592,860 research outputs found
Brokered Graph State Quantum Computing
We describe a procedure for graph state quantum computing that is tailored to
fully exploit the physics of optically active multi-level systems. Leveraging
ideas from the literature on distributed computation together with the recent
work on probabilistic cluster state synthesis, our model assigns to each
physical system two logical qubits: the broker and the client. Groups of
brokers negotiate new graph state fragments via a probabilistic optical
protocol. Completed fragments are mapped from broker to clients via a simple
state transition and measurement. The clients, whose role is to store the
nascent graph state long term, remain entirely insulated from failures during
the brokerage. We describe an implementation in terms of NV-centres in diamond,
where brokers and clients are very naturally embodied as electron and nuclear
spins.Comment: 5 pages, 3 figure
Computing graph gonality is hard
There are several notions of gonality for graphs. The divisorial gonality
dgon(G) of a graph G is the smallest degree of a divisor of positive rank in
the sense of Baker-Norine. The stable gonality sgon(G) of a graph G is the
minimum degree of a finite harmonic morphism from a refinement of G to a tree,
as defined by Cornelissen, Kato and Kool. We show that computing dgon(G) and
sgon(G) are NP-hard by a reduction from the maximum independent set problem and
the vertex cover problem, respectively. Both constructions show that computing
gonality is moreover APX-hard.Comment: The previous version only dealt with hardness of the divisorial
gonality. The current version also shows hardness of stable gonality and
discusses the relation between the two graph parameter
Using Canonical Forms for Isomorphism Reduction in Graph-based Model Checking
Graph isomorphism checking can be used in graph-based model checking to achieve symmetry reduction. Instead of one-to-one comparing the graph representations of states, canonical forms of state graphs can be computed. These canonical forms can be used to store and compare states. However, computing a canonical form for a graph is computationally expensive. Whether computing a canonical representation for states and reducing the state space is more efficient than using canonical hashcodes for states and comparing states one-to-one is not a priori clear. In this paper these approaches to isomorphism reduction are described and a preliminary comparison is presented for checking isomorphism of pairs of graphs. An existing algorithm that does not compute a canonical form performs better that tools that do for graphs that are used in graph-based model checking. Computing canonical forms seems to scale better for larger graphs
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