136 citations · 513 across the 37 of their papers we have counts for
39 papers
Extending quantum annealing to continuous domains: a hybrid method for quadratic programming
Hristo N. Djidjev
We propose Quantum Enhanced Simulated Annealing (QESA), a novel hybrid optimization framework that integrates quantum annealing (QA) into simulated annealing (SA) to tackle continu…
A quantum annealing approach to graph node embedding
Hristo N. Djidjev
Node embedding is a key technique for representing graph nodes as vectors while preserving structural and relational properties, which enables machine learning tasks like feature e…
Increasing the Hardness of Posiform Planting Using Random QUBOs for Programmable Quantum Annealer Benchmarking
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Posiform planting is a method for constructing QUBO problems with a single unique planted solution that can be tailored to arbitrary connectivity graphs. In this study we investiga…
Replication-based quantum annealing error mitigation
Hristo N. Djidjev
Quantum annealers like those from D-Wave Systems implement adiabatic quantum computing to solve optimization problems, but their analog nature and limited control functionalities p…
Posiform Planting: Generating QUBO Instances for Benchmarking
Georg Hahn, Elijah Pelofske, Hristo N. Djidjev
We are interested in benchmarking both quantum annealing and classical algorithms for minimizing Quadratic Unconstrained Binary Optimization (QUBO) problems. Such problems are NP-h…
Initial State Encoding via Reverse Quantum Annealing and h-gain Features
Elijah Pelofske, Georg Hahn, Hristo Djidjev
Quantum annealing is a specialized type of quantum computation that aims to use quantum fluctuations in order to obtain global minimum solutions of combinatorial optimization probl…