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The future of quantum physics and technology debated at the Royal Institution

As we enter the final stretch of the International Year of Quantum Science and Technology (IYQ), I hope you’ve enjoyed our extensive quantum coverage over the last 12 months. We’ve tackled the history of the subject, explored some of the unexplained mysteries that still make quantum physics so exciting, and examined many of the commercial applications of quantum technology. You can find most of our coverage collected into two free-to-read digital Quantum Briefings, available here and here on the Physics World website.

Over the last 100 years since Werner Heisenberg first developed quantum mechanics on the island of Helgoland in June 1925, quantum mechanics has proved to be an incredibly powerful, successful and logically consistent theory. Our understanding of the subatomic world is no longer the “lamentable hodgepodge of hypotheses, principles, theorems and computational recipes”, as the Israeli physicist and philosopher Max Jammer memorably once described it.

In fact, quantum mechanics has not just transformed our understanding of the natural world; it has immense practical ramifications too, with so-called “quantum 1.0” technologies – lasers, semiconductors and electronics – underpinning our modern world. But as was clear from the UK National Quantum Technologies Showcase in London last week, organized by Innovate UK, the “quantum 2.0” revolution is now in full swing.

The day-long event, which is now in its 10th year, featured over 100 exhibitors, including many companies that are already using fundamental quantum concepts such as entanglement and superposition to support the burgeoning fields of quantum computing, quantum sensing and quantum communication. The show was attended by more than 3000 delegates, some of whom almost had to be ushered out of the door at closing time, so keen were they to keep talking.

Intrigued by quantum? Explore the 2025 Physics World Quantum Briefing 2.0

Last week also saw a two-day conference at the historic Royal Institution (RI) in central London that was a centrepiece of IYQ in the UK and Ireland. Entitled Quantum Science and Technology: the First 100 Years; Our Quantum Future and attended by over 300 people, it was organized by the History of Physics and the Business Innovation and Growth groups of the Institute of Physics (IOP), which publishes Physics World.

The first day, focusing on the foundations of quantum mechanics, ended with a panel discussion – chaired by my colleague Tushna Commissariat and Daisy Shearer from the UK’s National Quantum Computing Centre – with physicists Fay Dowker (Imperial College), Jim Al-Khalili (University of Surrey) and Peter Knight. They talked about whether the quantum wavefunction provides a complete description of physical reality, prompting much discussion with the audience. As Al-Khalili wryly noted, if entanglement has emerged as the fundamental feature of quantum reality, then “decoherence is her annoying and ever-present little brother”.

Knight, meanwhile, who is a powerful figure in quantum-policy circles, went as far as to say that the limit of decoherence – and indeed the boundary between the classical and quantum worlds – is not a fixed and yet-to-be revealed point. Instead, he mused, it will be determined by how much money and ingenuity and time physicists have at their disposal.

Bridging the gap between scientists, policy makers and industry to build the quantum ecosystem

Illustration of two people writing legal documents surrounding by symbols of science

On the second day of the IOP conference at the RI, I chaired a discussion that brought together four future leaders of the subject: Mehul Malik (Heriot-Watt University) and Sarah Malik (University College London) along with industry insiders Nicole Gillett (Riverlane) and Muhammad Hamza Waseem (Quantinuum).

As well as outlining the technical challenges in their fields, the speakers all stressed the importance of developing a “skills pipeline” so that the quantum sector has enough talented people to meet its needs. Also vital will be the need to communicate the mysteries and potential of quantum technology – not just to the public but to industrialists, government officials and venture capitalists. By many measures, the UK is at the forefront of quantum tech – and it is a lead it should not let slip.

**Clear talker** Jim Al-Khalili giving his Friday night discourse at the Royal Institution on 7 November 2025. (Courtesy: Matin Durrani)

The week ended with Al-Khalili giving a public lecture, also at the Royal Institution, entitled “A new quantum world: ‘spooky’ physics to tech revolution”. It formed part of the RI’s famous Friday night “discourses”, which this year celebrate their 200th anniversary. Al-Khalili, who also presents A Life Scientific on BBC Radio 4, is now the only person ever to have given three RI discourses.

After the lecture, which was sold out, he took part in a panel discussion with Knight and Elizabeth Cunningham, a former vice-president for membership at the IOP. Al-Khalili was later presented with a special bottle of “Glentanglement” whisky made by Glasgow-based Fraunhofer UK for the Scottish Quantum Technology cluster.

Neural networks discover unstable singularities in fluid systems

Significant progress towards answering one of the Clay Mathematics Institute’s seven Millennium Prize Problems has been achieved using deep learning. The challenge is to establish whether or not the Navier-Stokes equation of fluid dynamics develops singularities. The work was done by researchers in the US and UK – including some at Google Deepmind. Some team members had already shown that simplified versions of the equation could develop stable singularities, which reliably form. In the new work, the researchers found unstable singularities, which form only under very specific conditions.

The Navier–Stokes partial differential equation was developed in the early 19th century by Claude-Louis Navier and George Stokes. It has proved its worth for modelling incompressible fluids in scenarios including water flow in pipes; airflow around aeroplanes; blood moving in veins; and magnetohydrodynamics in plasmas.

