Quantum Computing Paradigms: Biological QC

(For other quantum computing paradigms and architectures, see Taxonomy of Quantum Computing: Paradigms & Architectures)

What It Is

Biological Quantum Computing refers to speculative ideas that biological systems might perform quantum computations or that we could harness biological processes to implement quantum computing. This paradigm is highly exploratory and not yet realized in any form, lying at the intersection of quantum physics, biology, and computer science. There are two main interpretations:

1. Biology as the computer: Certain processes in living organisms might naturally exploit quantum effects to compute or process information. For example, it has been hypothesized that the brain could be a quantum computer, or that plants perform quantum optimizations in photosynthesis. These ideas suggest that evolution might have stumbled upon quantum mechanisms to enhance functionality (like efficiency of energy transfer or perhaps even consciousness via quantum processes in neurons).
2. Biology-inspired hardware: Using biological materials or biologically derived structures to build quantum computers. For instance, using proteins, DNA, or other biomolecules as qubits or as scaffolds to hold and manipulate qubits. This also covers hybrid approaches where biological systems interface with quantum systems (like a living organism that interacts with a quantum device).

At present, no clear evidence exists that any biological system performs non-trivial quantum algorithms. But there are intriguing phenomena:

• Photosynthetic complexes in certain algae and bacteria show quantum coherence in exciton transfer (energy transfer) at room temperature, which might help them transfer energy more efficiently​.
• Migratory birds like European robins appear to have a compass mechanism in their eyes that may involve entangled radical pairs (a quantum effect) to sense Earth’s magnetic field – a quantum biological sensor, essentially.
• The human sense of smell has been theorized (by Luca Turin) to involve quantum tunneling of electrons for odor discrimination.
• Most famously, Roger Penrose and Stuart Hameroff proposed the Orch-OR (Orchestrated Objective Reduction) theory, positing that quantum coherence in microtubule structures in neurons contributes to consciousness and that the brain might be tapping into quantum computation. This theory has been controversial, as the warm, wet brain environment seems hostile to sustaining quantum coherence, but it ignited discussion on quantum processes in neuroscience​.

Another serious proposal by physicist Matthew Fisher (2015) suggests that certain nuclear spins in biological molecules (specifically, phosphorus nuclear spins in phosphate ions) could have unusually long coherence times in the brain and act as qubits​. He outlines a hypothesis where Posner molecules (clusters of calcium and phosphate) protect entangled nuclear spins, potentially allowing quantum information processing in neural processes​. This is under experimental investigation now.

On the hardware side, one could imagine:

• Using DNA as a scaffold to precisely arrange qubits (like placing spin centers or quantum dots at bases along a DNA strand).
• Using engineered biological molecules as qubits (for example, certain molecular complexes whose states can be superposed).
• Harnessing biological polymers or protein dynamics for quantum annealing-like processes.
• Even living cells that might host quantum states (though cells are very noisy electrically and magnetically).

A wild idea: quantum computers might one day be grown organically or incorporate living components that self-assemble. There’s research on DNA self-assembly (DNA origami) to position nanomaterials including qubits​​, and one could see future tech where viruses or bacteria construct quantum circuits at the nanoscale.

Key Academic Papers

For quantum effects in biology (quantum biology), notable studies include Engel et al. (2007) who reported long-lived quantum coherence in a photosynthetic complex at room temperature​. Scholes et al. (2011) and Collini et al. extended such observations. The avian compass idea was developed by Thorsten Ritz, Klaus Schulten and colleagues in the early 2000s. The Penrose-Hameroff Orch-OR theory was first proposed in the 1990s (Penrose’s book “The Emperor’s New Mind” (1989) speculated on quantum consciousness, and later Penrose & Hameroff papers in the mid-90s fleshed it out). A critical rebuttal by Tegmark (2000) estimated decoherence in microtubules occurs in $10^{-13}$ seconds, far too fast for neurons to utilize. Nonetheless, Hameroff and Penrose have continued to update their theory​, citing things like anesthetics’ effects on microtubule quantum dipoles as support​.

Matthew Fisher’s work (2015) is a more concrete scientific hypothesis: he identified a specific nuclear spin (phosphorus-31) in a biochemical context that could be a qubit and suggested how entanglement might last long in Posner molecules​. His theory is being tested by experiments trying to measure long spin coherence in calcium phosphate and in vitro neural settings.

In terms of bio-inspired qubits, researchers have looked at things like NV centers in diamond coupled to biomolecules (for quantum sensing in biology), and proposals like using the electronic states of some aromatic amino acids as qubits (though not much success yet). DNA origami has been used to position conventional qubits (e.g., to arrange NV centers or quantum dots with nanometer precision)​, but that’s more bio-nanotech aiding regular QC than a true biological qubit.

