To understand why this matters, it helps to consider how quantum computers differ from the machines on our desks. Conventional computers store information in bits, each of which is either a 0 or a 1. Quantum computers use qubits, which can exist in a superposition, meaning they hold multiple possibilities between 0 and 1 until they are measured, at which point each produces one result or the other. This strange property, governed by the unusual rules that govern atoms and other extremely small particles, is what gives quantum computers their potential power. It is also what makes the information they carry so sensitive. Heat, stray electromagnetic signals, imperfect control pulses and tiny flaws in the hardware can all alter a qubit’s state, and over time the resulting errors can accumulate until a calculation can no longer be trusted.

The challenge of keeping a quantum computer on track is compounded by two fundamental restrictions that do not apply to ordinary computing. Unlike ordinary computer data, quantum information cannot simply be copied for safekeeping. And measuring it can change or destroy the very state being used. A fault-tolerant machine must therefore repeatedly detect and correct errors without losing the information it is meant to protect. Scaling quantum computers will require them to identify and correct errors while they are working, a capability known as error correction, which will allow for the construction of a fault-tolerant quantum computer. Such a device would be able to keep computing correctly even though its individual quantum bits are constantly making small mistakes. No one has yet built one.

“To build a fault-tolerant quantum computer, we have to make these checks and corrections not just once or twice, but a huge number of times during a calculation,” said Jedediah Pixley, a professor in the Department of Physics and Astronomy at the Rutgers School of Arts and Sciences and Director of the Center for Materials Theory, who is one of the senior authors of the study and a member of the Flatiron Institute, the scientific research division of the Simons Foundation in New York. “This experiment is a landmark on that road. It says, ‘OK, we’ve taken another important step to get there.'”

The experiment was conducted on a 156-qubit IBM Quantum Heron processor, one of the most powerful quantum processors in the world, with the researchers selecting a connected chain of as many as 100 qubits. The team programmed the system to alternate between two competing tasks. The first acted like a scrambler: it mixed information among neighboring qubits, spreading chaos through the system in much the same way that repeatedly shuffling a deck of cards makes the original order increasingly difficult to recognize. The second task served as a form of control: the computer checked individual qubits and reset them when necessary. Repeated often enough, those resets pushed the system toward a simple, orderly state selected by the researchers.

Overall, the team performed nearly 5,000 operations that linked pairs of qubits, along with nearly 5,000 checks and resets. According to the researchers, it was the largest successful demonstration of this approach to date. “The challenge was not simply to measure a qubit, but to do it repeatedly while the rest of the processor continued to operate,” said Maika Takita, principal research scientist for quantum error correction and dynamic circuits experiments at IBM, and another senior author of the study. “This experiment shows that present-day hardware can coordinate quantum operations, measurements and resets thousands of times across a large system.”

The researchers controlled how often the computer performed each task, and by changing that balance they could see whether scrambling or resetting would take over. When scrambling occurred more than half the time, chaos won and the system remained chaotic. When checking and resetting occurred more often, control won and the researchers could guide the qubits toward the orderly state they wanted. At roughly a fifty-fifty split, a slight change in which task occurred more often suddenly changed the entire system. Physicists call such a dramatic shift a phase transition, like water freezing into ice. Here, the qubits shifted together from chaotic behavior to a state the researchers could control, confirming a theoretical prediction that order can emerge from disorder in this setting.

“Small changes can lead to large and nearly impossible-to-predict consequences, like the classic idea that a butterfly flapping its wings in Africa could eventually contribute to a hurricane hitting Louisiana,” said Justin Wilson, an associate professor of physics at Louisiana State University and a study coauthor. “But this out-of-control behavior can be brought under control by randomly intervening, and there is a sharply defined rate at which that happens. It wasn’t obvious that this transition between chaos and control would survive in quantum physics, but our results show that it does.”

The project itself began in an unusually informal setting: a friendly bet at a 2021 birthday party for Pixley’s wife, while he was still at Rutgers and living in Highland Park, New Jersey. His longtime collaborator Sriram Ganeshan wagered that a puzzling quantum effect had a counterpart in ordinary physics, leading the group to develop a new theory about controlling quantum chaos. A chance conversation with IBM physicist Barbara Jones then helped turn that theory into an experiment. Pixley lost the bet and paid up with two scoops of blueberry ice cream, after Ganeshan and others joined him at the Aspen Center for Physics in Aspen, Colorado. What began as a party wager ultimately produced a theoretical framework robust enough to be tested on one of the world’s most advanced quantum processors.

