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Trump Administration slashes basic science research while boosting space, AI and quantum tech funding

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The new fiscal year 2021 budget proposal from the Trump Administration would increase funding for research and development by $142 billion over the Administration’s previous year’s budget, but will still reduce overall spending for science and technology from alternative proposals coming from the U.S. House of Representatives.

Basic science funding would be hard hit under the Trump Administration priorities.

A rundown of all of the programs that would be cut under the Administration’s budget was published by Science Magazine and it includes:

  • National Institutes of Health: a cut of 7%, or $2.942 billion, to $36.965 billion
  • National Science Foundation (NSF): a cut of 6%, or $424 million, to $6.328 billion
  • Department of Energy’s (DOE’s) Office of Science: a cut of 17%, or $1.164 billion, to $5.760 billion
  • NASA science: a cut of 11%, or $758 million, to $6.261 billion
  • DOE’s Advanced Research Projects Agency-Energy: a cut of 173%, which would not only eliminate the $425 million agency, but also force it to return $311 million to the U.S. Department of the Treasury
  • U.S. Department of Agriculture’s (USDA’s) Agricultural Research Service: a cut of 12%, or $190 million, to $1.435 billion
  • National Institute of Standards and Technology: a cut of 19%, or $154 million, to $653 million
  • National Oceanic and Atmospheric Administration: a cut of 31%, or $300 million, to $678 million
  • Environmental Protection Agency science and technology: a cut of 37%, or $174 million, to $318 million
  • Department of Homeland Security science and technology: a cut of 15%, or $65 million, to $357 million
  • U.S. Geological Survey: a cut of 30%, or $200 million, to $460 million

However, certain areas where venture investors and startups spend a lot of time should see a funding boost. These include new money for research and development in industries developing new machine learning and quantum computing technologies.

Artificial intelligence allocations across the National Science Foundation, the Department of Energy’s Office of science, and the Defense Advanced Research Projects Agency and the Department of Defense’s Joint AI Center will reach a combined $1.724 billion — with portions of an additional $150 million allocation for the Department of Agriculture and the National Institutes of Health going to AI research.

Quantum information science is another area that’s set for a windfall of government dollars under the proposed Trump Administration Budget. The National Science Foundation will receive $210 million for quantum research, while the Department of Energy will receive a $237 million boost and an additional carve out of $25 million for the Depart of Energy to begin development of a nationwide Quantum Internet.

“Quantum computing, networking and sensing technologies are areas of incredible potential,” said Paul Dabbar, the under secretary for science at the Department of Energy.

As part of this development, Dabbar pointed to the work underway at the University of Chicago, where partners including the Argonne National Laboratory, Fermi Laboratory and the university have already launched a 52 mile quantum communication loop in Chicago.

There are plans underway to create six quantum internet nodes in the midwest and another node in Long Island near New York City to create a Northeastern quantum network hub.

“This will be the backbone of a national quantum internet extending coast to coast and border to border,” said Dabbar. “If we don’t, others will do it,” he said. “China and the EU have announced plans for investments in the area.”

White House reportedly aims to double AI research budget to $2B

Space is another area where spending will see a boost, under the Trump budget.

A key part of the package is a 12 percent boost to the budget of the National Aeronautics and Space Administration, as the administration aims to get astronauts back on the surface of the moon by 2024. In all, the new budget will add $3 billion to funding for NASA to develop things like human landers and other technologies to capitalize on the potential assets and strategic importance of space. In all NASA will receive $25.2 billion, while the newly created Space Force will see an allocation of $15.4 billion in the new budget.

The budget will double research and development spending for quantum information science and non-defense artificial intelligence by the 2022 fiscal year, according to a statement from the administration.

2021 NASA budget request includes $3.3B for human lunar landers, $430M for Moon resource development

Much of the administration’s budget seems focused on spending to catch up in areas where the U.S. may be losing its technological edge. China already spends tens of billions of dollars on research in both quantum computing and artificial intelligence.

While spending on quantum computing and artificial intelligence advances, the Trump Administration continues to slash budgets in other areas dependent on scientific study — where the discoveries of the scientific community and their implications contradict the political wishes of the President.

That includes the Environmental Protection Agency, which would see its total budget slashed by 26.5 percent over the next year. The Department of Health and Human Services would see its budget allocation shrink by 9 percent — although the administration actually plans to avoid cutting the budget for combating infections diseases through the Centers for Disease Control and Prevention.

Few of these allocations will actually make it through the Congressional budgeting process, since the Democrats control the House of Representatives and the most draconian parts of the budget proposed by the administration couldn’t even pass a Congress controlled by Republicans.

Read more: https://techcrunch.com/2020/02/10/trump-administration-slashes-basic-science-research-while-boosting-space-ai-and-quantum-tech-funding/

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President Reif testifies before Congress on U.S. competitiveness

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No U.S. strategy to respond to competition from China will succeed unless it includes increased investment in research, a concerted effort to attract more students to key research fields, and a more creative approach to turning ideas into commercial products, MIT President L. Rafael Reif said in congressional testimony on Wednesday, Feb. 26.

Reif spoke at a hearing of the House Ways and Means Committee on “U.S.-China Trade and Competition.”

“Whatever else the U.S. does to counter the challenges posed by China, we must increase our investment in research in key technology areas, and we must enhance our capacity to get the most out of that investment,” he told the panel. “U.S. strategy is unlikely to succeed if it is merely defensive; to stay ahead, the U.S. needs to do more to capitalize on our own strengths.”

