Where quantum and quantum-like methods earn their place in behavioural-trial analysis: three simulation pilots across the RCT pipeline
Abstract
Behavioural randomized controlled trials (RCTs) are the empirical workhorse of behavioural science, yet quantum computing has reached the field only through opinion-dynamics demonstrations and decision-theory formalism, leaving the RCT analysis pipeline unexamined. I ask, stage by stage, where quantum or quantum-like computation earns its place against a strong classical baseline, and report three controlled simulation pilots across the pipeline. In a power-calculation pilot anchored on a 97-outcome preprocessing multiverse of real behavioural RCTs, quantum amplitude estimation recovers enumerated multiverse-significance fractions with absolute error 20-30 times smaller than classical sampled Monte Carlo at matched query budgets on the two non-degenerate outcomes, conditional on efficient state preparation. In an interference pilot, an entanglement-structured estimator recovers a four-way spillover that a pairwise model cannot represent at any sample size. In an adaptation pilot, a quantum-inspired contextual bandit is decisively beaten by a correctly specified classical baseline. The three results - one conditional win, one representational win, one honest loss - yield a decision rule for when quantum methods are worth adopting, piloting, or deferring, together with a five-gap research agenda.
Related
- A Kinetic Theory Approach To Ordered Fluids: Direct Simulation Monte Carlo Methods
- When Do Pilots Matter for OFDM Sensing? Pilot-Data Resource Design for ISAC
- Q-Edge: Symmetry-Reduced Quantum Simulation of Structured Extreme Dependence
- Methods for Deriving Nonlinear Kinetic Equations from the Dynamics of Quantum Systems
- Predicting Noise in a Trapped-ion Quantum Simulation of a Lattice Gauge Theory
BibTeX
@misc{veltri2026where,
title = {{Where quantum and quantum-like methods earn their place in behavioural-trial analysis: three simulation pilots across the RCT pipeline}},
author = {Giuseppe Alessandro Veltri},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21269},
eprint = {2609.21269},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21269},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Where quantum and quantum-like methods earn their place in behavioural-trial analysis: three simulation pilots across the RCT pipeline
AU - Giuseppe Alessandro Veltri
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21269
UR - https://arxiv.org/abs/2609.21269
AB - Behavioural randomized controlled trials (RCTs) are the empirical workhorse of behavioural science, yet quantum computing has reached the field only through opinion-dynamics demonstrations and decision-theory formalism, leaving the RCT analysis pipeline unexamined. I ask, stage by stage, where quantum or quantum-like computation earns its place against a strong classical baseline, and report three controlled simulation pilots across the pipeline. In a power-calculation pilot anchored on a 97-outcome preprocessing multiverse of real behavioural RCTs, quantum amplitude estimation recovers enumerated multiverse-significance fractions with absolute error 20-30 times smaller than classical sampled Monte Carlo at matched query budgets on the two non-degenerate outcomes, conditional on efficient state preparation. In an interference pilot, an entanglement-structured estimator recovers a four-way spillover that a pairwise model cannot represent at any sample size. In an adaptation pilot, a quantum-inspired contextual bandit is decisively beaten by a correctly specified classical baseline. The three results - one conditional win, one representational win, one honest loss - yield a decision rule for when quantum methods are worth adopting, piloting, or deferring, together with a five-gap research agenda.
ER -