The IBM processor allowed the researchers to test their theory on systems of up to 100 qubits, far beyond the roughly two dozen they could simulate on conventional computers, and the results closely matched their predictions. “What was striking was that the transition became more clearly defined as we studied larger systems,” said Haining Pan, a Rutgers postdoctoral researcher at the time of the study and a co-first author. “The agreement between the experiment and the theory showed that this was not an accidental feature of a small circuit. It was collective behavior emerging across the quantum system.” In addition to Rutgers and IBM, researchers affiliated with the City College of New York, the CUNY Graduate Center, Iowa State University, Ames National Laboratory, Pennsylvania State University and Louisiana State University contributed to the study. As the field moves toward machines that can correct their own mistakes in real time, this experiment offers both a technical milestone and a demonstration that theoretical predictions about quantum chaos can be verified at scales where collective behavior, not just individual qubits, takes center stage.

Subject of Research: Real-time feedback control and error correction in quantum computers

Article Title: Scientists move a step toward quantum computers that can correct their own mistakes

Article References: Scientists move a step toward quantum computers that can correct their own mistakes. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: quantum computing, error correction, fault tolerance, qubits, quantum chaos, phase transition, IBM Quantum Heron, feedback control, Rutgers University, Nature Physics, quantum information, scrambling

The study, conducted by Abdelmounaim Hadjira of the University of Mustapha Ben Boulaid-Batna 2, Abdelmounaim Rezki of the National Higher School of Statistics and Applied Economics, and Said Meziane of Mohamed Cherif Messaadia University, draws on annual national data spanning 1970 to 2023. The datasets combine agricultural production figures from FAOSTAT, historical climate records from the World Bank Climate Change Knowledge Portal, and socioeconomic indicators from the World Bank’s World Development Indicators. By assembling more than five decades of evidence into a single statistical framework, the researchers were able to separate short-lived fluctuations from durable long-run relationships that define the structural conditions of Algerian wheat farming.

Methodologically, the team employed an autoregressive distributed lag model of order ARDL(1,1,1,2,1,0,1), a flexible econometric specification that allows each explanatory variable to exert its influence over different time horizons. The variables included wheat production as the outcome, with rainfall, temperature, land under cereal cultivation, rural population, fertilizer consumption and carbon dioxide emissions as potential drivers. The authors first confirmed that each series displayed the appropriate statistical properties for the approach, and bounds testing procedures confirmed the existence of a stable long-run equilibrium relationship linking wheat production to its climatic and structural determinants. This is a crucial finding in itself, because it demonstrates that the variables move together over decades rather than drifting apart as unrelated trends.

Perhaps the most telling number in the study comes from the error-correction mechanism, which measures how quickly the system returns to equilibrium after a disturbance. The estimate indicates that 67.8 percent of any short-run disequilibrium in wheat production is corrected within a single year. In practical terms, this means that when a drought, a market disruption or another shock pushes Algerian wheat output away from its long-run path, the system snaps back with considerable speed, but it also means that roughly a third of any deviation carries over into the following season. The result quantifies the sensitivity of a rainfed cereal system in which farmers have limited capacity to buffer bad years through irrigation or storage.

The long-run coefficients tell a story of climatic vulnerability. Rainfall is positively associated with wheat production, an unsurprising but precisely quantified result in a country where the overwhelming majority of wheat acreage depends on precipitation rather than irrigation. Higher temperatures, by contrast, display a substantial negative long-run association with output. The finding aligns with a broad international literature, including landmark global modeling studies showing that rising temperatures reduce wheat yields, and with earlier regional research documenting the sensitivity of durum wheat across eastern Algeria’s high plateaus. Because wheat is particularly vulnerable to heat stress during flowering and grain filling, sustained warming in a semi-arid Mediterranean climate compresses the growing window and accelerates soil moisture depletion, leaving plants less able to complete their development.

