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Quantum adiabatic algorithm is of vital importance in quantum computation field. It offers us an alternative approach to manipulate the system instead of quantum gate model. Recently, an interesting work arXiv:1805.10549 indicated that we…

Quantum Physics · Physics 2019-01-23 Jingwei Wen , Xiangyu Kong , Shijie Wei , Bixue Wang , Tao Xin , Guilu Long

We demonstrate that with an optimally tuned scheduling function, adiabatic quantum computing (AQC) can readily solve a quantum linear system problem (QLSP) with $\mathcal{O}(\kappa~\text{poly}(\log(\kappa/\epsilon)))$ runtime, where…

Quantum Physics · Physics 2022-03-10 Dong An , Lin Lin

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

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic…

We propose a non-adiabatic scheme for geometric quantum computation with trapped ions. By making use of the Aharonov-Anandan phase, the proposed scheme not only preserves the globally geometric nature in quantum computation, but also…

Quantum Physics · Physics 2009-11-07 Xin-Qi Li , Li-Xiang Cen , Guo-Xiang Huang , Lei Ma , YiJing Yan

Keeping a quantum system in a given instantaneous eigenstate is a control problem with numerous applications, e.g., in quantum information processing. The problem is even more challenging in the setting of open quantum systems, where…

Quantum Physics · Physics 2016-11-02 Jun Jing , Marcelo S. Sarandy , Daniel A. Lidar , Da-Wei Luo , Lian-Ao Wu

Quantum computing promises significant improvements of computation capabilities in various fields such as machine learning and complex optimization problems. Recent technological advancements suggest that the adiabatic quantum computing…

Quantum Physics · Physics 2021-05-06 Veit Stooß , Martin Ulmke , Felix Govaers

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

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

We investigate the irreconcilability issue that raises in translating the search algorithm from the Continuous-Time Quantum Walk (CTQW) framework to the Adiabatic Quantum Computing (AQC) framework. For the AQC formulation to evolve along…

Quantum Physics · Physics 2023-04-24 Chen-Fu Chiang , Paul M. Alsing

Adiabatic limit is the presumption of the adiabatic geometric quantum computation and of the adiabatic quantum algorithm. But in reality, the variation speed of the Hamiltonian is finite. Here we develop a general formulation of adiabatic…

Quantum Physics · Physics 2009-11-10 Yu Shi , Yong-Shi Wu

We illustrate the adiabatic quantum computing solution of the knapsack problem with both integer profits and weights. For problems with $n$ objects (or items) and integer capacity $c$, we give specific examples using both an Ising class…

Quantum Physics · Physics 2017-01-23 Mark W. Coffey

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…

Designing quantum algorithms with a speedup over their classical analogs is a central challenge in quantum information science. Motivated by recent experimental observations of a superlinear quantum speedup in solving the Maximum…

Modern adiabatic quantum computers (AQC) are already used to solve difficult combinatorial optimisation problems in various domains of science. Currently, only a few applications of AQC in computer vision have been demonstrated. We review…

Computer Vision and Pattern Recognition · Computer Science 2020-03-31 Vladislav Golyanik , Christian Theobalt

The adiabatic quantum computation is a universal and robust method of quantum computing. In this architecture, the problem can be solved by adiabatically evolving the quantum processor from the ground state of a simple initial Hamiltonian…

Adiabatic evolution is a powerful technique in quantum information and computation. However, its performance is limited by the adiabatic theorem of quantum mechanics. In this scenario, shortcuts to adiabaticity, such as provided by the…

Quantum Physics · Physics 2016-03-17 Alan C. Santos

We present a method for accelerating adiabatic protocols for systems involving a coupling to a continuum, one that cancels both non-adiabatic errors as well as errors due to dissipation. We focus on applications to a generic quantum state…

Quantum Physics · Physics 2017-11-07 Alexandre Baksic , Ron Belyansky , Hugo Ribeiro , Aashish A. Clerk

Topological quantum computing promises error-resistant quantum computation without active error correction. However, there is a worry that during the process of executing quantum gates by braiding anyons around each other, extra anyonic…

Quantum Physics · Physics 2015-08-05 Chris Cesare , Andrew J. Landahl , Dave Bacon , Steven T. Flammia , Alice Neels

We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in [E. Farhi, et al., arXiv:quant-ph/0208135]. The algorithm is applied to a random binary optimization problem (a version…

Quantum Physics · Physics 2009-11-10 A. Boulatov , V. N. Smelyanskiy