Where Physics Meets Biology at the Nanoscale
The human brain achieves remarkable computational efficiency, performing complex decision-making while consuming only ~20 watts. My research explores how quantum mechanical processes in synaptic proteins and neural systems contribute to this efficiency, potentially enabling computational capabilities beyond classical limits.
↑ Watch above: Coherent state (cyan, synchronized) - quantum superposition enables efficient energy transfer.Decoherent state (purple, random) - environmental noise collapses quantum states into classical behavior.
Exploring quantum effects across biological scales
Investigating how photosystem II achieves 95% energy transfer efficiency through quantum coherence. My analysis revealed highly conserved aromatic residue networks creating "quantum corridors" that protect coherence at room temperature.
Developing the Quantum-Enhanced Spike Frequency Adaptation (Q-SFA) model that explains rapid learning in brain-computer interfaces. Quantum tunneling in ion channels creates non-classical adaptation patterns distinguishable from classical neurons.
Creating algorithms to detect quantum effects in neural recordings. The first 60 seconds of BCI learning show quantum coherence signatures that predict long-term performance, opening new avenues for enhanced neural interfaces.
Explore quantum effects in neural spike adaptation
This simulation shows how a neuron fires differently when quantum effects are present. The blue trace represents voltage changes over time. When the voltage crosses a threshold, the neuron "spikes" (fires).
From photosynthesis to neural computation
Below: Quantum tunneling in photosynthesis. The yellow wave shows an electron "tunneling" between protein complexes (P680 → Pheo → QA → QB). The distances shown (8-22 Å) are perfectly tuned for quantum effects - too close and the electron would move classically, too far and tunneling fails. This quantum design achieves 95% efficiency in converting light to chemical energy.
Through computational analysis of photosystem II structures across diverse species, I identified a network of highly conserved aromatic residues positioned at precise distances (8-22 Å) optimal for quantum tunneling. These "quantum corridors" maintain coherence by:
The Q-SFA model reveals three quantum mechanisms operating in neural systems:
Peer-reviewed papers and ongoing projects
Computational identification of structural and genetic determinants of quantum coherence in photosynthetic reaction centers. Analysis of sequence conservation patterns in photosystem II revealed quantum-protective features maintained across billions of years of evolution.
Read Full Paper →A multi-scale investigation of learning and adaptation, proposing that quantum mechanical processes in synaptic proteins enable the brain's remarkable computational efficiency. Includes experimental protocols for isotope substitution studies and BCI validation.
View Proposal →Open-source implementation of the Quantum-Enhanced Spike Frequency Adaptation model. Demonstrates measurable quantum signatures in neural adaptation patterns with applications to brain-computer interface optimization.
Access Code →From fundamental science to transformative applications
Quantum-enhanced architectures achieving 100x energy efficiency gains. By implementing biological quantum principles in silicon, we can create computing systems that match the brain's remarkable efficiency.
Quantum-informed decoders that adapt faster and perform better. Understanding quantum effects in neural learning enables next-generation brain-computer interfaces for medical and augmentation applications.
Targeting quantum processes in biological systems opens new therapeutic avenues. From enzyme design to neural therapeutics, quantum biology provides novel drug targets and mechanisms.