Comparison To Other Paradigms

Compared to main paradigms, biological quantum computing is extremely nascent. It doesn’t fit neatly against gate model or analog or others, because we don’t yet have a concrete model of computation from biology that is quantum. If some biological system were quantum computing, presumably it could be mapped to circuit or adiabatic models.

One might contrast it with quantum-inspired biological computing like DNA computing. DNA computing is a form of classical computing using biological molecules (DNA strands) to do massive parallel search – but it’s classical and not wavefunction-based. Biological quantum computing would instead involve quantum states (like spin states, electronic states) in biological entities.

If we consider the brain as a quantum computer argument: the brain (if quantum) would be an analog, highly parallel processor, possibly leveraging quantum principles that we don’t utilize yet. It might do things like pattern recognition or combinatorial optimization in ways conventional QCs aren’t programmed for. But this is speculative.

Advantages (Hypothetical)

• Room-Temperature Quantum Coherence: Biology, if it does quantum, does it in warm, noisy conditions. For instance, evidence of quantum coherence in photosynthetic proteins at ambient temperature lasted hundreds of femtoseconds​. That’s short but notable since most human-made systems lose coherence almost instantly at room temp. If biology has tricks to maintain coherence (like structural shielding, clever chemical arrangements, or error correction-like redundancy), learning those could help build room-temperature quantum devices. A biological quantum computer might not need dilution refrigerators if we can mimic those tricks.
• Self-Assembly and Repair: Biological systems can self-replicate and self-assemble according to genetic instructions. If we could encode the blueprint of a quantum computing architecture in DNA and have organisms assemble it, that might circumvent complex nanofabrication. Additionally, living systems can repair damage; a bio-quantum computer could, in theory, heal certain errors or regenerate faulty components, giving it longevity and robustness.
• Massive Parallelism and Connectivity: If the brain were quantum, it has ~10^11 neurons and 10^14 synapses – far beyond any man-made QC in count (though neurons are not qubits; but perhaps microtubule networks inside neurons, of which there are ~10^7 per neuron, could be qubit analogs as Hameroff suggests). The interconnect complexity of biological systems is staggering. If such connectivity could be utilized in a quantum fashion, it could solve highly complex problems quickly (e.g. maybe the brain solving problems we consider hard is partly due to some quantum search? Purely speculative).
• New Algorithms or Principles: If nature already uses quantum computing, studying it could reveal algorithms or principles we haven’t considered. For example, photosynthesis seems to do a form of quantum walk to sample energy transfer paths and find an efficient one​. That’s akin to a quantum optimization (finding the energy transfer minimum time path). Understanding that might inspire quantum algorithms for network optimization or transport problems. Similarly, if the brain uses quantum processing, it might be performing a yet unknown kind of computation that we could learn from and adapt to machines.
• Interdisciplinary Opportunities: Pushing on biological quantum computing fosters collaboration between fields – quantum physicists, neuroscientists, chemists. This cross-pollination can lead to creative new approaches to both quantum tech and biology (quantum biology is already an emerging field investigating these phenomena).
• Potential for Non-Traditional Qubits: Biological molecules like certain complexes of spins could act as qubits with natural coherence protection. For example, photosynthetic light-harvesting complexes have chromophores that maintain coherence across them; one could imagine using those complexes as qubits or qutrits in a device. Or radical pair spins in proteins could be manipulated as qubits by magnetic fields (they are at least two-level spin systems). Using these as qubits might allow leveraging biochemical techniques (like enzyme catalysts) to manipulate states in ways electronic systems can’t.

Disadvantages

• Lack of Evidence and Theory: The biggest drawback is that we don’t have solid evidence of any computationally relevant quantum processing in biology. The examples of coherence in photosynthesis and avian compasses are specific and limited – they don’t demonstrate algorithms or reprogrammable computing, just quantum effects aiding a biological function. The brain being quantum is a conjecture not supported by mainstream neuroscience; in fact, decoherence calculations suggest any quantum states in neurons would decohere orders of magnitude faster than neural firing times (brain is warm ~310K, noisy electrical environment). So, biological QC might simply not exist, or if it does, we haven’t identified it convincingly. Thus, designing a “biological quantum computer” currently has no blueprint.
• Decoherence and Noise: Living systems are messy. They’re aqueous, constantly interacting with environment, full of vibrations (phonons), chemical noise, etc. Precisely the conditions quantum computers hate. To keep a qubit coherent, we usually isolate it extremely. Biologically, any quantum coherence is fleeting (picoseconds to microseconds at best observed). Maintaining entanglement or superpositions in such media for computation seems incredibly difficult. Evolution might use quantum effects only in very restricted ways, not for general computing. So trying to exploit those effects for computing might run into the same decoherence wall – we may need to freeze or isolate the biological components anyway, negating their “bio” advantage.
• Control and Readout Difficulties: Suppose you have a quantum state in a protein inside a cell. How do you address it with a control field without perturbing everything else in the cell? Or how to read it out reliably? Biological systems are small, often requiring invasive or indirect measurement (like extracting molecules or using fluorescent markers). This doesn’t lend itself to the kind of precise single-qubit addressability we have with, say, trapped ions. There’s also a lot of variability in biological units – no two neurons or protein molecules are exactly identical, which is a nightmare for calibrating a quantum computer (which demands uniform qubits).
• Reproducibility and Scaling: Building a computer out of biological components would face issues of reproducibility (biology can be temperamental – conditions like pH, temperature, and genetic mutations all alter function). Scaling up might mean literally growing larger tissue or networks, which is slow and prone to defects. And connecting a biocomputer to classical interfaces or other quantum devices could be very challenging (imagine wiring a brain or bio-gel to a laser controller for qubit operations).
• Ethical and Unpredictability Concerns: If one seriously considered using living organisms or neural tissue as computing elements, it raises ethical questions (e.g., if a brain is computing, is it sentient? Are we exploiting consciousness?). Even aside from ethics, living systems can behave in unpredictable ways, adapt or evolve, which is not what you want in a deterministic computer. So “wetware” computing has many unknowns.

