Computational models for dual-layer cosmic reality (CCH 2.0)

I’m pulling back the curtain on my thinking process.

May 22, 2025

For this post, I’m pulling back the curtain on my thinking process. I’ve been working to formalize the Cosmic Computer Hypothesis 2.0 in computational terms, so I ran CCH through three distinct modeling frameworks: classical logic gate circuits, cellular automata, and quantum circuits. Below, you’ll find my comparative analysis and preliminary results. From here, I’ll share these ideas on Reddit and Substack for feedback and further development.

The Cosmic Computer Hypothesis (CCH) envisions the universe as a two-tiered computational system. An underlying CPU layer is a timeless information substrate containing all possible configurations, and an emergent GPU layer renders one classical outcome when an observer “queries” the system. In this view, each quantum superposition or entangled state exists in the CPU, and only upon measurement is a single result displayed in spacetime. We now compare three computational frameworks for modeling this CPU/GPU duality.

Classical logic gate models

Core structure: Classical logic models use binary bits and Boolean gates. They can be formalized as deterministic functions f:{0,1}n→{0,1}mf:{0,1}n→{0,1}m realized by circuits of AND/OR/NOT gates (or equivalently by finite-state machines). Mathematically, the circuit defines a fixed mapping from input bit-strings to output bit-strings.
CPU–GPU dual-layer mapping: One could attempt to map the CPU layer onto the set of all possible input bit combinations (the circuit’s full truth table) and the GPU onto the single output bit. However, a classical circuit yields one fixed output for any given input, there is no built-in superposition or multiple-outcome ensemble. In practice, classical logic lacks any indeterminacy or ensemble-outcome mechanism, so it cannot naturally implement the idea that an observer’s query to the CPU produces a different result.
Observer/consciousness integration: In a logic-circuit model, observation is passive. Reading an output bit just reveals the already-determined result; it does not change the computation. To mimic an observer, one would have to manually intervene (e.g. toggle a gate), but that lies outside the model’s natural dynamics. In other words, classical logic has no analogue of the collapse or query process that CCH envisions – an observer is completely passive.
Testable predictions/simulation: Classical circuits are trivial to simulate (Python, Mathematica, etc.) and can generate arbitrarily large logic networks. One could even simulate a toy “dual-layer” by computing an entire truth table and then picking an entry, but the predictions remain purely classical. In fact, purely deterministic digital models imply local hidden variables and conflict with quantum tests (Bell’s theorem) en.wikipedia.org. Thus, a classical logic model can reproduce any classical outcome but cannot produce genuine quantum randomness or correlations.
Strengths and limitations: Logic circuits are simple, transparent, and easy to implement. They clearly illustrate deterministic computation. The fatal limitation is that they lack superposition, randomness, and any observer-driven choice. Consequently, classical gate models are too rigid and deterministic to capture the CPU–GPU dynamics CCH requires.

Cellular automata

Core structure: A cellular automaton (CA) is a grid of cells (e.g. a 2D lattice), each in a finite state (commonly 0 or 1). All cells update in discrete steps according to a local rule that depends on neighbors (for example, Conway’s Game of Life is a famous 2D CA). Formally, the global CA state can be represented as a vector in {0,1}N{0,1}N, and the rule is a deterministic map on this state. Notably, many CA (like Life or Rule 110) are Turing-complete, meaning they can emulate any computation given suitable initial conditions.
CPU–GPU dual-layer mapping: One can imagine the CPU layer encoding the CA rule and the entire space of possible configurations, while the GPU layer is the single configuration that actually evolves. In other words, the CPU “contains” all conceivable CA histories, and the GPU shows one realized pattern. Indeed, some digital-physics proposals suggest the universe could be “a huge cellular automaton,” en.wikipedia.org. However, an ordinary CA is deterministic: once the rule and initial state are set, it evolves along one path. CCH’s notion of holding all alternative histories in a hidden layer is not inherent in a standard CA.
Observer/consciousness integration: Conventional CAs have no inherent observer effect. An external agent can inspect or even alter the grid, but the CA’s update rules do not depend on observation. To model CCH, one would have to declare that an observation triggers some change (for example, randomly choosing a new pattern or changing the rule). This is ad hoc. As Stephen Wolfram points out, standard CA have “rigid predefined notions of space and time” with no built-in mechanism for observer-driven change wolframphysics.org. In short, a CA will not collapse an ensemble of possibilities on its own.
Testable predictions/simulation: CAs are trivial to simulate and visualize (Mathematica has built-in CA functions, Python loops, etc.). One could easily code a CA and experiment: for example, simulate multiple potential evolutions in parallel and then have an observer function select one configuration to continue. Such toy simulations can illustrate how local rules produce structure, but by themselves they make no concrete physical predictions. Any pattern produced is purely a result of the chosen rule and initial condition, not emergent physics.
Strengths and limitations: CA models show how simple local rules can generate rich complexity, making them appealing as illustrative models. They are fully discrete and deterministic, which makes them easy to compute. Their limitation is similar to classical logic: they lack inherent randomness and nonlocal quantum correlations. Standard CA rely on a fixed grid and have no internal collapse mechanism. To mimic CCH’s observer-triggered collapse, one must inject randomness or selection externally. In practice, a CA can be a useful sandbox for exploring ideas, but by itself, it requires extra rules to reproduce CCH-like behavior.

