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Related papers: Classical Criticality via Quantum Annealing

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In this work we demonstrate a practical prospect of using quantum annealers for simulation of molecular dynamics. A methodology developed for this goal, dubbed Quantum Differential Equations (QDE), is applied to propagate classical…

Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to…

Quantum annealing was originally proposed as an approach for solving combinatorial optimisation problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and…

Disordered Systems and Neural Networks · Physics 2021-03-16 Takehito Sato , Masayuki Ohzeki , Kazuyuki Tanaka

A quantum annealer exploits quantum effects to solve a particular type of optimization problem. The advantage of this specialized hardware is that it effectively considers all possible solutions in parallel, thereby potentially…

Programming Languages · Computer Science 2018-04-03 Scott Pakin

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

Simulating quantum many-body systems is crucial for advancing physics but poses substantial challenges for classical computers. Quantum simulations overcome these limitations, with analog simulators offering unique advantages over digital…

Quantum Physics · Physics 2025-01-30 Rui-Cheng Guo , Yanwu Gu , Dong E. Liu

Quantum annealing is a generic solver of classical optimization problems that makes full use of quantum fluctuations. We consider work statistics given by a repetition of quantum annealing processes by employing the Jarzynski equality…

Disordered Systems and Neural Networks · Physics 2015-05-27 Masayuki Ohzeki , Hidestoshi Nishimori

Quantum annealers are specialized quantum computers for solving combinatorial optimization problems using special characteristics of quantum computing (QC), such as superposition, entanglement, and quantum tunneling. Theoretically, quantum…

Software Engineering · Computer Science 2024-07-29 Xinyi Wang , Asmar Muqeet , Tao Yue , Shaukat Ali , Paolo Arcaini

These lecture notes introduce quantum spin systems and several computational methods for studying their ground-state and finite-temperature properties. Symmetry-breaking and critical phenomena are first discussed in the simpler setting of…

Strongly Correlated Electrons · Physics 2015-03-17 Anders W. Sandvik

By contrasting the performance of two quantum annealers operating at different temperatures, we address recent questions related to the role of temperature in these devices and their function as `Boltzmann samplers'. Using a method to…

Quantum Physics · Physics 2018-01-03 Jeffrey Marshall , Eleanor G. Rieffel , Itay Hen

We present a quantum annealing-based solution method for topology optimization (TO). In particular, we consider TO in a more general setting, i.e., applied to structures of continuum domains where designs are represented as distributed…

Numerical Analysis · Mathematics 2023-01-30 Zisheng Ye , Xiaoping Qian , Wenxiao Pan

A recent experiment [Lanting et al., PRX, (2014)] claimed to provide evidence of up to $8$-qubit entanglement in a D-Wave quantum annealing device. However, entanglement was measured using qubit tunneling spectroscopy, a technique that…

Quantum Physics · Physics 2015-12-17 Tameem Albash , Itay Hen , Federico M. Spedalieri , Daniel A. Lidar

Annealing approach to quantum tomography is theoretically proposed. First, based on the maximum entropy principle, we introduce classical parameters to combine "quantum models (or quantum states)" given a prior for potentially representing…

Quantum Physics · Physics 2019-04-05 Kentaro Imafuku

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

In this dissertation, we explore how quantum annealing (QA) and its applications behave in an open system setting. We give derivations and numerical recipes for effective parallel simulation methods for time-dependent open dynamics of…

Quantum Physics · Physics 2021-07-16 Ka Wa Yip

Quantum algorithms for Hamiltonian simulation and linear differential equations more generally have provided promising exponential speed-ups over classical computers on a set of problems with high real-world interest. However, extending…

Quantum Physics · Physics 2025-05-14 Noah Brüstle , Nathan Wiebe

The recent emergence of novel computational devices, such as quantum computers, coherent Ising machines, and digital annealers presents new opportunities for hardware-accelerated hybrid optimization algorithms. Unfortunately, demonstrations…

Optimization and Control · Mathematics 2020-10-21 Yuchen Pang , Carleton Coffrin , Andrey Y. Lokhov , Marc Vuffray

Recent demonstrations on specialized benchmarks have reignited excitement for quantum computers, yet whether they can deliver an advantage for practical real-world problems remains an open question. Here, we show that probabilistic…

Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical devices that aim at this class of problems by exploiting quantum…

In this thesis, we focus on the problem of validating and benchmarking quantum annealers. To this end, we propose two algorithms for solving real-world problems and test how they perform on the current generation of quantum annealers. The…

Quantum Physics · Physics 2023-12-07 Konrad Jałowiecki