Enea Monzio Compagnoni

#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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PUBLICATION UPDATE!! A Chaos-Causality Approach to Principled Pruning of Dense Neural Networks: a new publication by Prof. Snehanshu Saha and other authors published in Chaos Theory and Applications. 🎉 Highlights Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. We formulate pruning as an optimization problem to minimize misclassifications by selecting specific weights. We have introduced the concept of chaos in learning (Lyapunov Exponents) through weight updates and used causality-based investigations to identify the causal weight connections responsible for misclassification. Two architectures are proposed in the current work - Lyapunov Exponent Granger Causality driven Fully Trained Network (LEGCNet-FT) and Lyapunov Exponent Granger Causality driven Partially Trained Network (LEGCNet-PT). The proposed methodology gauges causality between weight-specific Lyapunov Exponents (LEs) and misclassification, facilitating the identification of weights for pruning in the network. This work was carried out in collaboration with NITTE University, Bangalore and NIAS Bangalore. 📖 Read more: https://lnkd.in/gSj6P4xe Birla Institute of Technology and Science, Pilani | BITS Pilani K K Birla Goa Campus | BITS Pilani, Hyderabad Campus | Research & Innovation, BITS Pilani | Shibu Clement | Snehanshu Saha | Sanket Goel | Rajan Sahu | Shivam Chadha | Archana Mathur | Nithin Nagaraj | Surender Mohan | Vishwajeet Parihar | Arpita Sharma | Praloy Mondal | Apurva Vunnava | Dr. Radhika Vangala |Dr. Juhi Kumari| Navya Debbad | Dr. Trupti Sharma | Sindhu Reddy

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Our latest ICML paper on "Generative AI without training generative models" is on MIT News! We find out tokenizers that reduce data to use a few tokens compress information in unique ways. Tokens take on unique meaning. Once data is tokenized, optimization algorithms (instead of training new generative models) take a few iterations to carefully modify the tokens to take on typical generative AI tasks, like this in-painting task. Check out the MIT News article here: https://lnkd.in/etBbbQaa, and the paper here: https://lnkd.in/eJzsFiyM Led by Lukas Lao Beyer (MIT AeroAstro & LIDS). Collaboration with Kaming He (MIT EECS & CSAIL), Tianhong Li (MIT CSAIL), Xinlei Chen (Meta FAIR).

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Automated unit testing, including test oracles, using multiple, specialized, and collaborating LLM agents This is a very exciting piece of new work led by Qinghua Xu, a research fellow in my group at the Lero research centre in Ireland, in collaboration with Huawei. "Hallucination to Consensus: Multi-Agent LLMs for End-to-End JUnit Test Generation", Qinghua Xu, Guancheng Wang, Lionel Briand, Kui Liu https://lnkd.in/enu5adY3 We propose CANDOR, a novel end-to-end, prompt engineering-based LLM framework for automated unit test generation in Java. CANDOR orchestrates multiple specialized LLM agents to collaboratively generate complete JUnit tests, including both high-quality test prefixes and accurate oracles.

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New paper with Dipankar Maity on optimal covariance steering with output feedback with chance constraints that has just appeared in Systems and Control Letters. One of the key results of this paper is that the class of affine controllers contains the optimal controller. #controlsystems #controltheory https://lnkd.in/ey8J9jDh

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