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Jacob Biamonte
635 followers -
Lead of the Quantum Complexity Science Initiative
Lead of the Quantum Complexity Science Initiative

635 followers
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NetSci2018 goes (a bit) Quantum

En route to #netsci2018 Paris edition - Talks:

1. "Information theory unites quantum stat mech with complex network theory"
2. "Quantum walks on scale free networks"
3. "Information-Theoretic Tools for Complex Networks" with +Manlio De Domenico

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Research Scientist (multiple levels) – Tensor Networks/Hamiltonian Complexity/Machine Learning

Deep Quantum Labs is in the ongoing process of a expansion planned over the next several years as we continue to establish resident based international leadership in several core areas of theory related to quantum computation, tensor networks, Hamiltonian complexity and quantum machine learning.
We are seeking a Research Scientist(s) to join our lab (Level I, II or III depending on experience). We offer internationally competitive compensation, including a housing allowance, medical insurance and a sign-on bonus to cover moving expenses. As part of the expansion plan, we will open two Tenure Track Theory positions after the effort is further established.

Duties primarily include furthering the competitive research agenda of Deep Quantum Labs and assisting in the advising and day-to-day training of our extremely talented PhD Fellows. It would be preferable to also assist in some of the teaching actives related to our new MSc track on Quantum Computer Science.

Skolkovo Institute of Science and Technology (Skoltech) is a unique English-speaking international graduate only research university, located in Skolkovo, just outside of Moscow. Established in collaboration with M.I.T., Skoltech integrates the best Russian scientific traditions with twenty-first century innovation. Deep Quantum Labs is lead by Prof Jacob Biamonte and is proudly part of the Skolkovo Institute of Science and Technology.

Application procedure. Please send your CV (subject Research Scientist) and a statement (not more than 300 words) outlining your specific fit for this position and how you will contribute to our ongoing work to Jobs@DeepQuantum.AI

http://deepquantum.ai/research-scientist-multiple-levels-tensor-networks-hamiltonian-complexity-machine-learning/
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Quantum Hackathon
Join us 18 May and learn to program quantum computers.

More details: https://www.facebook.com/events/1794591293933358/
And here: DeepQuantum.AI
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My take on what current quantum processors should be used for.

“Quantum Machine Learning Matrix Product States”

Matrix product states minimize bipartite correlations to compress the classical data representing quantum states. Matrix product state algorithms and similar tools---called tensor network methods---form the backbone of modern numerical methods used to simulate many-body physics. Matrix product states have a further range of applications in machine learning. Finding matrix product states is in general a computationally challenging task, a computational task which we show quantum computers can accelerate. We present a quantum algorithm which returns a classical description of a k-rank matrix product state approximating an eigenvector given black-box access to a unitary matrix. Each iteration of the optimization requires O(n⋅k^2) quantum gates, yielding sufficient conditions for our quantum variational algorithm to terminate in polynomial-time.
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Looking forward to my lecture at Harvard next to a North American shortlist of "The Who's Who of the Quantum World."

Register here: https://cmsa.fas.harvard.edu/quantum-information/

Recent interview and filmed public lectures.
Recent public lecture in front of 1000+ attendees at #MLPrauge
- pannel discussion on machine learning https://bit.ly/2GKaBtd
- popular talk on quantum enhanced machine learning (download slides) https://bit.ly/2H5NYTb

Recent quantum computing interview (English and Italian, filmed in Trento)
- transcribed here https://bit.ly/2GwGR2N
- on youtube here https://bit.ly/2q5iGS3

Recent TASS interview (English and Russian)
- English https://bit.ly/2qfrS6k
- Russian https://bit.ly/2Et9rAp

Finished an N+1 interview last week and a video about new paper with a local news agency.
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A quantum algorithm to train neural networks using low-depth circuits

Verdon, Broughton, Biamonte

The question has remained open if near-term gate model quantum computers will offer a quantum advantage for practical applications in the pre-fault tolerance noise regime. A class of algorithms which have shown some promise in this regard are the so-called classical-quantum hybrid variational algorithms. Here we develop a low-depth quantum algorithm to train quantum Boltzmann machine neural networks using such variational methods. We introduce a method which employs the quantum approximate optimization algorithm as a subroutine in order to approximately sample from Gibbs states of Ising Hamiltonians. We use this approximate Gibbs sampling to train neural networks for which we demonstrate training convergence for numerically simulated noisy circuits with depolarizing errors of rates of up to 4%.

https://arxiv.org/abs/1712.05304
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Looking forward to speaking at Artificial Intelligence and Quantum Physics at Nanjing University
https://physics.nju.edu.cn/AIQP2017/ #Quantumcomputing #Quantum #MachineLearning #ArtificialIntelligence
AIQP-2017
AIQP-2017
physics.nju.edu.cn
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Excited to be speaking at Europe's premier machine learning mega event #MLPrague. The event looks epic. 1000+ attending, talks filmed and live streamed.
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Our quantum machine learning review has been published in Nature.

Early on the team burned the midnight oil over Skype debating what the field even was—our synthesis will hopefully solidify topical importance. We submitted our draft to Nature, going forward subject to significant changes: all-in-all we ended up writing three versions over 8 months with nothing more than the title in common.

Email me (on www.QuamPlexity.org) if you want a copy and can't get one.

It was a great collaboration and I'm sure glad it's done!

Direct link: http://www.nature.com/nature/journal/v549/n7671/full/nature23474.html

DOI: doi:10.1038/nature23474
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