No-one has yet proved, however, whether smooth, non-singular solutions to the equation always exist in three dimensions. “In the real world, there is no singularity…there is no energy going to infinity,” says fluid dynamics expert Pedram Hassanzadeh of the University of Chicago. “So if you have an equation that has a singularity, it tells you that there is some physics that is missing.” In 2000, the Clay Mathematics Institute in Denver, Colorado listed this proof as one of seven key unsolved problems in mathematics, offering a reward of $1,000,000 for an answer.

Computational approaches

Researchers have traditionally tackled the problem analytically, but in recent decades high-level computational simulations have been used to assist in the search. In a 2023 paper, mathematician Tristan Buckmaster of New York University and colleagues used a special type of machine learning algorithm called a physics-informed neural network to address the question.

“The main difference is…you represent [the solution] in a highly non-linear way in terms of a neural network,” explains Buckmaster. This allows it to occupy a lower-dimensional space with fewer free parameters, and therefore to be optimized more efficiently. Using this approach, the researchers successfully obtained the first stable singularity in the Euler equation. This is an analogy to the Navier-Stokes equation that does not include viscosity.

A stable singularity will still occur if the initial conditions of the fluid are changed slightly – although the time taken for them to form may be altered. An unstable singularity, however, may never occur if the initial conditions are perturbed even infinitesimally. Some researchers have hypothesized that any singularities in the Navier-Stokes equation must be unstable, but finding unstable singularities in a computer model is extraordinarily difficult.

“Before our result there hadn’t been an unstable singularity for an incompressible fluid equation found numerically,” says geophysicist Ching-Yao Lai of California’s Stanford University.

Physics-informed neural network

In the new work the authors of the original paper and others teamed up with researchers at Google Deepmind to search for unstable singularities in a bounded 3D version of the Euler equation using a physics-informed neural network. “Unlike conventional neural networks that learn from vast datasets, we trained our models to match equations that model the laws of physics,” writes Yongji Wang of New York University and Stanford on Deepmind’s blog. “The network’s output is constantly checked against what the physical equations expect, and it learns by minimizing its ‘residual’, the amount by which its solution fails to satisfy the equations.”

After an exhaustive search at a precision that is orders of magnitude higher than a normal deep learning protocol, the researchers discovered new families of singularities in the 3D Euler equation. They also found singularities in the related incompressible porous media equation used to model fluid flows in soil or rock; and in the Boussinesq equation that models atmospheric flows.

The researchers also gleaned insights into the strength of the singularities. This could be important as stronger singularities might be less readily smoothed out by viscosity when moving from the Euler equation to the Navier-Stokes equation. The researchers are now seeking to model more open systems to study the problem in a more realistic space.

Hassanzadeh, who was not involved in the work, believes that it is significant – although the results are not unexpected. “If the Euler equation tells you that ‘Hey, there is a singularity,’ it just tells you that there is physics that is missing and that physics becomes very important around that singularity,” he explains. “In the case of Euler we know that you get the singularity because, at the very smallest scales, the effects of viscosity become important…Finding a singularity in the Euler equation is a big achievement, but it doesn’t answer the big question of whether Navier-Stokes is a representation of the real world, because for us Navier-Stokes represents everything.”

He says the extension to studying the full Navier-Stokes equation will be challenging but that “they are working with the best AI people in the world at Deepmind,” and concludes “I’m sure it’s something they’re thinking about”.

The work is available on the arXiv pre-print server.

NASA’s Goddard Space Flight Center hit by significant downsizing

NASA’s Goddard Space Flight Center (GSFC) looks set to lose a big proportion of its budget as a two-decade reorganization plan for the centre is being accelerated. The move, which is set to be complete by March, has left the Goddard campus with empty buildings and disillusioned employees. Some staff even fear that the actions during the 43-day US government shutdown, which ended on 12 November, could see the end of much of the centre’s activities.

Based in Greenbelt, Maryland, the GSFC has almost 10 000 scientists and engineers, about 7000 of whom are directly employed by NASA contractors. Responsible for many of NASA’s most important uncrewed missions, telescopes, and probes, the centre is currently working on the Nancy Grace Roman Space Telescope, which is scheduled to launch in 2027, as well as the Dragonfly mission that is due to head for Saturn’s largest moon Titan in 2028.

The ability to meet those schedules has now been put in doubt by the Trump administration’s proposed budget for financial year 2026, which started in September. It calls for NASA to receive almost $19bn – far less than the $25bn it has received for the past two years. If passed, Goddard would lose more than 42% of its staff.

Congress, which passes the final budget, is not planning to cut NASA so deeply as it prepares its 2026 budget proposal. But on 24 September, Goddard managers began what they told employees was “a series of moves…that will reduce our footprint into fewer buildings”. The shift is intended to “bring down overall operating costs while maintaining the critical facilities we need for our core capabilities of the future”.

While this is part of a 20-year “master plan” for the GSFC that NASA’s leadership approved in 2019, the management’s memo stated that “all planned moves will take place over the next several months and be completed by March 2026″. A report in September by Democratic members of the Senate Committee on Commerce, Science, and Transportation, which is responsible for NASA, asserts that the cuts are “in clear violation of the [US] constitution [without] regard for the impacts on NASA’s science missions and workforce”.

