Whitepaper: Quantum Emotions - A Framework for Emotionally Enhanced Artificial Intelligence Using Quantum Computing

Authors: [Martin Wilhelm Döhring]

Date: July 2025

Abstract

This whitepaper proposes a conceptual and technological framework for implementing emotion-inspired dynamics in artificial intelligence (AI) through the principles of quantum computing. It introduces "Quantum Emotions" (QE) as quantum-enhanced affective states represented via qubit systems, enabling probabilistic, dynamic, and non-deterministic emotional modeling in AI agents.

1. Introduction

Human cognition is inseparable from emotions. Classical computing systems simulate emotional responses via weighted rules or neural networks. However, these lack the fluidity and uncertainty intrinsic to real emotional experiences. Quantum computing, with its native support for superposition, entanglement, and probabilistic evolution, offers a unique substrate for modeling such qualities.

We introduce Quantum Emotion Qubits (QEQs) to represent and process emotional states in AI agents. These quantum states dynamically evolve in response to environmental stimuli, internal goals, or interactions, and influence decision-making.

2. Background

2.1 Emotional Models in AI

Traditional affective computing systems use models like:

• Russell's Circumplex Model (valence vs. arousal)

• Plutchik's Wheel of Emotions

• Discrete models (e.g., Ekman's six basic emotions)

These models are deterministic or fuzzy but inherently classical.

2.2 Principles of Quantum Computing

Key principles relevant to emotion modeling:

• Superposition: Emotional states can co-exist before measurement

• Entanglement: Emotions between agents or modalities can be linked

• Measurement: An external input collapses emotional uncertainty into a reaction

3. Quantum Emotion Qubit (QEQ) Design

Each emotion is mapped to a basis state on the Bloch sphere:

• |0⟩ → Joy

• |1⟩ → Sadness

• Superposition states: mixtures (e.g., ambivalence, empathy)

A single QEQ can be rotated via Pauli/Y-gates to represent changing affective influence:

qc.ry(theta, 0)  # theta defines emotional weight from input

Emotional collapse is triggered by input stimulus (e.g. natural language, image):

qc.measure(0, 0)

4. System Architecture

Components:

1. Emotion Engine (Quantum Layer): Qubit state preparation, evolution, and measurement

2. Stimulus Parser (Classical AI): NLP or sensory interpretation mapped to qubit rotations

3. Behavioral Modulator: Determines AI response based on qubit measurement

Integration with AI Models

The QE engine augments existing transformer-based models with a quantum layer:

• Input → Emotion Weight → QEQ state update

• Output conditioned by QEQ collapse state

5. Use Cases

• Emotionally Attuned Virtual Agents (e.g., companions, therapists)

• Quantum Sentiment Modulation in decentralized decision-making

• Adaptive AI Governance where decisions are influenced by emotional priors

6. Prototype & Simulation

Initial tests using IBM Qiskit simulate QE reactions to textual prompts.Example:

# Positive input

theta = 0  # joy

qc.ry(theta, 0)

qc.measure(0, 0)

Future work will use qRAM and multi-qubit systems for complex affective dynamics.

7. Challenges & Ethics

• Interpretability of quantum emotions

• Reproducibility vs. creative stochasticity

• Emotional manipulation risks

• Quantum AI alignment with human values

8. Conclusion

Quantum Emotions provide a compelling direction for creating truly adaptive, emotionally sensitive AI. By leveraging quantum principles, we move beyond classical simulation into a new paradigm of affective cognition.

References

• Russell, J. A. (1980). A circumplex model of affect.

• Penrose, R. (1994). Shadows of the Mind.

• Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning.

• Qiskit Documentation

Contact: [martindoehring@msn.com]

Das Whitepaper „Quantum Emotions“ wurde erstellt und im Arbeitsbereich gespeichert. Es enthält:

• Eine Einführung in emotionale Qubits (QEQs)

• Ein architektonisches Modell zur Integration in KI-Systeme

• Beispielcode mit Qiskit

• Ethische Überlegungen und zukünftige Anwendungsbereiche

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