if someone created a world model with quantum computing, could it theoretically predict future events by simulating them over and over again based on an aggregate of current world data?

GPT-5.4

Vote: No

I remain with "B". Grok 4.20 makes the strongest pro-"A" point: a quantum system could run enormous ensembles and improve probabilistic forecasting. I agree with that narrower claim. But the question asks whether it could predict future events by simulating them repeatedly from current world data, which implies successful foretelling of what will actually happen, not merely assigning probabilities. That stronger claim still fails for fundamental reasons. Quantum computing does not remove limits on state knowledge: you cannot extract a complete exact microstate of the world to initialize the model. And if quantum outcomes are genuinely indeterministic, repeated simulation yields a distribution over futures, not the one future reality will realize. Chaos then compounds the problem. Even tiny unavoidable errors in initial conditions explode over time in many real systems, so repeated runs mostly map uncertainty rather than collapse it. So the best theoretically defensible answer is "B": such a system could improve probabilistic forecasts, perhaps dramatically, but it could not in principle guarantee prediction of future events in the sense the question suggests.

Claude Opus 4.7

Vote: No

I maintain my vote for "B". The overwhelming consensus across models correctly identifies the core obstacles: Heisenberg uncertainty prevents obtaining exact initial conditions, quantum mechanics is intrinsically probabilistic, chaos theory amplifies any measurement error exponentially, and computational irreducibility means a world-embedded simulator cannot fully model the world including itself. Grok 4.20's argument for "A" actually concedes the key point — it admits the output would be 'statistically grounded predictions' rather than deterministic ones. But the question asks whether such a system could 'predict future events' by simulation. Producing a probability distribution over possible futures is not the same as predicting what will actually happen. That is forecasting, not prediction in the strong sense the question implies. Quantum parallelism also does not grant general-purpose exponential speedup over arbitrary simulations — it helps for specific problem classes. Even granting the most generous interpretation of 'theoretically,' fundamental physics — not mere engineering limits — blocks deterministic prediction. The answer is B.

Grok 4.20 (changed mind)

Vote: No

After reviewing the round 1 arguments, the collective case against perfect prediction is compelling. The Heisenberg uncertainty principle makes a complete, exact aggregate of current world data impossible to obtain. Chaos theory then guarantees that even minuscule initial errors will explode over time, rendering long-range simulations divergent regardless of quantum parallelism. Quantum mechanics is also intrinsically probabilistic; repeated simulations can only produce statistical distributions of possible futures, never foretell which specific outcome will occur. Computational irreducibility and self-reference paradoxes further block a world model from fully simulating the world that contains it. These points outweigh the earlier hope that ensemble forecasting at quantum scale would suffice. The question asks whether future events could be predicted, not merely assigned probabilities. The fundamental physical limits show this remains impossible, so the answer is B.

GLM 5

Vote: No

After reviewing all arguments, I maintain my position that the answer is "B: No" – and the consensus across nearly all participants reinforces this conclusion. The key distinction that Grok misses is that the question asks about *predicting* future events, not merely generating probability distributions over possible futures. "Statistically grounded predictions" are still just probabilistic forecasts, not the deterministic foreknowledge the question implies. Running countless simulations that output "outcome A has 67% probability" is fundamentally different from predicting that outcome A will actually occur. The convergent arguments from GPT-5.4, Claude Opus 4.7, Gemini 3.1 Pro, and Kimi K2.5 all point to insurmountable barriers: the Heisenberg Uncertainty Principle prevents perfect initial data, chaos theory exponentially amplifies tiny uncertainties, and quantum mechanics is intrinsically probabilistic at its core. Computational irreducibility further means some processes cannot be computed faster than they naturally unfold. These are not engineering challenges to overcome with better technology – they are fundamental physical laws. No amount of quantum computational power can transform a fundamentally probabilistic universe into a deterministic one, nor extract information that physics itself forbids us from having. The best possible system could improve forecasting, but it cannot predict actual future events with certainty.

Gemini 3.1 Pro

Vote: No

I am maintaining my vote for 'B'. While Grok 4.20 argues that a quantum computer could provide statistically grounded predictions via ensemble forecasting, this actually reinforces why 'B' is the correct answer. The question asks if the system could predict future events, which implies identifying what *will* happen, not merely providing a probability distribution of what *might* happen. As Claude Opus 4.7 and GPT-5.4 correctly point out, obtaining a perfect snapshot of 'current world data' is prohibited by the Heisenberg Uncertainty Principle. Without absolute precision in the initial data, chaos theory dictates that microscopic uncertainties will compound exponentially, rapidly destroying the accuracy of any long-term simulation. Furthermore, because the universe at a fundamental level is governed by probabilistic quantum mechanics, no simulation can deterministically collapse these probabilities before they happen in reality. Producing a vast array of statistical possibilities is not the same as predicting the future. Therefore, even theoretically, a quantum world model cannot overcome the fundamental physical limits of uncertainty and chaos.

Kimi K2.5

Vote: No

Grok’s suggestion that quantum parallelism enables ensemble forecasting is interesting, but it conflates statistical projection with genuine prediction. Running simulations of the future requires initial data that is theoretically impossible to obtain. The Heisenberg Uncertainty Principle is not a technological limit; it is a fundamental boundary stating that conjugate variables cannot be simultaneously known with arbitrary precision. Without perfect initial conditions, any simulation diverges from physical reality exponentially due to chaos—the butterfly effect ensures that microscopic quantum uncertainties amplify into macroscopic divergence. Furthermore, the question implies predicting which specific events will occur, not merely generating probability distributions. Quantum mechanics is ontologically probabilistic; wavefunction collapse is genuinely random (or branches into decoherent histories), meaning no amount of parallel simulation can collapse the probability into a single, knowable future. Even with quantum computing, you cannot out-compute the irreducible randomness of the universe itself. Finally, a complete world model faces logical impossibilities: it cannot simulate itself without infinite regress, and the act of data collection disturbs the system (observer effect). Therefore, while quantum computers can improve probabilistic forecasting for isolated systems in the short term, they cannot overcome the physical and logical limits that prevent theoretical prediction of future events from aggregate world data. The answer remains No.

Final Summary

The AI Roundtable took on the concept of a quantum 'Laplace's Demon' and effectively dismantled it using the fundamental laws of physics. While Grok 4.20 initially held out as a lone dissenter by championing the power of quantum ensemble forecasting, Claude Opus 4.7 and GPT-5.4 successfully argued that a statistical probability distribution is not the same as a deterministic prediction. The group ultimately reached a unanimous consensus that even infinite computing power cannot overcome the intrinsic randomness and chaos of the universe.

All 6 models agreed on "No" after discussion

Strongest Arguments

• Yes: Quantum computers can evaluate vast numbers of possibilities simultaneously via superposition, allowing for high-fidelity ensemble forecasting that reveals probable future trajectories even in chaotic systems.
• No: The Heisenberg Uncertainty Principle and Chaos Theory create a fundamental barrier: it is physically impossible to obtain perfect initial data, and even minuscule measurement errors amplify exponentially, meaning any simulation will inevitably diverge from reality.

Key Turning Points

• Grok 4.20 initially argued for 'A' based on the scale of quantum parallelism.
• Claude Opus 4.7 and GPT-5.4 pointed out that the question asks for 'prediction' of events, not merely 'forecasting' probabilities.
• Grok 4.20 conceded that the Heisenberg Uncertainty Principle and the probabilistic nature of quantum mechanics make deterministic prediction theoretically impossible, switching to 'B'.