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.
This episode explores the scientific and technological significance of 2D materials such as graphene. My guest is Antonio Rossi, who is a researcher in 2D materials engineering at the Italian Institute of Technology in Genoa.
Rossi explains why 2D materials are fundamentally different than their 3D counterparts – and how these differences are driving scientific progress and the development of new and exciting technologies.
Graphene is the most famous 2D material and Rossi talks about today’s real-world applications of graphene in coatings. We also chat about the challenges facing scientists and engineers who are trying to exploit graphene’s unique electronic properties.
Rossi’s current research focuses on two other promising 2D materials – tungsten disulphide and hexagonal boron nitride. He explains why tungsten disulphide shows great technological promise because of its favourable electronic and optical properties; and why hexagonal boron nitride is emerging as an ideal substrate for creating 2D devices.
Artificial intelligence (AI) is becoming an important tool in developing new 2D materials. Rossi explains how his team is developing feedback loops that connect AI with the fabrication and characterization of new materials. Our conversation also touches on the use of 2D materials in quantum science and technology.
Microcirculation – the flow of blood through the smallest vessels – is responsible for distributing oxygen and nutrients to tissues and organs throughout the body. Mapping this flow at the whole-organ scale could enhance our understanding of the circulatory system and improve diagnosis of vascular disorders. With this aim, researchers at the Institute Physics for Medicine Paris (Inserm, ESPCI-PSL, CNRS) have combined 3D ultrasound localization microscopy (ULM) with a multi-lens array method to image blood flow dynamics in entire organs with micrometric resolution, reporting their findings in Nature Communications.
“Beyond understanding how an organ functions across different spatial scales, imaging the vasculature of an entire organ reveals the spatial relationships between macro- and micro-vascular networks, providing a comprehensive assessment of its structural and functional organization,” explains senior author Clement Papadacci.
The 3D ULM technique works by localizing intravenously injected microbubbles. Offering a spatial resolution roughly ten times finer than conventional ultrasound, 3D ULM can map and quantify micro-scale vascular structures. But while the method has proved valuable for mapping whole organs in small animals, visualizing entire organs in large animals or humans is hindered by the limitations of existing technology.
To enable wide field-of-view coverage while maintaining high-resolution imaging, the team – led by PhD student Nabil Haidour under Papadacci’s supervision – developed a multi-lens array probe. The probe comprises an array of 252 large (4.5 mm²) ultrasound transducer elements. The use of large elements increases the probe’s sensitive area to a total footprint of 104 x 82 mm, while maintaining a relatively low element count.
Each transducer element is equipped with an individual acoustic diverging lens. “Large elements alone are too directive to create an image, as they cannot generate sufficient overlap or interference between beams,” Papadacci explains. “The acoustic lenses reduce this directivity, allowing the elements to focus and coherently combine signals in reception, thus enabling volumetric image formation.”
Whole-organ imaging
After validating their method via numerical simulations and phantom experiments, the team used a multi-lens array probe driven by a clinical ultrasound system to perform 3D dynamic ULM of an entire explanted porcine heart – considered an ideal cardiac model as its vascular anatomies and dimensions are comparable to those of humans.
The heart was perfused with microbubble solution, enabling the probe to visualize the whole coronary microcirculation network over a large volume of 120 x 100 x 82 mm, with a spatial resolution of around 125 µm. The technique enabled visualization of both large vessels and the finest microcirculation in real time. The team also used a skeletonization algorithm to measure vessel radii at each voxel, which ranged from approximately 75 to 600 µm.
As well as structural imaging, the probe can also assess flow dynamics across all vascular scales, with a high temporal resolution of 312 frames/s. By tracking the microbubbles, the researchers estimated absolute flow velocities ranging from 10 mm/s in small vessels to over 300 mm/s in the largest. They could also differentiate arteries and veins based on the flow direction in the coronary network.
In vivo demonstrations
Next, the researchers used the multi-lens array probe to image the entire kidney and liver of an anaesthetized pig at the Veterinary school of Maison Alfort, with the probe positioned in front of the kidney or liver, respectively, and held using an articulated arm. They employed electrocardiography to synchronize the ultrasound acquisitions with periods of minimal respiratory motion and injected microbubble solution intravenously into the animal’s ear.
