Infleqtion is one of six teams that have reached the final stage of Quantum for Bio (Q4Bio), a 30-month-long competition meticulously designed to demonstrate the practical utility of quantum computing in human health. Organized by the nonprofit Wellcome Leap, the competition aims to prove that even today’s "messy and error-prone" quantum computers, which are still far from the large-scale, fault-tolerant machines envisioned by engineers, can deliver tangible benefits to healthcare. A successful outcome would represent a monumental stride in validating the immense potential of quantum computers. Crucially, the competition highlights a burgeoning trend: the power of hybrid quantum-classical approaches, where quantum computers are harnessed to augment, rather than entirely replace, conventional computing capabilities, thereby achieving outcomes unattainable by either technology in isolation.

The Q4Bio competition features two distinct prize categories, each designed to incentivize different levels of achievement. A $2 million prize is earmarked for any and all teams that can successfully execute a demonstrably useful healthcare algorithm on quantum computers equipped with 50 or more qubits. The ultimate accolade, the $5 million grand prize, will be awarded to a team that not only runs a quantum algorithm capable of solving a significant real-world healthcare problem but also achieves this feat using 100 or more qubits. Both prizes come with stringent performance criteria, and a critical prerequisite for winning is the demonstration that the solved problem is intractable for conventional computing methods – a formidable challenge given the rapid advancements in classical algorithms.

Despite the inherent difficulty of the task, a palpable sense of optimism permeates the finalist teams. "I think we’re in with a good shout," remarks Jonathan D. Hirst, a computational chemist at the University of Nottingham, UK, expressing confidence in his team’s prospects. Grant Rotskoff from Stanford University, whose collaborative effort is delving into the quantum properties of ATP, the molecule that powers biological cells, is equally optimistic, stating, "We’re very firmly within the criteria for the $2 million prize." The grand prize, however, is acknowledged to be a more ambitious target. Rotskoff concedes, "This is really at the very edge of doable." Insiders suggest that the sheer difficulty of meeting the grand prize criteria, given the current state of quantum computing technology, might mean that a substantial portion of the prize money could remain unclaimed by Wellcome Leap. With much of the Q4Bio work currently under non-disclosure agreements and the quantum computing landscape often characterized by competing claims, the final verdict rests solely with the judging panel.

The fundamental principle underpinning quantum computers lies in their ability to leverage the peculiar laws of quantum mechanics, utilizing quantum phenomena in small-scale objects like atoms and photons of light, to simulate complex real-world processes that are beyond the reach of even the most powerful classical machines. For decades, researchers have strived to build these systems, envisioning their application in fields ranging from the discovery of novel materials and the development of advanced pharmaceuticals to the optimization of chemical processes like fertilizer production. However, the practical realization of such systems is fraught with immense challenges. Building robust, large-scale quantum computers capable of shielding delicate quantum states from environmental "noise" – the ubiquitous disruptions that can easily derail quantum computations – remains an elusive goal, with no clear timeline for when such machines will become a reality. Wellcome Leap’s initiative, therefore, seeks to ascertain whether the smaller, more accessible quantum machines of today can be practically applied to healthcare challenges while the era of large-scale quantum computing matures. The competition, launched in 2024, initially provided $1.5 million in funding to each of the 12 selected teams, fostering innovation and exploration.

The six Q4Bio finalists have adopted a diverse array of strategies, all united by their ingenuity in circumventing the inherent limitations of current quantum computing hardware. Confronted with "noisy, limited machines," these teams have ingeniously offloaded significant computational burdens onto classical processors, employing newly developed algorithms that often surpass existing state-of-the-art classical methods. The quantum processors are then reserved for the specific, computationally intensive parts of a problem where classical methods falter as the scale of the calculation increases.

For instance, a team spearheaded by Sergii Strelchuk of Oxford University is employing a quantum computer to map human and pathogen genetic diversity onto complex graph-based structures. The researchers anticipate that this approach will unveil hidden connections and potentially identify novel treatment pathways. Strelchuk describes it as "a platform for solving difficult problems in computational genomics." The corresponding classical tools often struggle even with modest scaling to large databases. Strelchuk’s team has developed an automated pipeline that can predict whether classical solvers will encounter difficulties with a particular problem and how a quantum algorithm might be used to reformat the data for efficient processing on classical computers or for handling on noisy quantum hardware. This pre-computational assessment allows for strategic resource allocation, as Strelchuk notes, "You can do all this before you start spending money on computing."

