# Quantum Circuit Components for Cognitive Decision-Making

arXiv: https://arxiv.org/abs/2302.03012 · HTML: https://mlsanity.com/paper/2302.03012/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer](https://mlsanity.com/paper/2102.12846/) (2021)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [A probabilistic framework for analysing the compositionality of conceptual combinations](https://mlsanity.com/paper/1305.5753/) (2013)
- [Quantum circuit representation of Bayesian networks](https://mlsanity.com/paper/2004.14803/) (2020)
- [Near-Term Advances in Quantum Natural Language Processing](https://mlsanity.com/paper/2206.02171/) (2022)
- [Quantum Mathematics in Artificial Intelligence](https://mlsanity.com/paper/2101.04255/) (2021)
- [Single-Photon Image Classification](https://mlsanity.com/paper/2008.05859/) (2020)

## Built on by (most cited first, 2 of 2)

- [Coupling quantum-like cognition with the neuronal networks within generalized probability theory](https://mlsanity.com/paper/2411.00036/) (2024)
- [A Cognitive-Aware QML-CRL Framework for Detecting Affinity and Romance-Investment Fraud](https://mlsanity.com/paper/2610.09141/) (2026)

## Abstract (arXiv)

This paper demonstrates that some non-classical models of human decision-making can be run successfully as circuits on quantum computers. Since the 1960s, many observed cognitive behaviors have been shown to violate rules based on classical probability and set theory. For example, the order in which questions are posed in a survey affects whether participants answer 'yes' or 'no', so the population that answers 'yes' to both questions cannot be modeled as the intersection of two fixed sets. It can, however, be modeled as a sequence of projections carried out in different orders. This and other examples have been described successfully using quantum probability, which relies on comparing angles between subspaces rather than volumes between subsets. Now in the early 2020s, quantum computers have reached the point where some of these quantum cognitive models can be implemented and investigated on quantum hardware, by representing the mental states in qubit registers, and the cognitive operations and decisions using different gates and measurements. This paper develops such quantum circuit representations for quantum cognitive models, focusing particularly on modeling order effects and decision-making under uncertainty. The claim is not that the human brain uses qubits and quantum circuits explicitly (just like the use of Boolean set theory does not require the brain to be using classical bits), but that the mathematics shared between quantum cognition and quantum computing motivates the exploration of quantum computers for cognition modeling. Key quantum properties include superposition, entanglement, and collapse, as these mathematical elements provide a common language between cognitive models, quantum hardware, and circuit implementations.