of adverse scenarios, traditional optimisation techniques incorporate uncertainty on the basis of prior knowledge on its distribution. In…

Optimization and Control · Mathematics 2022-12-06 Julien Vaes , Vassilis M. Charitopoulos