A few weeks ago, I experienced a classic annoyance of modern life: one of my computer games stopped working. The cause? An “update” to the emulator that translates old games into programs that today’s machines can execute. In my case, this update broke the translation process, and the tenuous thread of hardware and software connecting my laptop to the game’s 30-year-old code was severed.
For individuals, failures like this are irritating. But for the wider digital ecosystem, they’re a real problem – so much so, in fact, that Vint Cerf, who’s known as one of the “fathers of the Internet”, made them the subject of his talk at last week’s Heidelberg Laureate Forum (HLF) in Heidelberg, Germany.
“My big worry is that all this digital stuff won’t be there when we would like it to be there, or when our descendants would like to have it,” Cerf said.
How it used to work
Historically, the best ways of preserving information involved writing it on durable materials such as clay tablets, high-quality paper, or a form of animal skin known as vellum. These media, Cerf observed, “have one thing in common: they don’t require electricity to be stored and preserved.”
Digital media, in contrast, are much less robust. “Many of them are magnetic, and the magnetic material wears away after a while,” Cerf explained. Consequently, some old tapes are now so fragile that attempting to read them can actually lift the magnetic material off the surface: “You read it once and that’s it. It’s now transparent tape,” he said.
Being able to read data is just the beginning, though. As my broken computer game shows, you also need programs and equipment that can persuade those data to do things. “That’s often the thing that goes first,” Cerf told me in a press conference after his talk. For example, when Cerf recently tried to retrieve data from an old three-and-a-half-inch floppy disk, he discovered that doing so would require three additional components: a drive that could read the disk, a program that could open the files stored on the disk and an old computer that could run the program. “I needed a whole lot of software help and several stages in order to make that digital content useful,” Cerf said.
Creating ‘digital vellum’
As for how to fix this problem and create a digital version of vellum, Cerf, who has been the “Chief Internet Evangelist” at Google since 2005, listed three ideas that he finds interesting. The first involves a New Jersey, US-based company called SPhotonix that does research and development work in the UK and Switzerland. It’s using lasers to write bits of data into chunks of quartz crystal, which is a very long-lasting medium. However, each crystal is roughly the size of a hockey puck, and Cerf thinks that “real work” still needs to be done to organize the information the material holds.
The second idea is partly inspired by the clay tablets that proved so successful at preserving cuneiform writing from ancient Mesopotamia. Cerabyte, a start-up with facilities in Austria, Germany and the US, has developed a ceramic material that its founders claim could “store all data virtually forever”.
The third idea, and the one that seems to appeal most to Cerf, is to write digital information into DNA. That might sound like an inherently fragile medium, but as Cerf pointed out, “It’s actually a very robust molecule – otherwise, life wouldn’t have persisted for several billion years.” Provided you dehydrate the DNA first, he added, it lasts for “quite a long time”.
The question of how to read such information is not an easy one, and Cerf doesn’t have an answer to it. He is, however, hopeful that someone will find one. At the HLF, where he is such a revered figure that even the journalists want to take photos with him, he issued a call to arms for the young researchers in the audience. “I want you to appreciate the scope of the work that is required to preserve digital things,” Cerf told them. Without that work, he added, “recreating a digital environment in 100 years is not going to be a trivial matter.”
An unconventional approach to solving the dark energy problem called the cosmologically coupled black hole (CCBH) hypothesis appears to be compatible with the observed masses of neutrinos. This new finding from researchers working at the DESI collaboration suggests that black holes may represent little Big Bangs played in reverse and could be used as a laboratory to study the birth and infancy of our universe. The study also confirms that the strength of dark energy has increased along with the formation rate of stars.
The Dark Energy Spectroscopic Instrument (DESI) is located on the Nicholas U Mayall four-metre Telescope at Kitt Peak National Observatory in Arizona. Its raison d’être is to shed more light on the “dark universe” – the 95% of the mass and energy in the universe that we know very little about. Dark energy is a hypothetical entity invoked to explain why the rate of expansion of the universe is (mysteriously) increasing – something that was discovered at the end of the last century.
According to standard theories of cosmology, matter is thought to comprise cold dark matter (CDM) and normal matter (mostly baryons and neutrinos). DESI can observe fluctuations in the matter density of the universe known as baryonic acoustic oscillations (BAOs), which are density fluctuations that were created after the Big Bang in the hot plasma of baryons and electrons that prevailed then. BAOs expand with the growth of the universe and represent a sort of “standard ruler” that allows cosmologists to map the universe’s expansion by statistically analysing the distance that separates pairs of galaxies and quasars.
