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Found 9 papers, 2 papers with code
A divide-and-conquer algorithm for quantum state preparation
Results show that we can efficiently load data in quantum devices using a divide-and-conquer strategy to exchange computational time for space.
Quantum One-class Classification With a Distance-based Classifier
We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers.
Quantum ensemble of trained classifiers
An approach of quantum machine learning named quantum ensembles of quantum classifiers consists of using superposition to build an exponentially large ensemble of classifiers to be trained with an optimization-free learning algorithm.
Validation of the rapid detection approach for enhancing the electronic nose systems performance, using different deep learning models and support vector machines
In this work, we used a Support Vector Machines (SVM) algorithm and three different DL models to validate the rapid detection approach (based on processing an early portion of raw signals and a rising window protocol) over different measurement conditions.
Wine quality rapid detection using a compact electronic nose system: application focused on spoilage thresholds by acetic acid
It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality.
Parametric Probabilistic Quantum Memory
Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory.
Quantum enhanced cross-validation for near-optimal neural networks architecture selection
This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures.
Quantum perceptron over a field and neural network architecture selection in a quantum computer
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF).
Weightless neural network parameters and architecture selection in a quantum computer
Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture.
