Quantum Thermodynamic Selection - a proposed framework

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Hellboy

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Hi folks, I think I may have had a valuable insight, but I need physicists familiar with Quantum Mechanics to tell me where I'm going wrong.

I've been working on an unanswered question in physics: when baths are thermodynamic (not just energetic), how does entropy affect decoherence?

The answer: decoherence rates scale with free energy, not just energy:

Γᵢ = Γ₀ × exp(ΔGᵢ/kT)

This comes from properly including entropic contributions to the bath spectral density in standard master equation derivations. Detailed balance then requires exp(-ΔG/kT) weighting, not exp(-ΔE/kT).

Implications:
  • Einselection in thermodynamic: systems selects minimum free energy states
  • Measurement has thermodynamic cost proportional to ΔG
This is falsifiable: molecules with same ΔE but different ΔS (via solvation) should have different decoherence rates. Measurable with existing 2D spectroscopy.
 
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Hellboy

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Quantum Thermodynamic Selection​

The Central Claim​

Decoherence rates in thermodynamic environments scale with free energy differences:

Γᵢ = Γ₀ · exp(ΔGᵢ/kT)

This yields selection probabilities:

P(i) ∝ |αᵢ|² · exp(−ΔGᵢ/kT)

This may already be implicit in standard decoherence theory. I'm looking to either:
  1. Verify this is derivable from Born-Markov/Redfield equations with thermal bath
  2. Identify where it differs and design experiments to test

Theoretical Basis - Starting Point: Path Integral + Wick Rotation​

The Euclidean path integral:

ψ[path] ∝ exp(−S_E/ℏ)

is mathematically identical to the partition function:

Z ∝ Σ exp(−E/kT)

Standard interpretation: This is a calculational tool (Wick rotation t → −iτ).

Proposed interpretation: This connection is physical. During decoherence, environmental coupling forces quantum amplitudes to thermally weight by free energy.

Question: Can we derive Γᵢ ∝ exp(ΔGᵢ/kT) from:

dρ_S/dt = −i[H_S, ρ_S] + Σᵢⱼ γᵢⱼ(Lᵢⱼ ρ_S Lᵢⱼ† − ½{Lᵢⱼ†Lᵢⱼ, ρ_S})

where γᵢⱼ couples to thermal bath at temperature T?

Expected Derivation Path​

For system + thermal environment:
  1. System-bath coupling: H_I couples to environment with temperature T
  2. Born-Markov approximation: Assume weak coupling, Markovian bath
  3. Thermal state of bath: ρ_B ∝ exp(−H_B/kT)
  4. Trace over bath: Lindblad operators depend on thermal occupation
Prediction: Decoherence rates γᵢⱼ should contain exp(±ΔE/kT) factors from bath thermal statistics.
Question: Does this naturally give γ ∝ exp(ΔG/kT) where ΔG includes entropic contributions?


Why This Matters: The ΔG ~ kT Regime​

Standard decoherence theory focuses on:
  • Strong coupling: Rapid decoherence, classical behavior
  • Weak coupling: Slow decoherence, quantum behavior
This framework highlights the intermediate regime ΔG ~ kT where:
  • Decoherence rate varies exponentially with ΔG
  • Smooth quantum-classical interpolation
  • Biology operates here
Testable Predictions in Accessible Systems

Molecular dyads in solution
(we can measure this now):
  • Vary ΔG via solvent polarity, molecular structure
  • Vary T from 77K to 350K
  • Measure coherence time via 2D electronic spectroscopy
  • Prediction: log(Γ) vs. ΔG/kT should be linear, slope = 1
Protein cavity engineering:
  • Mutate photosynthetic protein to alter ΔG landscape
  • Keep chromophore energetics constant
  • Measure coherence lifetimes
  • Prediction: τ_coherence ∝ exp(−ΔG/kT)

How You Can Help​

1. Theoretical Verification​

Can you derive (or show where it's already derived):

Γᵢ ∝ exp(ΔGᵢ/kT)

from standard master equation with thermal bath?

If yes: This formalizes existing physics. Useful framework.
If no: This is new hypothesis. Needs experimental test.

2. Critical Evaluation​

Where does this framework:
  • Agree with standard decoherence theory?
  • Extend standard theory with new predictions?
  • Conflict with established results?

Relationship to Existing Work​

Einselection (Zurek)​

Standard: Environment selects pointer states
This framework: ΔG/kT determines which states are robust pointers
Relationship: Potentially same physics, made quantitative

Quantum Biology (Engel, Lambert, etc.)​

Standard: Quantum effects in biology when protein environment structured
This framework: Structure creates favorable ΔG landscape, enabling coherence
Relationship: Mechanistic explanation for empirical observations

Decoherence Theory (Joos, Zeh, Caldeira-Leggett)​

Standard: System-bath coupling causes decoherence
This framework: Thermal bath weights rates by ΔG/kT
Relationship: May be implicit in detailed calculations, now made explicit


Open Questions​

  1. Is Γ ∝ exp(ΔG/kT) derivable from standard theory?
    • If yes: Show me the calculation
    • If no: How do we test it experimentally?
  2. Does this work at T = 0?
    • Framework requires thermal bath
    • What happens in quantum regime kT → 0?
    • Is this supplementary to QM or fundamental?
  3. What about many-body effects?
    • Framework stated for individual states
    • How does it extend to entangled systems?
    • Collective modes?
  4. Connection to quantum thermodynamics?
    • Recent field studying quantum + thermodynamic together
    • How does this relate to fluctuation theorems, Jarzynski equality, etc.?


Thanks for taking a look!​

 
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w00key

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Pro tip: you need to understand EVERY word from the chatbot before you ask others. And also use another model to verify. Gemini disagrees with a lot of points, so it's probably not that major of a discovery.

As a description of general quantum dynamics, it is likely incorrect or at least incomplete when compared to standard open quantum systems theory. Specifically, it appears to conflate the timescale of decoherence (T_2) with the timescale of thermalization (T_1), and it posits a rate law (\Gamma \propto \exp) that contradicts standard spontaneous emission results at low temperatures.
 
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redleader

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The AI chat bots "memory" contains a frozen snapshot of the past, and they have no sensory input. They are like a brain in a jar that we whisper to, and then we get confused that it's "hallucinating."
It's not even that, it's just probabilities of what words and letters should come next in a sequence trained on large samples of text. You're basically taking a bunch of science papers, looking at the word probabilities and regurgitating them back. The result is obviously nonsense because that's a nonsense thing to ask it to do.

Basically, learn to use the tool correctly.
 
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