Quenching Speculation in Quantum Markets via Entangled Neural Traders

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.

💡 Research Summary

The paper investigates whether quantum entanglement can be used as an endogenous stabilising mechanism in speculative markets. The authors construct a minimalist “quantum stock market” consisting of eight reinforcement‑learning agents that trade a single commodity. Each agent is a feed‑forward neural network that receives three inputs – current cash, current holdings, and the previous round’s average price – and outputs a real number whose sign determines buy or sell and whose magnitude determines the price at which the agent is willing to trade one unit. Agents are trained with the REINFORCE policy‑gradient algorithm and the Adam optimiser (learning rate 10⁻³) to maximise their net worth (cash plus holdings × price).

In the classical version of the market, agents’ price proposals $i are used directly for matching. In the quantum version, before matching, each $i is encoded into a qubit rotation θ_i = π $ i /$max, the qubits are globally entangled by the unitary J(γ)=exp(−iγ/2 ∑σₓ^{(k)}), then each qubit undergoes its individual rotation U_i(θ_i). After applying the inverse entangling operation J†(γ), the expectation value ⟨σ_z^{(i)}⟩ is read out, rescaled, and interpreted as the adjusted price ˜$i. The parameter γ∈