Ruslan Shaydulin

126 posts

Ruslan Shaydulin

Ruslan Shaydulin

@ruslanquantum

Quantum algorithms researcher @jpmorgan. Views my own.

Katılım Ekim 2020
126 Takip Edilen313 Takipçiler
Ruslan Shaydulin retweetledi
Google Quantum AI
Google Quantum AI@GoogleQuantumAI·
Tensor networks are becoming a vital tool in quantum computing. Originally used for simulating quantum systems, their applications now include: - Quantum circuit synthesis - Quantum error correction - Quantum machine learning Read more from Nature: goo.gle/4lQaKyB
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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
@Travis_Sch Quantum advantage, conventionally defined as a quantum computer performing a useful task that is impossible to do using classical computation alone, has already been attained: nature.com/articles/s4158… Attaining “large” $$ impact with quantum computers remains open, of course.
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Travis L. Scholten
Travis L. Scholten@Travis_Sch·
A completely-unscientific poll (re-post for further reach): Do you think quantum advantage will be attained prior to the realization of fault-tolerant quantum computers?
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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
It was a privilege to work on this milestone demonstration. This result proves beyond doubt that today's quantum computers combined with exascale supercomputers can perform a valuable task that no classical computer can to achieve on its own: certified randomness expansion.
Pradeep Niroula@NiroulaPradeep

Almost exactly a year ago, we ran an ambitious experiment. The goal was to make a quantum computer do something that was impossible before and would also be practically useful. Today, I am proud to announce the results of that work through a paper in Nature. In it, we use a quantum computer to realize the task of generating “Certified Randomness” — a special kind of randomness which comes with a ‘proof’ of randomness that can be verified by anyone. Randomness is used everywhere — in lotteries, in jury selections, in selecting who to audit. With certified randomness, you can be confident that such sensitive decisions are made fairly, using genuine randomness, untampered by a malicious adversary. You can be sure that the security keys you use for encryption come from a truly random source. What’s more, you can now prove the integrity of your system to others. This opens up new opportunities within cryptography. We can generally use certified randomness to augment existing entropy sources, towards making opaque decision processes more transparent, towards  ever more layers of trust. We present some initial thoughts in an accompanying paper. But clearly, there is a lot more to do. This work was made possible with a heroic collaboration between federal laboratories, universities, startups and financial institutions spread all across USA. This collaboration — which includes @jpmorgan, @QuantinuumQC , @argonne, @ORNL, @UTAustin — brought together theoretical expertise, state-of-the-art quantum computers and world’s most powerful supercomputers. From a long list of people behind this work, I would like to specially thank my closest collaborators @ruslanquantum and @henryken_liu for being a part of this journey, as well as JPMorganChase leadership — Marco Pistoia, Lori Beer and Jamie Dimon — for creating an environment that makes breakthroughs like this possible. pradeepniroula.com/certified-rand…

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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
@wjzeng @DulwichQuantum Note that Google’s result is not the only “beyond-classical” experiment that is robust against the new techniques. Another example is our recent work with Quantinuum: arxiv.org/abs/2406.02501 There’s no reason to believe that tensor networks can keep up with hardware progress.
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Will Zeng
Will Zeng@wjzeng·
The quote from Sergio Boxio in the article puts it well: "In our 2019 paper we said that classical algorithms would improve… but the key point is that quantum technology improves exponentially faster. So we don’t think this classical approach can keep up with quantum circuits in 2022 and beyond, despite significant improvements in the last few years."
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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
@alejomonbar Only a couple of minutes, with the runtime dominated by communication time. See Fig 5 in arxiv.org/abs/2309.04841 for details on scaling. The simulation is memory-bound, so if you have a bigger supercomputer, it’s relatively straightforward to run higher N.
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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
We show that the Quantum Approximate Optimization Algorithm (QAOA) achieves a quantum speedup over state-of-the-art classical algorithms. Our results are enabled by large-scale simulation using up to 1,024 GPUs on Polaris supercomputer hosted by @argonne_lcf
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Ruslan Shaydulin
Ruslan Shaydulin@ruslanquantum·
Consider registering for this excellent event! @marco_pistoia and myself will be there in person to chat with prospective candidates.
Chicago Quantum Exchange@ChicagoQuantum

Register for the Chicago Quantum Recruiting Forum by tomorrow @ 12:00 pm CT to be included in the resume book! This annual event brings together the rising generation of #quantum scientists with employers/educators for an event aimed at catalyzing careers: chicagoquantum.org/events/2024-ch…

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Ruslan Shaydulin retweetledi
Quantum
Quantum@quantumjournal·
Fresh in Quantum: Parameter Setting in Quantum Approximate Optimization of Weighted Problems by Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso, Shouvanik Chakrabarti, Yue Sun, and Marco Pistoia doi.org/10.22331/q-202…
Quantum tweet media
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