11 research outputs found
Most Frequent Itemset Optimization
In this paper we are dealing with the frequent itemset mining. We concentrate
on the special case that we only want to identify the most frequent itemset of
length N. To do that, we present a pattern on how to consider this search as an
optimization problem. First, we extract the frequency of all possible
2-item-sets. Then the optimization problem is to find the N objects, for which
the minimal frequency of all containing 2-item-sets is maximal. This
combinatorial optimization problem can be solved by any optimization algorithm.
We will solve them with Quantum Annealing and QUBO with QbSolv by D-Wave. The
advantages of MFIO in comparison to the state-of-the-art-approach are the
enormous reduction of time need, reduction of memory need and the omission of a
threshold. The disadvantage is that there is no guaranty for accuracy of the
result. The evaluation indicates good results
Influence of Different 3SAT-to-QUBO Transformations on the Solution Quality of Quantum Annealing: A Benchmark Study
To solve 3SAT instances on quantum annealers they need to be transformed to
an instance of Quadratic Unconstrained Binary Optimization (QUBO). When there
are multiple transformations available, the question arises whether different
transformations lead to differences in the obtained solution quality. Thus, in
this paper we conduct an empirical benchmark study, in which we compare four
structurally different QUBO transformations for the 3SAT problem with regards
to the solution quality on D-Wave's Advantage_system4.1. We show that the
choice of QUBO transformation can significantly impact the number of correct
solutions the quantum annealer returns. Furthermore, we show that the size of a
QUBO instance (i.e., the dimension of the QUBO matrix) is not a sufficient
predictor for solution quality, as larger QUBO instances may produce better
results than smaller QUBO instances for the same problem. We also empirically
show that the number of different quadratic values of a QUBO instance, combined
with their range, can significantly impact the solution quality
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Quantum computing provides powerful algorithmic tools that have been shown to
outperform established classical solvers in specific optimization tasks. A core
step in solving optimization problems with known quantum algorithms such as the
Quantum Approximate Optimization Algorithm (QAOA) is the problem formulation.
While quantum optimization has historically centered around Quadratic
Unconstrained Optimization (QUBO) problems, recent studies show, that many
combinatorial problems such as the TSP can be solved more efficiently in their
native Polynomial Unconstrained Optimization (PUBO) forms. As many optimization
problems in practice also contain continuous variables, our contribution
investigates the performance of the QAOA in solving continuous optimization
problems when using PUBO and QUBO formulations. Our extensive evaluation on
suitable benchmark functions, shows that PUBO formulations generally yield
better results, while requiring less qubits. As the multi-qubit interactions
needed for the PUBO variant have to be decomposed using the hardware gates
available, i.e., currently single- and two-qubit gates, the circuit depth of
the PUBO approach outscales its QUBO alternative roughly linearly in the order
of the objective function. However, incorporating the planned addition of
native multi-qubit gates such as the global Molmer-Sorenson gate, our
experiments indicate that PUBO outperforms QUBO for higher order continuous
optimization problems in general
Improving Primate Sounds Classification using Binary Presorting for Deep Learning
In the field of wildlife observation and conservation, approaches involving
machine learning on audio recordings are becoming increasingly popular.
Unfortunately, available datasets from this field of research are often not
optimal learning material; Samples can be weakly labeled, of different lengths
or come with a poor signal-to-noise ratio. In this work, we introduce a
generalized approach that first relabels subsegments of MEL spectrogram
representations, to achieve higher performances on the actual multi-class
classification tasks. For both the binary pre-sorting and the classification,
we make use of convolutional neural networks (CNN) and various
data-augmentation techniques. We showcase the results of this approach on the
challenging \textit{ComparE 2021} dataset, with the task of classifying between
different primate species sounds, and report significantly higher Accuracy and
UAR scores in contrast to comparatively equipped model baselines.Comment: DeLT
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
The safe application of reinforcement learning (RL) requires generalization
from limited training data to unseen scenarios. Yet, fulfilling tasks under
changing circumstances is a key challenge in RL. Current state-of-the-art
approaches for generalization apply data augmentation techniques to increase
the diversity of training data. Even though this prevents overfitting to the
training environment(s), it hinders policy optimization. Crafting a suitable
observation, only containing crucial information, has been shown to be a
challenging task itself. To improve data efficiency and generalization
capabilities, we propose Compact Reshaped Observation Processing (CROP) to
reduce the state information used for policy optimization. By providing only
relevant information, overfitting to a specific training layout is precluded
and generalization to unseen environments is improved. We formulate three CROPs
that can be applied to fully observable observation- and action-spaces and
provide methodical foundation. We empirically show the improvements of CROP in
a distributionally shifted safety gridworld. We furthermore provide benchmark
comparisons to full observability and data-augmentation in two different-sized
procedurally generated mazes.Comment: 9 pages, 5 figures, accepted for publication at IJCAI 202
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Stochastic partial observability poses a major challenge for decentralized
coordination in multi-agent reinforcement learning but is largely neglected in
state-of-the-art research due to a strong focus on state-based centralized
training for decentralized execution (CTDE) and benchmarks that lack sufficient
stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this paper, we
propose Attention-based Embeddings of Recurrence In multi-Agent Learning
(AERIAL) to approximate value functions under stochastic partial observability.
