When you look back at the early days of computing, some familiar names pop up, including John von Neumann, Nicholas Metropolis and Richard Feynman. But they were not lonely pioneers – they were part of a much larger group, using mechanical and then electronic computers to do calculations that had never been possible before.

These people, many of whom were women, were the first scientific programmers and computational scientists. Skilled in the complicated operation of early computing devices, they often had degrees in maths or science, and were an integral part of research efforts. And yet, their fundamental contributions are mostly forgotten.

This was in part because of their gender – it was an age when sexism was rife, and it was standard for women to be fired from their job after getting married. However, there is another important factor that is often overlooked, even in today’s scientific community – people in technical roles are often underappreciated and underacknowledged, even though they are the ones who make research possible.

Human and mechanical computers

Originally, a “computer” was a human being who did calculations by hand or with the help of a mechanical calculator. It is thought that the world’s first computational lab was set up in 1937 at Columbia University. But it wasn’t until the Second World War that the demand for computation really exploded; with the need for artillery calculations, new technologies and code breaking.

In the US, the development of the atomic bomb during the Manhattan Project (established in 1943) required huge computational efforts, so it wasn’t long before the New Mexico site had a hand-computing group. Called the T-5 group of the Theoretical Division, it initially consisted of about 20 people. Most were women, including the spouses of other scientific staff. Among them was Mary Frankel, a mathematician married to physicist Stan Frankel; mathematician Augusta “Mici” Teller who was married to Edward Teller, the “father of the hydrogen bomb”; and Jean Bacher, the wife of physicist Robert Bacher.

As the war continued, the T-5 group expanded to include civilian recruits from the nearby towns and members of the Women’s Army Corps. Its staff worked around the clock, using printed mathematical tables and desk calculators in four-hour shifts – but that was not enough to keep up with the computational needs for bomb development. In the early spring of 1944, IBM punch-card machines were brought in to supplement the human power. They became so effective that the machines were soon being used for all large calculations, 24 hours a day, in three shifts.

The computational group continued to grow, and among the new recruits were Naomi Livesay and Eleonor Ewing. Livesay held an advanced degree in mathematics and had done a course in operating and programming IBM electric calculating machines, making her an ideal candidate for the T-5 division. She in turn recruited Ewing, a fellow mathematician who was a former colleague. The two young women supervised the running of the IBM machines around the clock.

The frantic pace of the T-5 group continued until the end of the war in September 1945. The development of the atomic bomb required an immense computational effort, which was made possible through hand and punch-card calculations.

Electronic computers

Shortly after the war ended, the first fully electronic, general-purpose computer – the Electronic Numerical Integrator and Computer (ENIAC) – became operational at the University of Pennsylvania, following two years of development. The project had been led by physicist John Mauchly and electrical engineer J Presper Eckert. The machine was operated and coded by six women – mathematicians Betty Jean Jennings (later Bartik); Kathleen, or Kay, McNulty (later Mauchly, then Antonelli); Frances Bilas (Spence); Marlyn Wescoff (Meltzer) and Ruth Lichterman (Teitelbaum); as well as Betty Snyder (Holberton) who had studied journalism.

Polymath John von Neumann also got involved when looking for more computing power for projects at the new Los Alamos Laboratory, established in New Mexico in 1947. In fact, although originally designed to solve ballistic trajectory problems, the first problem to be run on the ENIAC was “the Los Alamos problem” – a thermonuclear feasibility calculation for Teller’s group studying the H-bomb.

Like in the Manhattan Project, several husband-and-wife teams worked on the ENIAC, the most famous being von Neumann and his wife Klara Dán, and mathematicians Adele and Herman Goldstine. Dán von Neumann in particular worked closely with Nicholas Metropolis, who alongside mathematician Stanislaw Ulam had coined the term Monte Carlo to describe numerical methods based on random sampling. Indeed, between 1948 and 1949 Dán von Neumann and Metropolis ran the first series of Monte Carlo simulations on an electronic computer.

Work began on a new machine at Los Alamos in 1948 – the Mathematical Analyzer Numerical Integrator and Automatic Computer (MANIAC) – which ran its first large-scale hydrodynamic calculation in March 1952. Many of its users were physicists, and its operators and coders included mathematicians Mary Tsingou (later Tsingou-Menzel), Marjorie Jones (Devaney) and Elaine Felix (Alei); plus Verna Ellingson (later Gardiner) and Lois Cook (Leurgans).

Early algorithms

The Los Alamos scientists tried all sorts of problems on the MANIAC, including a chess-playing program – the first documented case of a machine defeating a human at the game. However, two of these projects stand out because they had profound implications on computational science.

In 1953 the Tellers, together with Metropolis and physicists Arianna and Marshall Rosenbluth, published the seminal article “Equation of state calculations by fast computing machines” (J. Chem. Phys. 21 1087). The work introduced the ideas behind the “Metropolis (later renamed Metropolis–Hastings) algorithm”, which is a Monte Carlo method that is based on the concept of “importance sampling”. (While Metropolis was involved in the development of Monte Carlo methods, it appears that he did not contribute directly to the article, but provided access to the MANIAC nightshift.) This is the progenitor of the Markov Chain Monte Carlo methods, which are widely used today throughout science and engineering.

Marshall later recalled how the research came about when he and Arianna had proposed using the MANIAC to study how solids melt (AIP Conf. Proc. 690 22).

Edward Teller meanwhile had the idea of using statistical mechanics and taking ensemble averages instead of following detailed kinematics for each individual disk, and Mici helped with programming during the initial stages. However, the Rosenbluths did most of the work, with Arianna translating and programming the concepts into an algorithm.

The 1953 article is remarkable, not only because it led to the Metropolis algorithm, but also as one of the earliest examples of using a digital computer to simulate a physical system. The main innovation of this work was in developing “importance sampling”. Instead of sampling from random configurations, it samples with a bias toward physically important configurations which contribute more towards the integral.

Furthermore, the article also introduced another computational trick, known as “periodic boundary conditions” (PBCs): a set of conditions which are often used to approximate an infinitely large system by using a small part known as a “unit cell”. Both importance sampling and PBCs went on to become workhorse methods in computational physics.

In the summer of 1953, physicist Enrico Fermi, Ulam, Tsingou and physicist John Pasta also made a significant breakthrough using the MANIAC. They ran a “numerical experiment” as part of a series meant to illustrate possible uses of electronic computers in studying various physical phenomena.

The team modelled a 1D chain of oscillators with a small nonlinearity to see if it would behave as hypothesized, reaching an equilibrium with the energy redistributed equally across the modes (doi.org/10.2172/4376203). However, their work showed that this was not guaranteed for small perturbations – a non-trivial and non-intuitive observation that would not have been apparent without the simulations. It is the first example of a physics discovery made not by theoretical or experimental means, but through a computational approach. It would later lead to the discovery of solitons and integrable models, the development of chaos theory, and a deeper understanding of ergodic limits.

Although the paper says the work was done by all four scientists, Tsingou’s role was forgotten, and the results became known as the Fermi–Pasta–Ulam problem. It was not until 2008, when French physicist Thierry Dauxois advocated for giving her credit in a Physics Today article, that Tsingou’s contribution was properly acknowledged. Today the finding is called the Fermi–Pasta–Ulam–Tsingou problem.

The year 1953 also saw IBM’s first commercial, fully electronic computer – an IBM 701 – arrive at Los Alamos. Soon the theoretical division had two of these machines, which, alongside the MANIAC, gave the scientists unprecedented computing power. Among those to take advantage of the new devices were Martha Evans (whom very little is known about) and theoretical physicist Francis Harlow, who began to tackle the largely unexplored subject of computational fluid dynamics.