In vivo imaging Left: 3D microbubble density map of the porcine kidney. Centre: 3D flow map of microbubble velocity distribution. Right: 3D flow map showing arterial (red) and venous (blue) flow. (Courtesy: CC BY 4.0/Nat. Commun. 10.1038/s41467-025-64911-z)
The probe mapped the vascular network of the kidney over a 60 x 80 x 40 mm volume with a spatial resolution of 147 µm. The maximum 3D absolute flow velocity was approximately 280 mm/s in the large vessels and the vessel radii ranged from 70 to 400 µm. The team also used directional flow measurements to identify the arterial and venous flow systems.
Liver imaging is more challenging due to respiratory, cardiac and stomach motions. Nevertheless, 3D dynamic ULM enabled high-depth visualization of a large volume of liver vasculature (65 x 100 x 82 mm) with a spatial resolution of 200 µm. Here, the researchers used dynamic velocity measurement to identify the liver’s three blood networks (arterial, venous and portal veins).
“The combination of whole-organ volumetric imaging with high-resolution vascular quantification effectively addresses key limitations of existing modalities, such as ultrasound Doppler imaging, CT angiography and 4D flow MRI,” they write.
Clinical applications of 3D dynamic ULM still need to be demonstrated, but Papadacci suggests that the technique has strong potential for evaluating kidney transplants, coronary microcirculation disorders, stroke, aneurysms and neoangiogenesis in cancer. “It could also become a powerful tool for monitoring treatment response and vascular remodelling over time,” he adds.
Papadacci and colleagues anticipate that translation to human applications will be possible in the near future and plan to begin a clinical trial early in 2026.
Enigmatic Inge Lehmann around the time she quit her job at Denmark’s Geodetic Institute in 1953. (Courtesy: GEUS)
In the 1930s a little-known Danish seismologist calculated that the Earth has a solid inner core, within the liquid outer core identified just a decade earlier. The international scientific community welcomed Inge Lehmann as a member of the relatively new field of geophysics – yet in her home country, Lehmann was never really acknowledged as more than a very competent keeper of instruments.
It was only after retiring from her seismologist job aged 65 that Lehmann was able to devote herself full time to research. For the next 30 years, Lehmann worked and published prolifically, finally receiving awards and plaudits that were well deserved. However, this remarkable scientist, who died in 1993 aged 104, rarely appears in short histories of her field.
In a step to address this, we now have a biography of Lehmann: If I Am Right, and I Know I Am by Hanne Strager, a Danish biologist, science museum director and science writer. Strager pieces together Lehmann’s life in great detail, as well as providing potted histories of the scientific areas that Lehmann contributed to.
A brief glance at the chronology of Lehmann’s education and career would suggest that she was a late starter. She was 32 when she graduated with a bachelor’s degree in mathematics from the University of Copenhagen, 40 when she received her master’s degree in geodosy and was appointed state geodesist for Denmark. Lehmann faced a litany of struggles in her younger years, from health problems and money issues to the restrictions placed on most women’s education in the first decades of the 20th century.
The limits did not come from her family. Lehmann and her sister were sent to good schools, she was encouraged to attend university, and was never pressed to get married, which would likely have meant the end of her education. When she asked her father’s permission to go to the University of Cambridge, his objection was the cost – though the money was found and Lehmann duly went to Newnham College in 1910. While there she passed all the preliminary exams to study for Cambridge’s legendarily tough mathematical tripos but then her health forced her to leave.
Lehmann was suffering from stomach pains; she had trouble sleeping; her hair was falling out. And this was not her first breakdown. She had previously studied for a year at the University of Copenhagen before then, too, dropping out and moving to the countryside to recover her health.
The cause of Lehmann’s recurrent breakdowns is unknown. They unfortunately fed into the prevailing view of the time that women were too fragile for the rigours of higher learning. Strager attempts to unpick these historical attitudes from Lehmann’s very real medical issues. She posits that Lehmann had severe anxiety or a physical limitation to how hard she could push herself. But this conclusion fails to address the hostile conditions Lehmann was working in.