Largest 3D map
DESI has produced the largest such 3D map of the universe ever and it recently published the first set of BAO measurements determined from observations of over 14 million extragalactic targets going back 11 billion years in time.
In the new study, the DESI researchers combined measurements from these new data with cosmic microwave background (CMB) datasets (which measure the density of dark matter and baryons from a time when the universe was less than 400,000 years old) to search for evidence of matter converting into dark energy. They did this by focusing on a new hypothesis known as the cosmologically coupled black hole (CCBH), which was put forward five years ago by DESI team member Kevin Croker, who works at Arizona State University (ASU), and his colleague Duncan Farrah at the University of Hawaii. This physical model builds on a mathematical description of black holes as bubbles of dark energy in space that was introduced over 50 years ago. CCBH describes a scenario in which massive stars exhaust their nuclear fuel and collapse to produce black holes filled with dark energy that then grows as the universe expands. The rate of dark energy production is therefore determined by the rate at which stars form.
Neutrino contribution
Previous analyses by DESI scientists suggested that there is less matter in the universe today compared to when it was much younger. When they then added the additional, known, matter source from neutrinos, there appeared to be no “room” and the masses of these particles therefore appeared negative in their calculations. Not only is this unphysical, explains team member Rogier Windhorst of the ASU’s School of Earth and Space Exploration, it also goes against experimental measurements made so far on neutrinos that give them a greater-than-zero mass.
When the researchers re-interpreted the new set of data with the CCBH model, they were able to resolve this issue. Since stars are made of baryons and black holes convert exhausted matter from stars into dark energy, the number of baryons today has decreased in comparison to the CMB measurements. This means that neutrinos can indeed contribute to the universe’s mass, slowing down the expansion of the universe as the dark energy produced sped it up.
“The new data are the most precise measurements of the rate of expansion of the universe going back more than 10 billion years,” says team member Gregory Tarlé at the University of Michigan, “and it results from the hard work of the entire DESI collaboration over more than a decade. We undertook this new study to confront the CCBH hypothesis with these data.”
Black holes as a laboratory
“We found that the standard assumptions currently employed for cosmological analyses simply did not work and we had to carefully revisit and rewrite massive amounts of a lot of cosmological computer code,” adds Croker.
“If dark energy is being sourced by black holes, these structures may be used as a laboratory to study the birth and infancy of our own universe,” he tells Physics World. “The formation of black holes may represent little Big Bangs played in reverse, and to make a biological analogy, they may be the ‘offspring’ of our universe.”
The researchers say they studied the CCBH scenario in its simplest form in this work, and found that it performs very well. “The next big observational test will involve a new layer of complexity, where consistency with the large-scale features of the Big Bang relic radiation, or CMB, and the statistical properties of the distribution of galaxies in space will make or break the model,” says Tarlé.
Can quantum mechanics fully describe macroscopic reality? Everyday objects are typically well-described by classical mechanics, whereas atomic-scale objects are governed by quantum mechanics. Exploring the boundary between the two domains could enable fundamental tests of quantum mechanics and the development of new sensing technologies for gravitational measurements.
Quantum mechanics posits that even large objects behave as waves. However, the spatial extent of this wave-like behaviour, known as the “coherence length”, is far smaller than the size of large objects. This renders quantum phenomena effectively unobservable for such systems. “To push quantum physics into the macroscopic domain, we need to increase both [mass and coherence length] simultaneously”, explains lead researcher Massimiliano Rossi. This pursuit motivated the team’s recent study, which is described in Physical Review Letters.
Playing with light
The researchers studied large objects called silica nanoparticles, which are 100 nm in diameter. The nanoparticles were held and levitated in vacuum using a tightly-focused laser beam.
Nanoparticles naturally scatter the laser light, and the phase of the scattered photons encodes information about the nanoparticle’s centre-of-mass position. The researchers used this information in a feedback loop, applying electric fields to cool the nanoparticles close to their quantum ground state. The colder sample is in a more “pure” quantum state, such that the quantum wave-like behaviour extends farther in space and the coherence length is longer than in a hot sample. The team measured an initial coherence length of 21 pm (21 × 10-12 m).
Further extending the coherence length required careful manipulation of the laser light. The researchers started with high-power light, which provided a tight harmonic potential for the nanoparticles – like a marble trapped at the bottom of a steep bowl. An advantage of using a light-induced potential is that the curvature of the bowl is easily tuned over a large range by adjusting the laser power.
The researchers lowered the laser power in two pulses, each of which caused the bowl to become shallower, therefore allowing the marble to roll around and explore more of the bowl. In the experiment, this translated to an expansion of the nanoparticle’s coherence length to 73 pm, more than three-fold that of the initial value.
