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From football to physics




Zachary Hulcher was once set on becoming a lawyer. In high school, he took part in mock trials and competed in youth judicial, playing the role of legal counsel and presenting cases in front of a student jury. He says his inspiration came partly from the television show Law and Order: “There’s drama, there’s action, you send people to jail, and you get to argue with people — and I loved arguing with people.”

But all that changed one day, sometime during his junior year, when he happened to flip through his physics textbook. In an idle moment at school, he turned to the very back of the book and started to read the chapter about special relativity.

Physics, he discovered, put mathematics and science into an almost fantastical perspective. “Ideas that come out of that one chapter are time travel, atomic bombs, things warping when they go really fast, and all these things that shouldn’t be real, but are,” Hulcher says.

Hulcher is currently a senior at MIT, majoring in physics as well as computer science and electrical engineering, with a minor in math. “I love the creative process and figuring out how elegant solutions to real problems arise out of seeming chaos,” he says.

He is a recipient of the 2017 Marshall Scholarship, awarded each year to up to 40 U.S. students who will pursue graduate degrees at universities in the United Kingdom. Next year, Hulcher will be working toward a PhD in high energy physics at Cambridge University, where he hopes to work on both experimental and theoretical problems of the Standard Model of particle physics, which governs every aspect of the known universe except for gravity.

“Beautiful math”

Hulcher was born and raised in Montgomery, Alabama. His mother and father are managers for Alabama’s environmental management agency. Hulcher grew up playing basketball with his younger brother in the family’s backyard. The brothers, who towered over their classmates — Hulcher is 6 feet 4 inches tall and his “little” brother, Jacob, is 6 feet 8 inches — joined their church league, and eventually played for their middle and high school teams.  

Along with basketball, Hulcher played football and was on the track and field team, balancing an unrelenting schedule of games and practices with an increasingly challenging course load. Hulcher attended the Montgomery Catholic Preparatory School System from kindergarten through high school in Montgomery, where he was valedictorian and a National Merit Scholar. In his freshman year he began taking math and physics classes with Joe Profio, a teacher who, recognizing that Hulcher was one of the top students in his class, urged him to join the school’s math teams.

Hulcher soon found himself taking long drives to math competitions across the state with Profio and his classmates. During those drives, Profio would talk about math at a deeper level than he could present in class, and Hulcher credits his passion for physics and math to these inspiring talks.

“Our conversations obliterated the idea that the only beauty in the world is found in an imaginary place in a book — beauty was all around me, if I would only look through the right lens,” Hulcher says.

It was around that time that Hulcher says “the wheels started cranking to do science.” The answer to how and where to direct this newfound momentum came from an unlikely source, another TV show.

“I was watching NCIS one day, and one of the characters is from MIT, and I thought, ‘I’m starting to like more science. I should apply there,’ and I did,” Hulcher recalls.

Computing, a physics problem

When Hulcher set foot on the campus for the first time — also the first time he had been anywhere north of Washington, D.C. — he was immediately drawn to the physics seminars held during Campus Preview Weekend.

“I remember an event called something like ‘physics til you drop,’ and two students were standing at a blackboard, doing physics until 5 or 6 am, long past when I could stay awake,” Hulcher says. “People would ask them questions about quantum mechanics, string theory, general relativity, anything, and they would try to answer them on the board. I was pretty hooked.”

He quickly landed on physics as a major but also chose computer science and electrical engineering, a decision based largely on conversations with his roommate, who was also majoring in the subject. When Hulcher took classes that explored quantum computing — the idea that quantum elements such as elementary particles can perform certain calculations vastly more efficiently than classical computers — he realized “all of computing is not just a computer science problem. It’s a physics problem. That’s just cool.”

Seeing through plasma

In the summer following his sophomore year, Hulcher traveled to Geneva, Switzerland, to work at the Compact Muon Solenoid experiment (CMS) at CERN’s Large Hadron Collider, the world’s largest and most powerful particle accelerator. There, he helped to implement an alarm system that monitors the accelerator’s major systems and distributes information to key people in the event of a failure.

He returned again the following summer, this time as a theorist. The LHC uses giant magnets to steer beams of atoms, such as lead ions, toward each other at close to the speed of light. Hulcher, working as a research assistant with Krishna Rajagopal of MIT’s Department of Physics and the Center for Theoretical Physics, was interested in the hot plasma of quarks and gluons produced when two lead ions collide.

“The plasma doesn’t last very long before it returns to some other state of matter,” Hulcher says. “You don’t even have time to blast it with light to see it; it would just disappear before the light got there. So you need to use events inside it to study it.”

Those events involve jets of particles that spew out from the plasma following a collision between two lead ions. Hulcher worked with Rajagopal and Daniel Pablos, a University of Barcelona graduate student, to help implement a model for how these jets of particles propagate through the resulting plasma. Hulcher recently helped to present the team’s results at a workshop in Paris and is finishing up a paper to submit to a journal — his first publication.