AERIAL replaces the true state with a learned representation of multi-agent
recurrence, considering more accurate information about decentralized agent
decisions than state-based CTDE. We then introduce MessySMAC, a modified
version of SMAC with stochastic observations and higher variance in initial
states, to provide a more general and configurable benchmark regarding
stochastic partial observability. We evaluate AERIAL in Dec-Tiger as well as in
a variety of SMAC and MessySMAC maps, and compare the results with state-based
CTDE. Furthermore, we evaluate the robustness of AERIAL and state-based CTDE
against various stochasticity configurations in MessySMAC.Comment: Accepted at ICML 202
Dynamic Price Incentivization for Carbon Emission Reduction using Quantum Optimization
Demand Side Response (DSR) is a strategy that enables consumers to actively
participate in managing electricity demand. It aims to alleviate strain on the
grid during high demand and promote a more balanced and efficient use of
electricity resources. We implement DSR through discount scheduling, which
involves offering discrete price incentives to consumers to adjust their
electricity consumption patterns. Since we tailor the discounts to individual
customers' consumption, the Discount Scheduling Problem (DSP) becomes a large
combinatorial optimization task. Consequently, we adopt a hybrid quantum
computing approach, using D-Wave's Leap Hybrid Cloud. We observe an indication
that Leap performs better compared to Gurobi, a classical general-purpose
optimizer, in our test setup. Furthermore, we propose a specialized
decomposition algorithm for the DSP that significantly reduces the problem
size, while maintaining an exceptional solution quality. We use a mix of
synthetic data, generated based on real-world data, and real data to benchmark
the performance of the different approaches
Pattern QUBOs: Algorithmic Construction of 3SAT-to-QUBO Transformations
One way of solving 3sat instances on a quantum computer is to transform the 3sat instances into instances of Quadratic Unconstrained Binary Optimizations (QUBOs), which can be used as an input for the QAOA algorithm on quantum gate systems or as an input for quantum annealers. This mapping is performed by a 3sat-to-QUBO transformation. Recently, it has been shown that the choice of the 3sat-to-QUBO transformation can significantly impact the solution quality of quantum annealing. It has been shown that the solution quality can vary up to an order of magnitude difference in the number of correct solutions received, depending solely on the 3sat-to-QUBO transformation. An open question is: what causes these differences in the solution quality when solving 3sat-instances with different 3sat-to-QUBO transformations? To be able to conduct meaningful studies that assess the reasons for the differences in the performance, a larger number of different 3sat-to-QUBO transformations would be needed. However, currently, there are only a few known 3sat-to-QUBO transformations, and all of them were created manually by experts, who used time and clever reasoning to create these transformations. In this paper, we will solve this problem by proposing an algorithmic method that is able to create thousands of new and different 3sat-to-QUBO transformations, and thus enables researchers to systematically study the reasons for the significant difference in the performance of different 3sat-to-QUBO transformations. Our algorithmic method is an exhaustive search procedure that exploits properties of 4×4 dimensional pattern QUBOs, a concept which has been used implicitly in the creation of 3sat-to-QUBO transformations before, but was never described explicitly. We will thus also formally and explicitly introduce the concept of pattern QUBOs in this paper
Algorithmic QUBO formulations for k-SAT and hamiltonian cycles
Quadratic Unconstrained Binary Optimization (QUBO) can be seen as a generic language for optimization problems. QUBOs attract particular attention since they can be solved with quantum hardware, like quantum annealers or quantum gate computers running QAOA. In this paper, we present two novel QUBO formulations for k-SAT and Hamiltonian Cycles that scale significantly better than existing approaches. For k-SAT we reduce the growth of the QUBO matrix from O(k) to O(log(k)). For Hamiltonian Cycles the matrix no longer grows quadratically in the number of nodes, as currently, but linearly in the number of edges and logarithmically in the number of nodes. We present these two formulations not as mathematical expressions, as most QUBO formulations are, but as meta-algorithms that facilitate the design of more complex QUBO formulations and allow easy reuse in larger and more complex QUBO formulations.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Quantum Circuit Architectures and Technolog
NISQ-Ready Community Detection Based on Separation-Node Identification
The analysis of network structure is essential to many scientific areas ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO-based approach that only needs number-of-nodes qubits and is represented by a QUBO matrix as sparse as the input graph’s adjacency matrix. The substantial improvement in the sparsity of the QUBO matrix, which is typically very dense in related work, is achieved through the novel concept of separation nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which, upon its removal from the graph, yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept by achieving an up to 95% optimal solution quality on three established real-world benchmark datasets. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large-scale, real-world problem instances.Quantum Circuit Architectures and Technolog