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The standard approach to encoding constraints in quantum optimization is the quadratic penalty method. Quadratic penalties introduce additional couplings and energy scales, which can be detrimental to the performance of a quantum optimizer.…

Quantum Physics · Physics 2024-12-17 Puya Mirkarimi , David C. Hoyle , Ross Williams , Nicholas Chancellor

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

Quantum annealing is a method developed to solve combinatorial optimization problems by utilizing quantum bits. Solving such problems corresponds to minimizing a cost function defined over binary variables. However, in many practical cases,…

Quantum Physics · Physics 2025-06-26 Seiya Endo , Shohei Kawakatsu , Hiromichi Matsuyama , Kohei Suzuki , Yuichiro Matsuzaki

We present a new quantum adiabatic theorem that allows one to rigorously bound the adiabatic timescale for a variety of systems, including those described by unbounded Hamiltonians. Our bound is geared towards the qubit approximation of…

Quantum Physics · Physics 2024-01-17 Evgeny Mozgunov , Daniel A. Lidar

The theoretical investigation of non-adiabatic processes is hampered by the complexity of the coupled electron-nuclear dynamics beyond the Born-Oppenheimer approximation. Classically, the simulation of such reactions is limited by the…

Quantum Physics · Physics 2021-01-06 Pauline J. Ollitrault , Guglielmo Mazzola , Ivano Tavernelli

Combinatorial optimization problems are crucial for widespread applications but remain difficult to solve on a large scale with conventional hardware. Novel optical platforms, known as coherent or photonic Ising machines, are attracting…

Optics · Physics 2020-05-19 D. Pierangeli , G. Marcucci , C. Conti

Big Data is characterized by Volume, Velocity, Veracity and Complexity. The interaction between this huge data is complex with an associated free will having dynamic and non linear nature. We reduced big data based on its characteristics,…

Other Computer Science · Computer Science 2018-05-30 Sahil Imtiyaz

The adiabatic theorem has been recently used to design quantum algorithms of a new kind, where the quantum computer evolves slowly enough so that it remains near its instantaneous ground state which tends to the solution [Farhi et al.,…

Quantum Physics · Physics 2009-11-07 Jeremie Roland , Nicolas J. Cerf

Suppressing unwanted transitions out of the instantaneous ground state is a major challenge in unitary adiabatic quantum computation. A recent approach consists in building counterdiabatic potentials approximated using variational…

Quantum Physics · Physics 2022-03-14 Gianluca Passarelli , Rosario Fazio , Procolo Lucignano

Computational complexity theory contains a corpus of theorems and conjectures regarding the time a Turing machine will need to solve certain types of problems as a function of the input size. Nature {\em need not} be a Turing machine and,…

Quantum Physics · Physics 2015-05-20 Javier Rodriguez-Laguna , Silvia N. Santalla

Besides the traditional circuit-based model of quantum computation, several quantum algorithms based on a continuous-time Hamiltonian evolution have recently been introduced, including for instance continuous-time quantum walk algorithms as…

Quantum Physics · Physics 2007-05-23 Jeremie Roland , Nicolas J. Cerf

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not…

Optimization and Control · Mathematics 2022-10-11 Byron Tasseff , Tameem Albash , Zachary Morrell , Marc Vuffray , Andrey Y. Lokhov , Sidhant Misra , Carleton Coffrin

Quantum simulation with adiabatic annealing can provide insight into difficult problems that are impossible to study with classical computers. However, it deteriorates when the systems scale up due to the shrinkage of the excitation gap and…

Quantum annealing is a promising paradigm for building practical quantum computers. Compared to other approaches, quantum annealing technology has been scaled up to a larger number of qubits. On the other hand, deep learning has been…

Quantum Physics · Physics 2021-07-07 Michele Sasdelli , Tat-Jun Chin

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many…

Disordered Systems and Neural Networks · Physics 2017-08-23 Shu Tanaka , Ryo Tamura

The quantum adiabatic theorem ensures that a slowly changing system, initially prepared in its ground state, will evolve to its final ground state with arbitrary precision. As a first result this thesis extends the original theorem to…

Quantum Physics · Physics 2016-10-18 Friederike Anna Dziemba

We outline an algorithm for the Quantum Counting problem using Adiabatic Quantum Computation (AQC). We show that using local adiabatic evolution, a process in which the adiabatic procedure is performed at a variable rate, the problem is…

Quantum Physics · Physics 2014-06-02 Itay Hen

At present, several models for quantum computation have been proposed. Adiabatic quantum computation scheme particularly offers this possibility and is based on a slow enough time evolution of the system, where no transitions take place. In…

Quantum Physics · Physics 2012-10-12 P. J. Salas Peralta

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

The quest for real-time dynamic optimization solutions in the process industry represents a formidable computational challenge, particularly within the realm of applications like model-predictive control, where rapid and reliable…

Optimization and Control · Mathematics 2024-04-29 Dennis Michael Nenno , Adrian Caspari