On 3 November, the Goddard Engineers, Scientists and Technicians Association, a union representing NASA workers, reported that the GSFC had already closed over a third of its buildings, including some 100 labs. This had been done, it says, “with extreme haste and with no transparent strategy or benefit to NASA or the nation”. The union adds that the “closures are being justified as cost-saving but no details are being provided and any short-term savings are unlikely to offset a full account of moving costs and the reduced ability to complete NASA missions”.

Accounting for the damage

Zoe Lofgren, the lead Democrat on the House of Representatives Science Committee, has demanded of Sean Duffy, NASA’s acting administrator, that the agency “must now halt” any laboratory, facility and building closure and relocation activities at Goddard. In a letter to Duffy dated 10 November, she also calls for the “relocation, disposal, excessing, or repurposing of any specialized equipment or mission-related activities, hardware and systems” to also end immediately.

Lofgren now wants NASA to carry out a “full accounting of the damage inflicted on Goddard thus far” by 18 November. Owing to the government shutdown, no GSFC or NASA official was available to respond to Physics World’s requests for a response.

Meanwhile, the Trump administration has renominated billionaire entrepreneur Jared Isaacman as NASA’s administrator. Trump had originally nominated Isaacman, who had flown on a private SpaceX mission and carried out spacewalk, on the recommendation of SpaceX founder Elon Musk. But the administration withdrew the nomination in May following concerns among some Republicans that Isaacman had funded the Democrat party.

Designing better semiconductor chips: NP hard problems and forever chemicals

Like any major endeavour, designing and fabricating semiconductor chips requires compromise. As well as trade-offs between cost and performance, designers also consider carbon emissions and other environmental impacts.

In this episode of the Physics World Weekly podcast, Margaret Harris reports from the Heidelberg Laureate Forum where she spoke to two researchers who are focused on some of these design challenges.

Up first is Mariam Elgamal, who’s doing a PhD at Harvard University on the development of environmentally sustainable computing systems. She explains why sustainability goes well beyond energy efficiency and must consider the manufacturing process and the chemicals used therein.

Harris also chats with Andrew Gunter, who is doing a PhD at the University of British Columbia on circuit design for computer chips. He talks about the maths-related problems that must be solved in order to translate a desired functionality into a chip that can be fabricated.

High-resolution PET scanner visualizes mouse brain structures with unprecedented detail

Positron emission tomography (PET) is used extensively within preclinical research, enabling molecular imaging of rodent brains, for example, to investigate neurodegenerative disease. Such imaging studies require the highest possible spatial resolution to resolve the tiny structures in the animal’s brain. A research team at the National Institutes for Quantum Science and Technology (QST) in Japan has now developed the first PET scanner to achieve sub-0.5 mm spatial resolution.

Submillimetre-resolution PET has been demonstrated by several research groups. Indeed, the QST team previously built a PET scanner with 0.55 mm resolution – sufficient to visualize the thalamus and hypothalamus in the mouse brain. But identification of smaller structures such as the amygdala and cerebellar nuclei has remained a challenge.

“Sub-0.5 mm resolution is important to visualize mouse brain structures with high quantification accuracy,” explains first author Han Gyu Kang. “Moreover, this research work will change our perspective about the fundamental limit of PET resolution, which had been regarded to be around 0.5 mm due to the positron range of [the radioisotope] fluorine-18”.

System optimization

With Monte Carlo simulations revealing that sub-0.5 mm resolution could be achievable with optimal detector parameters and system geometry, Kang and colleagues performed a series of modifications to their submillimetre-resolution PET (SR-PET) to create the new high-resolution PET (HR-PET) scanner.

The HR-PET, described in IEEE Transactions on Medical Imaging, is based around two 48 mm-diameter detector rings with an axial coverage of 23.4 mm. Each ring contains 16 depth-of-interaction (DOI) detectors (essential to minimize parallax error in a small ring diameter) made from three layers of LYSO crystal arrays stacked in a staggered configuration, with the outer layer coupled to a silicon photomultiplier (SiPM) array.

Compared with their previous design, the researchers reduced the detector ring diameter from 52.5 to 48 mm, which served to improve geometrical efficiency and minimize the noncollinearity effect. They also reduced the crystal pitch from 1.0 to 0.8 mm and the SiPM pitch from 3.2 to 2.4 mm, improving the spatial resolution and crystal decoding accuracy, respectively.

Other changes included optimizing the crystal thicknesses to 3, 3 and 5 mm for the first, second and third arrays, as well as use of a narrow energy window (440–560 keV) to reduce the scatter fraction and inter-crystal scattering events. “The optimized staggered three-layer crystal array design is also a key factor to enhance the spatial resolution by improving the spatial sampling accuracy and DOI resolution compared with the previous SR-PET,” Kang points out.

Performance tests showed that the HR-PET scanner had a system-level energy resolution of 18.6% and a coincidence timing resolution of 8.5 ns. Imaging a NEMA ²²Na point source revealed a peak sensitivity at the axial centre of 0.65% for the 440–560 keV energy window and a radial resolution of 0.67±0.06 mm from the centre to 10 mm radial offset (using 2D filtered-back-projection reconstruction) – a 33% improvement over that achieved by the SR-PET.

