Su Yeon Chang

PhD student

[Marco Cerezo @MvsCerezo]

Extremely proud to see our paper "Quantum Convolutional Neural Networks are Effectively Classically Simulable" published in @PRX_Quantum

https://t.co/1UGukJYQLv

This is an instantiation of our work, provable absence of barren plateaus implies classical simulability.

[Martin Larocca @MartinLaroo]

New paper out: Classical Shadows with Arbitrary Group Representations (see https://t.co/O7zBDf52Ew)
1/11

[Su Yeon Chang @SyChang97]

Huge thanks to my co-authors @MartinLaroo and @MvsCerezo !

If you're attending the APS Global Physics Summit, we’d be happy to see you at our talk:
🗓️ Tuesday, March 17
⏰ 9:12 AM
📍Mile High Ballroom 1D

[Su Yeon Chang @SyChang97]

🚀 New paper out!

Happy to share our latest work:
“A Practical Framework for Simulating Permutation-Equivariant Quantum Circuits.”
https://t.co/m8RxNis1SJ

[Su Yeon Chang @SyChang97]

This work introduces a practical classical simulation framework for permutation-equivariant quantum circuits with runtime scaling O(n⁴), improving upon previous methods.
To demonstrate its practicality, we simulate circuits with up to 512 qubits, running in under 2 minutes.

[Ricard Puig @q_pvricard]

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.

[Marco Cerezo @MvsCerezo]

#QTML2025 Announced for Singapore!

[Mark M. Wilde @markwilde]

"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

[Su Yeon Chang @SyChang97]

Thrilled to announce that our paper has been finally published! A big thank you to all our collaborators!

[PRX Quantum @PRX_Quantum]

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

[PRX Quantum @PRX_Quantum]

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

[Marco Cerezo @MvsCerezo]

🚨🚨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 [email protected]

[Su Yeon Chang @SyChang97]

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.

[Su Yeon Chang @SyChang97]

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

[Su Yeon Chang @SyChang97]

✅ 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.

[CERN Quantum Technology Initiative (CERN QTI) @CERNquantum]

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

[Francesco Tacchino @quantum_of_me]

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)

[Marco Cerezo @MvsCerezo]

🚨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

[CERN Quantum Technology Initiative (CERN QTI) @CERNquantum]

@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