Preserving quantum information
Rossi notes that the main experimental challenge was limiting decoherence, a process that destroys quantum information. He explains that when a nanoparticle interacts with its surroundings, it becomes correlated with a noisy and unmeasurably complex environment. This interaction causes the nanoparticle’s motion to become increasingly random when measured. As a result, the nanoparticle’s quantum mechanical behaviour is washed out and the particle is well described as a classical ball.
It was therefore critical that the researchers expand the coherence length faster than the rate of any decoherence. To achieve this, they meticulously measured, identified, and suppressed all sources of decoherence, with the dominant source being laser light scattering. Scattering was reduced during the expansion pulses because of the lower laser power.
The achieved 73 pm remains orders of magnitude smaller than the size of the nanoparticle, which was 100 nm in diameter. However, Rossi remarks that “we do not know of any fundamental reason why achieving nanometre coherence lengths should be impossible.” One next step could be to use more expansion pulses to increase the coherence length further.
With a longer expansion time, the main challenge would be to outpace decoherence. Researchers propose using hybrid traps that employ both light and electric fields to confine the nanoparticles, since an electric trap would reduce the decoherence from light scattering. Rossi is now pursuing this direction in his new research group at the Delft University of Technology in the Netherlands.
Adding energy to a system usually heats it up, but physicists at the University of Innsbruck in Austria have now discovered a scenario in which this is not the case. Their new platform – a one-dimensional fluid of strongly interacting atoms cooled to just a few nanokelvin above absolute zero and periodically “kicked” using an external force – could be used to study how objects transition from being quantum and ordered to classical and chaotic.
Our everyday world is chaotic and chaos plays a crucial and often useful role in many areas of science – from nonlinear complex systems in mathematics, physics and biology to ecology, meteorology and economics. How a system evolves depends on its initial conditions, but this evolution is, by nature, inherently unpredictable.
While we know how chaos emerges in classical systems, how it does so in quantum materials is still little understood. When this happens, the quantum system reverts to being a classical one.
The quantum kicked rotor
Researchers have traditionally studied chaotic behaviour in driven systems – that is, rotating objects periodically kicked by an external force. The quantum version of these is the quantum kicked rotor (QKR). Here, quantum coherence effects can prevent the system from absorbing external energy, meaning that, in contrast to its classical counterpart, it doesn’t heat up – even if a lot of energy is applied. This “dynamical localization” effect has already been seen in dilute ultracold atomic gases.
The QKR is a highly idealized single-particle model system, explains study lead Hanns-Christoph Nägerl. However, real-world systems contain many particles that interact with each other – something that can destroy dynamical localization. Recent theoretical work has suggested that this localization may persist in some types of interacting, even strongly interacting, many-body quantum systems – for example, in 1D bosonic gases.
In the new work, Nägerl and colleagues made a QKR by subjecting samples of ultracold caesium (Cs) atoms to periodic kicks by means of a “flashed-on lattice potential”. They did this by loading a Bose–Einstein condensate of these atoms into an array of narrow 1D tubes created by a 2D optical lattice formed by laser beams propagating in the x–y plane at right angles to each other. They then increased the power of the beams to heat up the Cs atoms.
Many-body dynamical localization
The researchers expected the atoms to collectively absorb energy over the course of the experiment. Instead, when they recorded how their momentum distribution evolved, they found that it actually stopped spreading and that the system’s energy reached a plateau. “Despite being continually kicked and strongly interacting, it no longer absorbed energy,” says Nägerl. “We say that it had localized in momentum space – a phenomenon known as many-body dynamical localization (MBDL).”
In this state, quantum coherence and many-body interactions prevent the system from heating up, he adds. “The momentum distribution essentially freezes and retains whatever structure it has.”
Nägerl and colleagues repeated the experiment by varying the interaction between the atoms – from zero (non-interacting) to strongly interacting. They found that the system always localizes.
Quantum coherence is crucial for preventing thermalization
“We had already found localization for our interacting QKR in earlier work and set out to reproduce these results in this new study,” Nägerl tells Physics World. “We had not previously realised the significance of our findings and thought that perhaps we were doing something wrong, which turned out not to be the case.”
The MBDL is fragile, however – something the researchers proved by introducing randomness into the laser pulses. A small amount of disorder is enough the destroy the localization effect and restore diffusion, explains Nägerl: the momentum distribution smears out and the kinetic energy of the system rises sharply, meaning that it is absorbing energy.