To further evaluate the performance of the HR-PET, the researchers imaged a rod-based resolution phantom. Images reconstructed using a 3D ordered-subset-expectation-maximization (OSEM) algorithm clearly resolved all of the rods. This included the smallest rods with diameters of 0.5 and 0.45 mm, with average valley-to-peak ratios of 0.533 and 0.655, respectively – a 40% improvement over the SR-PET.

In vivo brain PET

The researchers then used the HR-PET for in vivo mouse brain imaging. They injected ¹⁸F-FITM, a tracer used to image the central nervous system, into an awake mouse and performed a 30 min PET scan (with the animal anesthetized) 42 min after injection. For comparison, they scanned the same mouse for 30 min with a preclinical Inveon PET scanner.

Mouse brain PET image — **Imaging the mouse brain** 3D maximum intensity projection image obtained from a 30-min HR-PET scan using ¹⁸F-FITM. High tracer uptake is seen in the cerebellum, thalamus and hypothalamus. Scale bar: 10 mm. (Courtesy: Han Gyu Kang)

After OSEM reconstruction, strong tracer uptake in the thalamus, hypothalamus, cerebellar cortex and cerebellar nuclei was clearly visible in the coronal HR-PET images. A zoomed image distinguished the cerebellar nuclei and flocculus, while sagittal and axial images visualized the cortex and striatum. Images from the Inveon, however, could barely resolve these brain structures.

The team also imaged the animal’s glucose metabolism using the tracer ¹⁸F-FDG. A 30 min HR-PET scan clearly delineated glucose transporter expression in the cortex, thalamus, hypothalamus and cerebellar nuclei. Here again, the Inveon could hardly identify these small structures.

The researchers note that the ¹⁸F-FITM and ¹⁸F-FDG PET images matched well with the anatomy seen in a preclinical CT scan. “To the best of our knowledge, this is the first separate identification of the hypothalamus, amygdala and cerebellar nuclei of mouse brain,” they write.

Future plans for the HR-PET scanner, says Kang, include using it for research on neurodegenerative disorders, with tracers that bind to amyloid beta or tau protein. “In addition, we plan to extend the axial coverage over 50 mm to explore the whole body of mice with sub-0.5 mm resolution, especially for oncological research,” he says. “Finally, we would like to achieve sub-0.3 mm PET resolution with more optimized PET detector and system designs.”

New experiments on static electricity cast doubt on previous studies in the field

Static electricity is an everyday phenomenon, but it remains poorly understood. Researchers at the Institute of Science and Technology Austria (ISTA) have now shed new light on it by capturing an “image” of charge distributions as charge transfers from one surface to another. Their conclusions challenge longstanding interpretations of previous experiments and enhance our understanding of how charge behaves on insulating surfaces.

Static electricity is also known as contact electrification because it occurs when charge is transferred from one object to another by touch. The most common laboratory example involves rubbing a balloon on someone’s head to make their hair stand on end. However, static electricity is also associated with many other activities, including coffee grinding, pollen transport and perhaps even the formation of rocky planets.

One of the most useful ways of studying contact electrification is to move a metal tip slowly over the surface of a sample without touching it, recording a voltage all the while. These so-called scanning Kelvin methods produce an “image” of voltages created by the transferred charge. At the macroscale, around 100 μm to 10 cm, the main method is termed scanning Kelvin probe microscopy (SKPM). At the nanoscale, around 10 nm to 100 μm, a related but distinct variant known as Kelvin probe force microscopy (KPFM) is used instead.

In previous fundamental physics studies using these techniques, the main challenges have been to make sense of the stationary patterns of charge left behind after contact electrification, and to investigate how these patterns evolve over space and time. In the latest work, the ISTA team chose to ask a slightly different question: when are the dynamics of charge transfer too fast for measured stationary patterns to yield meaningful information?

Mapping the charge on the contact-electrified surface of a polymer film

To find out, ISTA PhD student Felix Pertl built a special setup that could measure a sample’s surface charge with KPFM; transfer it below a linear actuator so that it could exchange charge when it contacted another material; and then transfer it underneath the KPFM again to image the resulting change in the surface charge.

“In a typical set-up, the sample transfer, moving the AFM to the right place and reinitiation and recalibration of the KPFM parameters can easily take as long as tens of minutes,” Pertl explains. “In our system, this happens in as little as around 30 s. As all aspects of the system are completely automated, we can repeat this process, and quickly, many times.”

An experimental set-up to measure static electricity — Whole setup side view of the experiment: the counter-sample (white rod with green sample holder and PDMS at the very end) approaches the sample and induces electric charge via contact. The AFM head is on the left waiting until the sample returns to its original position. (Courtesy: Felix Pertl)

This speed-up is important because static electricity dissipates relatively rapidly. In fact, the researchers found that the transferred charge disappeared from the sample’s surface quicker than the time required for most KPFM scans. Their data also revealed that the deposited charge was, in effect, uniformly distributed across the surface and that its dissipation depended on the material’s electrical conductivity. Additional mathematical modelling and subsequent experiments confirmed that the more insulating a material is, the slower it dissipates charge.

