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We demonstrate that the performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device.…

Quantum Physics · Physics 2015-04-03 Kristen L. Pudenz , Tameem Albash , Daniel A. Lidar

In a recent study (Ref. [1]), quantum annealing was reported to exhibit a scaling advantage for approximately solving Quadratic Unconstrained Binary Optimization (QUBO). However, this claim critically depends on the choice of classical…

Quantum Physics · Physics 2025-05-29 J. Pawlowski , P. Tarasiuk , J. Tuziemski , L. Pawela , B. Gardas

Quantum annealing is a heuristic optimization algorithm that exploits quantum evolution to approximately find lowest energy states. Quantum annealers have scaled up in recent years to tackle increasingly larger and more highly connected…

Quantum Physics · Physics 2025-07-04 Humberto Munoz Bauza , Daniel A. Lidar

Quantum annealing has emerged as a powerful platform for simulating and optimizing classical and quantum Ising models. Quantum annealers, like other quantum and/or analog computing devices, are susceptible to nonidealities including…

Quantum Physics · Physics 2024-10-15 Kevin Chern , Kelly Boothby , Jack Raymond , Pau Farré , Andrew D. King

We introduce quantum fluctuations into the simulated annealing process of optimization problems, aiming at faster convergence to the optimal state. The idea is tested by the two models, the transverse Ising model and the traveling salesman…

Quantum Physics · Physics 2007-05-23 Tadashi Kadowaki

Quantum devices are affected by intrinsic and environmental noises. An in-depth characterization of noise effects is essential for exploiting noisy quantum computing. To this end, we studied the energy dissipative behavior of a quantum…

Quantum Physics · Physics 2019-03-14 Tadashi Kadowaki , Masayuki Ohzeki

Quantum annealers are special-purpose quantum computers that primarily target solving Ising optimization problems. Theoretical work has predicted that the probability of a quantum annealer ending in a ground state can be dramatically…

Quantum Physics · Physics 2018-11-20 Juan I. Adame , Peter L. McMahon

New annealing schedules for quantum annealing are proposed based on the adiabatic theorem. These schedules exhibit faster decrease of the excitation probability than a linear schedule. To derive this conclusion, the asymptotic form of the…

Quantum Physics · Physics 2011-11-09 Satoshi Morita

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

We study a quantum annealer where bosons mediate the Ising-type interactions between qubits. We compare the efficiency of ground state preparation for direct and mediated couplings, for which Ising and spin-boson Hamiltonian are employed…

Quantum Physics · Physics 2018-12-05 Manuel Pino , Juan José García-Ripoll

Recent empirical results using quantum annealing hardware have shown that mid anneal pausing has a surprisingly beneficial impact on the probability of finding the ground state for of a variety of problems. A theoretical explanation of this…

Quantum Physics · Physics 2020-08-07 Huo Chen , Daniel A. Lidar

A large class of optimisation problems can be mapped to the Ising model where all details are encoded in the coupling of spins. The task of the original mathematical optimisation is then equivalent to finding the ground state of the…

The quantum Ising chain has shortcuts to adiabaticity when operated with weak processes. However, when exactly do the non-equilibrium effects of the Kibble-Zurek mechanism, inherent to the system, appear in the optimal protocols in such a…

Quantum Physics · Physics 2023-06-18 Pierre Nazé

Quantum annealing aims to solve combinatorial optimization problems mapped on to Ising interactions between quantum spins. A critical factor that limits the success of a quantum annealer is its sensitivity to noise, and intensive research…

Quantum Physics · Physics 2019-01-29 Shruti Puri , Christian Kraglund Andersen , Arne L. Grimsmo , Alexandre Blais

We study the effect of a thermal environment on the quantum annealing dynamics of a transverse-field Ising chain. The environment is modelled as a single Ohmic bath of quantum harmonic oscillators weakly interacting with the total…

Quantum Physics · Physics 2018-09-26 Luca Arceci , Simone Barbarino , Davide Rossini , Giuseppe E. Santoro

Prior work showed the efficacy of pausing midanneal: such a pause improved the probability of success by orders of magnitude in a class of native problem instances and improved the time to solution in a class of embedded problem instances.…

We use superconducting qubit quantum annealing devices to determine the ground state of Ising models with algebraically decaying competing long-range interactions in the thermodynamic limit. This is enabled by a unit-cell-based optimization…

Quantum Physics · Physics 2025-11-12 Jan Alexander Koziol , Kai Phillip Schmidt

We study quantum annealing in the quantum Ising model coupled to a thermal environment. When the speed of quantum annealing is sufficiently slow, the system evolves following the instantaneous thermal equilibrium. This quasistatic and…

Quantum Physics · Physics 2022-05-09 Hiroki Oshiyama , Sei Suzuki , Naokazu Shibata

Recent tests performed on the D-Wave Two quantum annealer have revealed no clear evidence of speedup over conventional silicon-based technologies. Here, we present results from classical parallel-tempering Monte Carlo simulations combined…

Quantum Physics · Physics 2016-01-19 Zheng Zhu , Andrew J. Ochoa , Stefan Schnabel , Firas Hamze , Helmut G. Katzgraber

Recently proposed analog solvers based on dynamical systems, such as Ising machines, are promising platforms for large-scale combinatorial optimization. Yet, given the heuristic nature of the field, there is very limited insight on…

Disordered Systems and Neural Networks · Physics 2026-03-27 Shu Zhou , K. Y. Michael Wong , Juntao Wang , David Shui Wing Hui , Daniel Ebler , Jie Sun