In Cambridge Lehmann formed firm friendships that lasted the rest of her life. But women there did not have the same access to learning as men. They were barred from most libraries and laboratories; could not attend all the lectures; were often mocked and belittled by professors and male students. They could sit exams but, even if they passed, would not be awarded a degree. This was a contributing factor when after the First World War Lehmann decided to complete her undergraduate studies in Copenhagen rather than Cambridge.
More than meets the eye
Lehmann is described as quiet, shy, reticent. But she could be eloquent in writing and once her career began she established connections with scientists all over the world by writing to them frequently. She was also not the wallflower she initially appeared to be. When she was hired as an assistant at Denmark’s Institute for the Measurement of Degrees, she quickly complained that she was being using as an office clerk, not a scientist, and she would not have accepted the job had she known this was the role. She was instead given geometry tasks that she found intellectually stimulating, which led her to seismology.
Unfortunately, soon after this Lehmann’s career development stalled. While her title of “state geodesist” sounds impressive, she was the only seismologist in Denmark for decades, responsible for all the seismographs in Denmark and Greenland. Her days were filled with the practicalities of instrument maintenance and publishing reports of all the data collected.
Intrepid Inge Lehmann at the Ittoqqortootmitt (Scoresbysund) seismic station in Greenland c. 1928. A keen hiker, Lehmann was comfortable in cold and remote environments. (Courtesy: GEUS)
Despite repeated requests Lehmann didn’t receive an assistant, which meant she never got round to completing a PhD, though she did work towards one in her evenings and weekends. Time and again opportunities for career advancement went to men who had the title of doctor but far less real experience in geophysics. Even after she co-founded the Danish Geophysical Society in 1934, her native country overlooked her.
The breakthrough that should have changed this attitude from the men around her came in 1936, when she published “P’ ”. This innocuous sounding paper was revolutionary, but based firmly in the P wave and S wave measurements that Lehmann routinely monitored.
In If I Am Right, and I Know I Am, Strager clearly explains what P and S waves are. She also highlights why they were being studied by both state seismologist Lehmann and Cambridge statistician Harold Jeffreys, and how they led to both scientists’ biggest breakthroughs.
After any seismological disturbance, P and S waves propagate through the Earth. P waves move at different speeds according to the material they encounter, while S waves cannot pass through liquid or air. This knowledge allowed Lehmann to calculate whether any fluctuations in seismograph readings were earthquakes, and if so where the epicentre was located. And it led to Jeffreys’ insight that the Earth must have a liquid core.
Lehmann’s attention to detail meant she spotted a “discontinuity” in P waves that did not quite match a purely liquid core. She immediately wrote to Jeffreys that she believed there was another layer to the Earth, a solid inner core, but he was dismissive – which led to her writing the statement that forms the title of this book. Undeterred, she published her discovery in the journal of the International Union of Geodesy and Geophysics.
Home from home
In 1951 Lehmann visited the institution that would become her second home: the Lamont Geological Observatory in New York state. Its director Maurice Ewing invited her to work there on a sabbatical, arranging all the practicalities of travel and housing on her behalf.
Here, Lehmann finally had something she had lacked her entire career: friendly collaboration with colleagues who not only took her seriously but also revered her. Lehmann took retirement from her job in Denmark and began to spend months of every year at the Lamont Observatory until well into her 80s.
Valued colleague A farewell party held for Inge Lehmann in 1954 at Lamont Geological Observatory after one of her research stays. (Courtesy: GEUS)
Though Strager tells us this “second phase” of Lehmann’s career was prolific, she provides little detail about the work Lehmann did. She initially focused on detecting nuclear tests during the Cold War. But her later work was more varied, and continued after she lost most of her vision. Lehmann published her final paper aged 99.
If I Am Right, and I Know I Am is bookended with accounts of Strager’s research into one particular letter sent to Lehmann, an anonymous (because the final page has been lost) declaration of love. It’s an insight into the lengths Strager went to – reading all the surviving correspondence to and from Lehmann; interviewing living relatives and colleagues; working with historians both professional and amateur; visiting archives in several countries.