Surface heterogeneity likely not a feature of static electricity

Pertl says that these results call into question the validity of some previous static electricity studies that used KPFM to study charge transfer. “The most influential paper in our field to date reported surface charge heterogeneity using KPFM,” he tells Physics World. At first, the ISTA team’s goal was to understand the origin of this heterogeneity. But when their own experiments showed an essentially homogenous distribution of surface charge, the researchers had to change tack.

“The biggest challenge in our work was realizing – and then accepting – that we could not reproduce the results from this previous study,” Pertl says. “Convincing both my principal investigator and myself that our data revealed a very different physical mechanism required patience, persistence and trust in our experimental approach.”

The discrepancy, he adds, implies that the surface heterogeneity previously observed was likely not a feature of static electricity, as was claimed. Instead, he says, it was probably “an artefact of the inability to image the charge before it had left the sample surface”.

A historical precedent

Studies of contact electrification studies go back a long way. Philippe Molinié of France’s GeePs Laboratory, who was not involved in this work, notes that the first experiments were performed by the English scientist William Gilbert clear back in the sixteenth century. As well as coining the term “electricity” (from the Greek “elektra”, meaning amber), Gilbert was also the first to establish that magnets maintain their electrical attraction over time, while the forces produced by contact-charged insulators slowly decrease.

“Four centuries later, many mysteries remain unsolved in the contact electrification phenomenon,” Molinié observes. He adds that the surfaces of insulating materials are highly complex and usually strongly disordered, which affects their ability to transfer charge at the molecular scale. “The dynamics of the charge neutralization, as Pertl and colleagues underline, is also part of the process and is much more complex than could be described by a simple resistance-capacitor model,” Molinié says.

Although the ISTA team studied these phenomena with sophisticated Kelvin probe microscopy rather than the rudimentary tools available to Gilbert, it is, Molinié says, “striking that the competition between charge transfer and charge screening that comes from the conductivity of an insulator, first observed by Gilbert, is still at the very heart of the scientific interrogations that this interesting new work addresses.”

“A more critical interpretation”

The Austrian researchers, who detail their work in Phys. Rev. Lett., say they hope their experiments will “encourage a more critical interpretation” of KPFM data in the future, with a new focus on the role of sample grounding and bulk conductivity in shaping observed charge patterns. “We hope it inspires KPFM users to reconsider how they design and analyse experiments, which could lead to more accurate insights into charge behaviour in insulators,” Pertl says.

“We are now planning to deliberately engineer surface charge heterogeneity into our samples,” he reveals. “By tuning specific surface properties, we aim to control the sign and spatial distribution of charge on defined regions of these.”

SEMICON Europa 2025 presents cutting-edge technology for semiconductor R&D and production

“Global collaborations for European economic resilience” is the theme of SEMICON Europa 2025. The event is coming to Munich, Germany on 18–21 November and it will attract 25,000 semiconductor professionals who will enjoy presentations from over 200 speakers.

The TechARENA portion of the event will cover a wide range of technology-related issues including new materials, future computing paradigms and the development of hi-tech skills in the European workface. There will also be an Executive Forum, which will feature leaders in industry and government and will cover topics including silicon geopolitics and the use of artificial intelligence in semiconductor manufacturing.

SEMICON Europa will be held at the Messe München, where it will feature a huge exhibition with over 500 exhibitors from around the world. The exhibition is spread out over three halls and here are some of the companies and product innovations to look out for on the show floor.

Accelerating the future of electro-photonic integration with SmarAct

As the boundaries between electronic and photonic technologies continue to blur, the semiconductor industry faces a growing challenge: how to test and align increasingly complex electro-photonic chip architectures efficiently, precisely, and at scale. At SEMICON Europa 2025, SmarAct will address this challenge head-on with its latest innovation – Fast Scan Align. This is a high-speed and high-precision alignment solution that redefines the limits of testing and packaging for integrated photonics.

In the emerging era of heterogeneous integration, electronic and photonic components must be aligned and interconnected with sub-micrometre accuracy. Traditional positioning systems often struggle to deliver both speed and precision, especially when dealing with the delicate coupling between optical and electrical domains. SmarAct’s Fast Scan Align solution bridges this gap by combining modular motion platforms, real-time feedback control, and advanced metrology into one integrated system.

At its core, Fast Scan Align leverages SmarAct’s electromagnetic and piezo-driven positioning stages, which are capable of nanometre-resolution motion in multiple degrees of freedom. Fast Scan Align’s modular architecture allows users to configure systems tailored to their application – from wafer-level testing to fibre-to-chip alignment with active optical coupling. Integrated sensors and intelligent algorithms enable scanning and alignment routines that drastically reduce setup time while improving repeatability and process stability.

Fast Scan Align’s compact modules allow various measurement techniques to be integrated with unprecedented possibilities. This has become decisive for the increasing level of integration of complex electro-photonic chips.

Apart from the topics of wafer-level testing and packaging, wafer positioning with extreme precision is as crucial as never before for the highly integrated chips of the future. SmarAct’s PICOSCALE interferometer addresses the challenge of extreme position by delivering picometer-level displacement measurements directly at the point of interest.

When combined with SmarAct’s precision wafer stages, the PICOSCALE interferometer ensures highly accurate motion tracking and closed-loop control during dynamic alignment processes. This synergy between motion and metrology gives users unprecedented insight into the mechanical and optical behaviour of their devices – which is a critical advantage for high-yield testing of photonic and optoelectronic wafers.

