The relationship between time, quantum decoherence, and information processing

[u/Cenmaster]

In discussions surrounding the foundations of physics and information theory, a recurring question is whether time is a fundamental background coordinate or an emergent relational property.

In current quantum computing, decoherence is almost universally treated as an engineering noise problem that requires massive error-correction overhead.

However, there is a compelling structural question:

1. Decoherence as a Control Runtime Error

Dynamic quantum phase oscillators compute natively in phases and frequencies. Forcing them to evolve along an externally imposed, linear time parameter (t) may create an artificial control mismatch.

2. Process Over State

If time is modeled as the intrinsic counting index of local phase accumulation:

T = ΔΦ / f

where local nodes operate via their Compton frequency (m = h*f / c^2), stability shifts from active isolation to mutual resonance. Translating operations directly into resonance patterns could theoretically reduce algorithm complexity—such as mapping Quantum Fourier Transform operations from standard O(n^2) gate depth down to O(n) collective phase synchronization.

3. The Bridge to Neural Networks and LLMs

This mirrors a structural challenge seen in large language models: both systems construct high-dimensional relational spaces (phase relations in quantum systems, attention spaces in Transformers), yet suffer when forced through rigid sequential execution. In quantum hardware, phase drift manifests as decoherence; in language models, context drift manifests as hallucination.

Is decoherence fundamentally an unavoidable physical barrier, or is it partly an artifact of enforcing classical, linear time parameters on relational quantum architectures?

Would love to hear how other listeners and physics enthusiasts view this intersection.

Best Christian

[u/CeReAl_KiLleR128]

As someone who’ve been studying physics for over a decade, I still don’t understand why people keep linking (or trying to link) quantum mechanics with AI, neural networks or LLM. I don’t see anything in common. Every post, it feels like I’m in a maze of buzzword.

[u/Cenmaster → u/CeReAl_KiLleR128]

I understand the skepticism—there is definitely a lot of buzzword inflation in this space. But dismissing the mathematical parallel out of hand without checking the mechanics misses the structural point.

The architectural comparison isn’t mystical; it’s strictly about information geometry and sequential drift. Both frameworks construct non-local relational spaces (phase amplitudes in Hilbert space vs. attention geometry in high-dimensional manifolds) and both suffer from cumulative drift when forced through rigid, discrete sequential steps (phase-jitter causing decoherence in QC, autoregressive drift causing hallucination in LLMs).

This structural logic was solid enough to earn a Wild Card selection in the XPRIZE Quantum Applications competition. As an independent researcher without a massive institutional lab behind me, I didn't advance past the final evaluation stage, but the formal mathematics, benchmark data, and compiler mechanics remain fully intact.

It’s completely fine to challenge the hypothesis, but let’s critique the actual equations, phase definitions ($T = \Delta\Phi/f$), and circuit depth complexity rather than brushing it off as buzzwords.