You can never truly empty a box. Suppose you want to empty a box. You remove all its visible contents, pump out any gases, and--applying some science-fiction technology--evacuate any unseeable material such as dark matter. According to quantum mechanics, what's left inside? It sounds like a trick question.

For the past year, I kept bringing the same story to my editor: quantum computers are on the edge of becoming useful for scientific discovery. Of course, that has always been the goal. The idea of using quantum computers to better understand our universe is part of their origin story, and it even featured in a 1981 speech by Richard Feynman. Contemplating the best way to simulate nature, he wrote: "We can give up on our rule about what the computer was, we can say: Let the computer itself be built of quantum mechanical elements which obey quantum mechanical laws." Today, Feynman's vision has been realised by Google, IBM and dozens more companies and academic teams. Their devices are now being used to simulate reality at the quantum level - and here are some highlights.

A group of quantum physicists at Spanish firm Multiverse Computing claims to have created a version of the powerful reasoning AI model DeepSeek R1 that strips out the censorship built into the original by its Chinese creators. In China, AI companies are subject to rules and regulations meant to ensure that content output aligns with laws and "socialist values." As a result, companies build in layers of censorship when training the AI systems. When asked questions that are deemed "politically sensitive," the models often refuse to answer or provide talking points straight from state propaganda. Multiverse Computing specializes in quantum-inspired AI techniques, which it used to create DeepSeek R1 Slim, a model that is 55% smaller but performs almost as well as the original model. It allowed them to identify and remove Chinese censorship so that the model answered sensitive questions in much the same way as Western models.

MARGE performs comparably to XLM-R, but with significant variation across languages. We only show results for languages in all model's Table 8: Number of documents per language used for pre-training. Katherine G. Johnson (née Coleman; August 26, 1918 - February 24, 2020) was an She contributed to the science of the U.S. Air Force and space programs,

Researchers at the quantum computing firm Quantinuum used a new Helios-1 quantum computer to simulate a mathematical model that has long been used to study superconductivity. These simulations are not out of reach for conventional computers, but this advance sets the stage for quantum computers to become useful tools for materials science . Superconductors conduct electricity with perfect efficiency, but they currently only work at temperatures too low to be practical. For decades, physicists have been trying to understand how to tweak their structure to make them work at room temperature, and many believe answers will come from a mathematical framework called the Fermi-Hubbard model. This potential makes it one of the most important models in all condensed matter physics, says Quantinuum's Henrik Dreyer . Conventional computers can run exceptional simulations of the Fermi-Hubbard model but struggle with very large samples or cases where the materials it describes change over time.

New studies of the "platypus of materials" help explain how their atoms arrange themselves into orderly, but nonrepeating, patterns. Since their discovery in 1982, exotic materials known as quasicrystals have bedeviled physicists and chemists. Their atoms arrange themselves into chains of pentagons, decagons, and other shapes to form patterns that never quite repeat. These patterns seem to defy physical laws and intuition. How can atoms possibly "know" how to form elaborate nonrepeating arrangements without an advanced understanding of mathematics?

The original version of this story appeared in Quanta Magazine. We were once promised self-driving cars and robot maids. Instead, we've seen the rise of artificial intelligence systems that can beat us in chess, analyze huge reams of text, and compose sonnets. This has been one of the great surprises of the modern era: physical tasks that are easy for humans turn out to be very difficult for robots, while algorithms are increasingly able to mimic our intellect. Another surprise that has long perplexed researchers is those algorithms' knack for their own, strange kind of creativity.

The original version of this story appeared in Quanta Magazine. There are precision measurements, and then there's the Laser Interferometer Gravitational-Wave Observatory. In each of LIGO's twin gravitational wave detectors (one in Hanford, Washington, and the other in Livingston, Louisiana), laser beams bounce back and forth down the four-kilometer arms of a giant L. When a gravitational wave passes through, the length of one arm changes relative to the other by less than the width of a proton. It's by measuring these minuscule differences--a sensitivity akin to sensing the distance to the star Alpha Centauri down to the width of a human hair--that discoveries are made. The design of the machine was decades in the making, as physicists needed to push every aspect to its absolute physical limits. Construction began in 1994 and took more than 20 years, including a four-year shutdown to improve the detectors, before LIGO detected its first gravitational wave in 2015: a ripple in the space-time fabric coming from the faraway collision of a pair of black holes.

The following is an extract from our Lost in Space-Time newsletter. Each month, we hand over the keyboard to a physicist or mathematician to tell you about fascinating ideas from their corner of the universe. You can sign up for Lost in Space-Time here. The Navier-Stokes equations have been used to model the flow of fluids for almost 200 years – but we still don't really understand them. This can often feel a little odd, especially as we rely on these equations every day to help build rockets, design drugs and understand climate change. But here is where you have to think like a mathematician.

Can you imagine the imprint a four-dimensional hexagon might leave as it passes through your three-dimensional kitchen table? One such person was mathematician Alicia Boole Stott, daughter of logician George Boole. Early in the 20th century, she made models of the shapes four-dimensional objects would create when passing through three-dimensional objects. Decades later, when mathematicians could check such things using computer programs, they found Boole Stott had possessed an uncanny gift for getting these shapes right. For most of us, geometry conjures up thoughts of pencils, rulers, triangles and circles.