Visitors to SEMICON Europa will also experience how all of SmarAct’s products – from motion and metrology components to modular systems and up to turn-key solutions – integrate seamlessly, offering intuitive operation, full automation capability, and compatibility with laboratory and production environments alike.

For more information visit SmarAct at booth B1.860 or explore more of SmarAct’s solutions in the semiconductor and photonics industry.

Optimized pressure monitoring: Efficient workflows with Thyracont’s VD800 digital compact vacuum meters

Thyracont Vacuum Instruments will be showcasing its precision vacuum metrology systems in exhibition hall C1. Made in Germany, the company’s broad portfolio combines diverse measurement technologies – including piezo, Pirani, capacitive, cold cathode, and hot cathode – to deliver reliable results across a pressure range from 2000 to 3e-11 mbar.

Front-and-centre at SEMICON Europa will be Thyracont’s new series of VD800 compact vacuum meters. These instruments provide precise, on-site pressure monitoring in industrial and research environments. Featuring a direct pressure display and real-time pressure graphs, the VD800 series is ideal for service and maintenance tasks, laboratory applications, and test setups.

The VD800 series combines high accuracy with a highly intuitive user interface. This delivers real-time measurement values; pressure diagrams; and minimum and maximum pressure – all at a glance. The VD800’s 4+1 membrane keypad ensures quick access to all functions. USB-C and optional Bluetooth LE connectivity deliver seamless data readout and export. The VD800’s large internal data logger can store over 10 million measured values with their RTC data, with each measurement series saved as a separate file.

Data sampling rates can be set from 20 ms to 60 s to achieve dynamic pressure tracking or long-term measurements. Leak rates can be measured directly by monitoring the rise in pressure in the vacuum system. Intelligent energy management gives the meters extended battery life and longer operation times. Battery charging is done conveniently via USB-C.

The vacuum meters are available in several different sensor configurations, making them adaptable to a wide range of different uses. Model VD810 integrates a piezo ceramic sensor for making gas-type-independent measurements for rough vacuum applications. This sensor is insensitive to contamination, making it suitable for rough industrial environments. The VD810 measures absolute pressure from 2000 to 1 mbar and relative pressure from −1060 to +1200 mbar.

Model VD850 integrates a piezo/Pirani combination sensor, which delivers high resolution and accuracy in the rough and fine vacuum ranges. Optimized temperature compensation ensures stable measurements in the absolute pressure range from 1200 to 5e-5 mbar and in the relative pressure range from −1060 to +340 mbar.

The model VD800 is a standalone meter designed for use with Thyracont’s USB-C vacuum transducers, which are available in two models. The VSRUSB USB-C transducer is a piezo/Pirani combination sensor that measures absolute pressure in the 2000 to 5.0e-5 mbar range. The other is the VSCUSB USB-C transducer, which measures absolute pressures from 2000 down to 1 mbar and has a relative pressure range from -1060 to +1200 mbar. A USB-C cable connects the transducer to the VD800 for quick and easy data retrieval. The USB-C transducers are ideal for hard-to-reach areas of vacuum systems. The transducers can be activated while a process is running, enabling continuous monitoring and improved service diagnostics.

With its blend of precision, flexibility, and ease of use, the Thyracont VD800 series defines the next generation of compact vacuum meters. The devices’ intuitive interface, extensive data capabilities, and modern connectivity make them an indispensable tool for laboratories, service engineers, and industrial operators alike.

To experience the future of vacuum metrology in Munich, visit Thyracont at SEMICON Europa hall C1, booth 752. There you will discover how the VD800 series can optimize your pressure monitoring workflows.

Physicists discuss the future of machine learning and artificial intelligence

Pierre Gentine, Jimeng Sun, Jay Lee and Kyle Cranmer — Looking ahead to the future of machine learning: (clockwise from top left) Jay Lee, Jimeng Sun, Pierre Gentine and Kyle Cranmer.

IOP Publishing’s Machine Learning series is the world’s first open-access journal series dedicated to the application and development of machine learning (ML) and artificial intelligence (AI) for the sciences.

Part of the series is Machine Learning: Science and Technology, launched in 2019, which bridges the application and advances in machine learning across the sciences. Machine Learning: Earth is dedicated to the application of ML and AI across all areas of Earth, environmental and climate sciences while Machine Learning: Health covers healthcare, medical, biological, clinical and health sciences and Machine Learning: Engineeringfocuses on applied AI and non-traditional machine learning to the most complex engineering challenges.

Here, the editors-in-chief (EiC) of the four journals discuss the growing importance of machine learning and their plans for the future.

Kyle Cranmer is a particle physicist and data scientist at the University of Wisconsin-Madison and is EiC of Machine Learning: Science and Technology (MLST). Pierre Gentine is a geophysicist at Columbia University and is EiC of Machine Learning: Earth. Jimeng Sun is a biophysicist at the University of Illinois at Urbana-Champaign and is EiC of Machine Learning: Health. Mechanical engineer Jay Lee is from the University of Maryland and is EiC of Machine Learning: Engineering.

What do you attribute to the huge growth over the past decade in research into and using machine learning?