The idea was to use a mesh of cells through which the fluid, represented as particles, would move. This computational method made it possible to solve complex hydrodynamics problems (involving large distortions and compressions of the fluid) in 2D and 3D. Indeed, the method proved so effective that it became a standard tool in plasma physics where it has been applied to every conceivable topic from astrophysical plasmas to fusion energy.

The resulting internal Los Alamos report – The Particle-in-cell Method for Hydrodynamic Calculations, published in 1955 – showed Evans as first author and acknowledged eight people (including Evans) for the machine calculations. However, while Harlow is remembered as one of the pioneers of computational fluid dynamics, Evans was forgotten.

A clear-cut division of labour?

In an age where women had very limited access to the frontlines of research, the computational war effort brought many female researchers and technical staff in. As their contributions come more into the light, it becomes clearer that their role was not a simple clerical one.

There is a view that the coders’ work was “the vital link between the physicist’s concepts (about which the coders more often than not didn’t have a clue) and their translation into a set of instructions that the computer was able to perform, in a language about which, more often than not, the physicists didn’t have a clue either”, as physicists Giovanni Battimelli and Giovanni Ciccotti wrote in 2018 (Eur. Phys. J. H 43 303). But the examples we have seen show that some of the coders had a solid grasp of the physics, and some of the physicists had a good understanding of the machine operation. Rather than a skilled–non-skilled/men–women separation, the division of labour was blurred. Indeed, it was more of an effective collaboration between physicists, mathematicians and engineers.

Even in the early days of the T-5 division before electronic computers existed, Livesay and Ewing, for example, attended maths lectures from von Neumann, and introduced him to punch-card operations. As has been documented in books including Their Day in the Sun by Ruth Howes and Caroline Herzenberg, they also took part in the weekly colloquia held by J Robert Oppenheimer and other project leaders. This shows they should not be dismissed as mere human calculators and machine operators who supposedly “didn’t have a clue” about physics.

Verna Ellingson (Gardiner) is another forgotten coder who worked at Los Alamos. While little information about her can be found, she appears as the last author on a 1955 paper (Science 122 465) written with Metropolis and physicist Joseph Hoffman – “Study of tumor cell populations by Monte Carlo methods”. The next year she was first author of “On certain sequences of integers defined by sieves” with mathematical physicist Roger Lazarus, Metropolis and Ulam (Mathematics Magazine 29 117). She also worked with physicist George Gamow on attempts to discover the code for DNA selection of amino acids, which just shows the breadth of projects she was involved in.

Evans not only worked with Harlow but took part in a 1959 conference on self-organizing systems, where she queried AI pioneer Frank Rosenblatt on his ideas about human and machine learning. Her attendance at such a meeting, in an age when women were not common attendees, implies we should not view her as “just a coder”.

With their many and wide-ranging contributions, it is more than likely that Evans, Gardiner, Tsingou and many others were full-fledged researchers, and were perhaps even the first computational scientists. “These women were doing work that modern computational physicists in the [Los Alamos] lab’s XCP [Weapons Computational Physics] Division do,” says Nicholas Lewis, a historian at Los Alamos. “They needed a deep understanding of both the physics being studied, and of how to map the problem to the particular architecture of the machine being used.”

Overlooked then, overlooked now

Credited or not, these pioneering women and their contributions have been mostly forgotten, and only in recent decades have their roles come to light again. But why were they obscured by history in the first place?

Secrecy and sexism seem to be the main factors at play. For example, Livesay was not allowed to pursue a PhD in mathematics because she was a woman, and in the cases of the many married couples, the team contributions were attributed exclusively to the husband. The existence of the Manhattan Project was publicly announced in 1945, but documents that contain certain nuclear-weapons-related information remain classified today. Because these are likely to remain secret, we will never know the full extent of these pioneers’ contributions.

But another often overlooked reason is the widespread underappreciation of the key role of computational scientists and research software engineers, a term that was only coined just over a decade ago. Even today, these non-traditional research roles end up being undervalued. A 2022 survey by the UK Software Sustainability Institute, for example, showed that only 59% of research software engineers were named as authors, with barely a quarter (24%) mentioned in the acknowledgements or main text, while a sixth (16%) were not mentioned at all.

The separation between those who understand the physics and those who write the code, understand and operate the hardware goes back to the early days of computing (see box above), but it wasn’t entirely accurate even then. People who implement complex scientific computations are not just coders or skilled operators of supercomputers, but truly multidisciplinary scientists who have a deep understanding of the scientific problems, mathematics, computational methods and hardware.

Such people – whatever their gender – play a key role in advancing science and yet remain the unsung heroes of the discoveries their work enables. Perhaps what this story of the forgotten pioneers of computational physics tells us is that some views rooted in the 1950s are still influencing us today. It’s high time we moved on.

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Gravity might be able to quantum-entangle particles even if the gravitational field itself is classical. That is the conclusion of a new study by Joseph Aziz and Richard Howl at Royal Holloway University of London. This challenges a popular view that such entanglement would necessarily imply that gravity must be quantized. This could be important in the ongoing attempt to develop a theory of quantum gravity that unites quantum mechanics with Einstein’s general theory of relativity.

“When you try to quantize the gravitational interaction in exactly the same way we tried to mathematically quantize the other forces, you end up with mathematically inconsistent results – you end up with infinities in your calculations that you can’t do anything about,” Howl tells Physics World.

“With the other interactions, we quantized them assuming they live within an independent background of classical space and time,” Howl explains. “But with quantum gravity, arguably you cannot do this [because] gravity describes space−time itself rather than something within space−time.”

Quantum entanglement occurs when two particles share linked quantum states even when separated. While it has become a powerful probe of the gravitational field, the central question is whether gravity can mediate entanglement only if it is itself quantum in nature.

General treatment

“It has generally been considered that the gravitational interaction can only entangle matter if the gravitational field is quantum,” Howl says. “We have argued that you could treat the gravitational interaction as more general than just the mediation of the gravitational field such that even if the field is classical, you could in principle entangle matter.”

Quantum field theory postulates that entanglement between masses arises through the exchange of virtual gravitons. These are hypothetical, transient quantum excitations of the gravitational field. Aziz and Howl propose that even if the field remains classical, virtual-matter processes can still generate entanglement indirectly. These processes, he says, “will persist even when the gravitational field is considered classical and could in principle allow for entanglement”.

The idea of probing the quantum nature of gravity through entanglement goes back to a suggestion by Richard Feynman in the 1950s. He envisioned placing a tiny mass in a superposition of two locations and checking whether its gravitational field was also superposed. Though elegant, the idea seemed untestable at the time.

Recent proposals − most notably by teams led by Sougato Bose and by Chiara Marletto and Vlatko Vedral – revived Feynman’s insight in a more practical form.

Feasible tests

“Recently, two proposals showed that one way you could test that the field is in a superposition (and thus quantum) is by putting two masses in a quantum superposition of two locations and seeing if they become entangled through the gravitational interaction,” says Howl. “This also seemed to be much more feasible than Feynman’s original idea.” Such experiments might use levitated diamonds, metallic spheres, or cold atoms – systems where both position and gravitational effects can be precisely controlled.

Aziz and Howl’s work, however, considers whether such entanglement could arise even if gravity is not quantum. They find that certain classical-gravity processes can in principle entangle particles, though the predicted effects are extremely small.

“These classical-gravity entangling effects are likely to be very small in near-future experiments,” Howl says. “This though is actually a good thing: it means that if we see entanglement…we can be confident that this means that gravity is quantized.”

The paper has drawn a strong response from some leading figures in the field, including Marletto at the University of Oxford, who co-developed the original idea of using gravitationally induced entanglement as a test of quantum gravity.