“This test highlights that quantum coherence is crucial for preventing thermalization in such driven many-body systems,” he says.
Simulating such a system on classical computers is only possible for two or three particles, but the one studied in this work, reported in Science, contains 20 or more. “Our new experiments now provide precious data to which we can compare the QKR model system, which is a paradigmatic one in quantum physics,” adds Nägerl.
Looking ahead, the researchers say they would now like to find out how stable MBDL is to various external perturbations. “In our present work, we report on MBDL in 1D, but would it happen in a 2D or a 3D system?” asks Nägerl. “I would like to do an experiment in which we have a 1D + 1D situation, that is, where the 1D is allowed to communicate with just one neighbouring 1D system (via tunnelling; by lowering the barrier to this system in a controlled way).”
Another way of perturbing the system would be to add a local defect – for example a bump in the potential of a different atom, he says. “Generally speaking, we would like to measure the ‘phase diagram’ for MBDL, where the axes of the graph would quantify the strength of the various perturbations we apply.”
Photographers Weitang Liang, Qi Yang and Chuhong Yu have beaten thousands of amateur and professional photographers from around the world to bag the 2025 Royal Observatory Greenwich’s ZWO Astronomy Photographer of the Year.
The image – The Andromeda Core – showcases the core of the Andromeda Galaxy (M31) in exceptional detail, revealing the intricate structure of the galaxy’s central region and its surrounding stellar population.
The image was taken with a long focal-length telescope from the AstroCamp Observatory, Nerpio, Spain.
“Not to show it all − this is one of the greatest virtues of this photo. The Andromeda Galaxy has been photographed in so many different ways and so many times with telescopes that it is hard to imagine a new photo would ever add to what we’ve already seen,” notes astrophotographer László Francsics who was a judge for this year’s competition. “But this does just that, an unusual dynamic composition with unprecedented detail that doesn’t obscure the overall scene.”
As well as winning the £10,000 top prize, the image has gone on display along with other selected pictures from the competition at an exhibition at the National Maritime Museum observatory that opened on 12 September.
The award – now in its 17th year – is run by the Royal Observatory Greenwich in association with the astrophotography firm ZWO and BBC Sky at Night Magazine.
A new optically addressable quantum bit (qubit) encoded in a fluorescent protein could be used as a sensor that can be directly produced inside living cells. The device opens up a new era for fluorescence microscopy to monitor biological processes, say the researchers at the University of Chicago Pritzker School of Molecular Engineering who designed the novel qubit.
Quantum technologies use qubits to store and process information. Unlike classical bits, which can exist in only two states, qubits can exist in a superposition of both these states. This means that computers employing these qubits can simultaneously process multiple streams of information, allowing them to solve problems that would take classical computers years to process.
Qubits can be manipulated and measured with high precision, and in quantum sensing applications they act as nanoscale probes whose quantum state can be initialized, coherently controlled and read out. This allows them to detect minute changes in their environment with exquisite sensitivity.
Optically addressable qubit sensors – that is, those that are read out using light pulses from a laser or other light source – are able to measure nanoscale magnetic fields, electric fields and temperature. Such devices are now routinely employed by researchers working in the physical sciences. However, their use in the life sciences is lagging behind, with most applications still at the proof-of-concept stage.
Difficult to position inside living cells
Many of today’s quantum sensors are based on nitrogen-vacancy (NV) centres, which are crystallographic defects in diamond. These centres occur when two neighbouring carbon atoms in diamond are replaced by a nitrogen atom and an empty lattice site and they act like tiny quantum magnets with different spins. When excited with laser pulses, the fluorescent signal that they emit can be used to monitor slight changes in the magnetic properties of a nearby sample of material. This is because the intensity of the emitted NV centre signal changes with the local magnetic field.
“The problem is that such sensors are difficult to position at well-defined sites inside living cells,” explains Peter Maurer, who co-led this new study together with David Awschalom. “And the fact that they are typically ten times larger than most proteins further restricts their applicability,” he adds.
“So, rather than taking a conventional quantum sensor and trying to camouflage it to enter a biological system, we therefore wanted to explore the idea of using a biological system itself and developing it into a qubit,” says Awschalom.
Fluorescent proteins, which are just 3 nm in diameter, could come into their own here as they can be genetically encoded, allowing cells to produce these sensors directly at the desired location with atomic precision. Indeed, fluorescent proteins have become the “gold standard” in cell biology thanks to this unique ability, says Maurer. And decades of biochemistry research has allowed researchers to generate a vast library of such fluorescent proteins that can be tagged to thousands of different types of biological targets.