Kyle Cranmer (KC): It is due to a convergence of multiple factors. The initial success of deep learning was driven largely by benchmark datasets, advances in computing with graphics processing units, and some clever algorithmic tricks. Since then, we’ve seen a huge investment in powerful, easy-to-use tools that have dramatically lowered the barrier to entry and driven extraordinary progress.

Pierre Gentine (PG): Machine learning has been transforming many fields of physics, as it can accelerate physics simulation, better handle diverse sources of data (multimodality), help us better predict.

Jimeng Sun (JS): Over the past decade, we have seen machine learning models consistently reach — and in some cases surpass — human-level performance on real-world tasks. This is not just in benchmark datasets, but in areas that directly impact operational efficiency and accuracy, such as medical imaging interpretation, clinical documentation, and speech recognition. Once ML proved it could perform reliably at human levels, many domains recognized its potential to transform labour-intensive processes.

Jay Lee (JL): Traditionally, ML growth is based on the development of three elements: algorithms, big data, and computing. The past decade’s growth in ML research is due to the perfect storm of abundant data, powerful computing, open tools, commercial incentives, and groundbreaking discoveries—all occurring in a highly interconnected global ecosystem.

What areas of machine learning excite you the most and why?

KC: The advances in generative AI and self-supervised learning are very exciting. By generative AI, I don’t mean Large Language Models — though those are exciting too — but probabilistic ML models that can be useful in a huge number of scientific applications. The advances in self-supervised learning also allows us to engage our imagination of the potential uses of ML beyond well-understood supervised learning tasks.

PG: I am very interested in the use of ML for climate simulations and fluid dynamics simulations.

JS: The emergence of agentic systems in healthcare — AI systems that can reason, plan, and interact with humans to accomplish complex goals. A compelling example is in clinical trial workflow optimization. An agentic AI could help coordinate protocol development, automatically identify eligible patients, monitor recruitment progress, and even suggest adaptive changes to trial design based on interim data. This isn’t about replacing human judgment — it’s about creating intelligent collaborators that amplify expertise, improve efficiency, and ultimately accelerate the path from research to patient benefit.

JL: One area is generative and multimodal ML — integrating text, images, video, and more — are transforming human–AI interaction, robotics, and autonomous systems. Equally exciting is applying ML to nontraditional domains like semiconductor fabs, smart grids, and electric vehicles, where complex engineering systems demand new kinds of intelligence.

What vision do you have for your journal in the coming years?

KC: The need for a venue to propagate advances in AI/ML in the sciences is clear. The large AI conferences are under stress, and their review system is designed to be a filter not a mechanism to ensure quality, improve clarity and disseminate progress. The large AI conferences also aren’t very welcoming to user-inspired research, often casting that work as purely applied. Similarly, innovation in AI/ML often takes a back seat in physics journals, which slows the propagation of those ideas to other fields. My vision for MLST is to fill this gap and nurture the community that embraces AI/ML research inspired by the physical sciences.

PG: I hope we can demonstrate that machine learning is more than a nice tool but that it can play a fundamental role in physics and Earth sciences, especially when it comes to better simulating and understanding the world.

JS: I see Machine Learning: Health becoming the premier venue for rigorous ML–health research — a place where technical novelty and genuine clinical impact go hand in hand. We want to publish work that not only advances algorithms but also demonstrates clear value in improving health outcomes and healthcare delivery. Equally important, we aim to champion open and reproducible science. That means encouraging authors to share code, data, and benchmarks whenever possible, and setting high standards for transparency in methods and reporting. By doing so, we can accelerate the pace of discovery, foster trust in AI systems, and ensure that our field’s breakthroughs are accessible to — and verifiable by — the global community.

JL: Machine Learning: Engineering envisions becoming the global platform where ML meets engineering. By fostering collaboration, ensuring rigour and interpretability, and focusing on real-world impact, we aim to redefine how AI addresses humanity’s most complex engineering challenges.

Playing games by the quantum rulebook expends less energy

Games played under the laws of quantum mechanics dissipate less energy than their classical equivalents. This is the finding of researchers at Singapore’s Nanyang Technological University (NTU), who worked with colleagues in the UK, Austria and the US to apply the mathematics of game theory to quantum information. The researchers also found that for more complex game strategies, the quantum-classical energy difference can increase without bound, raising the possibility of a “quantum advantage” in energy dissipation.

Game theory is the field of mathematics that aims to formally understand the payoff or gains that a person or other entity (usually called an agent) will get from following a certain strategy. Concepts from game theory are often applied to studies of quantum information, especially when trying to understand whether agents who can use the laws of quantum physics can achieve a better payoff in the game.

In the latest work, which is published in Physical Review Letters, Jayne Thompson, Mile Gu and colleagues approached the problem from a different direction. Rather than focusing on differences in payoffs, they asked how much energy must be dissipated to achieve identical payoffs for games played under the laws of classical versus quantum physics. In doing so, they were guided by Landauer’s principle, an important concept in thermodynamics and information theory that states that there is a minimum energy cost to erasing a piece of information.

This Landauer minimum is known to hold for both classical and quantum systems. However, in practice systems will spend more than the minimum energy erasing memory to make space for new information, and this energy will be dissipated as heat. What the NTU team showed is that this extra heat dissipation can be reduced in the quantum system compared to the classical one.

