Covariant quantum error correction in a three-layer quantum brain model: computational analysis of layer-specific coherence dynamics

• URL: http://arxiv.org/abs/2604.08587v2
• Date: Mon, 13 Apr 2026 11:27:25 GMT
• Title: Covariant quantum error correction in a three-layer quantum brain model: computational analysis of layer-specific coherence dynamics
• Abstract summary: We evaluate approximate covariant quantum error correction (CQEC) across two radical-pair proteins.<n>CQEC maintains coherence of 0.83 (95% CI [0.76, 0.79); versus 0.12 without tunneling, $times6.96.9 improvement.<n>A $T sensitivity analysis confirms: at $T = 26$ms, CQEC-protected coherence remains 0.69.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum brain proposals require coherence on behaviorally relevant timescales, yet the gap between spin coherence times and neural decision windows has remained a quantitative obstacle. We evaluate approximate covariant quantum error correction (CQEC) -- a purification protocol constrained by the Eastin-Knill theorem -- across two radical-pair proteins parameterized by \textit{ab initio} spin Hamiltonians: monoamine oxidase~A (MAO-A) and cryptochrome (CRY, PDB~4I6G). Both share a three-layer architecture (${}^{31}$P nuclear spin memory, electron spin interface, classical electrochemistry) and identical hyperfine coupling ($A = 200$~MHz), but differ 16-fold in nuclear $T_2$: 3.2~ms (MAO-A) versus 52~ms (CRY). We test whether CQEC preserves coherence over the 200~ms Schultze-Kraft veto window by mapping each protein's $T_2$ gap onto a simulation decoherence rate ($γ_\mathrm{veto} = T_2~\text{gap}/2T_\mathrm{sim}$): 3.08 for MAO-A, 0.19 for CRY. At $γ_\mathrm{veto} = 0.19$, CQEC maintains tunneling coherence of 0.83 (95\% CI [0.76, 0.79]; versus 0.12 without correction, $\times$6.9 improvement). At $γ_\mathrm{veto} = 3.08$, coherence collapses to 0.012 even with CQEC. A $T_2$ sensitivity analysis confirms robustness: at $T_2 = 26$~ms (half the CRY estimate), CQEC-protected coherence remains 0.69. A classical Markov baseline produces only monotonic relaxation, confirming that CQEC-maintained oscillatory dynamics are genuinely quantum. However, no single protein optimizes both layers: CRY's shorter $T_2^e$ (0.53~ns versus 1.1~ns) worsens Layer~2 fidelity. This layer-protein tradeoff, together with unresolved challenges in state preparation and entanglement distribution, defines the next targets for quantum brain research.

