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We argue the feasibility to study the phase structure of a quantum physical system on quantum devices via adiabatic preparation of states. We introduce a novel method and successfully test it in application to the Schwinger model in the…

High Energy Physics - Lattice · Physics 2024-12-11 Oleg Kaikov , Theo Saporiti , Vasily Sazonov , Mohamed Tamaazousti

Quantum annealing is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. Quantum annealing in the transverse field Ising model, implemented on D-Wave…

Quantum Physics · Physics 2023-06-13 Elijah Pelofske

Variational quantum circuits characterise the state of a quantum system through the use of parameters that are optimised using classical optimisation procedures that typically rely on gradient information. The circuit-execution complexity…

Quantum Physics · Physics 2023-07-28 Sayantan Pramanik , Chaitanya Murti , M Girish Chandra

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to approximately find lowest energy states. Quantum annealers have scaled up in recent years to tackle increasingly larger and more highly connected…

Quantum Physics · Physics 2025-07-04 Humberto Munoz Bauza , Daniel A. Lidar

Advances in quantum algorithms suggest a tentative scaling advantage on certain combinatorial optimization problems. Recent work, however, has also reinforced the idea that barren plateaus render variational algorithms ineffective on large…

Quantum Physics · Physics 2025-03-14 Tim Bode , Krish Ramesh , Tobias Stollenwerk

Quantum annealing is a heuristic algorithm for searching the ground state of an Ising model. Heuristic algorithms aim to obtain near-optimal solutions with a reasonable computation time. Accordingly, many algorithms have so far been…

Quantum Physics · Physics 2022-11-09 Shuntaro Okada , Masayuki Ohzeki

Recent advancements in quantum information and quantum technology has stimulated a good deal of interest in the development of quantum algorithms for energetics and properties of many-fermionic systems. While the variational quantum…

Quantum Physics · Physics 2023-02-08 Dibyendu Mondal , Sonaldeep Halder , Dipanjali Halder , Rahul Maitra

This work studies pulse based variational quantum algorithms (VQAs), which are designed to determine the ground state of a quantum mechanical system by combining classical and quantum hardware. In contrast to more standard gate based…

Quantum Physics · Physics 2023-02-01 Robert de Keijzer , Oliver Tse , Servaas Kokkelmans

In this review we consider the performance of the quantum adiabatic algorithm for the solution of decision problems. We divide the possible failure mechanisms into two sets: small gaps due to quantum phase transitions and small gaps due to…

Quantum Physics · Physics 2015-04-21 C. R. Laumann , R. Moessner , A. Scardicchio , S. L. Sondhi

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

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

Quantum systems have historically been formidable to simulate using classical computational methods, particularly as the system size grows. In recent years, advancements in quantum computing technology have offered new opportunities for…

Quantum Physics · Physics 2023-09-06 Jinao Wang , Rimika Jaiswal

Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics. In this work, we first propose a Lyapunov control-inspired strategy to accelerate…

Quantum Physics · Physics 2022-12-21 Yu-Cheng Chen , Yu-Qin Chen , Alice Hu , Chang-Yu Hsieh , Shengyu Zhang

Many physically interesting models show a quantum phase transition when a single parameter is varied through a critical point, where the ground state and the first excited state become degenerate. When this parameter appears as a coupling…

Quantum Physics · Physics 2008-09-24 Gernot Schaller

Quantum computing employs controllable interactions to perform sequences of logical gates and entire algorithms on quantum registers. This paradigm has been widely explored, e.g., for simulating dynamics of manybody systems by decomposing…

Quantum Physics · Physics 2025-05-21 S. Alipour , A. T. Rezakhani , Alireza Tavanfar , K. Mölmer , T. Ala-Nissila

The conventional method for generating entangled states in qubit systems relies on applying precise two-qubit entangling gates alongside single-qubit rotations. However, achieving high-fidelity entanglement demands high accuracy in…

We explore a non-variational quantum state preparation approach combined with the ADAPT operator selection strategy in the application of preparing the ground state of a desired target Hamiltonian. In this algorithm, energy gradient…

Variational quantum algorithms (VQAs), as one of the most promising routes in the noisy intermediate-scale quantum (NISQ) era, offer various potential applications while also confront severe challenges due to near-term quantum hardware…

Quantum Physics · Physics 2024-12-12 Shi-Xin Zhang , Jiaqi Miao , Chang-Yu Hsieh

Current quantum simulators suffer from multiple limitations such as short coherence time, noisy operations, faulty readout and restricted qubit connectivity in some platforms. Variational quantum algorithms are the most promising approach…

Quantum Physics · Physics 2023-05-31 Chufan Lyu , Xiaoyu Tang , Junning Li , Xusheng Xu , Man-Hong Yung , Abolfazl Bayat

Quantum annealing is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare…

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