Quenching Speculation in Quantum Markets via Entangled Neural Traders

• URL: http://arxiv.org/abs/2602.06367v1
• Date: Fri, 06 Feb 2026 03:51:40 GMT
• Title: Quenching Speculation in Quantum Markets via Entangled Neural Traders
• Authors: Kieran Hymas, Hiu Ming Lau, Kareem Raslan, Qiang Sun, Azhar Iqbal, Derek Abbott, Andrew D. Greentree, James Q. Quach,
• Abstract summary: We show a prototype quantum stock market in which entanglement between traders' valuations mitigates the runaway devaluation characteristic of speculative busts.<n>Using reinforcement-learning agents trading a single commodity, we show that replacing classical valuations with quantum-correlated qubit-encoded valuations stabilizes prices.<n>To explain this behavior, we formulate and analyze a quantized version of the $p$-guessing game, a canonical model of speculative dynamics.
• Score: 3.772855715019728
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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