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Quantum optimization algorithms offer a promising route to finding the ground states of target Hamiltonians on near-term quantum devices. None the less, it remains necessary to limit the evolution time and circuit depth as much as possible,…

Quantum Physics · Physics 2022-11-01 Chenfeng Cao , Yunlong Yu , Zipeng Wu , Nic Shannon , Bei Zeng , Robert Joynt

In this work we propose an approach for implementing time-evolution of a quantum system using product formulas. The quantum algorithms we develop have provably better scaling (in terms of gate complexity and circuit depth) than a naive…

In this paper we show quantum fluctuation effect of fully frustrated Ising spin systems. Quantum annealing has been expected to be an efficient method to find ground state of optimization problems. However it is not clear when to use the…

Disordered Systems and Neural Networks · Physics 2011-06-06 Shu Tanaka

We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its…

Statistical Mechanics · Physics 2020-08-05 Lalit Gupta , Tameem Albash , Itay Hen

We show how a digitized version of Quantum Annealing can be made optimal, realizing the best possible solution allowed by quantum mechanics in the shortest time, without any prior knowledge on the location and properties of the spectral…

Quantum Physics · Physics 2019-11-28 Glen Bigan Mbeng , Rosario Fazio , Giuseppe E. Santoro

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving…

Simulated quantum annealing based on the path-integral Monte Carlo is one of the most common tools to simulate quantum annealing on classical hardware. Nevertheless, it is in principle highly non-trivial whether or not this classical…

Quantum Physics · Physics 2021-08-25 Yuki Bando , Hidetoshi Nishimori

This paper summarizes a quantum algorithm of [R.D. Somma, et.al., Phys. Rev. Lett. 101, 130504 (2008)] that simulates a classical annealing process for solving discrete optimization problems. The complexity of the quantum algorithm scales…

Quantum Physics · Physics 2015-12-16 Sergio Boixo , Rolando D. Somma

We develop a quantum algorithm to solve combinatorial optimization problems through quantum simulation of a classical annealing process. Our algorithm combines techniques from quantum walks, quantum phase estimation, and quantum Zeno…

Quantum Physics · Physics 2007-12-07 R. Somma , S. Boixo , H. Barnum

Variational quantum algorithms (VQAs) are a modern family of quantum algorithms designed to solve optimization problems using a quantum computer. Typically VQAs rely on a feedback loop between the quantum device and a classical optimization…

Quantum Physics · Physics 2022-08-26 Alexey Uvarov

We introduce antiferromagnetic quantum fluctuations into quantum annealing in addition to the conventional transverse-field term. We apply this method to the infinite-range ferromagnetic p-spin model, for which the conventional quantum…

Quantum Physics · Physics 2012-05-14 Yuya Seki , Hidetoshi Nishimori

Non-Hermitian quantum systems have recently attracted considerable attention due to their exotic properties. Though many experimental realizations of non-Hermitian systems have been reported, the non-Hermiticity usually resorts to the…

Quantum Physics · Physics 2023-06-21 Hongfeng Liu , Xiaodong Yang , Kai Tang , Liangyu Che , Xinfang Nie , Tao Xin , Jun Li , Dawei Lu

We introduce an intermediate quantum computing model built from translation-invariant Ising-interacting spins. Despite being non-universal, the model cannot be classically efficiently simulated unless the polynomial hierarchy collapses.…

Quantum Physics · Physics 2017-02-01 Xun Gao , Sheng-Tao Wang , Lu-Ming Duan

Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of Quantum Field Theories (QFTs). This paper presents an alternative to traditional methods for…

Quantum Physics · Physics 2025-09-03 James Ingoldby , Michael Spannowsky , Timur Sypchenko , Simon Williams

To enhance the performance of quantum annealing machines, several methods have been proposed to reduce the number of spins by fixing spin values through preprocessing. We proposed a hybrid optimization method that combines a simulated…

Statistical Mechanics · Physics 2025-07-22 Shuta Kikuchi , Nozomu Togawa , Shu Tanaka

Variational Quantum Algorithm (VQA) is a hybrid algorithm for noisy quantum devices. However, statistical fluctuations and physical noise degrade the solution quality, so it is difficult to maintain applicability for large-scale problems.…

Quantum Physics · Physics 2025-04-18 Hiromichi Matsuyama , Yu Yamashiro

Quantum simulation can help us study poorly understood topics such as high-temperature superconductivity and drug design. However, existing quantum simulation algorithms for current quantum computers often have drawbacks that impede their…

Quantum Physics · Physics 2022-02-28 Kishor Bharti , Tobias Haug

We consider a wide class of quantum spin systems obtained by adding a transverse field to a classical Hamiltonian. We give explicit high-temperature conditions which guarantee exponential decay of correlations. A stochastic-geometric…

Probability · Mathematics 2010-05-21 Alessandra Cipriani , Paolo Dai Pra

Quantum annealing (QA) is an efficient method for finding the ground-state energy of the problem Hamiltonian. However, in practical implementation, the system suffers from decoherence. On the other hand, recently, ``Localized virtual…

The standard quantum annealing algorithm tries to approach the ground state of a classical system by slowly decreasing the hopping rates of a quantum random walk in the configuration space of the problem, where the on-site energies are…

Disordered Systems and Neural Networks · Physics 2018-12-26 A. Ramezanpour
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