“We recognized that these proteins possess optical and spin properties that are strikingly similar to those of qubits formed by crystallographic defects in diamond – namely that they have a metastable triplet state,” explain Awschalom and Maurer. “Building on this insight, we combined techniques from fluorescence microscopy with methods of quantum control to encode and manipulate protein-based qubits.”
In their work, which is detailed in Nature, the researchers used a near-infrared laser pulse to optically address a yellow fluorescent protein known as EYFP and read out its triplet spin state with up to 20% “spin contrast” – measured using optically detected magnetic resonance (ODMR) spectroscopy.
To test the technique, the team genetically modified the protein so that it was expressed in human embryonic kidney cells and Escherichia coli (E. coli) cells. The measured OMDR signals exhibited a contrast of up to 8%. While this performance is not as good as that of NV quantum sensors, the fluorescent proteins open the door to magnetic resonance measurements directly inside living cells – something that NV centres cannot do, says Maurer. “They could thus transform medical and biochemical studies by probing protein folding, monitoring redox states or detecting drug binding at the molecular scale,” he tells Physics World.
“A new dimension for fluorescence microscopy”
Beyond sensing, the unique quantum resonance “signatures” offer a new dimension for fluorescence microscopy, paving the way for highly multiplexed imaging far beyond today’s colour palette, Awschalom adds. Looking further ahead, using arrays of such protein qubits could even allow researchers to explore many-body quantum effects within biologically assembled structures.
Maurer, Awschalom and colleagues say they are now busy trying to improve the stability and sensitivity of their protein-based qubits through protein engineering via “directed evolution” – similar to the way that fluorescent proteins were optimized for microscopy.
“Another goal is to achieve single-molecule detection, enabling readout of the quantum state of individual protein qubits inside cells,” they reveal. “We also aim to expand the palette of available qubits by exploring new fluorescent proteins with improved spin properties and to develop sensing protocols capable of detecting nuclear magnetic resonance signals from nearby biomolecules, potentially revealing structural changes and biochemical modifications at the nanoscale.”
It is Peer Review Week and the theme for 2025 is “Rethinking Peer Review in the AI Era”. This is not surprising given the rapid rise in the use and capabilities of artificial intelligence. However, views on AI are deeply polarized for reasons that span its legality, efficacy and even its morality.
A recent survey done by IOP Publishing – the scientific publisher that brings you Physics World – reveals that physicists who do peer review are polarized regarding whether AI should be used in the process.
IOPP’s Laura Feetham-Walker is lead author of AI and Peer Review 2025, which describes the survey and analyses its results. She joins me in this episode of the Physics World Weekly podcast in a conversation that explores reviewers’ perceptions of AI and their views of how it should, or shouldn’t, be used in peer review.
“Imagine the day the aliens arrive.” So begins Do Aliens Speak Physics? by the US particle physicist Daniel Whiteson and the cartoonist and author Andy Warner. From that starting point, if you believe the plots of many works of science fiction, it wouldn’t be long before we’re communicating with emissaries of an extraterrestrial civilization. Quickly, we’d be marvelling at their advanced science and technology.
But is this a reasonable assumption? Would we really be able to communicate with aliens? Even if we could, would their way of doing science have any meaning to us? What if an advanced alien civilization had no science at all? These are some of the questions tackled by Whiteson and Warner in their entertaining and thought-provoking book.
While Do Aliens Speak Physics? focuses on the possible differences between human and alien science, it made me think about what science means to humans – and the role of science in our civilization. Indeed, when I spoke to Whiteson for a future episode of the Physics World Weekly podcast, he told me that his original plan for the book was to examine if physics is universal or shaped by human perspective.
But when he pitched the idea to his teenage son, Whiteson realized that approach was a bit boring and decided to spice things up using an alien landing. At the heart of the book is a new equation for estimating the number of alien civilizations that scientists could potentially communicate with – ideally, when the aliens arrive on Earth.
The authors aren’t the first people to do such a calculation. In 1961 the US astrophysicist Frank Drake famously did so by estimating how many habitable planets might exist and whether they could harbour life that’s evolved so far that it could communicate with us. Whiteson and Warner’s “extended Drake equation” adds four extra terms related to alien science.
The first is the probability that a civilization has developed science. The second is the likelihood that we would be able to communicate with the civilization, with the third being the probability that an alien civilization would ask scientific questions that are meaningful to us. The final term is whether human science would benefit from the answers to those questions.