But for me it hit the wrong tone. The preface and epilogue are mostly speculation about Lehmann’s love life. Lehmann destroyed a lot of her personal correspondence towards the end of her life, and chose what papers to donate to an archive. To me those are the actions of a woman who wants to control the narrative of her life – and does not want her romances to be written about. I would have preferred instead another chapter about her later work, of which we know she was proud.
But for the majority of its pages, this is a book of which Strager can be proud. I came away from it with great admiration for Lehmann and an appreciation for how lonely life was for many women scientists even in recent history.
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”.
Quantum error correction codes protect quantum information from decoherence and quantum noise, and are therefore crucial to the development of quantum computing and the creation of more reliable and complex quantum algorithms. One example is the five-qubit error correction code, five being the minimum number of qubits required to fix single-qubit errors. These contain five physical qubits (a basic off/on unit of quantum information made using trapped ions, superconducting circuits, or quantum dots) to correct one logical qubit (a collection of physical qubits arranged in such a way as to correct errors). Yet imperfections in the hardware can still lead to quantum errors.
A method of testing quantum error correction codes is self-testing. Self-testing is a powerful tool for verifying quantum properties using only input-output statistics, treating quantum devices as black boxes. It has evolved from bipartite systems consisting of two quantum subsystems, to multipartite entanglement, where entanglement is among three or more subsystems, and now to genuinely entangled subspaces, where every state is fully entangled across all subsystems. Genuinely entangled subspaces offer stronger, guaranteed entanglement than general multipartite states, making them more reliable for quantum computing and error correction.
In this research, self-testing techniques are used to certify genuinely entangled logical subspaces within the five-qubit code on photonic and superconducting platforms. This is achieved by preparing informationally complete logical states that span the entire logical space, meaning the set is rich enough to fully characterize the behaviour of the system. They deliberately introduce basic quantum errors by simulating Pauli errors on the physical qubit, which mimics real-world noise. Finally, they use mathematical tests known as Bell inequalities, adapted to the framework used in quantum error correction, to check whether the system evolves in the initial logical subspaces after the errors are introduced.
Extractability measures tell you how close the tested quantum system is to the ideal target state, with 1 being a perfect match. The certification is supported by extractability measures of at least 0.828 ± 0.006 and 0.621 ± 0.007 for the photonic and superconducting systems, respectively. The photonic platform achieved a high extractability score, meaning the logical subspace was very close to the ideal one. The superconducting platform had a lower score but still showed meaningful entanglement. These scores show that the self-testing method works in practice and confirm strong entanglement in the five-qubit code on both platforms.
This research contributes to the advancement of quantum technologies by providing robust methods for verifying and characterizing complex quantum structures, which is essential for the development of reliable and scalable quantum systems. It also demonstrates that device-independent certification can extend beyond quantum states and measurements to more general quantum structures.
For almost a century, physicists have tried to understand why and how materials become magnetic. From refrigerator magnets to magnetic memories, the microscopic origins of magnetism remain a surprisingly subtle puzzle — especially in materials where electrons behave both like individual particles and like a collective sea.
In most transition-metal compounds, magnetism comes from the dance between localized and mobile electrons. Some electrons stay near their home atoms and form tiny magnetic moments (spins), while others roam freely through the crystal. The interaction between these two types of electrons produces “double-exchange” ferromagnetism — the mechanism that gives rise to the rich magnetic behaviour of materials such as manganites, famous for their colossal magnetoresistance (a dramatic change in electrical resistance under a magnetic field). Traditionally, scientists modelled this behaviour by treating the localized spins as classical arrows — big and well-defined, like compass needles. This approximation works well enough for explaining basic ferromagnetism, but experiments over the last few decades have revealed strange features that defy the classical picture. In particular, neutron scattering studies of manganites showed that the collective spin excitations, called magnons, do not behave as expected. Their energy spectrum “softens” (the waves slow down) and their sharp signals blur into fuzzy continua — a sign that the magnons are losing their coherence. Until now, these effects were usually blamed on vibrations of the atomic lattice (phonons) or on complex interactions between charge, spin, and orbital motion.
