@LCincio

Staff scientist at Los Alamos National Laboratory. I work on quantum computing, tensor networks and machine learning.

Joined July 2020
  • Tweets39
  • Following26
  • Followers731
Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo5 Dec 2024
🚨Applications for LANL's 2025 Quantum Computing Summer School are open! Please apply here 👇 lanl-summer-school-programs.… Reposts appreciated!
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Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo3 May 2024
🚨Today I am very happy to announce our review of the Barren Plateau (BP) literature in variational quantum computing. 🎆 We hope that this work will be useful for those who want to catch up with the recent developments on BPs! Link: arxiv.org/abs/2405.00781 See 🧵below 1/6
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Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo16 Jan 2024
🚨⚛️Friendly reminder that the deadline for applications to the 2024 Los Alamos Quantum Computing Summer School is *this* Sunday 👉Jan 21st 👈! If you haven't finished, or started, the application go do it now! lanl-summer-school-programs.… RTs appreciated!
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Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo15 Nov 2023
🚨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: lanl-summer-school-programs.…
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A new classical simulation kid is on the block 👲And we are benchmarking it against everyone's favorite example. Via classical surrogate simulation of 127-qubit systems, we can evaluate new circuit angles in fractions of a second on a laptop. scirate.com/arxiv/2308.09109 🧵1/7
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Lukasz Cincio retweeted
Matt Sims-Goh@Matt_L_Goh5 Aug 2023
⚛️🔬 Our new work out today explores Lie-algebraic classical simulations for variational quantum computing. This work leverages the underlying Lie algebra of the circuit's gate generators to achieve (under certain conditions) efficient simulations. arxiv.org/abs/2308.01432

Lie-algebraic classical simulations for quantum computing

The classical simulation of quantum dynamics plays an important role in our understanding of quantum complexity, and in the development of quantum technologies. Efficient techniques such as those...

arxiv.org
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Applications are now open for the 2023 Los Alamos Quantum Computing Summer School! Our school focuses on theory, applications, and programming of quantum computers. Apply here: smr.to/p85950
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Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo11 Oct 2022
📢 Our team at LANL is looking to hire postdocs to work in quantum computing, quantum information, and quantum machine learning. 🚨 Please see the ad below! If you are interested please get in touch with me or @LCincio RTs appreciated! lanl.jobs/search/jobdetails/…
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Lukasz Cincio retweeted
Zoe Holmes@qZoeHolmes22 Apr 2022
Ok, paper 2⃣ of the day is on QML enhanced quantum simulation In one sense this is the ultimate refinement (i.e. most efficient) of our family of variational fast forwarding algorithms In another, it's opening up a new approach to designing algorithms for quantum simulation 👇
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Here goes my first #QML paper tweet in 2022: "Out-of-distribution generalization for learning quantum dynamics" (arxiv.org/abs/2204.10268)!

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of...

arxiv.org
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New paper out today! arxiv.org/abs/2204.03495 We show how to prepare the covariance matrix on a quantum computer to do quantum principal component analysis: use an ensemble average density matrix! Thanks to @MvsCerezo , @LCincio and @ColesQuantum for a great collaboration! 1/n

Covariance matrix preparation for quantum principal component analysis

Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been...

arxiv.org
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Our quantum computing group at Los Alamos National Laboratory is hiring a staff scientist to work in quantum information theory. Apply here: lanl.jobs/los-alamos-nm/quan…
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Applications are now open for the 2022 LANL Quantum Computing Summer School! Our school focuses on theory, applications, and programming of quantum computers. Apply here: smr.to/p78267
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PS: If you want to hear more about this work, please stop by my #QTML2021 talk tomorrow! You can livestream it via @riken_en's Youtube channel at youtu.be/9eqgFDIBpcQ.
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Today, it's time for me to tell you about a new paper on the theory of quantum machine learning (QML) that I'm particularly proud of: arxiv.org/abs/2111.05292

Generalization in quantum machine learning from few training data

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set...

arxiv.org
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New Result! Virtual distillation of purified states from noisy states can be made qubit efficient: scirate.com/arxiv/2102.06056 We achieve exponential error suppression using a constant number of qubits, by resetting and reusing qubits. @czarnik_piotr @AndrewArrasmit2 @LCincio
Andrew Arrasmith@AndrewArrasmit212 Feb 2021
Recently an exciting new approach to near-term quantum error suppression has arisen: virtual distillation. Unfortunately, this method is very qubit hungry. Piotr Czarnik, @LCincio, @ColesQuantum, and I show a way to make it more resource-efficient in the paper we just posted:
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Lukasz Cincio retweeted
Zoe Holmes@qZoeHolmes9 Feb 2021
Had a lot of fun working on this project with my summer students Joe and Kaitlin Here we battle the noise limitations of current quantum hardware to implement long-time high fidelity dynamical simulations 🥷⚛️🖥️ @gili_kaitlin @LCincio @ColesQuantum @sornborg
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Lukasz Cincio retweeted
Marco Cerezo@MvsCerezo18 Dec 2020
🔥Today on arXiv we bring you a Review on Variational Quantum Algorithms (VQAs) 🔥 scirate.com/arxiv/2012.09265 We present the framework of VQAs, their applications, challenges and potential solutions, and how they could bring quantum advantage in the near term.
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There has been some debate about how serious barren plateaus are for variational algorithms, with some people suggesting that just using a gradient free optimizer would solve the problem. Here is a new paper with @MvsCerezo Piotr Czarnik @LCincio @ColesQuantum proving otherwise.
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