A set of tournaments with many Hamiltonian cycles

For a random tournament on $3^n$ vertices, the expected number of Hamiltonian cycles is known to be $(3^n -1)!/2^{3^n}$. Let $T_1$ denote a tournament of three vertices ${v_1, v_2, v_3}$. Let the orientation be such that there are directed edges from $v_1$to $v_2$ , from $v_2$ to $v_3$ and from $v_3$ to $v_1$. Construct a tournament $T_i$ by making three copies of $T_{i-1}$, $T_{i-1}\u27$, $T_{i-1}\u27\u27$ and $T_{i-1}\u27\u27\u27$. Let each vertex in $T_{i-1}\u27$ have directed edges to all vertices in $T_{i-1}\u27\u27$, similarly place directed edges from each vertex in $T_{i-1}\u27\u27$ to all vertices in $T_{i-1}\u27\u27\u27$ and from $T_{i-1}\u27\u27\u27$ to $T_{i-1}\u27$. In this thesis, we shall study this family of highly symmetric tournaments. In particular we shall present two different algorithms to calculate the number of Hamiltonian cycles in these tournaments and compare them with the expected number and with known bounds for random tournaments. This thesis is motivated by the question of the maximum number of Hamiltonian cycles a tournament can have

What Moser Could Have Asked: Counting Hamilton Cycles in Tournaments

Moser asked for a construction of explicit tournaments on $n$ vertices having at least $(\frac{n}{3e})^n$ Hamilton cycles. We show that he could have asked for rather more

Detecting Multiple Communities Using Quantum Annealing on the D-Wave System

A very important problem in combinatorial optimization is partitioning a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the ``highest quality'' possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer

Network Community Detection On Small Quantum Computers

In recent years a number of quantum computing devices with small numbers of qubits became available. We present a hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size. The proposed approach is applied to the network community detection problem. QLS is hardware-agnostic and easily extendable to new quantum computing devices as they become available. We demonstrate it to solve the 2-community detection problem on graphs of size up to 410 vertices using the 16-qubit IBM quantum computer and D-Wave 2000Q, and compare their performance with the optimal solutions. Our results demonstrate that QLS perform similarly in terms of quality of the solution and the number of iterations to convergence on both types of quantum computers and it is capable of achieving results comparable to state-of-the-art solvers in terms of quality of the solution including reaching the optimal solutions

Quantum Local Search for Graph Community Detection

We present Quantum Local Search (QLS) approach and demonstrate its efficacy by applying it to the problem of community detection in real-world networks. QLS is a hybrid algorithm that combines a classical machine with a small quantum device. QLS starts with an initial solution and searches its neighborhood, iteratively trying to find a better candidate solution. One of the main challenges of the quantum computing in NISQ era is the small number of available qubits. QLS addresses this challenge by using the quantum device only for the neighborhood search, which can be restricted to be small enough to fit on near-term quantum device. We implement QLS for modularity maximization graph clustering using QAOA on IBM Q Experience as a quantum local solver. We demonstrate the potential for quantum acceleration by showing that existing state-of-the-art optimization solvers cannot find a good solution to the local problems quickly and provide an estimate of how larger quantum devices can improve the performance of QLS