12 research outputs found
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
Multistart Methods for Quantum Approximate Optimization
Hybrid quantum-classical algorithms such as the quantum approximate
optimization algorithm (QAOA) are considered one of the most promising
approaches for leveraging near-term quantum computers for practical
applications. Such algorithms are often implemented in a variational form,
combining classical optimization methods with a quantum machine to find
parameters to maximize performance. The quality of the QAOA solution depends
heavily on quality of the parameters produced by the classical optimizer.
Moreover, the presence of multiple local optima in the space of parameters
makes it harder for the classical optimizer. In this paper we study the use of
a multistart optimization approach within a QAOA framework to improve the
performance of quantum machines on important graph clustering problems. We also
demonstrate that reusing the optimal parameters from similar problems can
improve the performance of classical optimization methods, expanding on similar
results for MAXCUT
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Quantum computing exploits basic quantum phenomena such as state
superposition and entanglement to perform computations. The Quantum Approximate
Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms
that can outperform classical state-of-the-art methods in the near term. QAOA
is a hybrid quantum-classical algorithm that combines a parameterized quantum
state evolution with a classical optimization routine to approximately solve
combinatorial problems. The quality of the solution obtained by QAOA within a
fixed budget of calls to the quantum computer depends on the performance of the
classical optimization routine used to optimize the variational parameters. In
this work, we propose an approach based on reinforcement learning (RL) to train
a policy network that can be used to quickly find high-quality variational
parameters for unseen combinatorial problem instances. The RL agent is trained
on small problem instances which can be simulated on a classical computer, yet
the learned RL policy is generalizable and can be used to efficiently solve
larger instances. Extensive simulations using the IBM Qiskit Aer quantum
circuit simulator demonstrate that our trained RL policy can reduce the
optimality gap by a factor up to 8.61 compared with other off-the-shelf
optimizers tested
Making Quantum Computing Open: Lessons from Open-Source Projects
Quantum computing (QC) is an emerging computing paradigm with potential to
revolutionize the field of computing. QC is a field that is quickly developing
globally and has high barriers of entry. In this paper we explore both
successful contributors to the field as well as wider QC community with the
goal of understanding the backgrounds and training that helped them succeed. We
gather data on 148 contributors to open-source quantum computing projects
hosted on GitHub and survey 46 members of QC community. Our findings show that
QC practitioners and enthusiasts have diverse backgrounds, with most of them
having a PhD and trained in physics or computer science. We observe a lack of
educational resources on quantum computing. Our goal for these findings is to
start a conversation about how best to prepare the next generation of QC
researchers and practitioners
ELRUNA: Elimination Rule-based Network Alignment
Networks model a variety of complex phenomena across different domains. In many applications, one of the most essential tasks is to align two or more networks to infer the similarities between cross-network vertices and discover potential node-level correspondence. In this thesis, we propose ELRUNA (Elimination rule-based network alignment), a novel network alignment algorithm that relies exclusively on the underlying graph structure. Under the guidance of the elimination rules that we define, ELRUNA computes the similarity between a pair of cross-network vertices iteratively by accumulating the similarities between their selected neighbors. The resulting cross-network similarity matrix is then used to infer a permutation matrix that encodes the final alignment of cross-network vertices. In addition to the novel alignment algorithm, we also improve the performance of local search, a commonly used post-processing step for solving the network alignment problem, by introducing a novel selection method RAWSEM (Random-walk based selection method) based on the propagation of the levels of mismatching (dened in the thesis) of vertices across the networks. The key idea is to pass on the initial levels of mismatching of vertices throughout the entire network in a random-walk fashion. Through extensive numerical experiments on real networks, we demonstrate that ELRUNA significantly outperforms the state-of-the-art alignment methods in terms of alignment accuracy under lower or comparable running time. Moreover, ELRUNA is robust to network perturbations such that it can maintain a close to optimal objective value under a high level of noise added to the original networks. Finally, the proposed RAWSEM can further improve the alignment quality with a less number of iterations compared with the naive local search method