“The phenomenon of gravitationally induced entanglement … is a game changer in the search for quantum gravity, as it provides a way to detect quantum effects in the gravitational field indirectly, with laboratory-scale equipment,” she says. Detecting it would, she adds, “constitute the first experimental confirmation that gravity is quantum, and the first experimental refutation of Einstein’s relativity as an adequate theory of gravity”.

However, Marletto disputes Aziz and Howl’s interpretation. “No classical theory of gravity can mediate entanglement via local means, contrary to what the study purports to show,” she says. “What the study actually shows is that a classical theory with direct, non-local interactions between the quantum probes can get them entangled.” In her view, that mechanism “is not new and has been known for a long time”.

Despite the controversy, Howl and Marletto agree that experiments capable of detecting gravitationally induced entanglement would be transformative. “We see our work as strengthening the case for these proposed experiments,” Howl says. Marletto concurs that “detecting gravitationally induced entanglement will be a major milestone … and I hope and expect it will happen within the next decade.”

Howl hopes the work will encourage further discussion about quantum gravity. “It may also lead to more work on what other ways you could argue that classical gravity can lead to entanglement,” he says.

The research is described in Nature.

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Better together. That’s the headline take on a newly inked technology partnership between Bluefors, a heavyweight Finnish supplier of cryogenic measurement systems, and Delft Circuits, a Dutch manufacturer of specialist I/O cabling solutions designed for the scale-up and industrial deployment of next-generation quantum computers.

The drivers behind the tie-up are clear: as quantum systems evolve – think vastly increased qubit counts plus ever-more exacting requirements on gate fidelity – developers in research and industry will reach a point where current coax cabling technology doesn’t cut it anymore. The answer? Collaboration, joined-up thinking and product innovation.

In short, by integrating Delft Circuits’ Cri/oFlex^® cabling technology into Bluefors’ dilution refrigerators, the vendors’ combined customer base will benefit from a complete, industrially proven and fully scalable I/O solution for their quantum systems. The end-game: to overcome the quantum tech industry’s biggest bottleneck, forging a development pathway from quantum computing systems with hundreds of qubits today to tens of thousands of qubits by 2030.

Joined-up thinking

For context, Cri/oFlex^® cryogenic RF cables comprise a stripline (a type of transmission line) based on planar microwave circuitry – essentially a conducting strip encapsulated in dielectric material and sandwiched between two conducting ground planes. The use of the polyimide Kapton^® as the dielectric ensures Cri/oFlex^® cables remain flexible in cryogenic environments (which are necessary to generate quantum states, manipulate them and read them out), with silver or superconducting NbTi providing the conductive strip and ground layer. The standard product comes as a multichannel flex (eight channels per flex) with a range of I/O channel configurations tailored to the customer’s application needs, including flux bias lines, microwave drive lines, signal lines or read-out lines.

“Reliability is a given with Cri/oFlex^®,” says Robby Ferdinandus, global chief commercial officer for Delft Circuits and a driving force behind the partnership with Bluefors. “By integrating components such as attenuators and filters directly into the flex,” he adds, “we eliminate extra parts and reduce points of failure. Combined with fast thermalization at every temperature stage, our technology ensures stable performance across thousands of channels, unmatched by any other I/O solution.”

Technology aside, the new partnership is informed by a “one-stop shop” mindset, offering the high-density Cri/oFlex^® solution pre-installed and fully tested in Bluefors cryogenic measurement systems. For the end-user, think turnkey efficiency: streamlined installation, commissioning, acceptance and, ultimately, enhanced system uptime.

Scalability is front-and-centre too, thanks to Delft Circuits’ pre-assembled and tested side-loading systems. The high-density I/O cabling solution delivers up to 50% more channels per side-loading port to Bluefors’ (current) High Density Wiring, providing a total of 1536 input or control lines to an XLDsl cryostat. In addition, more wiring lines can be added to multiple KF ports as a custom option.

Doubling up for growth

Reciprocally, there’s significant commercial upside to this partnership. Bluefors is the quantum industry’s leading cryogenic systems OEM and, by extension, Delft Circuits now has access to the former’s established global customer base, amplifying its channels to market by orders of magnitude. “We have stepped into the big league here and, working together, we will ensure that Cri/oFlex^® becomes a core enabling technology on the journey to quantum advantage,” notes Ferdinandus.

That view is amplified by Reetta Kaila, director for global technical sales and new products at Bluefors (and, alongside Ferdinandus, a main-mover behind the partnership). “Our market position in cryogenics is strong, so we have the ‘muscle’ and specialist know-how to integrate innovative technologies like Cri/oFlex^® into our dilution refrigerators,” she explains.

A win-win, it seems, along several coordinates. “The Bluefors sales teams are excited to add Cri/oFlex^® into the product portfolio,” Kaila adds. “It’s worth noting, though, that the collaboration extends across multiple functions – technical and commercial – and will therefore ensure close alignment of our respective innovation roadmaps.”

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International research collaborations will be increasingly led by scientists in China over the coming decade. That is according to a new study by researchers at the University of Chicago, which finds that the power balance in international science has shifted markedly away from the US and towards China over the last 25 years (Proc. Natl. Acad. Sci. 122 e2414893122).

To explore China’s role in global science, the team used a machine-learning model to predict the lead researchers of almost six million scientific papers that involved international collaboration listed by online bibliographic catalogue OpenAlex. The model was trained on author data from 80 000 papers published in high-profile journals that routinely detail author contributions, including team leadership.

The study found that between 2010 and 2012 there were only 4429 scientists from China who were likely to have led China-US collaborations. By 2023, this number had risen to 12714, meaning that the proportion of team leaders affiliated with Chinese institutions had risen from 30% to 45%.

Key areas

If this trend continues, China will hit “leadership parity” with the US in chemistry, materials science and computer science by 2028, with maths, physics and engineering being level by 2031. The analysis also suggests that China will achieve leadership parity with the US in eight “critical technology” areas by 2030, including AI, semiconductors, communications, energy and high-performance computing.

For China-UK partnerships, the model found that equality had already been reached in 2019, while EU and China leadership roles will be on par this year or next. The authors also found that China has been actively training scientists in nations in the “Belt and Road Initiative” which seeks to connect China closer to the world through investments and infrastructure projects.

This, the researchers warn, limits the ability to isolate science done in China. Instead, they suggest that it could inspire a different course of action, with the US and other countries expanding their engagement with the developing world to train a global workforce and accelerate scientific advancements beneficial to their economies.

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The LIGO–Virgo–KAGRA collaboration has detected strong evidence for second-generation black holes, which were formed from earlier mergers of smaller black holes. The two gravitational wave signals provide one of the strongest confirmations to date for how Einstein’s general theory of relativity describes rotating black holes. Studying such objects also provides a testbed for probing new physics beyond the Standard Model.

Over the past decade, the global network of interferometers operated by LIGO, Virgo, and KAGRA have detected close to 300 gravitational waves (GWs) – mostly from the mergers of binary black holes.

In October 2024 the network detected a clear signal that pointed back to a merger that occurred 700 million light-years away. The progenitor black holes were 20 and 6 solar masses and the larger object was spinning at 370 Hz, which makes it one of the fastest-spinning black holes ever observed.

Just one month later, the collaboration detected the coalescence of another highly imbalanced binary (17 and 8 solar masses), 2.4 billion light-years away. This signal was even more unusual – showing for the first time that the larger companion was spinning in the opposite direction of the binary orbit.

Massive and spinning

While conventional wisdom says black holes should not be spinning at such high rates, the observations were not entirely unexpected. “With both events having one black hole, which is both significantly more massive than the other and rapidly spinning, [the observations] provide tantalizing evidence that these black holes were formed from previous black hole mergers,” explains Stephen Fairhurst at Cardiff University, spokesperson of the LIGO Collaboration. If this were the case, the two GW signals – called GW241011 and GW241110 – are first observations of second-generation black holes. This is because when a binary merges, the resulting second-generation object tends to have a large spin.

