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Efficient Identification of Permutation Symmetries in Many-Body Hamiltonians via Graph Theory
Saumya Shah, Patrick Rebentrost
The computational cost of simulating quantum many-body systems can often be reduced by taking advantage of physical symmetries. While methods exist for specific symmetry classes, a…
Fast-forwardable Lindbladians imply quantum phase estimation
Zhong-Xia Shang, Naixu Guo, Patrick Rebentrost +3
Quantum phase estimation (QPE) and Lindbladian dynamics are both foundational in quantum information science and central to quantum algorithm design. In this work, we bridge these…
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines rem…
Artificial intelligence for representing and characterizing quantum systems
Yuxuan Du, Yan Zhu, Yuan-Hang Zhang +8
Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quant…
Quantum Learning with Tunable Loss Functions
Yixian Qiu, Lirandë Pira, Patrick Rebentrost
Learning from quantum data presents new challenges to the paradigm of learning from data. This typically entails the use of quantum learning models to learn quantum processes that…
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
Yuxuan Du, Xinbiao Wang, Naixu Guo +6
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum compute…