Among the structural variables, land under cereal cultivation contributes positively to wheat production, confirming that expanding or maintaining the cultivated area remains a meaningful lever for output, even though yields on that land are increasingly hostage to the weather. Rural population, however, is negatively associated with production in the long run, a result the authors interpret in the context of demographic and structural change in the countryside. Fertilizer consumption shows no significant long-run effect on wheat output, a finding that may reflect the dominance of water availability as the binding constraint in rainfed systems: nutrients cannot compensate when moisture is the limiting factor for crop growth. Previous field research on nitrogen use efficiency in Algerian durum wheat has similarly emphasized that fertilizer response is tightly conditioned by water supply in semi-arid environments.

One result deserves careful handling: carbon dioxide emissions display a positive long-run coefficient in the estimated model. The authors are explicit that this should not be read as an agronomic benefit of emissions. Rather, it is a conditional statistical association within the model, more likely to reflect the broader technological, economic and structural changes that have accompanied Algeria’s development over the study period, including mechanization, input intensification and policy shifts, than any direct physiological effect of carbon dioxide on crops. The caution matters because a naive reading could suggest that a warming, higher-carbon world helps Algerian wheat, when the temperature and rainfall coefficients point in the opposite direction. International evidence supports this interpretation, with studies of the world’s top wheat-producing countries finding that six decades of warming and drought have offset the theoretical yield benefits of rising carbon dioxide concentrations.

The study’s most forward-looking contribution comes from Dynamic ARDL simulations, a technique that allows researchers to trace how wheat production would respond over time to hypothetical changes in the driving variables while holding the rest of the system at its long-run equilibrium. The simulations show that a positive rainfall shock generates persistent increases in wheat production, with the gains accumulating and remaining in place over the simulated horizon. A positive temperature shock, by contrast, produces sustained declines in output that deepen over time rather than fading. The asymmetry is sobering for a country on the front line of Mediterranean climate change, where climate projections generally point toward hotter conditions and more erratic precipitation. If the simulated dynamics hold, each increment of warming locks in a durable production penalty, while rainfall gains, though valuable, must repeatedly be re-earned season by season.

The implications for policy are direct. The authors argue that their findings underscore the importance of strengthening climate adaptation policies through improved water management, heat-resilient production practices and sustainable land-use strategies. For Algeria, where wheat imports weigh heavily on the national budget and food security is a standing political priority, the analysis suggests that adaptation investments, from supplemental irrigation and drought-tolerant varieties to adjusted planting calendars and soil moisture conservation, are not optional refinements but structural necessities. The rapid error-correction speed identified in the study is encouraging in one respect, indicating that the production system can absorb and recover from shocks, but the simulation evidence makes clear that recovery to a declining long-run trend is small consolation.

The authors are equally candid about the limits of their analysis. The model relies on annual, national-level data, which cannot capture the regional and seasonal heterogeneity that defines Algerian agriculture, from the subhumid coastal plains to the semi-arid high plateaus where most wheat is grown. Seasonal climate indicators, such as rainfall concentrated in the critical winter and spring growth phases, would likely sharpen the picture, and nonlinear modeling approaches could reveal thresholds beyond which heat and drought effects escalate sharply. The researchers call for future work employing subnational datasets, seasonal indicators and nonlinear methods to provide a more comprehensive assessment. For now, the study stands as a rigorous quantitative confirmation of what Algerian farmers have long known in their bones: in this corner of the semi-arid Mediterranean, wheat lives and dies by the rain, and the heat is a creditor whose bill only grows.

Subject of Research: Climate shocks and wheat production dynamics in Algeria analyzed with ARDL and Dynamic ARDL simulations

Article Title: How do climate shocks shape wheat production in Algeria? Evidence from dynamic autoregressive distributed lag simulations

Article References: Hadjira, A., Rezki, A., & Meziane, S. (2026). How do climate shocks shape wheat production in Algeria? Evidence from dynamic autoregressive distributed lag simulations. Theoretical and Applied Climatology, 157(10), Article 661. https://doi.org/10.1007/s00704-026-06590-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06590-4

Keywords: wheat production, Algeria, climate change, ARDL model, Dynamic ARDL simulations, rainfall, temperature, rainfed agriculture, food security, semi-arid climate, error correction, climate adaptation