Yashine H. Goolam Hossen

arXiv2026

Quantum in Biology, Quantum for Biology, and Biology for Quantum: Mapping the Evidence and the Road Ahead

Quantum science and biology now intersect in three complementary directions: quantum in biology, quantum for biology, and biology for quantum. This review provides a structured narrative evidence map of that interface rather than an exhaustive catalogue or formal systematic review. For each topic, we ask what the mechanistic or technological claim is, which quantum resource is invoked, what the strongest experiments and models establish, which classical alternatives or engineering confounds remain competitive, and what decisive tests or benchmarks would most strongly change confidence. The most mature quantum-in-biology cases remain mechanistically constrained tunneling in some enzymatic hydrogen-transfer reactions and radical-pair spin chemistry as a viable framework for magnetoreception, whereas several higher-visibility topics remain suggestive but unresolved under physiological conditions. In quantum for biology, the central issue is whether quantum-enabled tools improve biological inference relative to strong classical baselines under realistic calibration, dose, throughput, and uncertainty constraints. In biology for quantum, the strongest claims arise when biomolecular structure or self-assembly measurably improves fabrication, integration, or robustness in quantum devices. Summary tables in the Appendix provide a compact cross-map view of the current evidence, major confounds, and the experiments or benchmarks most likely to discriminate between competing explanations.

quant-ph↗

arXiv2026

A quantum-like benchmark for context-sensitive associative memory with adaptive plasticity

Learning and memory require a balance between plasticity and stability: synaptic connections must encode new information without collapsing, saturating, or erasing previously useful structure. Associative-memory models can appear to learn successfully when fixed background connectivity already carries part of the task, making it difficult to distinguish genuine recall dynamics from structural assistance. We test this issue using an order-sensitive adaptive-plasticity benchmark for staged associative recall. The benchmark compares a quantum-like associative-memory model with matched real-valued no-phase and Markov-rate controls under the same task schedule, perturbation profiles, weak-support conditions, and plasticity settings. Here, "quantum-like" refers to the modeling formalism, not to a biological claim about quantum computation. We first screen weak structural support and then fix a conservative operating point for factorial comparisons across model families and plasticity mechanisms. The useful weak-support regime is narrow and non-monotonic. Weak structure alone does not rescue recall in the no-plasticity ablation, whereas most useful recall gains arise from adaptive plasticity, especially homeostatic stabilization. The Markov-rate control often achieves stronger raw recall, but the quantum-like model more consistently preserves order sensitivity and stage-dependent organization. These results do not support a universal quantum-like advantage. Instead, they show that model classes are better distinguished by a multi-objective profile combining recall, temporal organization, and context sensitivity than by any single recall score. The benchmark therefore provides a controlled framework for studying context-sensitive memory dynamics under weak support, regulated plasticity, and matched classical comparison.

q-bio.NC↗