Changhun Oh

[QIP 2027 @QIPConference]

We're pleased to announce the QIP 2027 call for submissions. The key dates are as follows; refer to the website https://t.co/dDji42FJTD for detailed instructions.

[Changhun Oh @ChanghunOh6]

Research Group:
https://t.co/498TMIuNae

PI Homepage:
https://t.co/kZBvfbnUZK

[Changhun Oh @ChanghunOh6]

📢 We're hiring!

Postdoctoral position in Theoretical Quantum Information Science at KAIST (Daejeon, South Korea).

Research:
• Photonic Quantum Computing
• Bosonic QEC
• Quantum Benchmarking

Details:
https://t.co/wszllp0gxK

Please share!

#QuantumComputing #Postdoc

[Dmitry Grinko @dmitry_grinko]

A question for quantum info community and QIP program committee: how would you review AI-generated solutions to important problems in our field? There are already examples of very strong results which several teams claimed simultaneously. 1/n

[Michał Oszmaniec @MichalOszmaniec]

I am very happy to be part of this project on classical simulation of fermionic dynamics beyond the noninteracting regime.

In our new paper, we study fermionic systems initialized in a product of fermionic magic states and evolving under particle-number-preserving fermionic Gaussian transformations.

https://t.co/N0LyMNhQFm

While sampling from the resulting quantum states is believed to be hard for classical computers [1] , we prove that, quite surprisingly, several quantities related to this scenario can be classically approximated efficiently up to additive accuracy:
-output probabilities
-Majorana product expectation values in the output state
-transition amplitudes

These findings have consequences to quantum alghorithms used for quantum chemistry [2] and can be used to benchmark certain regimes of recent Phasecraft experiments implementing Fermi-Hubbard dynamics [3].

Many thanks to my amazing collaborators @ChanghunOh6 , @ZoltanZimboras , and Olivier Reardon-Smith.

[1] M Oszmaniec, N Dangniam, MES Morales, Z Zimborás, Fermion sampling: a robust quantum computational advantage scheme using fermionic linear optics and magic input states, PRX Quantum 3, 020328 (2022)

[2] W. J. Huggins, B. A. O’Gorman, N. C. Rubin, D. R. Reichman, R. Babbush, and J. Lee, Unbiasing fermionic quantum monte carlo with a quantum computer, Nature 603, 416 (2022).

[3] F Alam, Fermionic dynamics on a trapped-ion quantum computer beyond exact classical simulation, arXiv:2510.26300

[PRX Quantum @PRX_Quantum]

Noisy boson sampling is shown to retain classical hardness even when a logarithmic fraction of photons are mutually distinguishable.

https://t.co/YZEQhAs7Ck

[Changhun Oh @ChanghunOh6]

@chiaohsuan_wang Hey Chiao! my office is at this building. I didn’t know you were visiting KAIST. Let me know next time!

[Nature Physics @NaturePhysics]

Gaussian boson sampling reproduces distributions that are hard to calculate classically and were claimed to show quantum advantage in the noiseless limit. But now a classical algorithm is shown to reproduce experimental results when noise is large.
https://t.co/ztTYCq3vVR

[Senrui Chen @CSenrui]

Share our recent work (https://t.co/BKOQ8JYwba). We show that a bosonic random displacement channel can be learned to good precision efficiently and robustly with entanglement, while any entanglement-free scheme requires exponentially many samples to achieve the same task.

[Senrui Chen @CSenrui]

Our paper "Tight Bounds on Pauli Channel Learning without Entanglement" is now published at @PhysRevLett

https://t.co/SF99Ogixgs

[Nature Physics @NaturePhysics]

It has been suggested that Gaussian boson sampling may provide a quantum computational advantage for calculating the vibronic spectra of molecules. Now, an equally efficient classical algorithm has been identified.
https://t.co/TD7fltQddX

[PRX Quantum @PRX_Quantum]

A quantum-inspired classical method is employed to solve graph theory problems with a comparable performance to Gaussian boson sampling. @UChicagoPME

📝 https://t.co/LcBCHU146i