For the first time, an international team of researchers has used a quantum computer to create artificial life—a simulation of living organisms that scientists can use to understand life at the level of whole populations all the way down to cellular interactions.

With the quantum computer, individual living organisms represented at a microscopic level with superconducting qubits were made to “mate,” interact with their environment, and “die” to model some of the major factors that influence evolution.

The new research, published in Scientific Reports on Thursday, is a breakthrough that may eventually help answer the question of whether the origin of life can be explained by quantum mechanics, a theory of physics that describes the universe in terms of the interactions between subatomic particles.

Modeling quantum artificial life is a new approach to one of the most vexing questions in science: How does life emerge from inert matter, such as the “primordial soup” of organic molecules that once existed on Earth?

Erwin Schrödinger first proposed that the answer might lie in the quantum realm in 1944 in his seminal book on the topic, What is Life?. But progress has been delayed by difficulties in creating the powerful quantum computers needed to power the simulations that can answer this question.

Unlike the normal, “classical” computers you’re using to read this article, which only process information in binary bits—units of information whose value can either be a one or a zero—quantum computers make use of qubits, whose information value can be a combination of both one and zero. This property, known as superposition, means that large-scale quantum computers will have vastly more information-processing power than classical computers.

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The aim of the research team, led by physicists Enrique Solano and from Basque Foundation for Science, was to create a computer model that replicates the processes of Darwinian evolution on a quantum computer. To do this the researchers used a five qubit quantum processor developed by IBM that is accessible through the cloud.

This quantum algorithm simulated major biological processes such as self-replication, mutation, interaction between individuals, and death at the level of qubits. The end result was an accurate simulation of the evolutionary process that play out at the microscopic level.

“Life is a complex macroscopic feature emerging from inanimate matter, while quantum information is a feature of qubits—microscopic isolated objects happening in the universe of the very small,” Solano told me in an email. “Our research brought these amazingly sophisticated events called ‘life’ to the realm of the atomic and microscopic world… and it worked.”

Individuals were represented in the model using two qubits. One qubit represented the individual’s genotype, the genetic code behind a certain trait, and the other its phenotype, or the physical expression of that trait.

To model self-replication, the algorithm copied the expectation value (the average of the probabilities of all possible measurements) of the genotype to a new qubit through entanglement, a process that links qubits so that information is instantaneously exchanged between them. To account for mutations, the researchers encoded random qubit rotations into the algorithm that were applied to the genotype qubits.

The algorithm then modeled the interaction between the individual and its environment, which represented aging and eventually death. This was done by taking the new genotype from the self-replicating action in the previous step and transferring it to another qubit via entanglement. The new qubit represented the individual’s phenotype. The lifetime of the individual—that is, how long it takes the information to degrade or dissipate through interaction with the environment—depends on the information coded in this phenotype.

Finally, these individuals interacted with one another. This required four qubits (two genotypes and two phenotypes), but the phenotypes only interacted and exchanged information if they met certain criteria as coded in their genotype qubits.

The interaction produced a new individual and the process began again. In total, the researchers repeated this process more than 24,000 times.

“Our quantum individuals are driven by an adaptation effort along the lines of a quantum Darwinian evolution, which effectively transfer the quantum information through generations of larger multi-qubit entangled states,” the researchers wrote.

Now that a quantum artificial life algorithm has been demonstrated, the next step is to scale the algorithm to allow for more individuals and expand the features attributed to them. For example, Solano told me that he and his colleagues are working on the possibility of adding “gender features” to the qubits to further explore social and sexual interactions at the quantum level.

“We may find that more than two genders is better, or perhaps none, for the sake of species survival and propagation,” Solano said.

Beyond that, Solano said he and his colleagues want to scale the number of interactions occurring between the individual in their simulations of quantum artificial life. This, however, ultimately depends on the ability to scale up the quantum computing hardware itself.

While quantum computing has made major headway in recent years, it still has a long way to go, mostly due to the finicky nature of qubits. Qubits are incredibly sensitive to noise; they can only be implemented in complex and expensive systems that can shield them from outside interference, which typically involves lots of lasers, exotic materials, and/or freezing cold temperatures.

Even then, getting more than a few dozen qubits to cooperate with one another has proven difficult. Earlier this year, Google set a record with a 72-qubit processor, but this is still a far cry from quantum supremacy, the theoretical point at which quantum computers will be able to outperform the most powerful classical computers on Earth.