Left to right: Adriana Moreo and Elbio Dagotto from University of Tennessee (USA), Takami Tohyama from Tokyo University of Science (Japan), and Marcin Mierzejewski and Jacek Herbrych from Wrocław University of Technology (Courtesy: Herbrych/Wrocław University of Science and Technology)
A new theoretical study challenges that assumption. By going fully quantum mechanical — treating every localized spin not as a classical arrow but as a true quantum object that can fluctuate, entangle, and superpose — the researchers have reproduced these puzzling experimental observations without invoking phonons at all. Using two powerful model systems (a quantum version of the Kondo lattice and a two-orbital Hubbard model), the team simulated how electrons and spins interact when no semiclassical approximations are allowed. The results reveal a subtle quantum landscape. Instead of a single type of electron excitation, the system hosts two. One behaves like a spinless fermion — a charge carrier stripped of its magnetic identity. The other forms a broad, “incoherent” band of excitations arising from local quantum triplets. These incoherent states sit close to the Fermi level and act as a noisy background — a Stoner-like continuum — that the magnons can scatter off. The result: magnons lose their coherence and energy in just the way experiments observe.
Perhaps most surprisingly, this mechanism doesn’t rely on the crystal lattice at all. It’s an intrinsic consequence of the quantum nature of the spins themselves. Larger localized spins, such as those in classical manganites, tend to suppress the effect — explaining why decoherence is weaker in some materials than others. Consequently, the implications reach beyond manganites. Similar quantum interplay may occur in iron-based superconductors, ruthenates, and heavy-fermion systems where magnetism and superconductivity coexist. Even in materials without permanent local moments, strong electronic correlations can generate the same kind of quantum magnetism.
In short, this work uncovers a purely electronic route to complex magnetic dynamics — showing that the quantum personality of the electron alone can mimic effects once thought to require lattice distortions. By uniting electronic structure and spin excitations under a single, fully quantum description, it moves us one step closer to understanding how magnetism truly works in the most intricate materials.
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.
“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.”
Marking 100 years since the advent of quantum mechanics, IYQ aims to raise awareness of the impact of quantum physics and its myriad future applications, with a global diary of quantum-themed public talks, scientific conferences, industry events and more.
You can find out more about the contributions of Indian physicist Satyendra Nath Bose to quantum science; explore weird phenomena such as causal order and quantum superposition; and discover the latest applications of quantum computing.
A century after quantum mechanics was first formulated, many physicists are still undecided on some of the most basic foundational questions. There’s no agreement on which interpretation of quantum mechanics holds strong; whether the wavefunction is merely a mathematical tool or a true representation of reality; or what impact an observer has on a quantum state.
Some of the biggest unanswered questions in physics – such as finding the quantum/classical boundary or reconciling gravity and quantum mechanics – lie at the heart of these conundrums. So as we look to the future of quantum – from its fundamentals to its technological applications – let us hope that some answers to these puzzles will become apparent as we crack the quantum code to our universe.
Unless you’ve been living under a stone, you can’t have failed to notice that 2025 marks the first 100 years of quantum mechanics. A massive milestone, to say the least, about which much has been written in Physics World and elsewhere in what is the International Year of Quantum Science and Technology (IYQ). However, I’d like to focus on a specific piece of quantum technology, namely quantum computing.
I keep hearing about quantum computers, so people must be using them to do cool things, and surely they will soon be as commonplace as classical computers. But as a physicist-turned-engineer working in the aerospace sector, I struggle to get a clear picture of where things are really at. If I ask friends and colleagues when they expect to see quantum computers routinely used in everyday life, I get answers ranging from “in the next two years” to “maybe in my lifetime” or even “never”.
Before we go any further, it’s worth reminding ourselves that quantum computing relies on several key quantum properties, including superposition, which gives rise to the quantum bit, or qubit. The basic building block of a quantum computer – the qubit – exists as a combination of 0 and 1 states at the same time and is represented by a probabilistic wave function. Classical computers, in contrast, use binary digital bits that are either 0 or 1.