The GW241011 signal was particularly clear, which allowed the team to make the third-ever observation of higher harmonic modes. These are overtones in the GW signal that become far clearer when the masses of the coalescing bodies are highly imbalanced.

The precision of the GW241011 measurement provides one of the most stringent verifications so far of general relativity. The observations also support Roy Kerr’s prediction that rapid rotation distorts the shape of a black hole.

Kerr and Einstein confirmed

“We now know that black holes are shaped like Einstein and Kerr predicted, and general relativity can add two more checkmarks in its list of many successes,” says team member Carl-Johan Haster at the University of Nevada, Las Vegas. “This discovery also means that we’re more sensitive than ever to any new physics that might lie beyond Einstein’s theory.”

This new physics could include hypothetical particles called ultralight bosons. These could form in clouds just outside the event horizons of spinning black holes, and would gradually drain a black hole’s rotational energy via a quantum effect called superradiance.

The idea is that the observed second-generation black holes had been spinning for billions of years before their mergers occurred. This means that if ultralight bosons were present, they cannot have removed lots of angular momentum from the black holes. This places the tightest constraint to date on the mass of ultralight bosons.

“Planned upgrades to the LIGO, Virgo and KAGRA detectors will enable further observations of similar systems,” Fairhurst says. “They will enable us to better understand both the fundamental physics governing these black hole binaries and the astrophysical mechanisms that lead to their formation.”

Haster adds, “Each new detection provides important insights about the universe, reminding us that each observed merger is both an astrophysical discovery but also an invaluable laboratory for probing the fundamental laws of physics”.

The observations are described in The Astrophysical Journal Letters.

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Using a new type of low-power, compact, fluid-based prism to steer the beam in a laser scanning microscope could transform brain imaging and help researchers learn more about neurological conditions such as Alzheimer’s disease.

The “electrowetting prism” utilized was developed by a team led by Juliet Gopinath from the electrical, computer and energy engineering and physics departments at the University of Colorado at Boulder (CU Boulder) and Victor Bright from CU Boulder’s mechanical engineering department, as part of their ongoing collaboration on electrically controllable optical elements for improving microscopy techniques.

“We quickly became interested in biological imaging, and work with a neuroscience group at University of Colorado Denver Anschutz Medical Campus that uses mouse models to study neuroscience,” Gopinath tells Physics World. “Neuroscience is not well understood, as illustrated by the neurodegenerative diseases that don’t have good cures. So a great benefit of this technology is the potential to study, detect and treat neurodegenerative diseases such as Alzheimer’s, Parkinson’s and schizophrenia,” she explains.

The researchers fabricated their patented electrowetting prism using custom deposition and lithography methods. The device consists of two immiscible liquids housed in a 5 mm tall, 4 mm diameter glass tube, with a dielectric layer on the inner wall coating four independent electrodes. When an electric field is produced by applying a potential difference between a pair of electrodes on opposite sides of the tube, it changes the surface tension and therefore the curvature of the meniscus between the two liquids. Light passing through the device is refracted by a different amount depending on the angle of tilt of the meniscus (as well as on the optical properties of the liquids chosen), enabling beams to be steered by changing the voltage on the electrodes.

Beam steering for scanning in imaging and microscopy can be achieved via several means, including mechanically controlled mirrors, glass prisms or acousto-optic deflectors (in which a sound wave is used to diffract the light beam). But, unlike the new electrowetting prisms, these methods consume too much power and are not small or lightweight enough to be used for miniature microscopy of neural activity in the brains of living animals.

In tests detailed in Optics Express, the researchers integrated their electrowetting prism into an existing two-photon laser scanning microscope and successfully imaged individual 5 µm-diameter fluorescent polystyrene beads, as well as large clusters of those beads.

They also used computer simulation to study how the liquid–liquid interface moved, and found that when a sinusoidal voltage is used for actuation, at 25 and 75 Hz, standing wave resonance modes occur at the meniscus – a result closely matched by a subsequent experiment that showed resonances at 24 and 72 Hz. These resonance modes are important for enhancing device performance since they increase the angle through which the meniscus can tilt and thus enable optical beams to be steered through a greater range of angles, which helps minimize distortions when raster scanning in two dimensions.

Bright explains that this research built on previous work in which an electrowetting prism was used in a benchtop microscope to image a mouse brain. He cites seeing the individual neurons as a standout moment that, coupled with the current results, shows their prism is now “proven and ready to go”.

Gopinath and Bright caution that “more work is needed to allow human brain scans, such as limiting voltage requirements, allowing the device to operate at safe voltage levels, and miniaturization of the device to allow faster scan speeds and acquiring images at a much faster rate”. But they add that miniaturization would also make the device useful for endoscopy, robotics, chip-scale atomic clocks and space-based communication between satellites.

The team has already begun investigating two other potential applications: LiDAR (light detection and ranging) systems and optical coherence tomography (OCT). Next, the researchers “hope to integrate the device into a miniaturized microscope to allow imaging of the brain in freely moving animals in natural outside environments,” they say. “We also aim to improve the packaging of our devices so they can be integrated into many other imaging systems.”

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At the centre of most quantum labs is a large cylindrical cryostat that keeps the delicate quantum hardware at ultralow temperatures. These cryogenic chambers have expanded to accommodate larger and more complex quantum systems, but the scientists and engineers at UK-based cryogenics specialist ICEoxford have taken a radical new approach to the challenge of scalability. They have split the traditional cryostat into a series of cube-shaped modules that slot into a standard 19-inch rack mount, creating an adaptable platform that can easily be deployed alongside conventional computing infrastructure.

“We wanted to create a robust, modular and scalable solution that enables different quantum technologies to be integrated into the cryostat,” says Greg Graf, the company’s engineering manager. “This approach offers much more flexibility, because it allows different modules to be used for different applications, while the system also delivers the efficiency and reliability that are needed for operational use.”

The standard configuration of the ICE-Q platform has three separate modules: a cryogenics unit that provides the cooling power, a large payload for housing the quantum chip or experiment, and a patent-pending wiring module that attaches to the side of the payload to provide the connections to the outside world. Up to four of these side-loading wiring modules can be bolted onto the payload at the same time, providing thousands of external connections while still fitting into a standard rack. For applications where space is not such an issue, the payload can be further extended to accommodate larger quantum assemblies and potentially tens of thousands of radio-frequency or fibre-optic connections.

The cube-shaped form factor provides much improved access to these external connections, whether for designing and configuring the system or for ongoing maintenance work. The outer shell of each module consists of panels that are easily removed, offering a simple mechanism for bolting modules together or stacking them on top of each other to provide a fully scalable solution that grows with the qubit count.

The flexible design also offers a more practical solution for servicing or upgrading an installed system, since individual modules can be simply swapped over as and when needed. “For quantum computers running in an operational environment it is really important to minimize the downtime,” says Emma Yeatman, senior design engineer at ICEoxford. “With this design we can easily remove one of the modules for servicing, and replace it with another one to keep the system running for longer. For critical infrastructure devices, it is possible to have built-in redundancy that ensures uninterrupted operation in the event of a failure.”

Other features have been integrated into the platform to make it simple to operate, including a new software system for controlling and monitoring the ultracold environment. “Most of our cryostats have been designed for researchers who really want to get involved and adapt the system to meet their needs,” adds Yeatman. “This platform offers more options for people who want an out-of-the-box solution and who don’t want to get hands on with the cryogenics.”

Such a bold design choice was enabled in part by a collaborative research project with Canadian company Photonic Inc, funded jointly by the UK and Canada, that was focused on developing an efficient and reliable cryogenics platform for practical quantum computing. That R&D funding helped to reduce the risk of developing an entirely new technology platform that addresses many of the challenges that ICEoxford and its customers had experienced with traditional cryostats. “Quantum technologies typically need a lot of wiring, and access had become a real issue,” says Yeatman. “We knew there was an opportunity to do better.”

