Andrea Censi

#variational #QML #strikesback #discretely In the recent work (#link in the first comment) we show that adaptivity in variational quantum algorithms can give an exponential advantage over non-adaptive approaches. By framing circuit recompilation as a discrete optimization problem, we identify a setting where hybrid quantum–classical loops are not just helpful, but necessary. Big thanks to my collaborators — 𝗭𝗼ë 𝗛𝗼𝗹𝗺𝗲𝘀, for super-energising discussions and pushing this through, and Chukwudubem Umeano, for helping to compile this piece together. Careful, long read below ❗️ 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗳𝗼𝗿 𝗗𝗶𝘀𝗰𝗿𝗲𝘁𝗲 𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗶𝗻 𝗖𝗶𝗿𝗰𝘂𝗶𝘁 𝗥𝗲𝗰𝗼𝗺𝗽𝗶𝗹𝗮𝘁𝗶𝗼𝗻 The question of whether variational quantum algorithms really need the hybrid quantum–classical loop has been a matter of debate in the last two years. The challenge comes from the ever-growing power of surrogating quantum models, and advances in classical simulation methods for problems with structure. This has been framed as a question to the community: 𝘋𝘰𝘦𝘴 𝘵𝘩𝘦 𝘱𝘳𝘰𝘷𝘢𝘣𝘭𝘦 𝘢𝘣𝘴𝘦𝘯𝘤𝘦 𝘰𝘧 𝘣𝘢𝘳𝘳𝘦𝘯 𝘱𝘭𝘢𝘵𝘦𝘢𝘶𝘴 𝘪𝘮𝘱𝘭𝘺 𝘴𝘪𝘮𝘶𝘭𝘢𝘣𝘪𝘭𝘪𝘵𝘺? Counterexamples exist, but most of them correspond to specific Boolean function learning or Shor-type algorithms in disguise. What we tried to do is to identify a setting where adaptivity appears more naturally, and relies on a different structure. The starting point is a simple observation: in classical discrete optimization, exponential separations between adaptive and non-adaptive search are known. If this logic carries over, then there should be quantum problems where adaptivity gives the same kind of exponential edge. In our preprint, we propose such a problem — quantum circuit recompilation. The task is to recover a “hidden” circuit structure, starting with quantum data (a state) prepared with randomly placed T-gates (“puzzles”) embedded in partially random unitaries. The ansatz has the reverse structure, but we don’t know where the gates are, and there are exponentially many options. We show that discrete quantum optimizers can efficiently unravel these puzzles, finding the right locations with a quadratic number of circuit evaluations. In contrast, non-adaptive searches face an exponentially hard landscape: non-separable, highly entangled, and full of magic. Importantly, we also find that optimization remains feasible under noise, using finite-shot samplers, even as the solution space grows exponentially. This points to a very particular (and largely overlooked) role for discrete optimization in variational quantum algorithms. It also raises a bigger question: what new problems can benefit from this kind of adaptivity? While we remain open to new discoveries, we already see clear connections to fault-tolerant protocols with partial structure, and to blind quantum computing scenarios. Grateful for the support from EPSRC and the QCi3 Hub!

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Delighted that our working paper “Catching Bid-rigging Cartels with Graph Attention Neural Networks”, joint work with D. Imhof and E. Viklund, is out! We propose a novel #DeepLearning algorithm based on Graph Attention Neural Networks to detect collusive behavior in markets and tenders. Trained on data from 7 countries across 13 markets, our method achieves impressive accuracy rates of up to 91% in classifying cartels - significantly outperforming classical #MachineLearning approaches: https://lnkd.in/dmnFAt-3 #DataScience

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Fun with NNPs: I just re-optimized one of the largest TSs from my PhD, which took a week with M06-2x/6-31G(d), in <15 minutes. I suspect the energy is poor here, but the geometry and frequencies look close enough—single-point energies could probably fix this. This is one of the TSs for the catalytic stereo- and site-selective glycosylation that Qiuhan Li, Alison Wendlandt, Sam Levi, and Eric Jacobsen developed (Nature, 2022, 608, 74–79). Link to paper: https://lnkd.in/egPjKihY And link to calculation: https://lnkd.in/eFy4dVSS

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Imagine building a start-up on Deep Research system… without training a single model, and having the code open-sourced. 🤔 🚀 Excited to announce our new work from NVIDIA Research: Universal Deep Research (UDR): https://lnkd.in/gwRuU3Bw Deep research agents are one of the most widely used forms of AI agents. With UDR, we take a step forward: 🔍 Customizable strategy 🔛 research is not just about retrieving documents, but about shaping how the agent explores, verifies, and synthesizes knowledge. UDR shows that the research strategy itself can be adapted to the user’s goals. 🛠️ System-level behavior 🔛 the agent’s “deep research” workflow can be implemented without re-training or fine-tuning any LLMs. Instead, it’s orchestrated at the system level, making it lightweight, flexible, and easy to extend. 🌐 General-purpose design 🔛 UDR is universal: it can be applied across domains, whether for scientific discovery, technical due diligence, or business intelligence. AI agents are not just about better models - they are about better systems. We believe UDR opens the door to a new wave of startups and applications: anyone can now design a specialized “research assistant” that adapts to their domain and strategy. Learn more from products we offer in this domain: 🔗 AIQ Deep Research blueprint: https://lnkd.in/g2_4y3RR 🔗 NeMo Agent Toolkit: https://lnkd.in/gVPHTrzN Great project led by Peter Belcak.

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