SQL2Circuits: Estimating Metrics for SQL Queries with a Quantum Natural Language Processing Method
- URL: http://arxiv.org/abs/2306.08529v2
- Date: Wed, 19 Jun 2024 09:21:44 GMT
- Title: SQL2Circuits: Estimating Metrics for SQL Queries with a Quantum Natural Language Processing Method
- Authors: Valter Uotila,
- Abstract summary: This work employs a quantum natural language processing (QNLP)-inspired approach for constructing a quantum machine learning model.
The model consists of an encoding mechanism and a training phase, including classical and quantum subroutines.
We conclude that our model reaches an accuracy equivalent to that of the QNLP model in the binary classification tasks.
- Score: 1.5540058359482858
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In recent years, advances in quantum computing have led to accelerating research on quantum applications across fields. Here, we introduce a quantum machine learning model as a potential solution to the classical question in database research: the estimation of metrics for SQL queries. This work employs a quantum natural language processing (QNLP)-inspired approach for constructing a quantum machine learning model that can classify SQL queries with respect to their cardinalities, costs, and execution times. The model consists of an encoding mechanism and a training phase, including classical and quantum subroutines. The encoding mechanism encodes SQL queries as parametrized quantum circuits. In the training phase, we utilize classical optimization algorithms, such as SPSA and Adam, to optimize the circuit parameters to make predictions about the query metrics. We conclude that our model reaches an accuracy equivalent to that of the QNLP model in the binary classification tasks. Moreover, we extend the previous work by adding 4-class classification tasks and compare the cardinality estimation results to the state-of-the-art databases. We perform a theoretical analysis of the quantum machine learning model by calculating its expressibility and entangling capabilities. The analysis shows that the model has advantageous properties that make it expressible but also not too complex to be executed on the existing quantum hardware.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Quantum-Assisted Simulation: A Framework for Developing Machine Learning Models in Quantum Computing [0.0]
We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms.
We conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches.
arXiv Detail & Related papers (2023-11-17T07:33:42Z) - Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms [65.268245109828]
We take inspiration from Kearns' SQ oracle and Valiant's weak evaluation oracle.
We introduce an extensive yet intuitive framework that yields unconditional lower bounds for learning from evaluation queries.
arXiv Detail & Related papers (2023-10-26T18:23:21Z) - A Framework for Demonstrating Practical Quantum Advantage: Racing
Quantum against Classical Generative Models [62.997667081978825]
We build over a proposed framework for evaluating the generalization performance of generative models.
We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models.
Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
arXiv Detail & Related papers (2023-03-27T22:48:28Z) - A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - Learning capability of parametrized quantum circuits [2.51657752676152]
Variational quantum algorithms (VQAs) and their applications in the field of quantum machine learning through parametrized quantum circuits (PQCs) are thought to be one major way of leveraging noisy intermediate-scale quantum computing devices.
In this paper, we build upon the work by Schuld et al. and compare popular ans"atze for PQCs through the new measure of learning capability.
We also examine dissipative quantum neural networks (dQNN) as introduced by Beer et al. and propose a data re-upload structure for dQNNs to increase their learning capability.
arXiv Detail & Related papers (2022-09-21T13:26:20Z) - Copula-based Risk Aggregation with Trapped Ion Quantum Computers [1.541403735141431]
Copulas are mathematical tools for modeling joint probability distributions.
Recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages.
We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer.
arXiv Detail & Related papers (2022-06-23T18:39:30Z) - Quantum variational learning for entanglement witnessing [0.0]
This work focuses on the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits.
We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled.
We made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness.
arXiv Detail & Related papers (2022-05-20T20:14:28Z) - When BERT Meets Quantum Temporal Convolution Learning for Text
Classification in Heterogeneous Computing [75.75419308975746]
This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification.
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
arXiv Detail & Related papers (2022-02-17T09:55:21Z) - Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search
Framework [0.0]
We take the first steps towards Automated Quantum Machine Learning (AutoQML)
We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture.
As an application use-case, we train a quantum Geneversarative Adrial neural Network (qGAN) to generate energy prices that follow a known historic data distribution.
arXiv Detail & Related papers (2022-02-16T12:37:10Z) - Oracle separations of hybrid quantum-classical circuits [68.96380145211093]
Two models of quantum computation: CQ_d and QC_d.
CQ_d captures the scenario of a d-depth quantum computer many times; QC_d is more analogous to measurement-based quantum computation.
We show that, despite the similarities between CQ_d and QC_d, the two models are intrinsically, i.e. CQ_d $nsubseteq$ QC_d and QC_d $nsubseteq$ CQ_d relative to an oracle.
arXiv Detail & Related papers (2022-01-06T03:10:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.