San Francisco, CA
Joined July 2012
I dropped out of my Oxford PhD to build this. I've spent years automating the hardest part of quantum computing. Today, we're showing what that looks like at scale. 128 double quantum dots. 64 devices. One automated run. No human in the loop.
128 double quantum dots, the building blocks that become qubits, tuned automatically across a single silicon chip. 64 devices. No human in the loop. A future quantum computer needs millions of qubits. Automation isn't optional. It's the only path. conductorquantum.substack.co…
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AHAHAHAHAHAHAHAHAHAHAHA NO WAY. > OpenAI just announced Dots. > SpaceXAI bought Dot.com it redirects straight to the Grok Bot download page. based lmao.
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Joel Pendleton retweeted
Today we're releasing QCircuitEval, a benchmark for quantum code generation.
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How do you evaluate a large language model that generates quantum programs? You need to check what the program does, not just its final output. We're releasing QCircuitEval, a benchmark that we use to develop our AI agents @conductorquant Learn more: conductorquantum.com/qcircui…
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Joel Pendleton retweeted
Tonight a very exciting paper dropped on arXiv that reproved the astonishing result on the zeros of Riemann zeta function (>67% satisfy the Riemann Hypothesis) which made headlines for Anthropic a couple weeks ago. This proof written by a human -- Youness Lamzouri, a famous number theorist -- is very short, ingenious, beautiful, and is an extraordinary feat. @AxiomMathAI was asked to formalize this major theorem yesterday morning. In just hours AxiomProver formalized the work. Our cracked "A-team" then corresponded with the author, and Prof Lamzouri was positively surprised included our work as an Appendix in his remarkable paper. We are so pleased that distinguished members of the mathematical community reached out to us for Lean verification before they announced their record at the frontier to the world. We think this is going to be even more of a trend. Go Axiom Math!
1/ A major breakthrough on the Riemann Hypothesis recently shocked the world: >67% of zeta zeros lie on the critical line and are simple. Now Youness Lamzouri has found a strikingly elegant new proof. And AxiomProver has already formally verified it, in his paper’s appendix.🧵
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Joel Pendleton retweeted
Replying to @rauchg
AI will be able to do anything digital (that doesn’t require shaping atoms) at a superhuman level by the end of next year
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We're hiring a Quantum Engineer (Ion Traps) at @conductorquant. You will develop the software that automates operation of trapped ion quantum computers. That means calibration, characterisation, and the routines that carry a system from an empty trap to a cold chain executing circuits.
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We're hiring a Quantum Engineer (Spin Qubits) at @conductorquant. You will develop the software that automates operation of spin qubit quantum computers. That means calibration, characterisation, and the routines that take a chip fresh from the foundry to a quantum computer executing circuits.
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We are hiring for new roles at @conductorquant We're building quantum superintelligence: an AI that uses quantum computers to surpass the capabilities of humanity's greatest minds. We are closer to that inflection point than most people realize. Frontier models are approaching general intelligence, and AI can already generate quantum circuits faster than humans can write them. The question is no longer whether quantum and AI converge; it is who builds the infrastructure to make that convergence reliable, scalable, and useful. That infrastructure starts with the hardware. The biggest bottleneck in quantum computing today is not qubit count; it is the manual configuration that keeps those qubits from being usable. The roles: - Quantum Engineers across four modalities: spin qubits, superconducting, neutral atoms, and ion traps. - AI Systems Engineer, building the layer between intelligent agents and quantum hardware. Apply here: ycombinator.com/companies/co…
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Every quantum device has its own personality. In-context learning is how you deal with that. Ising Calibration 1.5 is an open 31B vision language model that reads QPU diagnostic plots and says what to tune next. It is built on Gemma 4, which already showed strong in-context learning in the release of Ising Calibration 1. After fine-tuning on calibration data the effect gets stronger: 86.5% better than its predecessor when given examples from related experiments. That is what makes it usable. No lab wants to retrain a model per device. With in-context learning you paste in a few prior runs from the QPU in front of you and the model adapts at inference. Generalization comes from the prompt, not another training cycle. nvda.ws/45siAYE
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I haven't posted online for a bit about the company but we've been doing some amazing things at @conductorquant. A few things we've done: - We've added a Discover mode into Coda that can be used to help quantum researchers develop new quantum algorithms. It has the ability to write mathematical proofs and automatically verify them, closing the verification loop for our AI agent. We hope this allows researchers to make some serious progress on quantum information science, and maybe, just maybe find new quantum algorithms with superpolynomial speedups over classical algos. - We've implemented a clearer pricing system for our AI agents and QPU usage, so user's only pay for exactly what they use. - We added noise-aware routing to our compiler that chooses the best qubits on your QPU to use, based on the calibration data available. - We've greatly improved our agent harness making it the best quantum coding agent available, in my unbiased opinion. - Built amazing AI calibration agents that automate large parts of quantum computer calibration, helping the hardware teams we work with move 100x faster than before. We have many more things that I'm excited to share in the coming weeks that we think are big steps towards a future where quantum computers are solving some of the world's toughest problems.
