Analyzing the effectiveness of quantum annealing with meta-learning

The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in particular on Quantum Annealing (QA), a meta-heuristic solver for Quadratic Unconstrained Binary Optimization (QUBO) problems. It is known that the effectiveness of QA is dependent on the task itself, as is the case for classical solvers, but there is not yet a clear understanding of which are the characteristics of a problem that make it difficult to solve with QA. In this work, we propose a new methodology to study the effectiveness of QA based on meta-learning models. To do so, we first build a dataset composed of more than five thousand instances of ten different optimization problems. We define a set of more than a hundred features to describe their characteristics and solve them with both QA and three classical solvers. We publish this dataset online for future research. Then, we train multiple meta-models to predict whether QA would solve that instance effectively and use them to probe which features with the strongest impact on the effectiveness of QA. Our results indicate that it is possible to accurately predict the effectiveness of QA, validating our methodology. Furthermore, we observe that the distribution of the problem coefficients representing the bias and coupling terms is very informative in identifying the probability of finding good solutions, while the density of these coefficients alone is not enough. The methodology we propose allows to open new research directions to further our understanding of the effectiveness of QA, by probing specific dimensions or by developing new QUBO formulations that are better suited for the particular nature of QA. Furthermore, the proposed methodology is flexible and can be extended or used to study other quantum or classical solvers

Feature Selection for Classification with QAOA

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real-world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step

Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area

Does the structure of the QUBO problem affect the effectiveness of quantum annealing? An empirical perspective

In recent years there has been a significant interest in exploring the potential of Quantum Annealers (QA) as heuristic solvers of Quadratic Unconstrained Binary Optimization (QUBO) problems. Some problems are more difficult to solve on QA and understanding why is not straightforward, because an analytical study of the underlying physical system is intractable for large QUBO problems. This work consists in an empirical analysis of the features making a QUBO problem difficult to solve on QA, based on clusters of QUBO instances identified with Hierarchical Clustering. The analysis reveals correlations between specific values of the features and the ability of QA to solve effectively the instances. These initial results open new research opportunities to inform the development of new AI methods supporting quantum computation (e.g., for minor embedding or error mitigation) that are better tailored to the characteristics of the problem, as well as to develop better QUBO formulations for known problems in order to improve the quality of the solutions found by QA

Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers

In recent years the interest in applying Quantum Computing to Information Retrieval and Recommendation Systems task has increased and several papers have proposed formulations of relevant tasks that can be solved with quantum devices (community detection, feature selection etc.), usually focusing on Quantum Annealers (QA), a special purpose device able to solve combinatorial optimization problems. However, most research only focuses on the mathematical aspect of the formulation, without accounting for the underlying physical processes of the quantum device. Indeed, theoretical studies indicate that certain characteristics make a problem difficult to solve on QA, but it is not clear how to use this knowledge to inform the development of better problem formulations that are equivalent but easier to solve on QA. This work presents a preliminary study which approaches this issue with an empirical perspective. We consider several problems both general and related to IR and Recommendation tasks to assess whether we can identify characteristics of the problem formulation or the solution space that affect the effectiveness of QA. The results indicate interesting correlations and suggest that this is a promising area to investigate further

Towards Recommender Systems with Community Detection and Quantum Computing

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves

QuantumCLEF - Quantum Computing at CLEF

Over the last few years, Quantum Computing (QC) has captured the attention of numerous researchers pertaining to different fields since, due to technological advancements, QC resources have become more available and also applicable in solving practical problems. In the current landscape, Information Retrieval (IR) and Recommender Systems (RS) need to perform computationally intensive operations on massive and heterogeneous datasets. Therefore, it could be possible to use QC and especially Quantum Annealing (QA) technologies to boost systems’ performance both in terms of efficiency and effectiveness. The objective of this work is to present the first edition of the QuantumCLEF lab, which is composed of two tasks that aim at:evaluating QA approaches compared to their traditional counterpart;identifying new problem formulations to discover novel methods that leverage the capabilities of QA for improved solutions;establishing collaborations among researchers from different fields to harness their knowledge and skills to solve the considered challenges and promote the usage of QA. evaluating QA approaches compared to their traditional counterpart; identifying new problem formulations to discover novel methods that leverage the capabilities of QA for improved solutions; establishing collaborations among researchers from different fields to harness their knowledge and skills to solve the considered challenges and promote the usage of QA. This lab will employ the QC resources provided by CINECA, one of the most important computing centers worldwide. We also describe the design of our infrastructure which uses Docker and Kubernetes to ensure scalability, fault tolerance and replicability

Towards the Evaluation of Recommender Systems with Impressions

In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study's goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain

Virtual Network Function Embedding with Quantum Annealing

In recent years, the growing number of devices connected to the internet led network operators to continuously expand their own infrastructures. In order to simplify this scaling process, the research community is currently investigating the opportunity to move the complexity from a hardware to a software domain, through the introduction of a new paradigm, called Network Functions Virtualisation (NFV). It considers standard hardware platforms where many virtual instances are allocated to implement specific network services. However, despite the theoretical benefits, the mapping of the different virtual instances to the available physical resources represents a complex problem, difficult to be solved classically. The present work proposes a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of this embedding process, exploring the implementation possibilities on D-Wave's Quantum Annealers. Many test cases, with realistic constraints, have been considered to validate and characterise the potential of the model, and the promising results achieved are discussed throughout the document. The technical discussion is enriched with comparisons of the results obtained through heuristic algorithms, highlighting the strengths and the limitations in the resolution of the QUBO formulation proposed on current quantum machines

Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

Recommender systems typically rely on past user interactions as the primary source of information for making predictions. However, although highly informative, past user interactions are strongly biased. Impressions, on the other hand, are a new source of information that indicate the items displayed on screen when the user interacted (or not) with them, and have the potential to impact the field of recommender systems in several ways. Early research on impressions was constrained by the limited availability of public datasets, but this is rapidly changing and, as a consequence, interest in impressions has increased. Impressions present new research questions and opportunities, but also bring new challenges. Several works propose to use impressions as part of recommender models in various ways and discuss their information content. Others explore their potential in off-policy-estimation and reinforcement learning. Overall, the interest of the community is growing, but efforts in this direction remain disconnected. Therefore, we believe that a workshop would be useful in bringing the community together