The proposed system centers on the U.S. Candidate Risk Score, or US-CRS, a measure first described by the same research group in a 2024 JAMA paper. The score is designed to predict the likelihood that a patient will die within six weeks of being placed on the waiting list if they do not receive a transplant. Rather than relying on which devices or therapies a patient happens to be given, the US-CRS incorporates clinical and laboratory measurements associated with end-stage heart failure, including blood levels of molecules that reflect heart, liver, and kidney dysfunction. The result, the researchers argue, is a more faithful portrait of medical urgency, one that is harder to game and better aligned with the goal of saving the most lives.

To test how the new score would perform in practice, the team analyzed more than 45,000 match runs, the computational exercises that generate ranked lists of transplant candidates whenever a donor heart becomes available. By replaying these allocation decisions under both the current system and the proposed policy, the researchers could compare how well each approach prioritized the sickest patients. The findings were striking: the lab-based scoring system did a better job of identifying the candidates most in need of transplant than the existing six-status model, which sorts patients into tiers of severity based on the treatments they are receiving, such as mechanical circulatory support devices or intra-aortic balloon pumps.

The problem with treatment-based ranking, according to senior author William F. Parker, MD, PhD, Associate Professor of Medicine and Public Health Sciences at the University of Chicago, is that treatment choices are inherently subjective. In the current system, physicians decide which therapies a patient receives, and those decisions directly determine the patient’s priority status. This creates what Parker describes as a natural incentive for well-meaning transplant physicians to treat their patients with more aggressive interventions than they might otherwise choose, simply to move them higher on the list. The system also permits physicians to file exception requests for higher status, giving clinicians additional room to negotiate their patients’ way up the queue.

None of this implies bad faith on the part of transplant cardiologists, who are often making wrenching decisions under enormous time pressure for patients who may die without an organ. But when allocation rules reward the choice of therapy rather than the severity of illness, the rules themselves shape clinical behavior. Patients at centers with more aggressive practices may gain an advantage over equally or more ill patients elsewhere. The aggregate effect is a waiting list that reflects, at least in part, institutional style and physician judgment rather than a consistent, measurable standard of medical urgency. For a national system allocating an irreplaceable and desperately scarce resource, that inconsistency carries a real cost in lives.

The path from a statistical model to a workable national policy is rarely straightforward, and the research team emphasized that collaboration was central to their effort. The scientists partnered with the Scientific Registry of Transplant Recipients, a national database of transplant statistics and analyses operating under the Health Resources and Services Administration, and with the heart committee of the Organ Procurement and Transplant Network, the body that sets organ allocation policy in the United States. Grace Lyden, PhD, an SRTR contract biostatistician and first author of the study, led improvements to the original US-CRS by incorporating more advanced measures of heart function, more detailed treatment information, and refined statistical methods.

Crucially, the team did more than refine a risk score. They built a complete heart allocation algorithm that could be adapted into policy, one that accounts for additional factors that any real-world system must handle, including the geographic distance between the donor hospital and the candidate. In their match run analysis, the proposed policy not only prioritized sicker candidates more effectively but also reduced the distance that donor hearts needed to travel. Shorter transport times can mean healthier organs at implantation and faster transplants for the patients who receive them, so the model’s gains on both urgency and logistics together strengthen the case that the policy could save more lives than the status quo.

The technical logic behind the US-CRS reflects a broader shift in transplant medicine toward data-driven urgency assessment. Blood biomarkers of end-organ failure, such as those signaling cardiac, hepatic, and renal deterioration, capture physiological decline in a way that treatment categories cannot, because the same therapy may be applied to patients at very different stages of illness. By anchoring priority to predicted short-term mortality without transplant, the score aims to allocate each donor heart to the candidate for whom the organ makes the greatest difference in survival. That framing also gives policymakers a quantifiable benchmark: if the score accurately predicts six-week mortality, then ranking candidates by the score directly maximizes the number of lives saved per organ transplanted.