Related papers

• Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise [49.730496294398726]
  We develop a novel quantum mean estimator for heavy-tailed random variables.<n>We derive stronger lower bounds for tail index>4/3$, showing that a nontrivial dependence on a dimension is unavoidable.<n>For a convex objective function, we propose a quantum projected gradient descent method.
  arXiv  Detail & Related papers  (2026-07-28T09:29:37Z)
• ORCHID: Orchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers [0.2538209532048867]
  We present textbfORCHID (textitOrchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers), a novel bio-inspired consensus protocol.<n>It maps the neuroscientific emphbinding problem onto the distributed systems emphconsensus problem, how blockchain nodes agree on a single ledger state under Byzantine faults.<n>Results establish ORCHID as a scalable, biologically plausible, and quantum-augmented consensus mechanism for post-quantum distributed ledgers.
  arXiv  Detail & Related papers  (2026-05-12T14:47:42Z)
• Quantum Causal Discovery via Amplitude Estimation of Kullback-Leibler Divergence [0.0]
  We present QKLA (Quantum Kullback-Leibler Amplitude estimation), a quantum algorithm that encodes a clipped logdensity ratio as amplitude estimation to recover a clipped KL expectation.<n>QKLA achieves a quadratic precision improvement, needing only $mathcalO((L/)log (1/)$ queries, where $L$ is the log-ratio clip bound.<n>Under per-stratum conditional-oracle access and a margin assumption for CI decisions, embedding this estimator in the PC algorithm compounds to an $widetilde (1/(L
  arXiv  Detail & Related papers  (2026-04-25T21:46:28Z)
• Toward Magnetic-Field-Free Quantum Computing and Quantum Reservoir Computing in Engineered Organic Materials: A Unified Framework from the 3-Layer Quantum Brain Hypothesis [0.0]
  We extend the spin-vortex-induced loop-current (SVILC) qubit to engineered organic materials operated without any applied magnetic field.<n>Four paths are proposed: (P1) a flavin--nitroxide radical-pair reservoir, (P2) a perchlorotriphenylmethyl (PTM) radical array in a covalent organic framework, and (P4) a Su-Schrieffer--Heeger soliton on trans-polyacetylene.
  arXiv  Detail & Related papers  (2026-04-22T09:12:12Z)
• Blind Catalytic Quantum Error Correction: Target-State Estimation and Fidelity Recovery Without \textit{A Priori} Knowledge [0.0]
  We introduce emphblind CQEC, which estimates the target from the noisy output alone before catalytic recovery.<n>Five estimation strategies are benchmarked across three noise models.
  arXiv  Detail & Related papers  (2026-04-13T08:27:08Z)
• Catalytic Quantum Error Correction: Theory, Efficient Catalyst Preparation, and Numerical Benchmarks [0.0]
  CQEC recovers a known target state from noisy copies without an error emphmagnitude threshold.<n>CQEC succeeds whenever the coherent modes satisfy $mathcalC(_0) subseteq mathcalC(_mathrmnoisy)$, regardless of noise strength.
  arXiv  Detail & Related papers  (2026-03-26T12:03:20Z)
• First-Principles Optical Descriptors and Hybrid Classical-Quantum Classification of Er-Doped CaF$_2$ [3.039491211255488]
  We present a classical-quantum machine learning framework for discriminating pristine CaF$ from Er-doped CaF$$ using first-principles optical descriptors.<n>From 1,589 energy-resolved points per system, physically interpretable descriptors including transition energy $E$, extinction coefficient $$, and absorption coefficient $$ were extracted.
  arXiv  Detail & Related papers  (2026-01-31T05:43:59Z)
• Programmable Quantum Matter: Heralding Large Cluster States in Driven Inhomogeneous Spin Ensembles [0.0]
  Atom-like emitters in solids are promising platforms for quantum sensing and information processing.<n>We present a framework that leverages this diversity to reduce the resources for generating optically heralded spin cluster states.<n>An optimized pulse sequence simultaneously corrects pulse-length and detuning errors, achieving single-qubit gate fidelities exceeding $99.99%$ for errors.
  arXiv  Detail & Related papers  (2025-09-03T03:59:22Z)
• Non-Hermitian Quantum Metrology Enhancement and Skin Effect Suppression in PT-Symmetric Bardeen-Cooper-Schrieffer Chains [0.0]
  We outline a theoretical framework for quantum metrology in non-Hermitian systems.<n>Through biorthogonal quantum Fisher information analysis, we identify two distinct regimes.<n>NHSE suppresses sensitivity exponentially, while $mathcalPT$-symmetry enables Heisenberg-limited enhancement.
  arXiv  Detail & Related papers  (2025-08-06T18:54:45Z)
• Near-Optimal Clustering in Mixture of Markov Chains [74.3828414695655]
  We study the problem of clustering $T$ trajectories of length $H$, each generated by one of $K$ unknown ergodic Markov chains over a finite state space of size $S$.<n>We derive an instance-dependent, high-probability lower bound on the clustering error rate, governed by the weighted KL divergence between the transition kernels of the chains.<n>We then present a novel two-stage clustering algorithm.
  arXiv  Detail & Related papers  (2025-06-02T05:10:40Z)
• KPZ scaling from the Krylov space [83.88591755871734]
  Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported. Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
  arXiv  Detail & Related papers  (2024-06-04T20:57:59Z)
• Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
  We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$. We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
  arXiv  Detail & Related papers  (2024-01-17T18:59:38Z)
• Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction [63.41789556777387]
  Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP) We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
  arXiv  Detail & Related papers  (2020-06-04T17:51:00Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.