Life Through Quantum Annealing
By: Vessel Project
Tags: Machine Learning, Science, Quantum Computing, Physics, Technology

Clues from Biology

In this section, we show that DNA programming can be achieved in a quantum annealer, that a broad range of biology exhibits characteristics of the Ising model, and that challenges to current evolutionary models can be resolved under the framework of quantum annealing.

Programming Life

We recognize DNA as the blueprint behind all living things — this remains true in a universe made through quantum annealing. Remember that we can only program quantum annealing using biases and couplers. Using strong biases, we can create “stationary points,” which are qubit spins that do not change — just like your DNA does not change. These spins can be used to encode the exact same information that is described by sequences of nucleotide bases in DNA. Couplers can then define how other variables interact with those stationary points to determine how life emerges. Ultimately, patterns and rules are needed to create life, and those can be programmed into quantum annealing using biases and couplers.

To make this idea less hypothetical, let’s look at what has already been done. Transcription factor-DNA binding and protein folding have already been mapped to the Ising model to be used in D-Wave computers — these are the fundamental processes that translate DNA into the proteins that form and regulate cells, tissues, and entire organisms. Also, protein folding is an NP-complete problem just like the Ising model, meaning that at their core, they are the same problem.

Biology & the Ising Model

Just like in physics and cognition, traces of the Ising model are found throughout biology. It has been used to model the dynamics and correlations of DNA, protein folding, and complex genetic systems — including epigenetics, which determines how the same DNA can be expressed as different traits. Interestingly, a 2003 study noticed that gene transcription is equivalent to “a ‘programmable’ computing machine, belonging formally to the class of Boltzmann machines.” — evidence that the Ising model is behind both.

gene transcription is equivalent to “a ‘programmable’ computing machine, belonging formally to the class of Boltzmann machines.”

The Ising model has also been used as a framework for entire cells and tissues, where phase transitions, critical points, and disorder play a constructive role for self-organization. Embryonic tissues even appear to undergo a fluid-like to solid-like phase transition; this is an example of “glassy” dynamics, which can also be attributed to the Ising model.

The dynamics of seemingly unrelated sciences can be attributed to the Ising model, too. Connections have been found in the fields of ecology, sociology and even financial markets. From the perspective of sociology, it explains things like public opinion, crowd behavior, and hierarchy formation. It has also been used to model tumor growth and seismic activity. As we would expect in a quantum annealer, traces of the Ising model are found everywhere and at all scales, whether it be in a single cell or an entire population.

Resolving Evolutionary Challenges

Evolution is especially interesting when viewed from the lens of quantum annealing. There is a proposal that life self-organizes to dissipate energy. The basic idea is that when a group of atoms is placed in the presence of an external energy source, they can restructure themselves to spread energy more efficiently in order to reach equilibrium. This suggests that life on earth evolved to dissipate the sun’s energy, but maybe the deeper source of the phenomenon is quantum annealing, where complexity emerges from a network of qubits that self-organize to minimize energy in the presence of a magnetic field.

There is also an exact mapping between evolution and the Ising model, and evolution seems to have undergone its own phase transition of sorts, where it started off fast, and then slowed down. Let’s recall that Ising-based processes like quantum annealing tend to exhibit larger changes early on, with smaller changes over time; it’s why Boltzmann machines and brains become less malleable over time, and it may explain why huge evolutionary leaps happened early on. We also know that quantum tunneling happens more frequently at the beginning of quantum annealing to help drive these early, rapid changes; this aligns with claims that quantum tunneling is essential to some of life’s earliest developments. These characteristics of quantum annealing can help explain the changing rate of evolution, but one mystery still remains: the random genetic mutations that drive evolution appear to be not-so-random.

Michael Desai, a biologist at Harvard University, performed a large-scale study in 2014 that showed that different strains of yeast unexpectedly evolved to the same outcome over many generations. While each strain initially appeared to mutate randomly, over time they ultimately converged on the same set of predictable traits. Similar results have been found in studies of fruit flies and E. coli, but researchers have no explanation for why this happens. In quantum annealing, however, we expect the system to converge on one physical state — the process may take many paths, but it always settles on the same qubit spins at the lowest energy state. From the perspective of quantum annealing, our observations of evolution are fitting.

By extension, we can think that our entire universe is trending toward something — which begs the question: What?