Tech
Quantum Machines and Nvidia use machine learning to get closer to an error-corrected quantum computer
About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia’s DGX Quantum computing platform and Quantum Machine’s advanced quantum control hardware. We didn’t hear much about the results of this partnership for a while, but it’s now starting to bear fruit and getting the industry one step closer to the holy grail of an error-corrected quantum computer.
In a presentation earlier this year, the two companies showed that they are able to use an off-the-shelf reinforcement learning model running on Nvidia’s DGX platform to better control the qubits in a Rigetti quantum chip by keeping the system calibrated.
Yonatan Cohen, the co-founder and CTO of Quantum Machines, noted how his company has long sought to use general classical compute engines to control quantum processors. Those compute engines were small and limited, but that’s not a problem with Nvidia’s extremely powerful DGX platform. The holy grail, he said, is to run quantum error correction. We’re not there yet. Instead, this collaboration focused on calibration, and specifically calibrating the so-called “π pulses” that control the rotation of a qubit inside a quantum processor.
At first glance, calibration may seem like a one-shot problem: You calibrate the processor before you start running the algorithm on it. But it’s not that simple. “If you look at the performance of quantum computers today, you get some high fidelity,” Cohen said. “But then, the users, when they use the computer, it’s typically not at the best fidelity. It drifts all the time. If we can frequently recalibrate it using these kinds of techniques and underlying hardware, then we can improve the performance and keep the fidelity [high] over a long time, which is what’s going to be needed in quantum error correction.”
Constantly adjusting those pulses in near real time is an extremely compute-intensive task, but since a quantum system is always slightly different, it is also a control problem that lends itself to being solved with the help of reinforcement learning.
“As quantum computers are scaling up and improving, there are all these problems that become bottlenecks, that become really compute-intensive,” said Sam Stanwyck, Nvidia’s group product manager for quantum computing. “Quantum error correction is really a huge one. This is necessary to unlock fault-tolerant quantum computing, but also how to apply exactly the right control pulses to get the most out of the qubits”
Stanwyck also stressed that there was no system before DGX Quantum that would enable the kind of minimal latency necessary to perform these calculations.
As it turns out, even a small improvement in calibration can lead to massive improvements in error correction. “The return on investment in calibration in the context of quantum error correction is exponential,” explained Quantum Machines Product Manager Ramon Szmuk. “If you calibrate 10% better, that gives you an exponentially better logical error [performance] in the logical qubit that is composed of many physical qubits. So there’s a lot of motivation here to calibrate very well and fast.”
It’s worth stressing that this is just the start of this optimization process and collaboration. What the team actually did here was simply take a handful of off-the-shelf algorithms and look at which one worked best (TD3, in this case). All in all, the actual code for running the experiment was only about 150 lines long. Of course, this relies on all of the work the two teams also did to integrate the various systems and build out the software stack. For developers, though, all of that complexity can be hidden away, and the two companies expect to create more and more open source libraries over time to take advantage of this larger platform.
Szmuk stressed that for this project, the team only worked with a very basic quantum circuit but that it can be generalized to deep circuits as well. If you can do this with one gate and one qubit, you can also do it with a hundred qubits and 1,000 gates,” he said.
“I’d say the individual result is a small step, but it’s a small step towards solving the most important problems,” Stanwyck added. “Useful quantum computing is going to require the tight integration of accelerated supercomputing — and that may be the most difficult engineering challenge. So being able to do this for real on a quantum computer and tune up a pulse in a way that is not just optimized for a small quantum computer but is a scalable, modular platform, we think we’re really on the way to solving some of the most important problems in quantum computing with this.”
Stanwyck also said that the two companies plan to continue this collaboration and get these tools into the hands of more researchers. With Nvidia’s Blackwell chips becoming available next year, they’ll also have an even more powerful computing platform for this project, too.
Tech
Volkswagen’s cheapest EV ever is the first to use Rivian software
Volkswagen’s ultra-cheap EV called the ID EVERY1 — a small four-door hatchback revealed Wednesday — will be the first to roll out with software and architecture from Rivian, according to a source familiar with the new model.
The EV is expected to go into production in 2027 with a starting price of 20,000 euros ($21,500). A second EV called the ID.2all, which will be priced in the 25,000 euro price category, will be available in 2026. Both vehicles are part of the automaker’s new of category electric urban front-wheel drive cars that are being developing under the so-called “Brand Group Core” that makes up the volume brands in the VW Group. And both vehicles are for the European market.
The EVERY1 will be the first to ship with Rivian’s vehicle architecture and software as part of a $5.8 billion joint venture struck last year between the German automaker and U.S. EV maker. The ID.2all is based on the E3 1.1 architecture and software developed by VW’s software unit Cariad.
VW didn’t name Rivian in its reveal Wednesday, although there were numerous nods to next-generation software. Kai Grünitz, member of the Volkswagen Brand Board of Management responsible for Technical Development, noted it would be the first model in the entire VW Group to use a “fundamentally new, particularly powerful software architecture.”
“This means the future entry-level Volkswagen can be equipped with new functions throughout its entire life cycle,” he said. “Even after purchase of a new car, the small Volkswagen can still be individually adapted to customer needs.”
Sources who didn’t want to be named because they were not authorized to speak publicly, confirmed to TechCrunch that Rivian’s software will be in the ID EVERY1 EV. TechCrunch has reached out to Rivian and VW and will update the article if the companies respond.
The new joint venture provides Rivian with a needed influx of cash and the opportunity to diversify its business. Meanwhile, VW Group gains a next-generation electrical architecture and software for EVs that will help it better compete. Both companies have said that the joint venture, called Rivian and Volkswagen Group Technologies, will reduce development costs and help scale new technologies more quickly.
The joint venture is a 50-50 partnership with co-CEOs. Rivian’s head of software, Wassym Bensaid, and Volkswagen Group’s chief technical engineer, Carsten Helbing, will lead the joint venture. The team will be based initially in Palo Alto, California. Three other sites are in development in North America and Europe, the companies have previously said.
“The ID. EVERY1 represents the last piece of the puzzle on our way to the widest model selection in the volume segment,” Thomas Schäfer, CEO of the Volkswagen Passenger Cars brand and Head of the Brand Group Core, said in a statement. “We will then offer every customer the right car with the right drive system–including affordable all-electric entry-level mobility. Our goal is to be the world’s technologically leading high-volume manufacturer by 2030. And as a brand for everyone–just as you would expect from Volkswagen.”
The Volkswagen ID EVERY1 is just a concept for now — and with only a few details attached to the unveiling. The concept vehicle reaches a top speed of 130 km/h (80 miles per hour) and is powered by a newly developed electric drive motor with 70 kW, according to Volkswagen. The German automaker said the range on the EVERY1 will be at least 250 kilometers (150 miles). The vehicle is small but larger than VW’s former UP! vehicle. The company said it will have enough space for four people and a luggage compartment volume of 305 liters.
Tech
The hottest AI models, what they do, and how to use them
AI models are being cranked out at a dizzying pace, by everyone from Big Tech companies like Google to startups like OpenAI and Anthropic. Keeping track of the latest ones can be overwhelming.
Adding to the confusion is that AI models are often promoted based on industry benchmarks. But these technical metrics often reveal little about how real people and companies actually use them.
To cut through the noise, TechCrunch has compiled an overview of the most advanced AI models released since 2024, with details on how to use them and what they’re best for. We’ll keep this list updated with the latest launches, too.
There are literally over a million AI models out there: Hugging Face, for example, hosts over 1.4 million. So this list might miss some models that perform better, in one way or another.
AI models released in 2025
Cohere’s Aya Vision
Cohere released a multimodal model called Aya Vision that it claims is best in class at doing things like captioning images and answering questions about photos. It also excels in languages other than English, unlike other models, Cohere claims. It is available for free on WhatsApp.
OpenAI’s GPT 4.5 ‘Orion’
OpenAI calls Orion their largest model to date, touting its strong “world knowledge” and “emotional intelligence.” However, it underperforms on certain benchmarks compared to newer reasoning models. Orion is available to subscribers of OpenAI’s $200 a month plan.
Claude Sonnet 3.7
Anthropic says this is the industry’s first ‘hybrid’ reasoning model, because it can both fire off quick answers and really think things through when needed. It also gives users control over how long the model can think for, per Anthropic. Sonnet 3.7 is available to all Claude users, but heavier users will need a $20 a month Pro plan.
xAI’s Grok 3
Grok 3 is the latest flagship model from Elon Musk-founded startup xAI. It’s claimed to outperform other leading models on math, science, and coding. The model requires X Premium (which is $50 a month.) After one study found Grok 2 leaned left, Musk pledged to shift Grok more “politically neutral” but it’s not yet clear if that’s been achieved.
OpenAI o3-mini
This is OpenAI’s latest reasoning model and is optimized for STEM-related tasks like coding, math, and science. It’s not OpenAI’s most powerful model but because it’s smaller, the company says it’s significantly lower cost. It is available for free but requires a subscription for heavy users.
OpenAI Deep Research
OpenAI’s Deep Research is designed for doing in-depth research on a topic with clear citations. This service is only available with ChatGPT’s $200 per month Pro subscription. OpenAI recommends it for everything from science to shopping research, but beware that hallucinations remain a problem for AI.
Mistral Le Chat
Mistral has launched app versions of Le Chat, a multimodal AI personal assistant. Mistral claims Le Chat responds faster than any other chatbot. It also has a paid version with up-to-date journalism from the AFP. Tests from Le Monde found Le Chat’s performance impressive, although it made more errors than ChatGPT.
OpenAI Operator
OpenAI’s Operator is meant to be a personal intern that can do things independently, like help you buy groceries. It requires a $200 a month ChatGPT Pro subscription. AI agents hold a lot of promise, but they’re still experimental: a Washington Post reviewer says Operator decided on its own to order a dozen eggs for $31, paid with the reviewer’s credit card.
Google Gemini 2.0 Pro Experimental
Google Gemini’s much-awaited flagship model says it excels at coding and understanding general knowledge. It also has a super-long context window of 2 million tokens, helping users who need to quickly process massive chunks of text. The service requires (at minimum) a Google One AI Premium subscription of $19.99 a month.
AI models released in 2024
DeepSeek R1
This Chinese AI model took Silicon Valley by storm. DeepSeek’s R1 performs well on coding and math, while its open source nature means anyone can run it locally. Plus, it’s free. However, R1 integrates Chinese government censorship and faces rising bans for potentially sending user data back to China.
Gemini Deep Research
Deep Research summarizes Google’s search results in a simple and well-cited document. The service is helpful for students and anyone else who needs a quick research summary. However, its quality isn’t nearly as good as an actual peer-reviewed paper. Deep Research requires a $19.99 Google One AI Premium subscription.
Meta Llama 3.3 70B
This is the newest and most advanced version of Meta’s open source Llama AI models. Meta has touted this version as its cheapest and most efficient yet, especially for math, general knowledge, and instruction following. It is free and open source.
OpenAI Sora
Sora is a model that creates realistic videos based on text. While it can generate entire scenes rather than just clips, OpenAI admits that it often generates “unrealistic physics.” It’s currently only available on paid versions of ChatGPT, starting with Plus, which is $20 a month.
Alibaba Qwen QwQ-32B-Preview
This model is one of the few to rival OpenAI’s o1 on certain industry benchmarks, excelling in math and coding. Ironically for a “reasoning model,” it has “room for improvement in common sense reasoning,” Alibaba says. It also incorporates Chinese government censorship, TechCrunch testing shows. It’s free and open source.