The prism of physics

In addition to his research work, Hulcher has racked up a good amount of teaching experience. As a teaching assistant for MIT’s Department of Physics, he has graded weekly problem sets for classes in classical mechanics and electricity and magnetism. He tutors fellow students in electrical engineering and computer science subjects, and he has spent the last year as eligibles chair of the MIT chapter of the engineering honor society Tau Beta Pi. Through the MIT International Science and Technology Initiatives (MISTI), Hulcher has traveled around the world, to Italy, Mexico, and most recently, Israel, teaching students subjects including physics, electrical engineering, and entrepreneurship.

Of all the relationships he’s developed through his time at MIT, he counts those with most of his teammates as some of the strongest. Hulcher joined MIT’s football team as a freshman offensive lineman; he says he will remember hanging out on long nights, p-setting with his friends from the football team. He will also remember MIT as a really long rollercoaster, he says.

As for what’s next, Hulcher says the plan for now is “to keep liking physics.” If that happens, he hopes to become a researcher and professor, to help students see the world through physics.

“I fell in love with physics,” Hulcher says. “I appreciate light bouncing off a mirror, and smoke billowing up, and light moving through it in a different way. I appreciate looking up at the stars and thinking about what’s out there. The small things I took for granted when I didn’t know much about them, I appreciate now. Everything is just a little prettier.”

Topics: Profile, Students, Awards, honors and fellowships, Energy, Mathematics, MISTI, Physics, Quantum computing, Undergraduate, School of Science, School of Engineering, SHASS, Athletics, Sports and fitness, Laboratory for Nuclear Science, Center for Theoretical Physics



Probing nonclassicality with matrices of phase-space distributions




Martin Bohmann1,2, Elizabeth Agudelo1, and Jan Sperling3

1Institute for Quantum Optics and Quantum Information – IQOQI Vienna, Austrian Academy of Sciences, Boltzmanngasse 3, 1090 Vienna, Austria
2QSTAR, INO-CNR, and LENS, Largo Enrico Fermi 2, I-50125 Firenze, Italy
3Integrated Quantum Optics Group, Applied Physics, Paderborn University, 33098 Paderborn, Germany

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We devise a method to certify nonclassical features via correlations of phase-space distributions by unifying the notions of quasiprobabilities and matrices of correlation functions. Our approach complements and extends recent results that were based on Chebyshev’s integral inequality [65]. The method developed here correlates arbitrary phase-space functions at arbitrary points in phase space, including multimode scenarios and higher-order correlations. Furthermore, our approach provides necessary and sufficient nonclassicality criteria, applies to phase-space functions beyond $s$-parametrized ones, and is accessible in experiments. To demonstrate the power of our technique, the quantum characteristics of discrete- and continuous-variable, single- and multimode, as well as pure and mixed states are certified only employing second-order correlations and Husimi functions, which always resemble a classical probability distribution. Moreover, nonlinear generalizations of our approach are studied. Therefore, a versatile and broadly applicable framework is devised to uncover quantum properties in terms of matrices of phase-space distributions.

The intuitively accessible representation of quantum effects via quasiprobabilities, defying the nonnegativity requirement of classical probabilities, is a common technique to identify quantum features. However, the complexity of the reconstruction of such distributions increases with their sensitivity to uncover nonclassical signatures. Conversely, approaches based on correlation functions are experimentally available but less intuitive.

The method devised in our paper overcomes such disadvantageous features by unifying both aforementioned techniques. That is, quasiprobabilities can be correlated to unveil nonclassical properties even if the individual distributions are not sensitive enough to identify quantum properties. For example, it is shown that this necessary and sufficient approach applies to discrete- and continuous-variable, single- and multimode, pure and mixed states of light using phase-space distributions that can never become negative.

Thereby, we demonstrate the usefulness of our novel method to certify quantum characteristics in a practical manner that formthe basis for current and future quantum technologies.

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► References

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[96] The approximate POVM element in Eq. (43) has a decomposition in terms of lossy even photon-number operators with the expansion coefficients $[(2n)!/​n!](chi/​eta^2)^n$, which diverge for $ntoinfty$. Using the bounds $sqrt{2pi}m^{m+1/​2}e^{-m}leq m!leq e m^{m+1/​2}e^{-m}$, one finds the bound $chill eeta^2/​[4n]$ to satisfy $[(2n)!/​n!](chi/​eta^2)^nleq [e/​sqrtpi]([4nchi]/​[eeta^2])^nleq 1$ for correctly applying this approximation for upto $2n$ photons. Also note that for coherent states, one obtains the nonnegative function $langlealpha|hatPi|alpharangle=exp(-eta|alpha|^2+chi|alpha|^4)geq0$, representing the non-Gaussian integration kernel $Omega$.

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[100] J. Park, J. Lee, and H. Nha Verifying nonclassicality beyond negativity in phase space, arXiv:2005.05739 [quant-ph]; J. Park and H. Nha, Efficient and faithful criteria on nonclassicality for continuous variables, presented at 15th International Conference on Squeezed States and Uncertainty Relations, Jeju, South Korea, 2017.

