52 citations · 131 across the 9 of their papers we have counts for
9 papers
Quantum-informed surrogate sampling for combinatorial optimization
Elisabeth Wybo, Jernej Rudi Finžgar
We introduce Quantum-Informed Surrogate Sampling (QISS), a post-processing framework that generates candidate solutions to combinatorial optimization problems from low-weight corre…
Quantum-Guided Cluster Algorithms for Combinatorial Optimization
Peter J. Eder, Aron Kerschbaumer, Jernej Rudi Finžgar +5
Finding the ground state of Ising spin glasses is notoriously difficult due to disorder and frustration. Often, this challenge is framed as a combinatorial optimization problem, fo…
Counterdiabatic Driving with Performance Guarantees
Jernej Rudi Finžgar, Simone Notarnicola, Madelyn Cain +2
Counterdiabatic (CD) driving has the potential to speed up adiabatic quantum state preparation by suppressing unwanted excitations. However, existing approaches either require intr…
Quantum Computing for Automotive Applications
Carlos A. Riofrío, Johannes Klepsch, Jernej Rudi Finžgar +7
Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engin…
Quantum Dissipative Search via Lindbladians
Peter J. Eder, Jernej Rudi Finžgar, Sarah Braun +1
Closed quantum systems follow a unitary time evolution that can be simulated on quantum computers. By incorporating non-unitary effects via, e.g., measurements on ancilla qubits, t…
Quantum and classical correlations in shrinking algorithms for optimization
Victor Fischer, Maximilian Passek, Friedrich Wagner +3
Understanding the benefits of quantum computing for solving combinatorial optimization problems (COPs) remains an open research question. In this work, we extend and analyze algori…
Quantum-Informed Recursive Optimization Algorithms
Jernej Rudi Finžgar, Aron Kerschbaumer, Martin J. A. Schuetz +2
We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for combinatorial optimization problems. Our approach leverages quantum resources to…
Designing Quantum Annealing Schedules using Bayesian Optimization
Jernej Rudi Finžgar, Martin J. A. Schuetz, J. Kyle Brubaker +2
We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with re…
QUARK: A Framework for Quantum Computing Application Benchmarking
Jernej Rudi Finžgar, Philipp Ross, Leonhard Hölscher +2
Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances…