Quantinuum filed for a US initial public offering on Thursday that could value the company at more than 20 billion dollars. In the year ended 31 December 2025, Quantinuum reported revenue of 30.9 million dollars and a net loss of 192.6 million dollars. The company is asking public market investors to pay a premium of more than 600 times revenue for a quantum computer that does not yet exist in its final form. The computer it is building, a universal fault-tolerant machine called Apollo, is scheduled for 2029.

The filing is significant not because of Quantinuum’s current financials, which are modest by any standard, but because of what the IPO market’s appetite for it will reveal about how investors price a technology that has been five to ten years away from commercial utility for the past twenty years. Quantinuum is backed by Honeywell, which owns 54 per cent of the company. JPMorgan and Morgan Stanley are leading the offering. The ticker will be QNT on the Nasdaq Global Select Market.

The company

Quantinuum was formed in 2021 from the merger of Honeywell Quantum Solutions and Cambridge Quantum Computing. It builds quantum computers based on trapped-ion architecture, a technology in which individual atoms are suspended in electromagnetic fields and manipulated with lasers to perform calculations. The company claims the highest average two-qubit gate fidelity in the industry as of December 2025, a measure of how accurately the machine performs the basic operations of quantum computation.

Its customers include BMW, Airbus, JPMorgan Chase, HSBC, Mitsui, and Thales. BMW expanded its multi-year partnership with Quantinuum in May 2026 to apply quantum computing to catalyst chemistry research for fuel cells. Airbus is exploring quantum simulation for hydrogen-powered aircraft. JPMorgan has been working with Quantinuum since 2020 and is one of the most active corporate users of its software development kit.

These are research partnerships, not production deployments. No company is running quantum computing in production at a scale that affects its bottom line. The partnerships exist because the companies believe quantum computing will eventually transform their industries and want to be ready when it does. The word “eventually” carries all the risk.

The numbers

Quantinuum’s 2025 revenue of 30.9 million dollars represented 34 per cent growth over the prior year’s 23 million dollars. The net loss of 192.6 million dollars represented 34 per cent growth over the prior year’s 144.1 million dollars. Revenue and losses grew at exactly the same rate.

The first quarter of 2026 was worse. Revenue fell to 5.2 million dollars from 19.1 million dollars in the same quarter a year earlier. The net loss expanded to 136.6 million dollars from 30.5 million dollars. The quarterly numbers suggest that revenue is lumpy and dependent on the timing of contract milestones, a pattern common in pre-commercial deep technology companies.

The target valuation of more than 20 billion dollars would represent a doubling from the 10 billion dollar pre-money valuation at which Quantinuum raised 600 million dollars in September 2025. Before that, it raised 300 million dollars in January 2024 at a 5 billion dollar valuation. The valuation has quadrupled in two years while the company’s revenue has grown from 23 million to 31 million dollars.

The roadmap

Quantinuum’s hardware roadmap has four generations. The current system, Helios, is commercially available. Sol is planned for 2027. Apollo, the system that the company describes as universal and fully fault-tolerant, is planned for 2029. A fault-tolerant quantum computer is one that can perform complex calculations with enough error correction to produce reliable results, the threshold at which quantum computing transitions from a research tool to a commercial platform.

Riverlane raised 75 million dollars to build chips that solve quantum error correction, targeting one million error-free operations by 2026. Error correction is the central engineering challenge of the field. Without it, quantum computers produce results that are too noisy to be useful for the complex simulations that justify the technology’s theoretical advantages. Quantinuum’s Apollo is designed to solve this problem at the system level. Whether it will, and whether 2029 is achievable, are the questions on which the IPO valuation rests.

Europe is spending billions on quantum computers, with governments in Germany, the Netherlands, France, and the United Kingdom launching or expanding national programmes. France has committed 500 million euros to five startups building fault-tolerant quantum machines. The public investment reflects a consensus among policymakers that quantum computing is a strategic capability, even as the private market struggles to determine what the capability is worth before it works.

The market

Quantinuum would join a small cohort of publicly traded quantum computing companies. IonQ, which uses the same trapped-ion technology, went public via SPAC in 2021 and is the only pure-play quantum stock with positive returns in 2026, up 16 per cent year to date after posting more than 100 million dollars in annual revenue. Rigetti Computing, which uses superconducting qubits, is down 10 per cent. D-Wave Quantum is down 9 per cent.

IQM has built 30 full-stack quantum computers from its facility in Finland and announced a 1.8 billion dollar SPAC merger to list on the NYSE. The quantum computing sector is pre-profit and largely sentiment-driven, with stock prices moving on milestone announcements, government contracts, and capital raises rather than fundamentals. Quantinuum’s IPO would be the largest quantum computing listing to date and would set a valuation benchmark for the entire sector.

The risk is that the benchmark reflects the market’s enthusiasm for a technology whose commercial timeline remains uncertain. Industry experts surveyed in 2025 said quantum utility is at most ten years away, a timeline that has not changed meaningfully in a decade. Google’s chief executive said five to ten years. NVIDIA’s chief executive said at least fifteen.

The bet

Honeywell’s decision to take Quantinuum public is part of a broader restructuring that includes the spin-off of its aerospace division and the separation of its advanced materials business. The IPO gives Quantinuum access to public capital markets and gives Honeywell a path to gradually reduce its 54 per cent stake. The 600 million dollar raise in September 2025 was led by investors including JPMorgan, which is now also leading the IPO underwriting, a dual role that reflects the degree to which the investment banking community’s interests are aligned with the offering’s success.

Quantinuum’s filing is a bet that public market investors will value a quantum computing company the way private markets have: on the promise of a technology that does not yet work at scale, priced against a future in which it does. The 30.9 million dollars in revenue is not the product. The product is Apollo, a machine that is three years and several fundamental engineering breakthroughs away. The IPO is a wager that the market will pay 20 billion dollars for the right to wait.

Robots are incredibly precise, but being gentle is not always their strong suit. A machine that can build a car with near-perfect accuracy can still apply too much pressure when working in places where even the smallest mistake matters, like inside a human eye or during delicate surgery. That is why researchers at Shanghai Jiao Tong University are developing a new type of force sensor that could help robots “feel” what they are touching more accurately.

The sensor is tiny, about the size of a grain of rice at just 1.7 millimeters wide, making it small enough to fit inside advanced surgical tools. What makes it especially interesting is that it does not rely on traditional electronics. Instead, it uses light to measure force from every direction, including pressure, sliding movements, and twisting. Here is how it works. At the tip of an optical fiber sits a soft material that slightly changes shape when it comes into contact with something. That tiny deformation alters how light travels through the sensor. The altered light pattern is then sent through optical fibers to a camera, which captures it like an image. Researchers then use a machine learning model to study those light patterns and translate them into precise force readings. In simple terms, the system learns how to “read” touch through light alone, without needing a bunch of wires or multiple separate sensors packed into such a tiny space.

Why robots need to feel, not just see

Modern surgical imaging is already incredibly advanced. Surgeons today can see inside the human body with impressive clarity. But one thing they still struggle with, especially during minimally invasive procedures, is actually feeling what their tools are touching. A surgeon may be able to see the area clearly on a screen, but distinguishing between healthy tissue and something problematic often comes down to experience and instinct rather than feedback from the instrument itself.

That is exactly the problem this new sensor is trying to solve. During testing, researchers used it on a soft gelatin block with a small hard sphere hidden underneath, meant to mimic a tumor inside human tissue. The sensor detected the hidden object by sensing differences in stiffness as it moved across the surface. In robotic surgeries, where doctors operate in extremely tight spaces and cannot always rely on direct touch, this kind of tactile feedback could make procedures safer, more precise, and far less dependent on guesswork.

There’s still work to do before this reaches an operating room

Right now, these results are still more of a proof that the idea works rather than a finished medical breakthrough. The researchers themselves admit there is still a lot left to figure out. Building sensors this tiny with consistent quality at scale is much harder than making a single working version in a lab. The setup process also still needs to become simpler and more reliable before it can realistically be used in hospitals. On top of that, the sensor has not yet undergone the long-term stress testing that medical devices need before doctors would trust them during real procedures.

Even so, the core idea behind the technology feels genuinely promising. Instead of relying on multiple complicated sensing parts, the system uses a much simpler setup built around a single optical channel and a camera. That kind of simpler design often makes technologies easier to improve and scale over time once the engineering matures. The team is now working on fitting the sensor into actual robotic surgical tools and testing it in environments closer to real operating rooms. And while a sensor the size of a grain of rice that can “feel” may sound like a tiny innovation on paper, it could become incredibly important for surgeons guiding robotic instruments through spaces smaller than a fingernail.

Unlike the other glasses I tested, Even doesn’t sell a subscription plan; everything’s included out of the box.

The only downside I could find with the G2 is that it is largely devoid of offline features, so the glasses have to be connected to the internet to do much of anything. Considering the G2’s capabilities, it’s a trade-off I am more than happy to make.

Other Captioning Glasses I Tested

There are plenty of capable captioning eyeglasses on the market, but they are surprisingly similar in both looks and features. While many are quite capable, none had the combination of power and affordability that I got with Even’s G2. Here’s a rundown of everything else I tested.

Leion’s Hey 2 is the price leader in this market, and even its prescription lenses ($90 to $299) are pretty affordable. The hardware, however, is heavy: 50 grams without lenses, 60 grams with them. A full charge gets you six to eight hours of operation; the case adds juice for up to 12 recharges.

I like the Leion interface, which lays out caption, translation, “free talk” (two-way translation), and a teleprompter feature on its clean app. You get access to nine languages; using Pro minutes expands that to 143. Leion sells its premium plan by the minute, not the month, so you need to remember to toggle this mode off when you don’t need it. Pricing is $10 for 120 minutes, $50 for 1,200 minutes, and $200 for 6,000 minutes. There’s no offline use supported, and I often struggled to get AI summaries to show up in English instead of Chinese (regardless of the recorded language).

You’re not seeing double: XRAI and Leion use the same manufacturer for their hardware, and the glasses weigh the same. The battery spec is also similar, with up to eight hours on the frames and another 96 hours when recharging with the case. XRAI claims its display is significantly brighter than competitors’, but I didn’t see much of a difference in day-to-day use.

The features and user experience are roughly the same, though Leion’s teleprompter feature isn’t implemented in XRAI’s app, and it doesn’t offer AI summaries of conversations. I also didn’t find XRAI’s app as user-friendly as Leion’s version, particularly when trying to switch among the admittedly exhaustive 300 language options. Only 20 of these are included without ponying up for a Pro subscription, which is sold both by the month and minute: $20/month gets you a max of 600 upgraded transcription minutes and 300 translation minutes; $40/month gets you 1,800 and 1,200 minutes, respectively. On the plus side, XRAI does have a rudimentary offline mode that works better than most. For prescription lenses, add $140 to $170.

AirCaps does not make its own prescription lenses. Instead, you must purchase a pair of $39 “lens holders” and take them to an optician if you want prescription inserts. I was unable to test these with prescription lenses and ultimately had to try them out over my regular glasses, which worked well enough for short-term testing. Frames weigh a hefty 53 grams without add-on lenses; the company couldn’t tell me how much extra weight prescription lenses would add to that, but it’s safe to say these are the bulkiest and heaviest captioning glasses on the market. Despite the weight, they only carry two to four hours of battery life, with 10 or so recharges packed into the comically large case. Another option is to clip one of AirCaps’ rechargeable 13-gram Power Capsules ($79 for two) to one of the arms, which can provide 12 to 18 extra hours of juice.

The AirCaps feature list and interface make it perhaps the simplest of all these devices, with just a single button to start and stop recording. Transcriptions and translations are available for free in nine languages. For $20/month, you can add the Pro package, which offers better accuracy, access to more than 60 languages, and the option to generate AI summaries on demand (though only if recordings are long enough). As a bonus: Five hours of Pro features are free each month. Offline mode works pretty well, too. The only bad news is that these bulky frames just aren’t comfortable enough for long-term wear.

The most expensive option on the market (up to $1,399 with prescription lenses!) weighs a relatively svelte 40 grams (52 grams with lenses) and offers about four hours of battery life. There’s no charging case; the glasses must be charged directly using the included USB-connected dongle.

The glasses are extremely simple, offering transcription and translation features—with support for about 80 languages, which is impressive. I unfortunately found the prescription lenses Captify sent to be the blurriest of the bunch, making the captions comparatively hard to read. And while the device supports offline transcription, performance suffered badly when disconnected from the internet. I couldn’t get translations to work at all when offline. For $15/month, you get better accuracy and speaker differentiation, and access to AI summaries of conversations. Prescription lenses cost between $99 and $600.

In an increasingly AI-driven and digital world, analog instant film and retro-style cameras remain popular, fueled by a combination of both nostalgia and charm. Instant cameras, in particular, stand out for their simplicity and an experience that has remained largely unchanged over time.

Fujifilm’s $175 Instax Wide 400 builds on the familiar instant photography experience and expands it with a wider format. As someone who appreciates simple, easy-to-use instant cameras and often shoots with an Instax Square, I was interested in trying a model that offers larger prints and support for landscape photography. 

The Instax Wide 400 is built for capturing group shots, scenic landscapes, and moments where fitting more of the scene into the frame really matters. It produces 62 × 99 mm prints, about twice the size of Instax Mini photos, giving you a lot more space to work with in every shot.

Since it’s a simple point-and-shoot camera, it requires no prior photography experience and is designed to be easy and intuitive to use. Its appeal lies in fun and simplicity rather than advanced controls.

With its one-button design, the camera offers no manual exposure controls. It automatically manages flash, focus, and other settings, leaving you to simply frame the image and take the shot.

When lighting is ideal, the images print with nice color and a good amount of detail with some extra saturation. I was quite impressed by the quality because it managed to pick up accurate colors and distant details. 

However, the simplicity of the camera does have some trade-offs because there isn’t much you can do to make photos brighter if you’re not in an ideal lighting situation. I did end up with some less-than-ideal shots because, in scenes with strong contrast, like dark trees against a bright sky, the details tended to blur together.

The rest of the camera is equally straightforward. To power on the camera, you rotate the lens counterclockwise. The first click activates close-up mode for subjects about 0.9 to three meters away. Turning it once more switches to landscape mode for distant subjects beyond three meters. Rotating the lens clockwise powers the camera off. 

The camera comes with a close-up lens attachment that snaps onto the front for close-up shots. I found the attachment was good at helping for avoiding focus and blur issues when taking close-ups. 

Loading the cartridge is simple, with guide lines to help you align it correctly in the camera. The door closes securely using a twist lock that latches shut. There is also a small indicator on the back that shows how much film is left.

Coming in at 162 mm x 98 mm x 123 mm and 1.4 pounds, the camera is quite bulky. It’s noticeably bigger than the Instax Mini cameras and the Instax Square models. However, I wasn’t too surprised by the size, since it makes sense that larger photos would require a bigger camera. 

That said, it’s not something you’d want to carry around without a bag, and while the shoulder strap helps, I was still very conscious of it and careful not to bump it into people or objects in busy spaces. Although Instax does offer a camera case sold separately, I didn’t receive one alongside my review unit, so I can’t speak to it. 

Since it’s a bit bulky, it’s pretty noticeable, and it ended up attracting the attention of a few people when I was testing it. A worker at a downtown gelato shop even stopped to compliment it, calling it a “cool looking camera.”

That kind of attention is part of the appeal. 

The camera’s self-timer is a nice feature, as it makes it possible to include everyone in a shot without needing someone to press the shutter button. It works by pushing a lever on the side and choosing one of four durations, ranging from two to 10 seconds. The camera emits a ticking sound to indicate that the timer is active, and red lights provide a visual countdown.

If you don’t have a tripod, the camera’s strap includes two angle adjustment accessories that can help prop and position the camera on a flat surface. To use one, slide it into the indented space on the camera’s underside, set the self-timer, and step back into the frame.

Film for the camera is available in standard white frame, black frame, metallic borders, and monochrome. Fujifilm sent me the standard white film alongside my review unit, which is the film I used for this review. Each box of film comes with 20 sheets and costs between $25-$28.

As for colors, the Instax Wide 400 comes in two options: green and jet black. As someone who loves a good shade of green, I liked that the color of the camera gave it a more playful, fun vibe.

Overall, I think this camera is a great option for people of all ages, as it offers high-quality images while remaining simple enough for kids and teens who are just starting out in photography.

The 2026 World Cup starts on June 11, and while a lot of people will be thinking about a new TV, there’s still one crucial component that can make or break the whole experience: audio.

A new soundbar can really add another dimension to the World Cup viewing experience, delivering clearer commentary and a more immersive atmosphere by capturing the roar of the crowds. Let’s face it, even the best TVs have average built-in audio, and it’d be a shame to miss out on the full World Cup feeling.