Planning for future contingencies

To understand why, consider that when a classical agent creates a strategy, it must plan for all possible future contingencies. This means it stores possibilities that never occur, wasting resources. Thompson explains this with a simple analogy. Suppose you are packing to go on a day out. Because you are not sure what the weather is going to be, you must pack items to cover all possible weather outcomes. If it’s sunny, you’d like sunglasses. If it rains, you’ll need your umbrella. But if you only end up using one of these items, you’ll have wasted space in your bag.

“It turns out that the same principle applies to information,” explains Thompson. “Depending on future outcomes, some stored information may turn out to be unnecessary – yet an agent must still maintain it to stay ready for any contingency.”

For a classical system, this can be a very wasteful process. Quantum systems, however, can use superposition to store past information more efficiently. When systems in a quantum superposition are measured, they probabilistically reveal an outcome associated with only one of the states in the superposition. Hence, while superposition can be used to store both pasts, upon measurement all excess information is automatically erased “almost as if they had never stored this information at all,” Thompson explains.

The upshot is that because information erasure has close ties to energy dissipation, this gives quantum systems an energetic advantage. “This is a fantastic result focusing on the physical aspect that many other approaches neglect,” says Vlatko Vedral, a physicist at the University of Oxford, UK who was not involved in the research.

Implications of the research

Gu and Thompson say their result could have implications for the large language models (LLMs) behind popular AI tools such as ChatGPT, as it suggests there might be theoretical advantages, from an energy consumption point of view, in using quantum computers to run them.

Another, more foundational question they hope to understand regarding LLMs is the inherent asymmetry in their behaviour. “It is likely a lot more difficult for an LLM to write a book from back cover to front cover, as opposed to in the more conventional temporal order,” Thompson notes. When considered from an information-theoretic point of view, the two tasks are equivalent, making this asymmetry somewhat surprising.

In Thompson and Gu’s view, taking waste into consideration could shed light on this asymmetry. “It is likely we have to waste more information to go in one direction over the other,” Thompson says, “and we have some tools here which could be used to analyse this”.

For Vedral, the result also has philosophical implications. If quantum agents are more optimal, he says, it is “surely is telling us that the most coherent picture of the universe is the one where the agents are also quantum and not just the underlying processes that they observe”.

This article was amended on 19 November 2025 to correct a reference to the minimum energy cost of erasing information. It is the Landauer minimum, not the Landau minimum.

Teaching machines to understand complexity

Complex systems model real-world behaviour that is dynamic and often unpredictable. They are challenging to simulate because of nonlinearity, where small changes in conditions can lead to disproportionately large effects; many interacting variables, which make computational modelling cumbersome; and randomness, where outcomes are probabilistic. Machine learning is a powerful tool for understanding complex systems. It can be used to find hidden relationships in high-dimensional data and predict the future state of a system based on previous data.

This research develops a novel machine learning approach for complex systems that allows the user to extract a few collective descriptors of the system, referred to as inherent structural variables. The researchers used an autoencoder (a type of machine learning tool) to examine snapshots of how atoms are arranged in a system at any moment (called instantaneous atomic configurations). Each snapshot is then matched to a more stable version of that structure (an inherent structure), which represents the system’s underlying shape or pattern after thermal noise is removed. These inherent structural variables enable the analysis of structural transitions both in and out of equilibrium and the computation of high-resolution free-energy landscapes. These are detailed maps that show how a system’s energy changes as its structure or configuration changes, helping researchers understand stability, transitions, and dynamics in complex systems.

The model is versatile, and the authors demonstrate how it can be applied to metal nanoclusters and protein structures. In the case of Au147 nanoclusters (well-organised structures made up of 147 gold atoms), the inherent structural variables reveal three main types of stable structures that the gold nanocluster can adopt: fcc (face-centred cubic), Dh (decahedral), and Ih (icosahedral). These structures represent different stable states that a nanocluster can switch between, and on the high-resolution free-energy landscape, they appear as valleys. Moving from one valley to another isn’t easy, there are narrow paths or barriers between them, known as kinetic bottlenecks.

The researchers validated their machine learning model using Markov state models, which are mathematical tools that help analyse how a system moves between different states over time, and electron microscopy, which images atomic structures and can confirm that the predicted structures exist in the gold nanoclusters. The approach also captures non-equilibrium melting and freezing processes, offering insights into polymorph selection and metastable states. Scalability is demonstrated up to Au309 clusters.

The generality of the method is further demonstrated by applying it to the bradykinin peptide, a completely different type of system, identifying distinct structural motifs and transitions. Applying the method to a biological molecule provides further evidence that the machine learning approach is a flexible, powerful technique for studying many kinds of complex systems. This work contributes to machine learning strategies, as well as experimental and theoretical studies of complex systems, with potential applications across liquids, glasses, colloids, and biomolecules.

Read the full article

Inherent structural descriptors via machine learning

Emanuele Telari et al 2025 Rep. Prog. Phys. 88 068002

Do you want to learn more about this topic?

Complex systems in the spotlight: next steps after the 2021 Nobel Prize in Physics by Ginestra Bianconi et al (2023)