One of Whiteson and Warner’s more interesting ideas is that aliens could perceive science and technology in very different ways to us. After all, an alien civilization could be completely focused on developing technology and not be at all interested in the underlying science. Technology without science might seem deeply foreign to us today, but for most of history humans have focused on how things work – not why.
Blacksmiths of the past, for example, developed impressive swords and other metal implements without any understanding of how the materials they worked with behaved at a microscopic level. So perhaps our alien visitors will come from a planet of blacksmiths rather than materials scientists.
Mind you, communicating with alien scientists could be a massive challenge given that we do so mainly using sound and visual symbols, whereas an alien might use smells or subatomic particles to get their point across. As the authors point out, it’s difficult even translating the Danish/Norwegian word hygge into English, despite the concept’s apparent popularity in the English-speaking world. Imagine how much harder things would be if we used a different form of communication altogether.
But could physics function as a kind of Rosetta Stone, offering a universal way of translating one language into another? We could then get the aliens to explain various physical processes – such as how a mass falls under the influence of gravity – and compare their reasoning to our understanding of the same phenomena.
Of course, an alien scientist’s questions might depend on how they perceive the universe. In a chapter titled “Can aliens taste electrons?”, the authors explore what might happen if aliens were so small that they experience quantum effects such as entanglement in their daily lives. What if an organism were so big that it feels the gravitational tug of dark matter? Or what if an intelligent alien could exist in an ultracold environment where everything moves so slowly that their perception of physics is completely different to ours?
The final term in the authors’ extended Drake equation looks at whether the answers to the questions of alien physics would be meaningful to humans. We naturally assume there are deep truths about nature that can be explored using experimental and mathematical tools. But what if there are no deep truths out there – and what if our alien friends are already aware of that fact?
When Drake proposed his equation, humans did not know of any planets beyond the solar system. Today, however, we have discovered nearly 6000 such exoplanets, and it is possible that there are billions of habitable, Earth-like exoplanets in the Milky Way. So it does not seem at all fanciful that we could soon be communicating with an alien civilization.
But when I asked Whiteson if he’s worried that visiting aliens could be hostile towards humans, he said he hoped for a “peaceful” visit. In fact, Whiteson is unable to think of a good reason why an advanced civilization would be hostile to Earth – pointing out that there is probably nothing of material value here for them. Fingers crossed, any visit will be driven by curiosity, peace and goodwill.
4 November 2025 WW Norton & Company 272pp £23.00 hb; £21.84 ebook
What role does the Hartree Centre play in quantum computing?
The Hartree Centre gives industry fast-track access to next-generation supercomputing, AI and digital capabilities. We are a “connector” when it comes to quantum computing, helping UK businesses and public-sector organizations to de-risk the early-stage adoption of a technology that is not yet ready to buy off-the-shelf. Our remit spans quantum software, theoretical studies and, ultimately, the integration of quantum computing into existing high-performance computing (HPC) infrastructure and workflows.
What does industry need when it comes to quantum computing?
It’s evident that industry wants to understand the commercial upsides of quantum computing, but doesn’t yet have the necessary domain knowledge and skill sets to take full advantage of the opportunities. By working with the STFC Hartree Centre, businesses can help their computing and R&D teams to bridge that quantum knowledge gap.
How does the interaction with industry partners work?
The Hartree Centre’s quantum computing effort is built around a cross-disciplinary team of scientists and a mix of expertise spanning physics, chemistry, mathematics, computer science and quantum information science. We offer specialist quantum consultancy to clients across industries as diverse as energy, pharmaceuticals and food manufacturing.
How does that work in practice?
We begin by doing the due diligence on the client’s computing challenge, understanding the computational bottlenecks and, where appropriate, translating the research problem so that it can be executed, in whole or in part, on a quantum computer or a mixture of hybrid and quantum computing resources.
What are the operational priorities for the Hartree Centre in quantum computing?
Integrating classical HPC and quantum computing is a complex challenge along three main pathways: infrastructure – bridging fundamentally different hardware architectures; software – workflow management, resource scheduling and organization; and finally applications – adapting and optimizing computing workflows across quantum and classical domains. All of these competencies are mandatory for successful exploitation of quantum computing systems.
So it’s likely these pathways will converge?
Correct. Ultimately, the task is how do we distribute a workload to run on an HPC platform, also on a quantum computer, when many of the algorithms and data streams must loop back and forth between the two systems.
How do you link up classical computing and quantum resources?
We have been addressing this problem with our quantum technology partners – IBM and Pasqal – and a team at Rensselaer Polytechnic in New York. Together, we have introduced a Quantum Resource Management Interface – an open-source tool that supports unified job submission for quantum and classical computing tasks and that’s scalable to cloud computing environments. It’s the “black-box” solution industry has been looking for to bridge the established HPC and emerging quantum domains.
