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Extreme Quantum Cognition Machines for Deliberative Decision Making

Francesco Romeo, Jacopo Settino

Extreme Quantum Cognition Machines use an input-dependent Hamiltonian term to bias quantum feature maps toward relevant correlations, enabling tolerance to noisy training data in decision tasks.

arxiv:2603.05430 v2 · 2026-03-05 · quant-ph · cond-mat.dis-nn

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Claims

C1strongest claim

We introduce Extreme Quantum Cognition Machines, a class of quantum learning architectures for deliberative decision making that is tolerant to noisy and contradictory training data.

C2weakest assumption

That an input-dependent interaction term in the Hamiltonian can reliably bias the quantum feature embedding toward task-relevant correlations in linguistic classification tasks, without the paper showing that this bias survives hardware noise or outperforms classical attention mechanisms.

C3one line summary

Extreme Quantum Cognition Machines combine quantum reservoir-style evolution with a dynamical attention mechanism in the Hamiltonian to produce robust nonlinear embeddings for decision making from noisy training data.

References

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[1] The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review, 65(6):386 1958
[2] Neural networks and physical systems with emergent collective computational abilities 1982
[3] Spin-glass models of neural networks 1985
[4] A learning algorithm for boltzmann machines.Cognitive science, 9(1):147–169, 1985 1985
[5] A logical calculus of the ideas immanent in nervous activity 1943

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First computed 2026-05-17T23:38:59.748899Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

bc8ce30b3d1ce0bdf8493d9660f3d9732eb67fb078dc120b3c4147396919730c

Aliases

arxiv: 2603.05430 · arxiv_version: 2603.05430v2 · doi: 10.48550/arxiv.2603.05430 · pith_short_12: XSGOGCZ5DTQL · pith_short_16: XSGOGCZ5DTQL36CJ · pith_short_8: XSGOGCZ5
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XSGOGCZ5DTQL36CJHWLGB46ZOM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: bc8ce30b3d1ce0bdf8493d9660f3d9732eb67fb078dc120b3c4147396919730c
Canonical record JSON
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