Giacomo Torlai

Happy to introduce PastaQ, a Julia package for simulating and benchmarking near-term #quantum computers, using a combination of tensor networks and #MachineLearning algorithms. github.com/GTorlai/PastaQ… Really fun project with Matt Fishman @FlatironCCQ, supported by @SimonsFdn.

@michael_nielsen Let’s not forget the ten sex chromosomes

Platypi are self-advertising! A semiaquatic, egg-laying, duck-billed, beaver-tailed, venomous mammal, who sense prey using electrolocation, and whose males have a venomous spur on the back foot(!!!) I mean, really? (Text adapted from wikipedia.)

@ITensorLib Amazing

Our new ITensor tech support and discussion forum is live! itensor.discourse.group Be one of the first to make an account and start a new discussion. Thanks to discourse.org for hosting this for us.

@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)

Hey #QuantumTwitter, what are your "must-read" quantum information/optics books? (review papers count) 📚🧐

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: arxiv.org/abs/2203.15719 @QManyBody @MKebric @harvardphysics @MCQST_cluster

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 arxiv.org/abs/2203.04984

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: arxiv.org/abs/2203.04988 @MossSchuyler @MRadzihovsky @rgmelko

Check out my new preprint: arxiv.org/abs/2202.05850 Stripe formation in systems of strongly interacting electrons can lead to a "fragmentation" of the superconducting condensate.

@sCzischek @uOttawa @uOttawaResearch Congrats!! 🎉🎉

I'm starting 2022 with great excitement! It will for sure be an interesting year as I'm looking forward to joining @uOttawa's Physics Department as an assistant professor in summer 🤩 @uOttawaResearch

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 bit.ly/HybridJobs

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 scirate.com/arxiv/2111.080…

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) discourse.julialang.org/t/scientific-s…

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!

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 go.aps.org/3F9HNrM

Excited to be in our new building with an extraordinary team, working hard to push forward quantum computing @awscloud @Caltech washingtonpost.com/technology/202…

[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. washingtonpost.com/technology/202…

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 rdcu.be/cz3VS nature.com/articles/s4225…

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 arxiv.org/abs/2110.03676

From the @awscloud Quantum Computing Blog: Mario Berta on generating randomness with quantum processors, including an implementation on Amazon Braket you can try yourself. aws.amazon.com/blogs/quantum-…

Are you interested in quantum circuit compilation and optimization? We are looking for applied scientists to join our team at Amazon Braket. amazon.jobs/en/jobs/146882…

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! colab.research.google.com/github/CRC183-…