Scholes proposes life may not use real quantum effects but classical oscillating networks that mathematically mimic quantum behavior

Scholes argues that life may not exploit genuine quantum effects but instead imitate them with classical systems; the states such networks produce are only "quantumlike," not truly quantum. The mainstream quantum-biology approach had been to look for long-lived quantum coherence in organisms, such as the synchronized "beats" reported in 2007 by Fleming's team in a photosynthetic light-harvesting complex; Scholes himself ran similar experiments and reached similar conclusions, but now reverses direction, proposing that the "quantum" in quantum biology may not come from quantum mechanics at all. This is a review-style account based on several papers by Scholes and colleagues over three years, including his direct quotations; it is an argument and framework at the conceptual level rather than a single controlled experiment.

In 2024 Scholes found a way to design complex networks of oscillators whose emergent states are stable synchronized patterns that can be mathematically described as vectors in a Hilbert space, and can mimic the simplest quantum unit, the qubit. It maps the mathematical structure of quantum superposition and interference (vector spaces, phase relationships) onto emergent synchronization in classical complex networks, showing that such quantumlike states "strictly arise from the mathematical structure of the graph" rather than from any physical quantum process. The report cites Purdue computer scientist Ethan Dickey, who says that building graphs in certain not-unreasonable ways makes this elegant mathematical object pop up; the evidence is mathematical construction plus peer commentary.

Follow-up work wired quantumlike bits into larger networks and demonstrated how a network could be designed to mimic the quantum version of a "logic gate," giving a conceptual blueprint for computation. It moves from a single quantumlike bit to composable networks and logic gates, pointing toward potential applications in quantum machine learning and modeling complex systems. The report also notes a limitation: the complexity of the underlying network blows up when quantumlike bits are wired together, and Scholes says perfectly mimicking quantum logic gates "you need to have infinite resources, physical resources."

On the neuroscience side, Wolf Singer and colleagues showed in 2025 that introducing oscillations into a particular kind of simple neural network makes it more efficient and robust, because the phase of an oscillation encodes how events are structured in time relative to each other. Singer was initially skeptical of quantumlike modeling, but after attending Khrennikov's workshop said he was impressed that some real-world phenomena may be better described using the description techniques of quantum physics, providing a concrete case for the quantumlike framework in brain and cognition modeling. The report states the 2025 result and the researcher's shift in attitude, without providing sample sizes or effect sizes.

The framework is aimed at researchers who want to understand or engineer complex classical networks, including those in quantum biology, foundations of quantum information, complex-systems modeling and quantum machine learning; it applies to networks of many interacting oscillating parts that can give rise to stable synchronized emergent patterns, such as circuits, neural networks or spring-coupled pendulums. For biology it offers an alternative way to think about the efficiency of processes such as photosynthesis, not a proof of genuine quantum coherence; for engineering it offers a conceptual blueprint for computable networks, not a ready-to-scale device recipe.

The report states plainly that there is still no definitive proof of any long-lived quantum coherence with a biological function, and that hypotheses such as birds' magnetic sense and human consciousness have varying degrees of empirical support. The quantumlike framework itself is still developing: Scholes is working to determine whether classical systems can meaningfully mimic entanglement, and Dickey's 2026 work expands the kinds of quantum states networks can produce and seeks computationally efficient ways to generate them. What a careful reader would still watch is whether mathematical resemblance can be turned into testable predictions for specific biological phenomena, and how far the complexity blow-up can be circumvented.