Significant hurdles remain before the US-CRS could become national policy. The score must clear additional administrative review, undergo further testing, and be revised in response to feedback from clinicians, patients, and regulators. Allocation changes in transplantation are high-stakes and closely scrutinized, since any new rule redistributes access to a life-saving resource. Parker said he is nonetheless encouraged by the progress the team has made in refining and validating the model over the past several years, and he credited the direct collaboration with policymakers for providing critical feedback about turning the research into a practical reality.

For the patients and families touched by heart failure, the stakes of this policy debate are starkly concrete. Parker’s stated hope is that the system could be implemented within a few years, so that fewer patients are lost while waiting and the allocation process no longer depends on what he calls arbitrary subjective decision making, but instead reflects the true medical urgency of each candidate. If the match run analysis holds up under further scrutiny, the United States could move toward a heart transplant system in which the sickest patients, measured by the chemistry of their failing organs rather than the devices attached to them, stand first in line for the hearts that become available. In a field where one in five waiting-list patients may not survive long enough to receive a transplant, even a modest improvement in how organs are matched to need could translate into a meaningful number of lives saved.

Subject of Research: A laboratory-based risk score for prioritizing heart transplant candidates by medical urgency

Article Title: New policy could save more lives by prioritizing heart transplant candidates based on medical urgency, not treatment choices

Article References: New policy could save more lives by prioritizing heart transplant candidates based on medical urgency, not treatment choices. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: heart transplant, organ allocation, US-Candidate Risk Score, medical urgency, JAMA, University of Chicago, match run analysis, OPTN, SRTR, heart failure, transplant policy, biomarkers

The heart of the design is a tool that quantum physicists have refined over the past two decades: optical tweezers. Unlike the tiny mechanical tongs used by watchmakers, optical tweezers are better imagined as science fiction tractor beams. A single, tightly focused laser beam creates a spot in space that attracts an atom, holding it in place without any physical contact. As Covey describes it, one beam and one focused spot are enough, and the atom simply falls into the light. Arrays of such beams can hold dozens or hundreds of individual atoms, each one acting as a qubit, the fundamental unit of quantum information. The approach has already produced some of the most impressive results in neutral-atom quantum computing, but the choice of atom matters enormously, and that is where helium comes in.

Trapping an atom with light requires the laser to supply enough energy to promote the atom from its ground state to an excited state, and the amount of energy needed depends on the species of atom. Hydrogen, the lightest element of all, is currently out of reach because the jump to its excited state demands more energy than modern technology can conveniently deliver. Helium is also a high-energy atom, but it possesses a crucial feature that hydrogen lacks: a second electron. That extra electron allows helium to settle into a temporary metastable state, an energy level sitting between the ground state and the excited state. Covey offers a vivid analogy: if reaching the excited state is like leaping onto a table, the metastable state is like pulling up a stepstool first. Atoms with only one electron on their outer shell, including hydrogen and lithium, have no such stepstool and must make the jump in a single bound.

The metastable state of helium is remarkable in its own right. In many other elements, comparable states last only seconds before the atom decays back down. Helium’s metastable state persists for roughly two hours, an extraordinarily long lifetime by atomic standards. That longevity gives experimentalists a generous window in which to laser-cool the atoms, trap them, and manipulate them before they decay. Helium’s well-resolved energy structure also makes it easier to laser-cool than lithium, according to Zheyuan Li, a PhD student in Covey’s lab and a co-first author of the paper. Cooling is an essential prerequisite for trapping, because atoms must be slowed almost to a standstill before optical tweezers can grip them reliably.

The second advantage is speed. Because helium is even lighter than lithium, the third-lightest element and the basis of the first fermionic quantum computing demonstrations earlier this year, quantum tunneling rates in a helium-based machine would be about three times faster at minimum, Li explained. Tunneling — the quantum phenomenon in which particles pass through barriers that classical physics says they cannot cross — underlies how atoms interact and exchange information in these simulators. Faster tunneling means faster transport of atoms and faster gate-like operations between them, which translates directly into a machine that can run more complex calculations before decoherence and other imperfections erode the fragile quantum states. In quantum computing, where coherence times are measured in fleeting fractions of a second, a threefold speedup is not a cosmetic improvement; it is a fundamental upgrade to the platform’s capabilities.