Quantum hub The STFC Hartree Centre employs more than 160 scientists and technologists who specialize in supercomputing, applied scientific computing, data science, AI, cloud and quantum computing. (Courtesy: STFC)
The Hartree Centre has a flagship collaboration with IBM in quantum computing. Can you tell us more?
The Hartree National Centre for Digital Innovation (HNCDI) is a £210m public–private partnership with IBM to create innovative digital technologies spanning HPC, AI, data analytics and quantum computing. HNCDI is the cornerstone of IBM’s quantum technology strategy in the UK and, over the past four years, the collaboration has clocked up more than 30 joint projects with industry. In each of these projects, HNCDI is using quantum computers to tackle problems that are out of reach for classical computers.
Do you have any examples of early wins for HNCDI in quantum?
One is streamlining drug discovery and development. As part of a joint effort with the pharmaceutical firm AstraZeneca and quantum-software developer Algorithmiq, we have improved the accuracy of molecular modelling with the help of quantum computing and, by extension, developed a better understanding of the molecular interactions and processes involved in drug synthesis. Another eye-catching development is Qiskit Machine Learning (ML), an open-source library for quantum machine-learning tasks on quantum hardware and classical simulators. While Qiskit ML started as a proof-of-concept library from IBM, our team at the Hartree Centre has, over the past couple of years, developed it into a modular tool for non-specialist users as well as quantum computational scientists and developers.
So quantum computing could play a big role in healthcare?
Healthcare has yielded productive lines of enquiry, including a proof-of-concept study to demonstrate the potential of quantum machine-learning in cancer diagnostics. Working with Royal Brompton and Harefield Hospitals and Imperial College London, we have evaluated histopathology datasets to categorize different types of breast-cancer cells through AI workflows. It’s research that could eventually lead to better predictions regarding the onset and progression of disease.
And what about other sectors?
We have been collaborating with the German power utility E.ON to study the complex challenges that quantum computing may be able to address in the energy sector – such as strategic infrastructure development, effective energy demand management and streamlined integration of renewable energy sources.
What does the next decade look like for the Hartree Centre’s quantum computing programme?
Longer term, the goal is to enable our industry partners to become at-scale end-users of quantum computing, delivering economic and societal impact along the way. As for our own development roadmap at the Hartree Centre, we are evaluating options for the implementation of a large-scale quantum computing platform to further diversify our existing portfolio of HPC, AI, data science and visual computing technologies.
STFC Hartree Centre: helping UK industry deliver societal impact
Quantum returns “Our goal is to help UK industry generate economic growth and societal impact,” says Vassil Alexandrov, CSO of the STFC Hartree Centre. (Courtesy: STFC)
The Hartree Centre is part of the Science and Technology Facilities Council (STFC), one of the main UK research councils supporting fundamental and applied initiatives in astronomy, physics, computational science and space science.
Based at the Daresbury Laboratory, part of the Sci-Tech Daresbury research and innovation campus in north-west England, the Hartree Centre has more than 160 scientists and technologists specializing in supercomputing, applied scientific computing, data science, AI, cloud and quantum computing.
“Our goal is to help UK industry generate economic growth and societal impact by exploiting advanced HPC capabilities and digital technologies,” explains Vassil Alexandrov, chief science officer at STFC Hartree Centre.
One of the core priorities for Alexandrov and his team is the interface between “exascale” computing and scalable AI. It’s a combination of technologies that’s being lined up to tackle “grand challenges” like the climate crisis and the transition from fossil fuels to clean energy.
A case in point is the Climate Resilience Demonstrator, which uses “digital twins” to simulate how essential infrastructure like electricity grids and telecoms networks might respond to extreme weather events. “These kinds of insights are critical to protect communities, maintain service delivery and build more resilient public infrastructure,” says Alexandrov.
Elsewhere, as part of the Fusion Computing Lab, the Hartree Centre is collaborating with the UK Atomic Energy Authority on sustainable energy generation from nuclear fusion. “We have a joint team of around 60 scientists and engineers working on this initiative to iterate and optimize the building blocks for a fusion power plant,” notes Alexandrov. “The end-game is to deliver net power safely and affordably to the grid from magnetically confined fusion.”
Exascale computing and AI also underpin the Research Computing and Innovation Centre, a collaboration with AWE, the organization that runs research, development and support for the UK’s nuclear-weapons stockpile.
