Quenching Speculation in Quantum Markets via Entangled Neural Traders

TL;DR

This paper demonstrates that quantum entanglement between traders' valuations can stabilize markets and prevent crashes, using a quantum stock market prototype and reinforcement learning agents to show improved stability and profitability.

Contribution

It introduces a quantum market model where entanglement mitigates speculative crashes, revealing quantum correlations as a new endogenous stabilization mechanism.

Findings

Quantum entanglement stabilizes prices in simulated markets.

Quantum correlations eliminate market collapse equilibria.

Reinforcement learning agents perform better with quantum valuations.

Abstract

Speculative trading can drive pronounced market instabilities, yet existing regulatory and macroprudential tools intervene only after such dynamics emerge. Quantum technologies offer a fundamentally new means of shaping economic behavior by introducing non-classical correlations between decision-makers. Here we demonstrate a prototype quantum stock market in which entanglement between traders' valuations mitigates the runaway devaluation characteristic of speculative busts. Using reinforcement-learning agents trading a single commodity, we show that replacing classical valuations with quantum-correlated qubit-encoded valuations stabilizes prices and increases the AI traders' net worth relative to a classical market, where instead agents rapidly converge to liquidation strategies that collapse the asset value. To explain this behavior, we formulate and analyze a quantized version of the $p$-guessing game, a canonical model of speculative dynamics. Quantum entanglement and phase coherence reshape the strategic landscape, eliminating the pathological pure-strategy Nash equilibrium that drives market collapse in the classical game, while mixed-strategy equilibria remain non-degenerate and avoid bust-type outcomes. These results identify quantum correlations as a novel, endogenous mechanism for market stabilization and, more broadly, demonstrate the utility of multi-agent reinforcement learning algorithms for uncovering optimal strategies in complex decision-making frameworks with quantum degrees of freedom.