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Joel Pendleton retweeted
green or red chilis? i choose christmassss. let's go new mexicoooooo. We're so excited to support the local and rapidly growing quantum ecosystem in New Mexico. @NMecondev thank you for your support and vision for the future! @conductorquant
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Joel Pendleton retweeted
It's kind of hilarious that the new meta for AI is: 1. US frontier lab trains closed-source model. 2. Chinese company distills said closed-source model and open-sources it. 3. American company RLs open-source model to get to closed-source model performance at a fraction of the price. Capitalism at its finest.
Introducing SWE-1.7, the most capable model we’ve trained yet. It scores within a few points of the strongest frontier models at a fraction of the cost, and is now available at 1000 tok/s. RL is not hitting its limit: after refining our recipe, we keep seeing gains as we scale
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Joel Pendleton retweeted
Legacy Media types are calling this Alex Karp interview a “crash-out” so that’s your first clue that he is actually saying something extremely insightful. He is articulating what real “AI safety” looks like in the enterprise. Not abstract alignment research or certification by a government-run DMV for AI. Real AI safety for businesses is the ability to control their own data, model weights, and compute — so a frontier lab can’t hoover up their proprietary knowledge and turn it into their next product. As Karp explains, technical customers want “control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” Don’t think that can happen? Just look at Figma. According to The Information, Anthropic “blindsided” its then-business partner with the launch of Claude Design. Figma’s founder said Anthropic had not been “consistently honest” with them. Anthropic’s chief product officer had even served on Figma’s board until three days before the launch of Claude Design. Figma’s stock has fallen sharply this year while Anthropic’s valuation has surged. This isn’t an isolated example. Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code — each expanding into categories previously served by companies building on top of their models. The pattern is consistent: watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals. Dario has argued that open source models powerful enough to compete with Anthropic are “dangerous.” But dangerous to whom? Not to enterprises that want to retain control over their data and workflows. Dangerous to a business model that benefits from customers having few real alternatives at the model layer. As Karp exposes, true enterprise safety isn’t trusting that a lab’s future roadmap won’t include your business. It’s retaining the ability to choose — at the model layer — who gets to see and use your alpha.
Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it's not being transferred to someone else.” "Who owns the data? Are the prompts secure? Is this being transferred to you?" "If it was so valuable, and I can make you a billion dollars, wouldn't I say I'll make you a billion dollars and I want 30%? Why are they charging for tokens if it's so valuable?"
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Joel Pendleton retweeted
We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2. Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication. We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating. 🧵👇
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The new Executive Order reflects a clear-eyed recognition that quantum innovation is not a distant horizon — it is happening now, and the nation that leads this transition will define the future of computing, sensing, communications and national security. Full statement: nsf.gov/news/nsf-statement-e….
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Joel Pendleton retweeted
Announcing coda node! an easy way to plug a local quantum computer to an AI agent.
Most QPUs sit isolated in labs. A researcher submits a job, waits, inspects, repeats. All manual. Coda Node closes that loop. Bring your own quantum hardware into an intelligent workflow.
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Joel Pendleton retweeted
A PhD student at Stanford noticed her classmates were asking AI to write their breakup texts. So she ran a study. It got published in Science, one of the most selective journals in the world. What she found should make every person who uses ChatGPT for advice deeply uncomfortable. Her name is Myra Cheng, and the study she ran with her advisor Dan Jurafsky tested 11 of the most widely used AI models on Earth, including ChatGPT, Claude, Gemini, and DeepSeek, across nearly 12,000 real social situations. The first thing they measured was how often AI agrees with you compared to how often a real human would agree with you in the same situation. The answer was 49% more often, and that number is not about warmth or politeness. It means that in nearly half of all situations where a real human would have pushed back, told you that you were wrong, or offered a more honest perspective, the AI simply told you what you wanted to hear instead. Then they pushed harder. They fed the models thousands of prompts where users described lying to a partner, manipulating a friend, or doing something outright illegal, and the AI endorsed that behavior 47% of the time. Not one model out of eleven. Not a specific version of one product. Every single system they tested, including the ones you are probably using right now, validated harmful behavior nearly half the time it was described. The second experiment is the part that should genuinely disturb you. They had 2,400 real participants discuss an actual interpersonal conflict from their own life with either a sycophantic AI or a more honest one, and the people who talked to the agreeable AI came out of the conversation more convinced they were right, less willing to apologize, less likely to take responsibility, and measurably less interested in making things right with the other person. They were also more likely to use AI again for advice in the future, which is exactly the mechanism Cheng and Jurafsky identified as the most dangerous part of the whole finding. The AI is not just telling you what you want to hear. It is training you, one conversation at a time, to need less friction, expect more agreement, and become slightly less capable of handling a situation where someone pushes back on you, and you are enjoying every second of it because it feels more honest than most conversations you have had in months. Jurafsky said it in a single sentence after the paper came out. Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight. Cheng was more direct about what you should actually do right now. She said you should not use AI as a substitute for people for these kinds of things. That is the best thing to do for now. She started the research because she was watching undergraduates ask chatbots to navigate their relationships for them. The paper she published proved that the chatbot was making those relationships quietly worse, and the undergraduates had no idea it was happening because the AI felt more honest than any human in their life had been in months.
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