Cybersecurity Implications

At present, none – because biological quantum computing is not at a stage to impact cryptography. If one day someone discovered that, say, microtubules can solve certain NP-hard problems quantumly (Penrose’s dream), that would shake up computation and cryptography tremendously, but right now this is not taken as an immediate practical threat.

However, thinking long-term and hypothetically: if living systems could be harnessed for quantum computing, it might offer a path to ubiquitous quantum computers. For example, perhaps one could bio-engineer a “quantum organelle” in cells that performs Shor’s algorithm; then many organisms could break cryptography without a large quantum mainframe. That’s a sci-fi scenario, but it underscores a potential paradigm of cheap, scalable production (biology scales through reproduction). A malicious actor could grow a vat of quantum-bacteria that collectively break encryption, rather than building a multi-billion dollar lab. Again, extremely speculative, but a different vector from industrial quantum computing.

On the defensive side, the investigation into biological quantum processes might inform new quantum-safe cryptographic primitives. For example, if the brain (or any biological process) used quantum effects in learning or optimization, that might lead to new algorithms for pattern recognition or complexity analysis which could influence cryptographic constructions (maybe making them stronger or providing new ways to hide information in hard problems).

Another aspect: if quantum effects are found in biology, that could also threaten some security assumptions. For instance, one thought experiment – if the brain did quantum computing subconsciously, perhaps humans could solve certain puzzles faster (not proven, but if it were, say humans factoring 10-digit numbers instantly because their brain does quantum search – then our assumptions on human-intractability in protocols might change). Currently, there’s no evidence humans can do that, of course.

In summary, biological quantum computing has no direct impact on cybersecurity today, except perhaps inspiring interdisciplinary research. It’s more a fascinating concept that could broaden how we think about computing. If some day proven, it would suggest nature found ways to compute that might outclass our current machines, which in turn would push tech in new directions.

Who’s Pursuing

The idea of quantum computing in the brain or biology is mostly pursued by a small number of interdisciplinary researchers. Stuart Hameroff (anesthesiologist) and Sir Roger Penrose (mathematician/physicist) are key proponents for the brain’s quantum computing theory; they continue to publish in this area, often in the Journal of Consciousness Studies or Physics of Life Reviews. Matthew Fisher at UCSB founded a research collaboration (Qubits and the Quantum Brain) to test his Posner molecule theory – experimental groups from UCSB, UCLA, Oxford and others have joined in this effort as of late 2010s.

In quantum biology (non-brain), groups like Gregory Scholes (Princeton), Shaul Mukamel (UC Irvine), and Vladimír Šťovíček (with interest in quantum effects in enzymes) have been active. There was an EU FET project “QuProCS” on quantum probes in biology, and an NSF-funded project “Quantum Leap Challenge Institute for Quantum Biology” might emerge given interest. These projects aren’t directly about computing, but if they find something interesting, it could be a stepping stone.

No company is building a “biological quantum computer” – it’s far too early. But companies like IBM and Google have shown some interest in quantum biology as a domain (e.g., supporting research or workshops) because it broadens quantum tech applications. There are also startups focusing on bio-inspired computing in a classical sense (like DNA computing or neuromorphic chips) – e.g., DNA computing startups for storage (Catalog DNA) – but those aren’t quantum.

In conclusion, biological quantum computing is a fascinating, speculative paradigm. It pushes the boundaries of what we consider a “computer” by asking if nature already has quantum computers in living organisms and if we can emulate or harness that. While still highly theoretical, it reminds us that quantum information science spans not just engineered devices in labs, but potentially the fundamental processes of life itself. For now, it remains a thought-provoking idea rather than a practical platform.