However, converting a large cylindrical cryostat into a slimline and modular form factor demanded some clever engineering solutions. Perhaps the most obvious was creating a frame that allows the modules to be bolted together while still remaining leak tight. Traditional cryostats are welded together to ensure a leak-proof seal, but for greater flexibility the ICEoxford team developed an assembly technique based on mechanical bonding.

The side-loading wiring module also presented a design challenge. To squeeze more wires into the available space, the team developed a high-density connector for the coaxial cables to plug into. An additional cold-head was also integrated into the module to pre-cool the cables, reducing the overall heat load generated by such large numbers of connections entering the ultracold environment.

Meanwhile, the speed of the cooldown and the efficiency of operation have been optimized by designing a new type of heat exchanger that is fabricated using a 3D printing process. “When warm gas is returned into the system, a certain amount of cooling power is needed just to compress and liquefy that gas,” explains Kelly. “We designed the heat exchangers to exploit the returning cold gas much more efficiently, which enables us to pre-cool the warm gas and use less energy for the liquefaction.”

The initial prototype has been designed to operate at 1 K, which is ideal for the photonics-based quantum systems being developed by ICEoxford’s research partner. But the modular nature of the platform allows it to be adapted to diverse applications, with a second project now underway with the Rutherford Appleton Lab to develop a module that that will be used at the forefront of the global hunt for dark matter.

Already on the development roadmap are modules that can sustain temperatures as low as 10 mK – which is typically needed for superconducting quantum computing – and a 4 K option for trapped-ion systems. “We already have products for each of those applications, but our aim was to create a modular platform that can be extended and developed to address the changing needs of quantum developers,” says Kelly.

As these different options come onstream, the ICEoxford team believes that it will become easier and quicker to deliver high-performance cryogenic systems that are tailored to the needs of each customer. “It normally takes between six and twelve months to build a complex cryogenics system,” says Graf. “With this modular design we will be able to keep some of the components on the shelf, which would allow us to reduce the lead time by several months.”

More generally, the modular and scalable platform could be a game-changer for commercial organizations that want to exploit quantum computing in their day-to-day operations, as well as for researchers who are pushing the boundaries of cryogenics design with increasingly demanding specifications. “This system introduces new avenues for hardware development that were previously constrained by the existing cryogenics infrastructure,” says Kelly. “The ICE-Q platform directly addresses the need for colder base temperatures, larger sample spaces, higher cooling powers, and increased connectivity, and ensures our clients can continue their aggressive scaling efforts without being bottlenecked by their cooling environment.”

• You can find out more about the ICE-Q platform by contacting the ICEoxford team at iceoxford.com, or via email at sales@iceoxford.com. They will also be presenting the platform at the UK’s National Quantum Technologies Showcase in London on 7 November, with a further launch at the American Physical Society meeting in March 2026.

The post Modular cryogenics platform adapts to new era of practical quantum computing appeared first on Physics World.

When it comes to building a fully functional “fault-tolerant” quantum computer, companies and government labs all over the world are rushing to be the first over the finish line. But a truly useful universal quantum computer capable of running complex algorithms would have to entangle millions of coherent qubits, which are extremely fragile. Because of environmental factors such as temperature, interference from other electronic systems in hardware, and even errors in measurement, today’s devices would fail under an avalanche of errors long before reaching that point.

So the problem of error correction is a key issue for the future of the market. It arises because errors in qubits can’t be corrected simply by keeping multiple copies, as they are in classical computers: quantum rules forbid the copying of qubit states while they are still entangled with others, and are thus unknown. To run quantum circuits with millions of gates, we therefore need new tricks to enable quantum error correction (QEC).

Protected states

The general principle of QEC is to spread the information over many qubits so that an error in any one of them doesn’t matter too much. “The essential idea of quantum error correction is that if we want to protect a quantum system from damage then we should encode it in a very highly entangled state,” says John Preskill, director of the Institute for Quantum Information and Matter at the California Institute of Technology in Pasadena.

There is no unique way of achieving that spreading, however. Different error-correcting codes can depend on the connectivity between qubits – whether, say, they are coupled only to their nearest neighbours or to all the others in the device – which tends to be determined by the physical platform being used. However error correction is done, it must be done fast. “The mechanisms for error correction need to be running at a speed that is commensurate with that of the gate operations,” says Michael Cuthbert, founding director of the UK’s National Quantum Computing Centre (NQCC). “There’s no point in doing a gate operation in a nanosecond if it then takes 100 microseconds to do the error correction for the next gate operation.”

At the moment, dealing with errors is largely about compensation rather than correction: patching up the problems of errors in retrospect, for example by using algorithms that can throw out some results that are likely to be unreliable (an approach called “post-selection”). It’s also a matter of making better qubits that are less error-prone in the first place.

According to Maria Maragkou, commercial vice-president of quantum error-correction company Riverlane, the goal of full QEC has ramifications for the design of the machines all the way from hardware to workflow planning. “The shift to support error correction has a profound effect on the way quantum processors themselves are built, the way we control and operate them, through a robust software stack on top of which the applications can be run,” she explains. The “stack” includes everything from programming languages to user interfaces and servers.

With genuinely fault-tolerant qubits, errors can be kept under control and prevented from proliferating during a computation. Such qubits might be made in principle by combining many physical qubits into a single “logical qubit” in which errors can be corrected (see figure 1). In practice, though, this creates a large overhead: huge numbers of physical qubits might be needed to make just a few fault-tolerant logical qubits. The question is then whether errors in all those physical qubits can be checked faster than they accumulate (see figure 2).

That overhead has been steadily reduced over the past several years, and at the end of last year researchers at Google announced that their 105-qubit Willow quantum chip passed the break-even threshold at which the error rate gets smaller, rather than larger, as more physical qubits are used to make a logical qubit. This means that in principle such arrays could be scaled up without errors accumulating.

Fault-tolerant quantum computing is the ultimate goal, says Jay Gambetta, director of IBM research at the company’s centre in Yorktown Heights, New York. He believes that to perform truly transformative quantum calculations, the system must go beyond demonstrating a few logical qubits – instead, you need arrays of at least a 100 of them, that can perform more than 100 million quantum operations (10⁸ QuOps). “The number of operations is the most important thing,” he says.

It sounds like a tall order, but Gambetta is confident that IBM will achieve these figures by 2029. By building on what has been achieved so far with error correction and mitigation, he feels “more confident than I ever did before that we can achieve a fault-tolerant computer.” Jerry Chow, previous manager of the Experimental Quantum Computing group at IBM, shares that optimism. “We have a real blueprint for how we can build [such a machine] by 2029,” he says (see figure 3).

Others suspect the breakthrough threshold may be a little lower: Steve Brierley, chief executive of Riverlane, believes that the first error-corrected quantum computer, with around 10 000 physical qubits supporting 100 logical qubits and capable of a million QuOps (a megaQuOp), could come as soon as 2027. Following on, gigaQuOp machines (10⁹ QuOps) should be available by 2030–32, and teraQuOps (10¹² QuOp) by 2035–37.

Platform independent

Error mitigation and error correction are just two of the challenges for developers of quantum software. Fundamentally, to develop a truly quantum algorithm involves taking full advantage of the key quantum-mechanical properties such as superposition and entanglement. Often, the best way to do that depends on the hardware used to run the algorithm. But ultimately the goal will be to make software that is not platform-dependent and so doesn’t require the user to think about the physics involved.