Also vital for quantum computers is the notion of entanglement, which is when two or more qubits are co-ordinated, allowing them to share their quantum information. In a highly correlated system, a quantum computer can explore many paths simultaneously. This “massive scale” parallel processing is how quantum may solve certain problems exponentially faster than a classical computer.
The other key phenomenon for quantum computers is quantum interference. The wave-like nature of qubits means that when different probability amplitudes are in phase, they combine constructively to increase the likelihood of the right solution. Conversely, destructive interference occurs when amplitudes are out of phase, making it less likely to get the wrong answer.
Quantum interference is important in quantum computing because it allows quantum algorithms to amplify the probability of correct answers and suppress incorrect ones, making calculations much faster. Along with superposition and entanglement, it means that quantum computers could process and store vast numbers of probabilities at once, outstripping even the best classical supercomputers.
Towards real devices
To me, it all sounds exciting, but what have quantum computers ever done for us so far? It’s clear that quantum computers are not ready to be deployed in the real world. Significant technological challenges need to be overcome before they become fully realisable. In any case, no-one is expecting quantum computers to displace classical computers “like for like”: they’ll both be used for different things.
Yet it seems that the very essence of quantum computing is also its Achilles heel. Superposition, entanglement and interference – the quantum properties that will make it so powerful – are also incredibly difficult to create and maintain. Qubits are also extremely sensitive to their surroundings. They easily lose their quantum state due to interactions with the environment, whether via stray particles, electromagnetic fields, or thermal fluctuations. Known as decoherence, it makes quantum computers prone to error.
That’s why quantum computers need specialized – and often cryogenically controlled – environments to maintain the quantum states necessary for accurate computation. Building a quantum system with lots of interconnected qubits is therefore a major, expensive engineering challenge, with complex hardware and extreme operating conditions. Developing “fault-tolerant” quantum hardware and robust error-correction techniques will be essential if we want reliable quantum computation.
As for the development of software and algorithms for quantum systems, there’s a long way to go, with a lack of mature tools and frameworks. Quantum algorithms require fundamentally different programming paradigms to those used for classical computers. Put simply, that’s why building reliable, real-world deployable quantum computers remains a grand challenge.
What does the future hold?
Despite the huge amount of work that still lies in store, quantum computers have already demonstrated some amazing potential. The US firm D-Wave, for example, claimed earlier this year to have carried out simulations of quantum magnetic phase transitions that wouldn’t be possible with the most powerful classical devices. If true, this was the first time a quantum computer had achieved “quantum advantage” for a practical physics problem (whether the problem was worth solving is another question).
There is also a lot of research and development going on around the world into solving the qubit stability problem. At some stage, there will likely be a breakthrough design for robust and reliable quantum computer architecture. There is probably a lot of technical advancement happening right now behind closed doors.
The first real-world applications of quantum computers will be akin to the giant classical supercomputers of the past. If you were around in the 1980s, you’ll remember Cray supercomputers: huge, inaccessible beasts owned by large corporations, government agencies and academic institutions to enable vast amounts of calculations to be performed (provided you had the money).
And, if I believe what I read, quantum computers will not replace classical computers, at least not initially, but work alongside them, as each has its own relative strengths. Quantum computers will be suited for specific and highly demanding computational tasks, such as drug discovery, materials science, financial modelling, complex optimization problems and increasingly large artificial intelligence and machine-learning models.
These are all things beyond the limits of classical computer resource. Classical computers will remain relevant for everyday tasks like web browsing, word processing and managing databases, and they will be essential for handling the data preparation, visualization and error correction required by quantum systems.
And there is one final point to mention, which is cyber security. Quantum computing poses a major threat to existing encryption methods, with potential to undermine widely used public-key cryptography. There are concerns that hackers nowadays are storing their stolen data in anticipation of future quantum decryption.
Having looked into the topic, I can now see why the timeline for quantum computing is so fuzzy and why I got so many different answers when I asked people when the technology would be mainstream. Quite simply, I still can’t predict how or when the tech stack will pan out. But as IYQ draws to a close, the future for quantum computers is bright.