Anthropic’s Computer Use
Claude’s Computer Use is meant to take control of your computer to complete tasks like coding or booking a plane ticket, making it a predecessor of OpenAI’s Operator. Computer use, however, remains in beta. Pricing is via API: $0.80 per million tokens of input and $4 per million tokens of output.
x.AI’s Grok 2
Elon Musk’s AI company, x.AI, has launched an enhanced version of its flagship Grok 2 chatbot it claims is “three times faster.” Free users are limited to 10 questions every two hours on Grok, while subscribers to X’s Premium and Premium+ plans enjoy higher usage limits. x.AI also launched an image generator, Aurora, that produces highly photorealistic images, including some graphic or violent content.
OpenAI o1
OpenAI’s o1 family is meant to produce better answers by “thinking” through responses through a hidden reasoning feature. The model excels at coding, math, and safety, OpenAI claims, but has issues deceiving humans, too. Using o1 requires subscribing to ChatGPT Plus, which is $20 a month.
Anthropic’s Claude Sonnet 3.5
Claude Sonnet 3.5 is a model Anthropic claims as being best in class. It’s become known for its coding capabilities and is considered a tech insider’s chatbot of choice. The model can be accessed for free on Claude although heavy users will need a $20 monthly Pro subscription. While it can understand images, it can’t generate them.
OpenAI GPT 4o-mini
OpenAI has touted GPT 4o-mini as its most affordable and fastest model yet thanks to its small size. It’s meant to enable a broad range of tasks like powering customer service chatbots. The model is available on ChatGPT’s free tier. It’s better suited for high-volume simple tasks compared to more complex ones.
Cohere Command R+
Cohere’s Command R+ model excels at complex Retrieval-Augmented Generation (or RAG) applications for enterprises. That means it can find and cite specific pieces of information really well. (The inventor of RAG actually works at Cohere.) Still, RAG doesn’t fully solve AI’s hallucination problem.
Tech
Not all cancer patients need chemo. Ataraxis AI raised $20M to fix that.
Artificial intelligence is a big trend in cancer care, and it’s mostly focused detecting cancer at the earliest possible stage. That makes a lot of sense, given that cancer is less deadly the earlier it’s detected.
But fewer are asking another fundamental question: if someone does have cancer, is an aggressive treatment like chemotherapy necessary? That’s the problem Ataraxis AI is trying to solve.
The New York-based startup is focused on using AI to accurately predict not only if a patient has cancer, but also what their cancer outcome looks like in 5 to 10 years. If there’s only a small chance of the cancer coming back, chemo can be avoided altogether – saving a lot of money, while avoiding the treatment’s notorious side effects.
Ataraxis AI now plans to launch their first commercial test, for breast cancer, to U.S. oncologists in the coming months, its co-founder Jan Witowski tells TechCrunch. To bolster the launch and expand into other types of cancer, the startup has raised a $20.4 million Series A, it told TechCrunch exclusively.
The round was led by AIX Ventures with participation from Thiel Bio, Founders Fund, Floating Point, Bertelsmann, and existing investors Giant Ventures and Obvious Ventures. Ataraxis emerged from stealth last year with a $4 million seed round.
Ataraxis was co-founded by Witowski and Krzysztof Geras, an assistant professor at NYU’s medical school who focuses on AI.
Ataraxis’ tech is powered by an AI model that extracts information from high-resolution images of cancer cells. The model is trained on hundreds of millions of real images from thousands of patients, Witowski said. A recent study showed Ataraxis’ tech was 30% more accurate than the current standard of care for breast cancer, per Ataraxis.
Long term, Ataraxis has big ambitions. It wants its tests to impact at least half of new cancer cases by 2030. It also views itself as a frontier AI company that builds its own models, touting Meta’s chief AI scientist Yann LeCun as an AI advisor.
“I think at Ataraxis we are trying to build what is essentially an AI frontier lab, but for healthcare applications,” Witowski said. “Because so many of those problems require a very novel technology.”
The AI boom has led to a rush of fundraises for cancer care startups. Valar Labs raised $22 million to help patients figure out their treatment plan in May 2024, for example. There’s also a bevvy of AI-powered drug discovery firms in the cancer space, like Manas AI which raised $24.6 million in January 2025 and was co-founded by Reid Hoffman, the LinkedIn co-founder.
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