A search for hidden variables in the domain of high energy physics

Emilio Santos

Y. Fang, Christina Gao, Ying-Ying Li +6 more

Patrick Suppes, Mario Zanotti

The primary criterion of adequacy of a probabilistic causal analysis is that the causal variable should render the simultaneous phenomenological data conditionally independent. The intuition back of this idea is that the common cause of the phenomena should factor out the observed correlations. So we label the principle the common cause criterion . If we find that the barometric pressure and temperature are both dropping at the same time, we do not think of one as the cause of the other but look for a common dynamical cause within the physical theory of meteorology. If we find fever and headaches positively correlated, we look for a common disease as the source and do not consider one the cause of the other. But we do not want to suggest that satisfaction of this criterion is the end of the search for causes or probabilistic explanations. It does represent a significant and important milestone in any particular investigation. Under another banner the search for common causes in quantum mechanics is the search for hidden variables. A hidden variable that satisfies the common cause criterion provides a satisfactory explanation “in classical terms” of the quantum phenomenon. Much of the earlier discussion of hidden variables in quantum mechanics has centered around the search for deterministic underlying processes, but for some time now the literature has also been concerned with the existence of probabilistic hidden variables. It is a striking and important fact that even probabilistic hidden variables do not always exist when certain intuitive criteria are imposed.

Carter, John

This preprint introduces Quantum Hidden Variable Extraction (Q-HVE), a quantum algorithm that applies Grover-style amplitude amplification to identify which hidden-variable model best explains residual signal in a dataset after classical baselines have been subtracted. The algorithm achieves an unconditional O(√m) quadratic speedup over classical sequential model evaluation for libraries of m candidate models. The work argues that the four XPRIZE Quantum Applications problem domains — human health, climate science, energy systems, and materials engineering — share a common epistemic defect: their classical baseline models systematically exclude documented hidden variables. The Presignal Research Initiative has identified those variables across its published portfolio. Q-HVE provides the quantum acceleration layer that makes rapid model selection over those libraries computationally feasible. The four instantiations are: (1) Immunological Tinnitus Subtype (ITS), supported by peer-reviewed meta-analysis and active clinical trials; (2) heliospheric-ISM climate forcing, supported by published astrophysical modeling and isotopic evidence; (3) Vertical Arcology Hydraulic Spine (VAHS), a conceptual high-head pumped storage architecture; and (4) transport-regime-optimal battery electrode porosity derived from generalized Murray's Law. Epistemic status is explicitly graded for each domain. Resource estimates indicate Q-HVE is practical on a fault-tolerant quantum computer with approximately 85 logical qubits. No new experimental data are presented. This is a theoretical proposal posted for public scientific scrutiny under CC BY 4.0.

David Böhm

Ian Randall

How well do you know your quantum physics? Find out with this fun, cryptic word search, created by Ian Randall. All answers are hidden in the grid vertically, horizontally or diagonally, with no letters skipped.