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

Los Angeles, CA
Joined December 2015
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.jl Really fun project with Matt Fishman @FlatironCCQ, supported by @SimonsFdn.
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Giacomo Torlai retweeted
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
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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
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Giacomo Torlai retweeted
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
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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
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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/2111.08035
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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!
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Giacomo Torlai retweeted
[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/technolog…
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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/s42256-0…
It took a while but our Variational Neural annealing paper has been published. Great work @MedHibatAllah @InackEstelle nature.com/articles/s42256-0…
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Giacomo Torlai retweeted
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
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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/1468825/…
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Giacomo Torlai retweeted
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/gi…
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Check out this overview of ITensors.jl by lead developer Matt Fishman! Sky is the limit when you have this amazing tensor-network package in your toolbox!! @FlatironCCQ @MStoudenmire
"What's new in ITensors.jl" by Matt Fishman happening now! youtube.com/watch?v=EIiq3TfC… live.juliacon.org
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As much as I enjoy Julia programming, this is spot on: “What’s bad about Julia?” viralinstruction.com/posts/b…
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Giacomo Torlai retweeted
Bosonic, Steane, QLDPC, surface
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Giacomo Torlai retweeted
Check out my @AWSblog post with Chris on the surface-GKP code! We tried to explain basic concepts of fault-tolerance, GKP qubits, and analog information from GKP error correction.
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