A hybrid PCA+VQC quantum classifier achieves at best 55% recall on unseen ransomware detection, far below the 97.7% classical baseline, with performance degrading from 4 to 8 qubits before improving at 12.
Encoding classical data into a quantum computer
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abstract
In this article we describe a technique to transfer data from classical domain to quantum domain. We consider a set of $N (=2^n)$ classical data in the form of a column matrix and prepare a $n$-qubit quantum state, whose components correspond to the $N$ classical data. To prepare this $n$-qubit quantum vector we use Schmidt decomposition and singular value decomposition techniques respectively and construct the corresponding family of quantum circuits. To strengthen our argument we also give specific examples by considering a set of 4 and 16 classical data and constructing the corresponding 2 and 4-qubit quantum vector respectively.
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A Non-Monotonic Relationship: An Empirical Analysis of Hybrid Quantum Classifiers for Unseen Ransomware Detection
A hybrid PCA+VQC quantum classifier achieves at best 55% recall on unseen ransomware detection, far below the 97.7% classical baseline, with performance degrading from 4 to 8 qubits before improving at 12.