Abstract: Intrusion Detection Systems (IDS) play a fundamental role in safeguarding digital infrastructures against cyber threats. Quantum Machine Learning (QML) presents a promising frontier in this ...

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In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Telstra has completed a trial with Silicon Quantum Computing (SQC) that sought to apply quantum machine learning to boost network automation. The 12-month trial saw the pair leverage Watermelon, SQC’s ...

In the first two articles of this series, we introduced the foundations of Quantum Machine Learning (QML) and explored how quantum properties such as superposition and entanglement can enhance machine ...

In the first article of this series, we introduced the idea of Quantum Machine Learning (QML), explained how quantum computing differs from classical computing and why researchers believe the ...

Upconversion (UC) materials, such as Yb/Er-doped fluorides and oxides, are widely applied in bioimaging, lighting, and quantum photonics. However, interpreting UC spectra to extract material ...

If you’re learning machine learning with Python, chances are you’ll come across Scikit-learn. Often described as “Machine Learning in Python,” Scikit-learn is one of the most widely used open-source ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...