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Quantum annealing has emerged as a promising approach for solving NP-hard optimization problems, leveraging quantum phenomena such as quantum tunneling to navigate complex energy landscapes. However, the extent to which quantum tunneling…

Quantum Physics · Physics 2026-03-02 Vishwajeet Ohal , Pierre Boulanger

We present a comparison study of state-of-the-art classical optimisation methods to a D-Wave 2000Q quantum annealer for the planning of Earth observation missions. The problem is to acquire high value images while obeying the attitude…

The first quantum computers are expected to perform well at quadratic optimisation problems. In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such…

Portfolio Management · Quantitative Finance 2020-12-03 Frank Phillipson , Harshil Singh Bhatia

Quantum annealing is a heuristic algorithm for solving combinatorial optimization problems, and D-Wave Systems Inc. has developed hardware for implementing this algorithm. The current version of the D-Wave quantum annealer can solve…

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

Both simulated quantum annealing and physical quantum annealing have shown the emergence of "heavy tails" in their performance as optimizers: The total time needed to solve a set of random input instances is dominated by a small number of…

Quantum Physics · Physics 2016-06-22 Andrew D. King , Emile Hoskinson , Trevor Lanting , Evgeny Andriyash , Mohammad H. Amin

This paper investigates the performance of quantum, classical, and hybrid solvers on the NP-hard Max-Cut and QUBO problems, examining their solution quality relative to the global optima and their computational efficiency. We benchmark the…

Optimization and Control · Mathematics 2024-12-11 Jaka Vodeb , Vid Eržen , Timotej Hrga , Janez Povh

The availability of quantum annealing devices with hundreds of qubits has made the experimental demonstration of a quantum speedup for optimization problems a coveted, albeit elusive goal. Going beyond earlier studies of random Ising…

Quantum Physics · Physics 2015-11-03 Itay Hen , Joshua Job , Tameem Albash , Troels F. Rønnow , Matthias Troyer , Daniel Lidar

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

Quantum annealers (QA), such as D-Wave systems, become increasingly efficient and competitive at solving combinatorial optimization problems. However, solving problems that do not directly map the chip topology remains challenging for this…

Quantum Physics · Physics 2024-07-30 Valentin Gilbert , Stéphane Louise

The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly…

Quantum annealing has emerged as a powerful tool for solving combinatorial optimization problems efficiently, making use of the principles of quantum mechanics. Companies are increasingly investing in the market of quantum computers,…

Quantum Physics · Physics 2025-09-29 Lorenzo Mazzei , Giada Beccari , Mirko Laruina , Marco Cococcioni

One of the problems frequently mentioned as a candidate for quantum advantage is that of selecting a portfolio of financial assets to maximize returns while minimizing risk. In this paper we formulate several real-world constraints for use…

Materials Science · Physics 2022-03-10 Salvatore Certo , Anh Dung Pham , Daniel Beaulieu

Quantum annealing has shown promise for finding solutions to difficult optimization problems, including protein folding. Recently, we used the D-Wave Advantage quantum annealer to explore the folding problem in a coarse-grained lattice…

Quantum Physics · Physics 2024-02-15 Anders Irbäck , Lucas Knuthson , Sandipan Mohanty , Carsten Peterson

This paper summarizes a quantum algorithm of [R.D. Somma, et.al., Phys. Rev. Lett. 101, 130504 (2008)] that simulates a classical annealing process for solving discrete optimization problems. The complexity of the quantum algorithm scales…

Quantum Physics · Physics 2015-12-16 Sergio Boixo , Rolando D. Somma

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

Calibration of quantum computing technologies is essential to the effective utilization of their quantum resources. Specifically, the performance of quantum annealers is likely to be significantly impaired by noise in their programmable…

We discuss quantum annealing of the two-dimensional transverse-field Ising model on a D-Wave device, encoded on $L \times L$ lattices with $L \le 32$. Analyzing the residual energy and deviation from maximal magnetization in the final…

In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial…

The D-wave processor is a partially controllable open quantum system which exchanges energy with its surrounding environment (in the form of heat) and with the external time dependent control fields (in the form of work). Despite being…

Quantum Physics · Physics 2020-06-11 Lorenzo Buffoni , Michele Campisi

We present a general error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. Given any Ising model optimization problem, the encoding replaces each…

Quantum Physics · Physics 2016-09-01 Walter Vinci , Tameem Albash , Daniel A. Lidar
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