Giacomo Torlai

Research Scientist at @awscloud Center for Quantum Computing | Prev @FlatironInst, @Perimeter/@UWaterloo, @LMU_Muenchen. Views are my own.

[Giacomo Torlai @giactorlai]

Happy to introduce PastaQ, a Julia package for simulating and benchmarking near-term #quantum computers, using a combination of tensor networks and #MachineLearning algorithms.

https://t.co/LEeoAOUjKQ

Really fun project with Matt Fishman @FlatironCCQ, supported by @SimonsFdn.

[Giacomo Torlai @giactorlai]

@michael_nielsen Let’s not forget the ten sex chromosomes

[Giacomo Torlai @giactorlai]

@ITensorLib Amazing

[Giacomo Torlai @giactorlai]

@AzizaS A good addition/alternative to Nielsen and Chuang is “Principles of Quantum Computation and Information”, by Benenti, Casati and Strini (there is a recent edition too)

[Annabelle Bohrdt @aBohrdt]

In our latest work, we introduce an active learning 🤓 scheme to efficiently reconstruct a quantum state ⚛️ by choosing the measurement configurations adaptively — check out the details here: https://t.co/ftnXKJHs2E @QManyBody @MKebric @harvardphysics @MCQST_cluster

[Juan Felipe Carrasquilla Álvarez @carrasqu]

Roeland Wiersema's new preprint: if your quantum algorithm uses a small number of gates that can't be physically implemented, you can emulate their action classically through measurement and re-preparation on the qubits where the gates act https://t.co/MEILDUkBoH

[Stef Czischek @sCzischek]

Your RNN-based VMC needs long runtimes to converge? Speed it up with a data-driven initialization!
Check out our preprint to see how a small amount of measurement data can significantly improve the performance of VMC: https://t.co/lpeqJhXezz
@MossSchuyler @MRadzihovsky @rgmelko

[Alexander Wietek @AlexanderWietek]

Check out my new preprint: https://t.co/Rm0MNZAqCG

Stripe formation in systems of strongly interacting electrons can lead to a "fragmentation" of the superconducting condensate.

[Giacomo Torlai @giactorlai]

@sCzischek @uOttawa @uOttawaResearch Congrats!! 🎉🎉

[Eric Kessler @QuantumKessler]

Running quantum-classical workloads in the @awscloud just became a lot easier and more performant. With today’s launch of Amazon Braket Hybrid Jobs, you just need to provide your algorithm, and Braket will take care of the rest. Try it out today https://t.co/BZzWBSfzyL

[Juan Felipe Carrasquilla Álvarez @carrasqu]

New preprint in collaboration Roeland Wiersema, Cunlu Zhou, and Yong-Baek Kim. We provide evidence that variational quantum circuits display measurement induced entanglement transitions. Also interspersed measurements could alleviate barren plateaus https://t.co/52iXqB59Jm

[ITensor @ITensorLib]

We are hiring! We have a 2-3 year postdoctoral equivalent position focused on scientific software development of the ITensor Library in the Julia language (1/3) https://t.co/adi4qmXeVC

[Katiuscia Cassemiro @KatiusciaNC]

We are pleased to offer this hands-on guide that elucidates key operational aspects of machine learning and how to apply it for tackling condensed-matter problems. Thanks to @giactorlai and @carrasqu for the exceptional work!

[PRX Quantum @PRX_Quantum]

Tutorial: A newcomer guide on applying tools from #MachineLearning to solve problems in condensed-matter physics and quantum information: From key ingredients to the implementation. @giactorlai @carrasqu

https://t.co/GdNgBlRgUa

[Fernando Brandao @fgslbrandao]

Excited to be in our new building with an extraordinary team, working hard to push forward quantum computing @awscloud @Caltech

https://t.co/JIP2phsrYb

[John Preskill @preskill]

[Amazon] will benefit hugely from having access to the Caltech community, the physicists said. “This is really one of the best places on Earth for quantum computing,” [Fernando] Brandao said.

https://t.co/HOwR5nKHaZ

[Mohamed Hibat-Allah @MedHibatAllah]

Very honored to share “Variational Neural Annealing” published in Nature Machine Intelligence. In collaboration with @InackEstelle, Roeland Wiersema, @rgmelko, and @carrasqu. @UWaterloo @VectorInst @Perimeter @SpringerNature
https://t.co/XgnmKQKfBR
https://t.co/V6sNOHe9vT

[Roger Melko @rgmelko]

Anna Golubeva @_anna_go shows how pruning can be used to produce more efficient neural networks for the reconstruction of quantum states @UWaterloo @Perimeter @iaifi_news @MIT
https://t.co/NzHUy2Pvxm

[John Preskill @preskill]

From the @awscloud Quantum Computing Blog: Mario Berta on generating randomness with quantum processors, including an implementation on Amazon Braket you can try yourself.
https://t.co/ONXroYISRo

[Eric Kessler @QuantumKessler]

Are you interested in quantum circuit compilation and optimization? We are looking for applied scientists to join our team at Amazon Braket. https://t.co/X04H9SIBvB

[Giuseppe Carleo @gppcarleo]

Interested in neural quantum states? Check out this NetKet 3.0 @NetKetOrg tutorial I recently gave! You can learn how to build and use complex JAX models for wave functions, learn ground states, apply symmetries, train wave-function phases and more! https://t.co/NueYmjpSYk