Applications on quantum annealers

Ka-Wa Yip

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 annealing devices. We consider the weak-coupling limit to an environment and also the case of $1/f$ noise. The stochastic time-dependent quantum trajectories technique can be utilized in studying weak measurements and feedback error correction in quantum annealing. Then we focus on opensystem descriptions of reverse annealing (RA), which is a promising variant and application of quantum annealing. We show that, with various simulation tools, reverse annealing can benefit from the interaction between the annealer and its environment.

Dvir Kafri, Chris Quintana, Yu Chen +2 more

Y. S. Yang, P. Tyson, A. B Murphy

The weighted Max-Cut problem is an NP-hard problem with application implications. It is investigated on a cubic lattice with 113 nodes and mixed-signed random edge weights. For a fixed upper bound on edge weights, it has been demonstrated that the computational difficulty increases as the lower bound on edge weights becomes more negative. The solution time for the problem using a novel error-mitigated quantum annealing approach is compared with standard D-Wave quantum annealing (QA) and BQM hybrid solvers, as well as various classical solvers. For the QPU-embeddable weighted Max-Cut instances with mixed-signed edge weights, it has been quantitatively demonstrated that the SEMO (spin-error mitigation for optimisation) error-mitigated quantum annealing achieved substantially shorter time-to solution than standard D-Wave QA, D-Wave BQM, simulated annealing and Tabu search baselines. The error-mitigated quantum annealing approach presented in this article potentially elevates the efficiency and application scope of quantum annealing and would be applicable in solving other discrete optimisation problems that can be formulated as QUBO or Ising instances. The promising solution time advantage would be particularly impactful for time-critical optimisation applications.

Masayuki Ohzeki

Itay Hen, Víctor Martín-Mayor

Mayar Mohamed

This thesis studies several aspects of the quantum annealing (QA) computing approach. Quantum annealers' primary objective is to solve hard computational optimization problems. Because these optimization problems are in the NP-Hard complexity class, they are of great interest in several fields. One of the leading open questions concerning quantum annealers asks whether they will outperform other classical methods for solving these problems; Some aspects of this question are addressed in this thesis. The first part of the thesis investigates whether quantum annealing provides improved performance for solving a particular family of NP problems, called the Quadratic Knapsack Problem (QKP), using the D-Wave Quantum Annealer. The performance metrics used to assess QKP solving are the solution quality and the total runtime, and are benchmarked against other classical solvers. \n \nFurthermore, we extend our research on quantum annealers to propose two use cases for such systems. One is for Blockchain technology, and the second is in the area of quantum chaos. For the first use case of QA, an application for Blockchain's Proof of Work (PoW) is proposed, based on having hard optimization problems as an alternative to PoW hashing challenge, and using quantum annealers as solvers. For the second use case of QA, we propose simulating quantum chaos on the D-Wave Quantum Annealer to study the transition between the deep quantum realm and the classical limit in a chaotic system, and obtain insights into the “quantumness" of quantum annealers.