From the 1 of 10 linked papers with an AI index.
10 papers
Hardware-Aware QUBO Reformulation of Constrained Binary Optimization via the Walsh-Fourier Transform
Loong Kuan Lee, Harsha Nagarajan, Thore Gerlach +3
The paper proposes a slack‑free, penalty‑based method that reformulates constrained binary optimization problems into QUBO form using a Walsh‑Fourier projection that respects the c…
Standardization of Multi-Objective QUBOs
Loong Kuan Lee, Thore Gerlach, Nico Piatkowski
Multi-objective optimization involving Quadratic Unconstrained Binary Optimization (QUBO) problems arises in various domains. A fundamental challenge in this context is the effecti…
Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
Lukas TheiÃinger, Thore Gerlach, David Berghaus +1
Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasi…
Quadratic Continuous Quantum Optimization
Sascha Mücke, Thore Gerlach, Nico Piatkowski
Quantum annealers can solve QUBO problems efficiently but struggle with continuous optimization tasks like regression due to their discrete nature. We introduce Quadratic Continuou…
Multi-Objective Quantum Power System Redispatch
Loong Kuan Lee, Johannes Knaute, Florian Gerhardt +4
The rising energy production costs and the increasing reliance on volatile renewable sources have driven the need for more efficient power system redispatch strategies. In this wor…
Kernel -Medoids as General Vector Quantization
Thore Gerlach, Sascha Mücke, Christian Bauckhage
Vector Quantization (VQ) is a widely used technique in machine learning and data compression, valued for its simplicity and interpretability. Among hard VQ methods, -medoids clu…