Minimizing Computational Costs for Quantum Testing

New pre-print from PhD student Hang Zou on warm-starting the variational quantum eigensolver using flows: Flow-VQE! Flow-VQE is parameter transfer on steroids: it learns how to solve a family of related problems, dramatically reducing the aggregate compute cost! The cost-advantages from from the embedding of a generative model into the VQE optimization loop, and learning it via preference based optimization, alleviating the need to evaluate gradients of the quantum circuit. Flow-VQE outperforms baseline optimization algorithms, achieving computational accuracy with fewer circuit evaluations (up to 100x improvement) and in the warm-start context of new systems, accelerates subsequent fine-tuning by up to 50x compared to HF initialization. Curious to read more about the experiments and the method? Check out the pre-print here: https://lnkd.in/dcYDGRBf Code will follow soon. Feedback and input very welcome! Collaboration with Anton Frisk Kockum and Martin Rahm

❓ Ever wondered how Neural Networks (NNs) could revolutionize #quantum research? #NeuralNetworks aren't just transforming #AI —they're also pivotal in the quantum realm! In the work entitled "Parameter Estimation by Learning Quantum Correlations in Continuous Photon-Counting Data Using Neural Networks." Quantinuum proudly collaborated with global partners, such as the Universidad Autónoma de Madrid, Chalmers University of Technology, and the University of Michigan, uniting expertise from every corner of the world. 🌍 https://lnkd.in/gj8qttdN 🔍 Key Findings: 1️⃣ The study introduces a novel inference method employing artificial neural networks for quantum probe parameter estimation. 2️⃣ This method leverages quantum correlations in discrete photon-counting data, offering a fresh perspective compared to existing techniques focusing on diffusive signals. 3️⃣ The approach achieves performance on par with Bayesian inference - renowned for its optimal information retrieval capability - yet does so at a fraction of the computational cost. 4️⃣ Beyond efficiency, the method stands robust against imperfections in measurement and training data. 5️⃣ Potential applications span from quantum sensing and imaging to precise calibration tasks in laboratory setups. 🤔 Curious About the Unknowns? The authors are sharing EVERYTHING on Zenodo! 🎉 The codes used to generate these results, including the proposed NN architectures as TensorFlow models, are available here https://lnkd.in/gVdzJycM as well as all the data necessary to reproduce the results openly available here: https://lnkd.in/gVdzJycM Enrico Rinaldi, Manuel González Lastre, Sergio Garcia Herreros, Shahnawaz Ahmed, Maryam Khanahmadi, Franco Nori, and Carlos Sánchez Muñoz

It is no secret that the number one killer of anything useful in near-term quantum computing (i.e., before error correction) for quantum simulations is circuit depth. As the system gets bigger and more complicated, the required circuit gets deeper—until the device is mostly producing noise instead of useful signal. In our recent Q-SENSE method ( https://lnkd.in/gRCJQGnZ ), we tackle this by writing the wavefunction as a linear combination of short-depth circuits (a subspace expansion), instead of one huge, fragile circuit. Anyone who has ever done subspace expansion knows the catch: it usually kills you on measurement cost because you need many Hamiltonian matrix elements. In Q-SENSE, we use seniority symmetries so that most Hamiltonian terms don’t couple our symmetry-adapted states. The result: a dramatic reduction in measurements—in our benchmarks, the cost is lower than a single VQE cycle for the same systems. #quantumcomputing, #quantumphysics, #quantumchemistry

> Sharing Resource < Interesting: "TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms" by Yuewen Hou, Dhanvi Bharadwaj, Gokul Subramanian Ravi Abstract: Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This demands an enormous number of execution shots, making practical use prohibitively expensive. We observe that VQA costs can be significantly reduced by exploiting execution similarities across an application's tasks. Based on this insight, we propose TreeVQA, a tree-based execution framework that begins by executing tasks jointly and progressively branches only as their quantum executions diverge. Implemented as a VQA wrapper, TreeVQA integrates with typical VQA applications. Evaluations on scientific and combinatorial benchmarks show shot count reductions of 25.9×on average and over 100×  for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements. Link: https://lnkd.in/e9kkZZX5 #quantumcomputing #quantummachinelearning #research #paper