Quantum Fighter

Quantum Fighter

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D-Wave's New Chip and How to Use it
Even if it is still a prototype.
 • Iraitz
Implementation of a Quantum Random Forest
This article will guide you through an exploration of a Quantum Random Forest (QRF) adapting information from papers, GitHub, and proprietary…
 • Javier Mancilla
About How to Use Automated Feature Map Design in your QML Pipeline
In this article, we will show you how to integrate the paper "Automatic Design of Quantum Feature Maps" in your QML exploration.
 • Javier Mancilla
Quantum Feature Selection Made Easy
How to use Falcondale OSS for an easy quantum feature selection process.
 • Iraitz
GPUs vs CPUs for Quantum Simulation
Or when should you use each one.
 • Iraitz
Variational Quantum Classifier Using Cirq with Optimized Encoding Parameters
This series of code snippets demonstrates the implementation of a quantum machine learning model using Cirq, a Python SDK from Google for quantum…
 • Javier Mancilla
Quantum (Simulated) and Classical Benchmark for Feature Selection
A comparison using a real dataset between classical RFE, with D-Wave CQM and QAOA using Qiskit.
 • Javier Mancilla
Dimensionality Reduction for Quantum Machine Learning: Integrating LDA and K-Means
This article provides an illustration of how a dataset could be effectively reduced to execute QML that requires a low range of dimensions.
 • Javier Mancilla
Feature Selection with Quantum Approximate Optimization Algorithm (QAOA)
This article presents a method to perform feature selection using the Quantum Approximate Optimization Algorithm (QAOA).
 • Javier Mancilla
Quantum Fighter
Quantum Fighter
"Quantum Fighter" is your insightful guide into the world of quantum machine learning. It's not just about the latest news; here, you'll find book reviews, practical guides for aspiring quantum data scientists, and code, among other contents.
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