In this paper, we investigate the relationship between two elementary operations on derivations in the framework of graph transformation based on adhesive categories: moving a derivation along a derivation based on parallel and sequential independence on one hand and restriction of a derivation with respect to a monomorphism into the start object on the other hand. Intuitively, a restriction clips off parts of the start object that are never matched by a rule application throughout the derivation on the other hand. As main result, it is shown that moving a derivation preserves its spine being the minimal restriction.

Abstract In this paper, we continue the investigation of graph-based reaction systems. We extend the notion by input and output states as well as admitted context sequences to model explicitly input–output relations and decision problems on the inputs. Moreover, we combine extended graph-based reaction systems into families to cover infinite input–output relations and decision problems on infinite sets of graphs. This is used to model NP-problems on graphs and reductions between them as well as to prove their correctness.

In the literature, one encounters the intensely studied classical set-based reaction systems and the more recently introduced generalization to graph-based reaction systems where the considered graphs are directed, simple, and edge-labeled. In this paper, we propose a categorical approach to reaction systems so that a wider spectrum of data structures becomes available on which reaction systems can be based including various types of graphs and of graph-like structures like unlabeled graphs, vertex-labeled graphs, bipartite graphs, and a variety of types of hypergraphs. But also algebraic structures like monoids fit into the framework.

Motivated by the promises of significant speed-ups for certain problems, quantum computing received significant attention in the past. While much progress has been made in the development of synthesis methods for quantum circuits, new physical developments constantly lead to new constraints to be addressed. The limited interaction distance between the respective qubits (i.e. nearest neighbor optimization) has already been considered intensely. But with the emerge of multi-dimensional quantum architectures, new physical requirements came up for which only a few automatic synthesis solutions exist yet all of them of heuristic nature. In this work, we propose an exact scheme for nearest neighbor optimization in multi-dimensional quantum circuits. Although the complexity of the problem is a serious obstacle, our experimental evaluation shows that the proposed solution is sufficient to allow for a qualitative evaluation of the respective optimization steps. Besides that, this enabled an exact comparison to heuristical results for the first time.

Motivated by its promising applications e.g. for database search or factorization, significant progress has been made in the development of automated design methods for quantum circuits. But in order to keep up with recent physical developments in this domain, new technological constraints have to be considered. Limited interaction distance between gate qubits is one of the most common of these constraints. This led to the development of several strategies aiming at making a given quantum circuit nearest neighbor-compliant by inserting SWAP gates into the existing structure. Usually these strategies are of heuristic nature. In this work, we present an exact approach that enables nearest neighbor-compliance by inserting a minimal number of SWAP gates. Experiments demonstrate the applicability of the approach which enabled a comparison of results obtained by heuristic methods to the actual optimum.

Research in the domain of quantum computation is mainly driven by their promising applications e.g., for factorization or database search. At the same time, physical developments for this emerging technology constantly lead to new constraints to be addressed by logic designers. The limited interaction distance between qubits, the elementary information storage in quantum circuits, is one of the most common restrictions, leading to the fact that, for many quantum architectures, computations can only be performed on adjacent (i.e., nearest neighbor) qubits. Motivated by that, optimization of quantum circuits with respect to this restriction has become an intensely considered research topic. In this paper, we briefly review existing approaches that have been proposed in the past for this purpose. We particularly consider that almost all existing solutions are of heuristic nature, i.e., do not guarantee an optimal solution. In order to address this, exact alternatives are introduced which make use of the deductive power of constraint solvers. By this, we are able to perform a qualitative evaluation of the performance of existing (heuristic) solutions for linear nearest neighbor quantum circuit optimization.