Applying machine learning to combinatorial optimization problems has the potential to
improve both efficiency and accuracy. However, existing learning-based solvers often …

Recently, methods such as DeepCubeA have used deep reinforcement learning to learn
domain-specific heuristic functions in a largely domain-independent fashion. However, such …

Heuristic search is often used for motion planning and pathfinding problems, for finding the
shortest path in a graph while also promising completeness and optimal efficiency. The …

Deep reinforcement learning has been shown to be able to train deep neural networks to
implement effective heuristic functions that can be used with A* search to solve problems …

Efficient quantum compiling tactics greatly enhance the capability of quantum computers to
execute complicated quantum algorithms. Due to its fundamental importance, a plethora of …

The A* algorithm is commonly used to solve NP-hard combinatorial optimization problems.
When provided with a completely informed heuristic function, A* can solve such problems in …

Recent machine-learning approaches to deterministic search and domain-independent
planning employ policy learning to speed up search. Unfortunately, when attempting to …

In this work, we examine a line of recent publications that propose to use deep neural
networks to approximate the goal distances of states for heuristic search. We present a first …

Efficiently training a multi-task neural solver for various combinatorial optimization problems
(COPs) has been less studied so far. In this paper, we propose a general and efficient …

To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ)
processors is a central task in modern quantum technology. NISQ processors feature tens to …