“At the moment, a lot of the platforms require you to come right down into the quantum physics, which is a necessity to maximize performance,” says Richard Murray of photonic quantum-computing company Orca. Try to generalize an algorithm by abstracting away from the physics and you’ll usually lower the efficiency with which it runs. “But no user wants to talk about quantum physics when they’re trying to do machine learning or something,” Murray adds. He believes that ultimately it will be possible for quantum software developers to hide those details from users – but Brierly thinks this will require fault-tolerant machines.

“In due time everything below the logical circuit will be a black box to the app developers”, adds Maragkou over at Riverlane. “They will not need to know what kind of error correction is used, what type of qubits are used, and so on.” She stresses that creating truly efficient and useful machines depends on developing the requisite skills. “We need to scale up the workforce to develop better qubits, better error-correction codes and decoders, write the software that can elevate those machines and solve meaningful problems in a way that they can be adopted.” Such skills won’t come only from quantum physicists, she adds: “I would dare say it’s mostly not!”

Yet even now, working on quantum software doesn’t demand a deep expertise in quantum theory. “You can be someone working in quantum computing and solving problems without having a traditional physics training and knowing about the energy levels of the hydrogen atom and so on,” says Ashley Montanaro, who co-founded the quantum software company Phasecraft.

On the other hand, insights can flow in the other direction too: working on quantum algorithms can lead to new physics. “Quantum computing and quantum information are really pushing the boundaries of what we think of as quantum mechanics today,” says Montanaro, adding that QEC “has produced amazing physics breakthroughs.”

Early adopters?

Once we have true error correction, Cuthbert at the UK’s NQCC expects to see “a flow of high-value commercial uses” for quantum computers. What might those be?

In this arena of quantum chemistry and materials science, genuine quantum advantage – calculating something that is impossible using classical methods alone – is more or less here already, says Chow. Crucially, however, quantum methods needn’t be used for the entire simulation but can be added to classical ones to give them a boost for particular parts of the problem.

For example, last year researchers at IBM teamed up with scientists at several RIKEN institutes in Japan to calculate the minimum energy state for the iron sulphide cluster (4Fe-4S) at the heart of the bacterial nitrogenase enzyme that fixes nitrogen. This cluster is too big and complex to be accurately simulated using the classical approximations of quantum chemistry. The researchers used a combination of both quantum computing (with IBM’s 72-qubit Heron chip) and RIKEN’s Fugaku high performance computing (HPC). This idea of “improving classical methods by injecting quantum as a subroutine” is likely to be a more general strategy, says Gambetta. “The future of computing is going to be heterogeneous accelerators [of discovery] that include quantum.”

Likewise, Montanaro says that Phasecraft is developing “quantum-enhanced algorithms”, where a quantum computer is used, not to solve the whole problem, but just to help a classical computer in some way. “There are only certain problems where we know quantum computing is going to be useful,” he says. “I think we are going to see quantum computers working in tandem with classical computers in a hybrid approach. I don’t think we’ll ever see workloads that are entirely run using a quantum computer.” Among the first important problems that quantum machines will solve, according to Montanaro, are the simulation of new materials – to develop, for example, clean-energy technologies (see figure 4).

“For a physicist like me,” says Preskill, “what is really exciting about quantum computing is that we have good reason to believe that a quantum computer would be able to efficiently simulate any process that occurs in nature.”

Montanaro believes another likely near-term goal for useful quantum computing is solving optimization problems – both here and in quantum simulation, “we think genuine value can be delivered already in this NISQ era with hundreds of qubits.” (NISQ, a term coined by Preskill, refers to noisy intermediate-scale quantum computing, with relatively small numbers of rather noisy, error-prone qubits.)

One further potential benefit of quantum computing is that it tends to require less energy than classical high-performance computing, which is notoriously high. If the energy cost could be cut by even a few percent, it would be worth using quantum resources for that reason alone. “Quantum has real potential for an energy advantage,” says Chow. One study in 2020 showed that a particular quantum-mechanical calculation carried out on a HPC used many orders of magnitude more energy than when it was simulated on a quantum circuit. Such comparisons are not easy, however, in the absence of an agreed and well-defined metric for energy consumption.

Building the market

Right now, the quantum computing market is in a curious superposition of states itself – it has ample proof of principle, but today’s devices are still some way from being able to perform a computation relevant to a practical problem that could not be done with classical computers. Yet to get to that point, the field needs plenty of investment.

The fact that quantum computers, especially if used with HPC, are already unique scientific tools should establish their value in the immediate term, says Gambetta. “I think this is going to accelerate, and will keep the funding going.” It is why IBM is focusing on utility-scale systems of around 100 qubits or so and more than a thousand gate operations, he says, rather than simply trying to build ever bigger devices.

Montanaro sees a role for governments to boost the growth of the industry “where it’s not the right fit for the private sector”. One role of government is simply as a customer. For example, Phasecraft is working with the UK national grid to develop a quantum algorithm for optimizing the energy network. “Longer-term support for academic research is absolutely critical,” Montanaro adds. “It would be a mistake to think that everything is done in terms of the underpinning science, and governments should continue to support blue-skies research.”

It’s not clear, though, whether there will be a big demand for quantum machines that every user will own and run. Before 2010, “there was an expectation that banks and government departments would all want their own machine – the market would look a bit like HPC,” Cuthbert says. But that demand depends in part on what commercial machines end up being like. “If it’s going to need a premises the size of a football field, with a power station next to it, that becomes the kind of infrastructure that you only want to build nationally.” Even for smaller machines, users are likely to try them first on the cloud before committing to installing one in-house.

According to Cuthbert , the real challenge in the supply-chain development is that many of today’s technologies were developed for the science community – where, say, achieving millikelvin cooling or using high-power lasers is routine. “How do you go from a specialist scientific clientele to something that starts to look like a washing machine factory, where you can make them to a certain level of performance,” while also being much cheaper, and easier to use?

But Cuthbert is optimistic about bridging this gap to get to commercially useful machines, encouraged in part by looking back at the classical computing industry of the 1970s. “The architects of those systems could not imagine what we would use our computation resources for today. So I don’t think we should be too discouraged that you can grow an industry when we don’t know what it’ll do in five years’ time.”

Montanaro too sees analogies with those early days of classical computing. “If you think what the computer industry looked like in the 1940s, it’s very different from even 20 years later. But there are some parallels. There are companies that are filling each of the different niches we saw previously, there are some that are specializing in quantum hardware development, there are some that are just doing software.” Cuthbert thinks that the quantum industry is likely to follow a similar pathway, “but more quickly and leading to greater market consolidation more rapidly.”

However, while the classical computing industry was revolutionized by the advent of personal computing in the 1970s and 80s, it seems very unlikely that we will have any need for quantum laptops. Rather, we might increasingly see apps and services appear that use cloud-based quantum resources for particular operations, merging so seamlessly with classical computing that we don’t even notice.

That, perhaps, would be the ultimate sign of success: that quantum computing becomes invisible, no big deal but just a part of how our answers are delivered.

• In the first instalment of this two-part article, Philip Ball explores the latest developments in the quantum-computing industry

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Female university students do much better in introductory physics exams if they have the option of retaking the tests. That’s according to a new analysis of almost two decades of US exam results for more than 26 000 students. The study’s authors say it shows that female students benefit from lower-stakes assessments – and that the persistent “gender grade gap” in physics exam results does not reflect a gender difference in physics knowledge or ability.

The study has been carried out by David Webb from the University of California, Davis, and Cassandra Paul from San Jose State University. It builds on previous work they did in 2023, which showed that the gender gap disappears in introductory physics classes that offer the chance for all students to retake the exams. That study did not, however, explore why the offer of a retake has such an impact.

In the new study, the duo analysed exam results from 1997 to 2015 for a series of introductory physics classes at a public university in the US. The dataset included 26 783 students, mostly in biosciences, of whom about 60% were female. Some of the classes let students retake exams while others did not, thereby letting the researchers explore why retakes close the gender gap.

