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Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for…

Machine Learning · Computer Science 2024-05-02 Jan-Nico Zaech , Martin Danelljan , Tolga Birdal , Luc Van Gool

Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms for Hamiltonian simulation, such as the Variational Quantum…

Quantum Physics · Physics 2020-01-27 Arthur G. Rattew , Shaohan Hu , Marco Pistoia , Richard Chen , Steve Wood

Transportation systems such as urban logistics, vehicle routing, and infrastructure planning require solving large-scale combinatorial optimization problems under complex constraints. Problems such as the vehicle routing problem (VRP),…

Quantum Physics · Physics 2026-04-30 Talha Azfar , Ruimin Ke , Sean He , Cara Wang , José Holguín-Veras

Resource-efficient, low-depth implementations of quantum circuits remain a promising strategy for achieving reliable and scalable computation on quantum hardware, as they reduce gate resources and limit the accumulation of noisy operations.…

We present an extension of Adiabatic Quantum Computing (AQC) algorithm for the unstructured search to the case when the number of marked items is unknown. The algorithm maintains the optimal Grover speedup and includes a small counting…

Mathematical Physics · Physics 2015-03-17 Zhenwei Cao , Alexander Elgart

Quantum computing holds promise across various fields, particularly with the advent of Noisy Intermediate-Scale Quantum (NISQ) devices, which can outperform classical supercomputers in specific tasks. However, challenges such as noise and…

Eigenstate preparation is ubiquitous in quantum computing, and a standard approach for generating the lowest-energy states of a given system is by employing adiabatic state preparation (ASP). In the present work, we investigate a…

Quantum Physics · Physics 2021-08-30 Shunji Matsuura , Samantha Buck , Valentin Senicourt , Arman Zaribafiyan

Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods such as adiabatic quantum computing (AQC) have been employed in…

Today's Noisy Intermediate-Scale Quantum (NISQ) computers support only limited sets of available quantum gates and restricted connectivity. Therefore, quantum algorithms must be transpiled in order to become executable on a given NISQ…

Quantum Physics · Physics 2023-04-13 Yanjun Ji , Sebastian Brandhofer , Ilia Polian

We propose analog counterdiabatic quantum computing (ACQC) to tackle combinatorial optimization problems on neutral-atom quantum processors. While these devices allow for the use of hundreds of qubits, adiabatic quantum computing struggles…

Analog models of quantum information processing, such as adiabatic quantum computation and analog quantum simulation, require the ability to subject a system to precisely specified Hamiltonians. Unfortunately, the hardware used to implement…

Quantum Physics · Physics 2014-02-25 Kevin C. Young , Robin Blume-Kohout , Daniel A. Lidar

We propose a modification of the Variational Quantum Eigensolver algorithm for electronic structure optimization using quantum computers, named non-unitary Variational Quantum Eigensolver (nu-VQE), in which a non-unitary operator is…

Noisy Intermediate-Scale Quantum (NISQ) devices lack error correction, limiting scalability for quantum algorithms. In this context, digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm that…

Quantum Physics · Physics 2024-10-11 Paula García-Molina , Ana Martin , Mikel Garcia de Andoin , Mikel Sanz

We present high-precision quantum computing simulations of three-body atoms (He, H$^-$) and molecules (H$_2^+$, HD$^+$), the latter being studied beyond the Born-Oppenheimer approximation. The Non-Iterative Disentangled Unitary Coupled…

Quantum Physics · Physics 2025-10-22 Mohammad Haidar , Hugo D. Nogueira , J. -Ph. Karr

The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-size quantum (NISQ) era, and is generally speculated to deliver one of the first quantum advantages for the…

Quantum Physics · Physics 2022-04-13 Shi-Xin Zhang , Zhou-Quan Wan , Chee-Kong Lee , Chang-Yu Hsieh , Shengyu Zhang , Hong Yao

We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple homogeneous or heterogeneous quantum processors in hybrid classical-quantum workflows. In this manner, we are able to overcome…

For a large number of tasks, quantum computing demonstrates the potential for exponential acceleration over classical computing. In the NISQ era, variable-component subcircuits enable applications of quantum computing. To reduce the…

Quantum Physics · Physics 2022-12-12 Junhan Qin

Hybrid quantum-classical (HQC) algorithms make it possible to use near-term quantum devices supported by classical computational resources by useful control schemes. In this paper, we develop an HQC algorithm using an efficient variational…

Quantum Physics · Physics 2020-08-13 Mahmoud Mahdian , H. Davoodi Yeganeh

Current noisy intermediate-scale quantum (NISQ) devices remain limited in their ability to perform accurate quantum chemistry simulations due to restricted numbers of high-fidelity qubits and short coherence times. To overcome these…

Quantum Physics · Physics 2026-04-23 Yuhan Zheng , Yibin Guo , Huili Zhang , Jie Liu , Xiongzhi Zeng , Xiaoxia Cai , Zhenyu Li , Jinlong Yang

Unsupervised visual clustering has garnered significant attention in recent times, aiming to characterize distributions of unlabeled visual images through clustering based on a parameterized appearance approach. Alternatively, clustering…

Quantum Physics · Physics 2025-02-19 Xuan Bac Nguyen , Hugh Churchill , Khoa Luu , Samee U. Khan