Quantum circuit models

Core structure: Quantum circuits operate on qubits (quantum bits). An nn-qubit system has a state in a 2n2n-dimensional complex Hilbert space, allowing all classical bitstrings in superposition. Quantum gates are unitary (reversible) operators on this space. For example, the Hadamard gate creates a superposition and the CNOT gate entangles two qubits. These operations are formally represented by unitary matrices acting on the state vector.
CPU–GPU dual-layer mapping: This framework matches CCH directly. The pre-measurement quantum state encodes all possible outcomes (analogous to the CPU’s layer of possibilities). When one qubit is measured, the state collapses to a single classical output, which is the GPU. In other words, performing a measurement is like querying the CPU and having the GPU render the selected result. As one commentator explains, the GPU “takes that huge set of possibilities from the CPU … and renders just one definite result”.
Observer/consciousness integration: In quantum theory, the observer’s choice of measurement is the active ingredient. Applying a measurement gate forces the superposition to randomly choose a definite outcome (with probabilities given by the Born rule). This behavior aligns with CCH’s idea that conscious observation triggers collapse. In practice, one can simply equate a conscious decision to observe with performing the quantum measurement. While standard QM does not ascribe special power to consciousness, the circuit model at least provides a mechanism: collapse happens whenever a measurement is made.
Testable predictions/simulation: Quantum circuits reproduce standard quantum predictions by design. Small circuits (few-qubit systems) can be simulated on classical computers or run on experimental hardware (IBM/Qiskit, etc.). For example, one could simulate a simple entangled two-qubit circuit and verify that its measurement statistics match quantum mechanics. These simulations inherently include quantum randomness and correlations. Though scaling to many qubits is computationally hard, even toy circuits (dozens of qubits) are within reach of modern simulators and illustrate the CPU→GPU transition.
Strengths and limitations: The quantum-circuit approach naturally incorporates all of CCH’s ingredients: superposition, entanglement, and measurement collapse. Its mathematical formalism is well-developed and experimentally validated. The limitation is practical: simulating or building a large quantum system is resource-intensive, and the formalism relies on the standard quantum postulates. Nonetheless, as a proof-of-concept framework, it directly shows how an observer-triggered measurement yields a single outcome from many possibilities.

Conclusion and recommendations

For an independent researcher, cellular automata and small quantum circuits offer the most accessible proof-of-concept. Classical logic-gate models, while easiest to code, lack any true CPU/GPU dynamic. In contrast, one could quickly implement a CA (using Mathematica’s built-in functions or simple Python loops) and then introduce an “observer” rule. For example, simulate a 2D Life-like CA and at each “measurement” step randomly select one allowed next configuration to continue. This would illustrate how a hidden ensemble of CPU states yields one realized GPU outcome.

Alternatively, one could simulate a tiny quantum circuit: Python libraries (Qiskit, Cirq) or Mathematica’s quantum functions allow creating a few-qubit circuit, applying gates, and measuring. By preparing an entangled state and then measuring, the output will be a single classical bitstring drawn from the superposition, directly mirroring the CCH collapse. In practice, starting with a CA-based model is the simplest route (fast to code and visualize), while a small quantum-circuit simulation can serve as a complementary demonstration of the CCH idea.

Sources: Explanations and quotes are adapted from analyses of the Cosmic Computer Hypothesis and standard references on cellular automata and quantum circuits wolframphysics.org en. The cited transcripts summarize the CPU–GPU framework and observer role, while the Wikipedia sources define the underlying computation models.

Citations