When Webb and Paul examined the data for classes that offered retakes, they found that in first-attempt exams female students slightly outperformed their male counterparts. But male students performed better than female students in retakes.

This, the researchers argue, discounts the notion that retakes close the gender gap by allowing female students to improve their grades. Instead, they suggest that the benefit of retakes is that they lower the stakes of the first exam.

The team then compared the classes that offered retakes with those that did not, which they called high-stakes courses. They found that the gender gap in exam results was much larger in the high-stakes classes than the lower-stakes classes that allowed retakes.

“This suggests that high-stakes exams give a benefit to men, on average, [and] lowering the stakes of each exam can remove that bias ” Webb told Physics World. He thinks that as well as allowing students to retake exams, physics might benefit from not having comprehensive high-stakes final exams but instead “use final exam time to let students retake earlier exams”.

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Improving the efficiency of solar cells will likely be one of the key approaches to achieving net zero emissions in many parts of the world. Many types of solar cells will be required, with some of the better performances and efficiencies expected to come from multi-junction solar cells. Multi-junction solar cells comprise a vertical stack of semiconductor materials with distinct bandgaps, with each layer converting a different part of the solar spectrum to maximize conversion of the Sun’s energy to electricity.

When there are no constraints on the choice of materials, triple-junction solar cells can outperform double-junction and single-junction solar cells, with a power conversion efficiency (PCE) of up to 51% theoretically possible. But material constraints – due to fabrication complexity, cost or other technical challenges – mean that many such devices still perform far from the theoretical limits.

Perovskites are one of the most promising materials in the solar cell world today, but fabricating practical triple-junction solar cells beyond 1 cm² in area has remained a challenge. A research team from Australia, China, Germany and Slovenia set out to change this, recently publishing a paper in Nature Nanotechnology describing the largest and most efficient triple-junction perovskite–perovskite–silicon tandem solar cell to date.

When asked why this device architecture was chosen, Anita Ho-Baillie, one of the lead authors from The University of Sydney, states: “I am interested in triple-junction cells because of the larger headroom for efficiency gains”.

Addressing surface defects in perovskite solar cells

Solar cells formed from metal halide perovskites have potential to be commercially viable, due to their cost-effectiveness, efficiency, ease of fabrication and their ability to be paired with silicon in multi-junction devices. The ease of fabrication means that the junctions can be directly fabricated on top of each other through monolithic integration – which leads to only two terminal connections, instead of four or six. However, these junctions can still contain surface defects.

To enhance the performance and resilience of their triple-junction cell (top and middle perovskite junctions on a bottom silicon cell), the researchers optimized the chemistry of the perovskite material and the cell design. They addressed surface defects in the top perovskite junction by replacing traditional lithium fluoride materials with piperazine-1,4-diium chloride (PDCl). They also replaced methylammonium – which is commonly used in perovskite cells – with rubidium. “The rubidium incorporation in the bulk and the PDCl surface treatment improved the light stability of the cell,” explains Ho-Baillie.

To connect the two perovskite junctions, the team used gold nanoparticles on tin oxide. Because the gold was in a nanoparticle form, the junctions could be engineered to maximize the flow of electric charge and light absorption by the solar cell.

“Another interesting aspect of the study is the visualization of the gold nanoparticles [using transmission electron microscopy] and the critical point when they become a semi-continuous film, which is detrimental to the multi-junction cell performance due to its parasitic absorption,” says Ho-Baillie. “The optimization for achieving minimal particle coverage while achieving sufficient ohmic contact for vertical carrier flow are useful insights”.

Record performance for a large-scale perovskite triple-junction cell

Using these design strategies, Ho-Baillie and colleagues developed a 16 cm² triple-junction cell that achieved an independently certified steady-state PCE of 23.3% – the highest reported for a large-area device. While triple-junction perovskite solar cells have exhibited higher PCEs – with all-perovskite triple-junction cells reaching 28.7% and perovskite–perovskite–silicon devices reaching 27.1% – these were all achieved on a 1 cm² cell, not a large-area cell.

In this study, the researchers also developed a 1 cm² cell that was close to the best, with a PCE of 27.06%, but it is the large-area cell that’s the record breaker. The 1 cm² cell also passed the International Electrotechnical Commission’s (IEC) 61215 thermal cycling test, which exposes the cell to 200 cycles under extreme temperature swings, ranging from –40 to 85°C. During this test, the 1 cm² cell retained 95% of its initial efficiency after 407 h of continuous operation.

The combination of the successful thermal cycling test combined with the high efficiencies on a larger cell shows that there could be potential for this triple-junction architecture in real-world settings in the near future, even though they are still far away from their theoretical limits.

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It’s rare to come across someone who’s been responsible for enabling a seismic shift in society that has affected almost everyone and everything. Tim Berners-Lee, who invented the World Wide Web, is one such person. His new memoir This is for Everyone unfolds the history and development of the Web and, in places, of the man himself.

Berners-Lee was born in London in 1955 to parents, originally from Birmingham, who met while working on the Ferranti Mark 1 computer and knew Alan Turing. Theirs was a creative, intellectual and slightly chaotic household. His mother could maintain a motorbike with fence wire and pliers, and was a crusader for equal rights in the workplace. His father – brilliant and absent minded – taught Berners-Lee about computers and queuing theory. A childhood of camping and model trains, it was, in Berners-Lee’s view, idyllic.

Berners-Lee had the good fortune to be supported by a series of teachers and managers who recognized his potential and unique way of working. He studied physics at the University of Oxford (his tutor “going with the flow” of Berners-Lee’s unconventional notation and ability to approach problems from oblique angles) and built his own computer. After graduating, he married and, following a couple of jobs, took a six-month placement at the CERN particle-physics lab in Geneva in 1985.

This placement set “a seed that sprouted into a tool that shook up the world”. Berners-Lee saw how difficult it was to share information stored in different languages in incompatible computer systems and how, in contrast, information flowed easily when researchers met over coffee, connected semi-randomly and talked. While at CERN, he therefore wrote a rough prototype for a program to link information in a type of web rather than a structured hierarchy.

Back at CERN, Tim Berners-Lee developed his vision of a “universal portal” to information

The placement ended and the program was ignored, but four years later Berners-Lee was back at CERN. Now divorced and soon to remarry, he developed his vision of a “universal portal” to information. It proved to be the perfect time. All the tools necessary to achieve the Web – the Internet, address labelling of computers, network cables, data protocols, the hypertext language that allowed cross-referencing of text and links on the same computer – had already been developed by others.

Berners-Lee saw the need for a user-friendly interface, using hypertext that could link to information on other computers across the world. His excitement was “uncontainable”, and according to his line manager “few of us if any could understand what he was talking about”. But Berners-Lee’s managers supported him and freed his time away from his actual job to become the world’s first web developer.

Having a vision was one thing, but getting others to share it was another. People at CERN only really started to use the Web properly once the lab’s internal phone book was made available on it. As a student at the time, I can confirm that it was much, much easier to use the Web than log on to CERN’s clunky IBM mainframe, where phone numbers had previously been stored.

Wider adoption relied on a set of volunteer developers, working with open-source software, to make browsers and platforms that were attractive and easy to use. CERN agreed to donate the intellectual property for web software to the public domain, which helped. But the path to today’s Web was not smooth: standards risked diverging and companies wanted to build applications that hindered information sharing.

Feeling the “the Web was outgrowing my institution” and “would be a distraction” to a lab whose core mission was physics, Berners-Lee moved to the Massachusetts Institute of Technology in 1994. There he founded the World Wide Web Consortium (W3C) to ensure consistent, accessible standards were followed by everyone as the Web developed into a global enterprise. The progression sounds straightforward although earlier accounts, such as James Gillies and Robert Caillau’s 2000 book How the Web Was Born, imply some rivalry between institutions that is glossed over here.

