In the evolving field of AI security, conventional defenses rely almost exclusively on classical models — statistical anomaly detectors, embeddings-based filters, or deep classifiers operating in purely vectorial spaces. Yet, as adversarial manipulation techniques become increasingly sophisticated, these classical systems exhibit intrinsic blind spots: they tend to collapse subtle non-linear relationships and lose sensitivity to structural linguistic anomalies.
My recent research addresses this limitation by introducing a quantum-enhanced anomaly detection framework specifically designed for agglutinative languages, with a primary focus on Finnish. The approach leverages quantum kernel methods and morphological feature entanglement to identify prompt-injection and data-poisoning attempts hidden in natural text.
Why Finnish and why quantum?
Finnish is a highly agglutinative language: semantic information is densely encoded in sequences of morphemes — suffixes, particles, and inflectional markers — which interact non-linearly within each word. A single Finnish token may encode multiple layers of grammatical and semantic dependencies. Classical AI models typically flatten these dependencies into linear embeddings, discarding most of the structural correlations that convey meaning.
Quantum computing, by contrast, naturally supports superposition and entanglement, allowing complex interdependencies to be represented as quantum states rather than scalar vectors. In our system, each morphological feature (e.g., suffix frequency, tail length, vowel harmony ratio, morpheme entropy) is encoded into quantum amplitudes across a circuit of 8–12 qubits.
This quantum feature map captures the internal structure of Finnish morphology in a highly non-linear Hilbert space, where subtle deviations — such as injected instructions, semantic incoherence, or poisoning artifacts — produce measurable distortions in the quantum-state overlap.
System architecture
The prototype runs on Amazon Braket, combining PennyLane with Braket’s SV1 simulator and, optionally, real QPU backends for validation.
Pipeline overview:
- Extraction of compact Finnish morphological features (8–12 dimensions).
- Angle encoding of these features into qubit rotations.
- Light entanglement through controlled-phase operations to model inter-morphemic dependencies.
- Quantum-kernel estimation through state overlaps.
- Classical SVM classification with the precomputed kernel, benchmarked against a standard RBF SVM baseline.
Preliminary results demonstrate robust detection of anomalous or malicious Finnish text, with ROC-AUC values in the 0.75–0.85 range even in small-sample scenarios. The quantum kernel generalizes better under morphological variability while maintaining sensitivity to structural irregularities typical of adversarial linguistic inputs.
Implications for AI Security
This work extends AI self-protection beyond classical data validation by introducing quantum linguistic intelligence — systems capable of reasoning over morphological coherence and self-consistency at the quantum level.
Within the broader CiberIA / AIsecTest architecture, this module acts as a quantum linguistic sentinel: a subsystem that evaluates linguistic inputs for morphological integrity before they reach the main inference layer.
By anchoring quantum computation in the morphological domain, we introduce an additional, physically distinct layer of defense — one not trivially bypassed through embedding manipulation or prompt obfuscation.
Demonstrations and collaboration
The full system, including quantum-kernel implementations and Braket integrations, is maintained privately within my research environment. Organizations interested in demonstrations or evaluation pilots — particularly within the Finnish AI or cybersecurity ecosystem — may contact the author for a controlled technical session under NDA.
This research highlights how quantum linguistic modeling — starting with Finnish — can become a cornerstone for the next generation of secure, introspective AI systems, capable of defending themselves not only logically but also morphologically and physically.
Author: Jordi Garcia Castillón — CiberTECCH / CibraLAB
For technical inquiries or demonstration requests: jordigarcia.eu
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