In a remarkable collaboration with the Cleveland Clinic, Helsinki-based Algorithmiq has utilized a superconducting quantum computer, developed by IBM, to simulate a light-activated cancer drug. Guillermo García-Pérez, Algorithmiq’s Chief Scientific Officer, explains the drug’s mechanism: "The idea is you take the drug, and it’s everywhere in your body, but it’s doing nothing, just sitting there, until there’s light on it of a certain wavelength." Upon activation, it acts as a highly targeted "molecular bullet," attacking the tumor precisely where the light is directed. The drug Algorithmiq is currently studying is already in phase II clinical trials for bladder cancers. The quantum-computed simulation, which refines and improves upon existing classical algorithms, promises to enable its redesign for treating other conditions. Sabrina Maniscalco, Algorithmiq’s CEO and co-founder, highlights the drug’s previous limitation: "It has remained a niche treatment precisely because it can’t be simulated classically." Maniscalco, who also expresses confidence in securing prize money, believes the algorithmic methodologies developed within the Q4Bio program possess broad applicability, stating, "What we’ve done in the period of the Q4Bio program is something unique that can change how to simulate chemistry for health care and life sciences."

Infleqtion’s submission, running on its cesium-powered quantum computer, focuses on enhancing the identification of cancer signatures within medical data. Collaborating with scientists from the University of Chicago and MIT, Infleqtion has developed a quantum algorithm designed to mine vast datasets, such as the Cancer Genome Atlas. The objective is to identify patterns that can assist clinicians in determining critical factors, such as the likely origin of a patient’s metastasized cancer. "It’s very important to know where it came from because that can inform the best treatment," emphasizes Teague Tomesh, Infleqtion’s Q4Bio project lead and a quantum software engineer. Unfortunately, these crucial patterns are often buried within datasets so immense that they overwhelm conventional computing approaches. Infleqtion’s strategy involves using the quantum computer to identify correlations within the data, thereby reducing the computational complexity of the problem. "Then we hand the reduced problem back to the classical solver," Teague explains. "I’m basically trying to use the best of my quantum and my classical resources."

Meanwhile, a team based in Nottingham is leveraging quantum computing to pinpoint a drug candidate capable of treating myotonic dystrophy, the most prevalent adult-onset form of muscular dystrophy. Notably, one team member, David Brook, was instrumental in identifying the gene responsible for this condition back in 1992. Over three decades later, Brook, Hirst, and their colleagues – which include QuEra, a Boston-based company developing neutral-atom quantum computers – have employed quantum computation to devise a method by which drugs can form chemical bonds with the disease-causing protein, thereby inhibiting the mechanism that triggers the disorder.

Despite the evident enthusiasm of the competing teams, Shihan Sajeed, a quantum computing entrepreneur and the program director for Q4Bio, expresses more measured expectations. Sajeed believes that the inherently error-prone nature of current quantum machines makes it highly improbable that they will meet all the stringent criteria for the grand prize. "It is very difficult to achieve something with a noisy quantum computer that a classical machine can’t do," he states. Nevertheless, Sajeed acknowledges being pleasantly surprised by the progress made during the competition. "When we started the program, people didn’t know about any use cases where quantum can definitely impact biology," he recalls, adding that the teams have unearthed promising applications, leading to a clearer understanding of "the fields where quantum can matter." Sajeed further describes the advancements in "hybrid quantum-classical" processing employed by the finalists as "transformational."

Whether these advancements will be sufficient to warrant the disbursement of Wellcome Leap’s substantial prize money remains to be seen. The ultimate decision rests with a panel of judges whose identities are closely guarded to ensure impartiality. The outcome of this groundbreaking competition is anticipated in mid-April. Should no team ultimately meet the rigorous criteria for the prizes, Sajeed offers words of encouragement, emphasizing that the competition’s core objective has always been to demonstrate the feasibility of running useful algorithms on existing quantum hardware. Missing the mark, he suggests, does not invalidate the potential of an algorithm for future quantum computers; it simply signifies that the necessary hardware has not yet been developed.