Initially inclined to advise people to share good things and not search for bad things, Berners-Lee had reckoned without the insidious power of “manipulative and coercive” algorithms on social networks

The rest is history, but not quite the history that Berners-Lee had in mind. By 1995 big business had discovered the possibilities of the Web to maximize influence and profit. Initially inclined to advise people to share good things and not search for bad things, Berners-Lee had reckoned without the insidious power of “manipulative and coercive” algorithms on social networks. Collaborative sites like Wikipedia are closer to his vision of an ideal Web; an emergent good arising from individual empowerment. The flip side of human nature seems to come as a surprise.

The rest of the book brings us up to date with Berners-Lee’s concerns (data, privacy, misuse of AI, toxic online culture), his hopes (the good use of AI), a third marriage and his move into a data-handling business. There are some big awards and an impressive amount of name dropping; he is excited by Order of Merit lunches with the Queen and by sitting next to Paul McCartney’s family at the opening ceremony to the London Olympics in 2012. A flick through the index reveals names ranging from Al Gore and Bono to Lucien Freud. These are not your average computing technology circles.

There are brief character studies to illustrate some of the main players, but don’t expect much insight into their lives. This goes for Berners-Lee too, who doesn’t step back to particularly reflect on those around him, or indeed his own motives beyond that vision of a Web for all enabling the best of humankind. He is firmly future focused.

Still, there is no-one more qualified to describe what the Web was intended for, its core philosophy, and what caused it to develop to where it is today. You’ll enjoy the book whether you want an insight into the inner workings that make your web browsing possible, relive old and forgotten browser names, or see how big tech wants to monetize and monopolize your online time. It is an easy read from an important voice.

The book ends with a passionate statement for what the future could be, with businesses and individuals working together to switch the Web from “the attention economy to the intention economy”. It’s a future where users are no longer distracted by social media and manipulated by attention-grabbing algorithms; instead, computers and services do what users want them to do, with the information that users want them to have.

Berners-Lee is still optimistic, still an incurable idealist, still driven by vision. And perhaps still a little naïve too in believing that everyone’s values will align this time.

• 2025 Macmillan 400pp £25.00/$30.00hb

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This talk shows how integrating p-type NiO to form NiO/Ga_₂O_₃ heterojunction rectifiers overcomes that barrier, enabling record-class breakdown and Ampere-class operation. It will cover device structure/process optimization, thermal stability to high temperatures, and radiation response – with direct ties to today’s priorities: EV fast charging, AI data‑center power systems, and aerospace/space‑qualified power electronics.

An interactive Q&A session follows the presentation.

 

Jian-Sian Li received the PhD in chemical engineering from the University of Florida in 2024, where his research focused on NiO/β-Ga_₂O_₃ heterojunction power rectifiers, includes device design, process optimization, fast switching, high-temperature stability, and radiation tolerance (γ, neutron, proton). His work includes extensive electrical characterization and microscopy/TCAD analysis supporting device physics and reliability in harsh environments. Previously, he completed his BS and MS at National Taiwan University (2015, 2018), with research spanning phoretic/electrokinetic colloids, polymers for OFETs/PSCs, and solid-state polymer electrolytes for Li-ion batteries. He has since transitioned to industry at Micron Technology.

The post Fabrication and device performance of Ni0/Ga₂O₃ heterojunction power rectifiers appeared first on Physics World.

A new integrated “snapshot spectroscopy” system developed in China can determine the spectral and spatial composition of light from an object with much better precision than other existing systems. The instrument uses randomly textured lithium niobate and its developers have used it for astronomical imaging and materials analysis – and they say that other applications are possible.

Spectroscopy is crucial to analysis of all kinds of objects in science and engineering, from studying the radiation emitted by stars to identifying potential food contaminants. Conventional spectrometers – such as those used on telescopes – rely on diffractive optics to separate incoming light into its constituent wavelengths. This makes them inherently large, expensive and inefficient at rapid image acquisition as the light from each point source has to be spatially separated to resolve the wavelength components.

In recent years researchers have combined computational methods with advanced optical sensors to create computational spectrometers with the potential to rival conventional instruments. One such approach is hyperspectral snapshot imaging, which captures both spectral and spatial information in the same image. There are currently two main snapshot-imaging techniques available. Narrowband-filtered snapshot spectral imagers comprise a mosaic pattern of narrowband filters and acquire an image by taking repeated snapshots at different wavelengths. However, these trade spectral resolution with spatial resolution, as each extra band requires its own tile within the mosaic. A more complex alternative design – the broadband-modulated snapshot spectral imager – uses a single, broadband detector covered with a spatially varying element such as a metasurface that interacts with the light and imprints spectral encoding information onto each pixel. However, these are complex to manufacture and their spectral resolution is limited to the nanometre scale.

Random thicknesses

In the new work, researchers led by Lu Fang at Tsinghua University in Beijing unveil a spectroscopy technique that utilizes the nonlinear optical properties of lithium niobate to achieve sub-Ångström spectral resolution in a simply fabricated, integrated snapshot detector they call RAFAEL. A lithium niobate layer with random, sub-wavelength thickness variations is surrounded by distributed Bragg reflectors, forming optical cavities. These are integrated into a stack with a set of electrodes. Each cavity corresponds to a single pixel. Incident light enters  from one side of a cavity, interacting with the lithium niobate repeatedly before exiting and being detected. Because lithium niobate is nonlinear, its response varies with the wavelength of the light.

The researchers then applied a bias voltage using the electrodes. The nonlinear optical response of lithium niobate means that this bias alters its response to light differently at different wavelengths. Moreover, the random variation of the lithium niobate’s thickness around the surface means that the wavelength variation is spatially specific.

The researchers designed a machine learning algorithm and trained it to use this variation of applied bias voltage with resulting wavelength detected at each point to reconstruct the incident wavelengths on the detector at each point in space.

“The randomness is useful for making the equations independent,” explains Fang; “We want to have uncorrelated equations so we can solve them.”

Thousands of stars

The researchers showed that they could achieve 88 Hz snapshot spectroscopy on a grid of 2048×2048 pixels with a spectral resolution of 0.5 Å (0.05 nm) between wavelengths of 400–1000 nm. They demonstrated this by capturing the full atomic absorption spectra of up to 5600 stars in a single snapshot. This is a two to four orders of magnitude improvement in observational efficiency over world-class astronomical spectrometers. They also demonstrated other applications, including a materials analysis challenge involving the distinction of a real leaf from a fake one. The two looked identical at optical wavelengths, but, using its broader range of wavelengths, RAFAEL was able to distinguish between the two.

The researchers are now attempting to improve the device further: “I still think that sub-Ångstrom is not the ending – it’s just the starting point,” says Fu. “We want to push the limit of our resolution to the picometre.” In addition, she says, they are working on further integration of the device – which requires no specialized lithography – for easier use in the field. “We’ve already put this technology on a drone platform,” she reveals. The team is also working with astronomical observatories such as Gran Telescopio Canarias in La Palma, Spain.

The research is described in Nature.

Computational imaging expert David Brady of Duke University in North Carolina is impressed by the instrument. “It’s a compact package with extremely high spectral resolution,” he says; “Typically an optical instrument, like a CMOS sensor that’s used here, is going to have between 10,000 and 100,000 photo-electrons per pixel.  That’s way too many photons for getting one measurement…I think you’ll see that with spectral imaging as is done here, but also with temporal imaging. People are saying you don’t need to go at 30 frames second, you can go at a million frames per second and push closer to the single photon limit, and then that would require you to do computation to figure out what it all means.”

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