There is a subtlety in the choice of isotope, and it is here that the Chicago design makes its most distinctive move. Ordinary helium, the helium-4 that cools MRI machines and fills party balloons, is a boson, one of the two great families into which quantum particles divide. The other family, fermions, is named for University of Chicago legend Enrico Fermi, while bosons take their name from Indian physicist Satyendra Nath Bose. The two families obey fundamentally different rules: fermions cannot occupy the same quantum state at the same time, a prohibition known as the Pauli exclusion principle, while bosons are free to pile into identical states. That antisocial character of fermions turns out to be a blessing for quantum computing, because it frees fermionic machines from many of the errors that plague computers built from bosonic atoms.

Fermionic quantum computing has been a dream since the 1990s, but it was only cracked in early 2026, when two lithium-based models achieved the milestone. The Chicago team proposes to go one better by using helium-3, which has one fewer neutron than helium-4 and is therefore a fermion. Using the lightest fermionic atom that can be trapped means fermionic quantum computing can be implemented natively, Li said, rather than using bosonic atoms and then trying to simulate fermionic structure on top of them. Co-author Zoe Yan, an assistant professor of physics at UChicago who has previously worked with lithium-6, the next-heaviest fermion after helium-3, helped make the case that the extra effort of helium-3 is worthwhile. Hydrogen-1 is a boson, and while fermionic hydrogen-2, or deuterium, exists in principle, Covey noted that deuterium is far more difficult to work with than helium and is not even much lighter than helium-3, erasing most of the mass advantage that motivates the whole design.

The concept has drawn praise from outside the collaboration. Waseem Bakr, a professor of physics at Princeton University who was not involved in the research, said that by using the lightest trappable atom the work turns low mass into a real advantage, delivering faster tunneling, faster transport, and controllable motional qubits, and he called it a compelling blueprint for the next generation of fermionic quantum simulators. Such endorsements matter in a field where competing platforms — superconducting circuits, trapped ions, photonic chips, and neutral atoms — are racing to demonstrate practical advantage, and where the choice of physical substrate can determine how far a design can ultimately scale.

Of course, a concept paper is not a working machine, and the team’s next step is to build one. In collaboration with Yan, the researchers plan to trap and control individual helium-3 atoms for the first time. The foundation is there, Covey said, and progress is advancing to the point where the team hopes to have these atoms in tweezers for the first time probably within the next year or two. The path will begin, somewhat surprisingly, with the wrong isotope. Helium-3 is very expensive, explained Rupsa De, a PhD student in Covey’s lab and the paper’s other co-first author, so the team will start with helium-4 and then proceed toward helium-3. Because helium-4 is bosonic, the early experiments will validate the trapping and control techniques rather than the full fermionic architecture, but they will de-risk the hardest experimental steps before the costly isotope is committed.

Anyone worried that a quantum computer might worsen the world’s helium shortages can rest easy. Covey pointed out that the early experiments will have no impact on global helium-4 supplies, and the arithmetic is striking: two grams of helium, roughly enough to fill a single party balloon, contains a number of atoms that is a three followed by twenty-three zeroes. What the team ultimately wants is only tens of atoms, and a five-liter tank of helium, Covey noted, can last the lab for many years. The contrast between the scale of the ambition and the modesty of the material requirements is part of what makes the proposal so striking. If the team succeeds in suspending single fermionic helium atoms in beams of light over the next couple of years, the lightest trappable atom in the universe may find itself at the center of one of the most consequential technologies of the century, trading its party-trick reputation for a role in machines that compute with the strange rules of quantum mechanics itself.

Subject of Research: A proposed quantum computing architecture using laser-trapped metastable helium-3 atoms for fermionic quantum simulation

Article Title: Helium lifts new quantum computing concept

Article References: Helium lifts new quantum computing concept. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: helium-3, quantum computing, optical tweezers, fermions, metastable atoms, laser cooling, qubits, quantum tunneling, neutral atoms, PRX Quantum, University of Chicago, quantum simulation