Su Yeon Chang
@SyChang97
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Have you ever wondered how much work can be offloaded from quantum computers when simulating an expectation landscape of a parametrized quantum circuit🤔? In our new work “Efficient quantum-enhanced classical simulation for patches of quantum landscapes” we tackle this question.
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"Quantum Boltzmann machine learning of ground-state energies" now available: https://t.co/a8qt0YAAPa Our paper solves a problem that has been open in the theory of quantum Boltzmann machines since they were originally proposed eight years ago in https://t.co/K4KE3puwME. 1/2
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Thrilled to announce that our paper has been finally published! A big thank you to all our collaborators!
The impact of hardware noise on the capabilities of equivariant quantum neural networks is explored, and strategies for mitigation are addressed. @cenk_tuysuz @SyChang97 @maria_demidik @GrosQmichi @desy_cqta @CERNquantum
https://t.co/cqRsPEp9qf
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The impact of hardware noise on the capabilities of equivariant quantum neural networks is explored, and strategies for mitigation are addressed. @cenk_tuysuz @SyChang97 @maria_demidik @GrosQmichi @desy_cqta @CERNquantum
https://t.co/cqRsPEp9qf
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🚨🚨Some colleagues recently posted a report about potential applications of quantum computers at LANL https://t.co/xIsX13jxOs We are asking the QIS community’s feedback and critiques on the report and would appreciate your input 🙏. Please send any feedback to cjc@lanl.gov
arxiv.org
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural to presume that...
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Overall, our work highlights LaSt-QGAN's potential for practical image generation through empirical experiments and theoretical analysis, paving the way for future applications on larger datasets.
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✅ We address the barren plateau problem by showing that a polynomially deep generator circuit can be trained with a small angle initialization, providing a practical solution. We also provide a scaling of the initialization range w.r.t the number of qubits to mitigate BP.
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✅We showcase the model on different datasets, achieving performance comparable to classical GANs, even surpassing in some cases, with similar resources. ✅Thanks to the classical decoder, we attenuate the impact of statistical noise, allowing image generation with < 1000 shots.
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✅ We propose a hybrid quantum-classical GAN approach to generate large-size, high-quality images. ✅ We embed the data into a smaller latent space with a classical autoencoder, and train the style-based quantum GAN in this latent space.
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Are you interested in generating images using quantum computing? Check out our new approach: LaSt-QGAN! https://t.co/xC5js854Qe
@GrosQmichi @s_thanasilp @blesa_ux
#MachineLearning #QuantumComputing #GenerativeModeling #GenerativeAdversarialNetworks
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The #QTML2023 conference, hosted for the 1st time @CERN, has now officially come to a close. Thank you, everyone, for being with us this year! It has been fantastic to host you! #CERNqti #quantumtechnologies #machinelearning
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Equivariant Quantum Machine Learning on the (force) field ⚛️ https://t.co/0Hdrd2sa8F We have recently learned, from seminal works in the QML literature, that quantum learning models can very naturally embed group symmetric structures. (1/6)
arxiv.org
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum...
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🚨Applications are now open for the 2024 Los Alamos Quantum Computing Summer School! ⚛️Our school focuses on theory, applications, and programming of quantum computers. Apply here: https://t.co/9F0n0A3F2H
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@MvsCerezo Do you know how to enhance #Quantum Neural Networks' performance on #imageclassification? @SyChang97 of #CERNqti will tell you all about it in her upcoming presentation at #QTML2023. 🔗 https://t.co/ZzYsLMtXeF
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[Press Release] CERN inaugurates Science Gateway, its new outreach centre for science education Find out more: https://t.co/arEwEZAtmO
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Applications are now open to intern with IBM Quantum for summer of 2024! Interns have the opportunity to work directly with researchers, developers, and business experts to advance the field of quantum computing. Learn more and apply today:
ibm.com
IBM Research Global Internship Program applications for 2024 are now closed.
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Thrilled to share the preprint version of my latest research paper, "Approximately Equivariant Quantum Neural Network for p4m Group Symmetries in Images"!! Stay also tuned for its formal debut in the #IEEE Quantum Week 2023 proceedings 😊
🔔New paper alert: Explore how Equivariant Quantum Convolutional Neural Networks can improve #imageclassification on quantum devices. The network considers the dataset symmetry for better optimisation and generalisation: https://t.co/n2Dd6CHRcN
#CERNqti #QuantumMachineLearning
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Very nice work from our group! @SyChang97 congrats!
🔔New paper alert: Explore how Equivariant Quantum Convolutional Neural Networks can improve #imageclassification on quantum devices. The network considers the dataset symmetry for better optimisation and generalisation: https://t.co/n2Dd6CHRcN
#CERNqti #QuantumMachineLearning
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Excited for our new paper showing in a quantum context that backpropagation is not "just the chain rule" with a deep need for information reuse that is counter to our intuition about quantum measurement collapse! Highlights below, but check out https://t.co/12ExYijYoR (1/n)
In collaboration with researchers at @Caltech and @GoogleQuantumAI we have a new research paper demonstrating a core problem in current quantum machine learning proposals: optimization using gradients will NOT scale as efficiently as neural networks equipped with backpropagation
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