Today’s artificial intelligence (AI) systems are built on data generated by humans. They’re trained on huge repositories of writing, images and videos, most of which have been scraped from the Internet without the knowledge or consent of their creators. It’s a vast and sometimes ill-gotten treasure trove of information – but for machine-learning pioneer David Silver, it’s nowhere near enough.
“I think if you provide the knowledge that humans already have, it doesn’t really answer the deepest question for AI, which is how it can learn for itself to solve problems,” Silver told an audience at the 12th Heidelberg Laureate Forum (HLF) in Heidelberg, Germany, on Monday.
Silver’s proposed solution is to move from the “era of human data”, in which AI passively ingests information like a student cramming for an exam, into what he calls the “era of experience” in which it learns like a baby exploring its world. In his HLF talk on Monday, Silver played a sped-up video of a baby repeatedly picking up toys, manipulating them and putting them down while crawling and rolling around a room. To murmurs of appreciation from the audience, he declared, “I think that provides a different perspective of how a system might learn.”
Silver, a computer scientist at University College London, UK, has been instrumental in making this experiential learning happen in the virtual worlds of computer science and mathematics. As head of reinforcement learning at Google DeepMind, he was instrumental in developing AlphaZero, an AI system that taught itself to play the ancient stones-and-grid game of Go. It did this via a so-called “reward function” that pushed it to improve over many iterations, without ever being taught the game’s rules or strategy.
More recently, Silver coordinated a follow-up project called AlphaProof that treats formal mathematics as a game. In this case, AlphaZero’s reward is based on getting correct proofs. While it isn’t yet outperforming the best human mathematicians, in 2024 it achieved silver-medal standard on problems at the International Mathematical Olympiad.
Learning in the physics playroom
Could a similar experiential learning approach work in the physical sciences? At an HLF panel discussion on Tuesday afternoon, particle physicist Thea Klaeboe Åarrestad began by outlining one possible application. Whenever CERN’s Large Hadron Collider (LHC) is running, Åarrestad explained, she and her colleagues in the CMS experiment must control the magnets that keep protons on the right path as they zoom around the collider. Currently, this task is performed by a person, working in real time.
Up for discussion: A panel discussion on machine learning in physical sciences at the Heidelberg Laureate Forum. l-r: Moderator George Musser, Kyle Cranmer, Thea Klaeboe Åarrestad, David Silver and Maia Fraser. (Courtesy: Bernhard Kreutzer/HLFF)
In principle, Åarrestad continued, a reinforcement-learning AI could take over that job after learning by experience what works and what doesn’t. There’s just one problem: if it got anything wrong, the protons would smash into a wall and melt the beam pipe. “You don’t really want to do that mistake twice,” Åarrestad deadpanned.
For Åarrestad’s fellow panellist Kyle Cranmer, a particle physicist who works on data science and machine learning at the University of Wisconsin-Madison, US, this nightmare scenario symbolizes the challenge with using reinforcement learning in physical sciences. In situations where you’re able to do many experiments very quickly and essentially for free – as is the case with AlphaGo and its descendants – you can expect reinforcement learning to work well, Cranmer explained. But once you’re interacting with a real, physical system, even non-destructive experiments require finite amounts of time and money.
Another challenge, Cranmer continued, is that particle physics already has good theories that predict some quantities to multiple decimal places. “It’s not low-hanging fruit for getting an AI to come up with a replacement framework de novo,” Cranmer said. A better option, he suggested, might be to put AI to work on modelling atmospheric fluid dynamics, which are emergent phenomena without first-principles descriptions. “Those are super-exciting places to use ideas from machine learning,” he said.
Not for nuclear arsenals
Silver, who was also on Tuesday’s panel, agreed that reinforcement learning isn’t always the right solution. “We should do this in areas where mistakes are small and it can learn from those small mistakes to avoid making big mistakes,” he said. To general laughter, he added that he would not recommend “letting an AI loose on nuclear arsenals”, either.
Reinforcement learning aside, both Åarrestad and Cranmer are highly enthusiastic about AI. For Cranmer, one of the most exciting aspects of the technology is the way it gets scientists from different disciplines talking to each other. The HLF, which aims to connect early-career researchers with senior figures in mathematics and computer science, is itself a good example, with many talks in the weeklong schedule devoted to AI in one form or another.
For Åarrestad, though, AI’s most exciting possibility relates to physics itself. Because the LHC produces far more data than humans and present-day algorithms can handle, Åarrestad explained, much of it is currently discarded. The idea that, as a result, she and her colleagues could be throwing away major discoveries sometimes keeps her up at night. “Is there new physics below 1 TeV?” Åarrestad wondered.