Cited by

[1] Jiyong Park, Jaehak Lee, and Hyunchul Nha, “Verifying nonclassicality beyond negativity in phase space”, arXiv:2005.05739.

[2] Nicola Biagi, Martin Bohmann, Elizabeth Agudelo, Marco Bellini, and Alessandro Zavatta, “Experimental certification of nonclassicality via phase-space inequalities”, arXiv:2010.00259.

The above citations are from SAO/NASA ADS (last updated successfully 2020-10-16 02:17:01). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2020-10-16 02:16:59).


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A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time




Jonathan Allcock and Chang-Yu Hsieh

Tencent Quantum Laboratory

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We propose a quantum algorithm for training nonlinear support vector machines (SVM) for feature space learning where classical input data is encoded in the amplitudes of quantum states. Based on the classical SVM-perf algorithm of Joachims [1], our algorithm has a running time which scales linearly in the number of training examples $m$ (up to polylogarithmic factors) and applies to the standard soft-margin $ell_1$-SVM model. In contrast, while classical SVM-perf has demonstrated impressive performance on both linear and nonlinear SVMs, its efficiency is guaranteed only in certain cases: it achieves linear $m$ scaling only for linear SVMs, where classification is performed in the original input data space, or for the special cases of low-rank or shift-invariant kernels. Similarly, previously proposed quantum algorithms either have super-linear scaling in $m$, or else apply to different SVM models such as the hard-margin or least squares $ell_2$-SVM which lack certain desirable properties of the soft-margin $ell_1$-SVM model. We classically simulate our algorithm and give evidence that it can perform well in practice, and not only for asymptotically large data sets.

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► References

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Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design




Xiu-Zhe Luo1,2,3,4, Jin-Guo Liu1, Pan Zhang2, and Lei Wang1,5

1Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
2Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
3Department of Physics and Astronomy, University of Waterloo, Waterloo N2L 3G1, Canada
4Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada
5Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China

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We introduce $texttt{Yao}$, an extensible, efficient open-source framework for quantum algorithm design. $texttt{Yao}$ features generic and differentiable programming of quantum circuits. It achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications. We introduce the design principles and critical techniques behind $texttt{Yao}$. These include the quantum block intermediate representation of quantum circuits, a builtin automatic differentiation engine optimized for reversible computing, and batched quantum registers with GPU acceleration. The extensibility and efficiency of $texttt{Yao}$ help boost innovation in quantum algorithm design.

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Cited by

[1] Feng Pan, Pengfei Zhou, Sujie Li, and Pan Zhang, “Contracting Arbitrary Tensor Networks: General Approximate Algorithm and Applications in Graphical Models and Quantum Circuit Simulations”, Physical Review Letters 125 6, 060503 (2020).

[2] Jin-Guo Liu, Liang Mao, Pan Zhang, and Lei Wang, “Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits”, arXiv:1912.11381.

[3] Sirui Lu, Lu-Ming Duan, and Dong-Ling Deng, “Quantum adversarial machine learning”, Physical Review Research 2 3, 033212 (2020).

[4] Tatiana A. Bespalova and Oleksandr Kyriienko, “Hamiltonian operator approximation for energy measurement and ground state preparation”, arXiv:2009.03351.

[5] Tong Liu, Jin-Guo Liu, and Heng Fan, “Probabilistic Nonunitary Gate in Imaginary Time Evolution”, arXiv:2006.09726.

[6] Jin-Guo Liu, Lei Wang, and Pan Zhang, “Tropical Tensor Network for Ground States of Spin Glasses”, arXiv:2008.06888.

[7] Jin-Guo Liu and Taine Zhao, “Differentiate Everything with a Reversible Domain-Specific Language”, arXiv:2003.04617.

[8] Carsten Bauer, “Fast and stable determinant quantum Monte Carlo”, arXiv:2003.05286.

[9] Chen Zhao and Xiao-Shan Gao, “QDNN: DNN with Quantum Neural Network Layers”, arXiv:1912.12660.

[10] The Quingo Development Team, “Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features”, arXiv:2009.01686.

[11] Andrea Mari, Thomas R. Bromley, and Nathan Killoran, “Estimating the gradient and higher-order derivatives on quantum hardware”, arXiv:2008.06517.

[12] Stavros Efthymiou, Sergi Ramos-Calderer, Carlos Bravo-Prieto, Adrián Pérez-Salinas, Diego García-Martín, Artur Garcia-Saez, José Ignacio Latorre, and Stefano Carrazza, “Qibo: a framework for quantum simulation with hardware acceleration”, arXiv:2009.01845.

[13] Vincent Paul Su, “Variational Preparation of the Sachdev-Ye-Kitaev Thermofield Double”, arXiv:2009.04488.

The above citations are from SAO/NASA ADS (last updated successfully 2020-10-16 04:52:30). The list may be incomplete as not all publishers provide suitable and complete citation data.

On Crossref’s cited-by service no data on citing works was found (last attempt 2020-10-16 04:52:29).


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