Reif’s Capitol Hill appearance came immediately after he delivered an opening talk at a National Academy of Sciences (NAS)_event commemorating the 75th anniversary of “Science, The Endless Frontier,” a 1945 report to U.S. President Harry S. Truman that is seen as the founding document of the post-World War II research system in the U.S. The report was written by the late Vannevar Bush, who had a long career at MIT, including service as the Institute’s vice president and dean of engineering.

At both the NAS and on Capitol Hill, Reif called for a “visible, focused, and sustained” federal program that would increase funding for research and target the increase at key technologies, such as artificial intelligence, quantum computing, and advanced communications.

“The U.S. lacks an effective, coordinated way to target research toward specific areas and funding has fallen far behind what’s needed to stay ahead of our competitors,” Reif told Congress. “One promising proposal is to create a new directorate at the National Science Foundation with that mission, and giving that new unit the authority to be run more like the Defense Advanced Research Projects Agency (DARPA).”

Reif also said that attracting top talent is another essential element of a successful strategy. “At the university level, that requires two parallel tasks — attracting top U.S. students to key fields, and attracting and retaining the best researchers from around the world,” he said.

Specifically, he called for new programs to offer federal support to undergraduates, graduate students, and postdocs who are willing to study in fields related to key technologies. He also said foreign students who receive a U.S. doctorate should immediately be given a green card to settle in the U.S., and he warned against anti-immigrant rhetoric.

Finally, Reif said the U.S. needs to experiment with ways to speed the transition of ideas from lab to market. He called for new ways to de-risk technologies and to create more patient capital, and suggested that the Ways and Means Committee, which has jurisdiction over tax policy, should look at tax policies to create incentives for longer-term investment and to foster more university-industry cooperation.

“The U.S. edge in science and technology has been a foundation for U.S. security, prosperity, and quality of life,” Reif said, in conclusion. “But that edge has to be regularly honed; it is not ours by right or by nature. We can best sharpen it with a strategy founded on confidence in ourselves, not fear of others.”

Two weeks ago, Vice President for Research Maria Zuber delivered a similar message to Congress, in testimony before the House Permanent Select Committee on Intelligence on how to improve the intelligence services’ access to science and technology.

Zuber said that to help the intelligence services, the U.S. needs to capitalize on its strengths, which she said include “world-class universities, an open research system, and the ability to attract and retain top talent from around the world.”

Like Reif, Zuber highlighted a proposal to create a new technology directorate at the National Science Foundation, as well as the need to attract talent domestically and from abroad. She also cited MIT’s AI Accelerator — a cooperative project between MIT and the U.S. Air Force — as the kind of cooperative work that the intelligence services could foster.

In her testimony, Zuber emphasized the need to maintain an open U.S. research system: “The U.S. faces new challenges and competitors,” she said, “but we are well-placed to succeed if we get the most from our unrivaled strengths.”


Source: http://news.mit.edu/2020/president-reif-testifies-congress-us-competitiveness-0227

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Classical and Quantum Algorithms for Tensor Principal Component Analysis

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Matthew B. Hastings

Station Q, Microsoft Research, Santa Barbara, CA 93106-6105, USA
Microsoft Quantum and Microsoft Research, Redmond, WA 98052, USA

Find this paper interesting or want to discuss? Scite or leave a comment on SciRate.

Abstract

We present classical and quantum algorithms based on spectral methods for a problem in tensor principal component analysis. The quantum algorithm achieves a $quartic$ speedup while using exponentially smaller space than the fastest classical spectral algorithm, and a super-polynomial speedup over classical algorithms that use only polynomial space. The classical algorithms that we present are related to, but slightly different from those presented recently in Ref. [1]. In particular, we have an improved threshold for recovery and the algorithms we present work for both even and odd order tensors. These results suggest that large-scale inference problems are a promising future application for quantum computers.

► BibTeX data

► References

[1] Alexander S Wein, Ahmed El Alaoui, and Cristopher Moore. The kikuchi hierarchy and tensor pca. 2019. arXiv:1904.03858.
arXiv:1904.03858

[2] Andrea Montanari and Emile Richard. A statistical model for tensor pca. In Advances in Neural Information Processing Systems, pages 2897–2905, 2014.

[3] Thibault Lesieur, Leo Miolane, Marc Lelarge, Florent Krzakala, and Lenka Zdeborova. Statistical and computational phase transitions in spiked tensor estimation. In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, jun 2017. doi:10.1109/​isit.2017.8006580.
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[4] Samuel B Hopkins, Jonathan Shi, and David Steurer. Tensor principal component analysis via sum-of-square proofs. In Conference on Learning Theory, pages 956–1006, 2015.

[5] Samuel B. Hopkins, Tselil Schramm, Jonathan Shi, and David Steurer. Fast spectral algorithms from sum-of-squares proofs: tensor decomposition and planted sparse vectors. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing – STOC 2016. ACM Press, 2016. doi:10.1145/​2897518.2897529.
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[6] Vijay V. S. P. Bhattiprolu, Mrinalkanti Ghosh, Euiwoong Lee, Venkatesan Guruswami, and Madhur Tulsiani. Multiplicative approximations for polynomial optimization over the unit sphere. CoRR, abs/​1611.05998, 2016. URL: http:/​/​arxiv.org/​abs/​1611.05998, arXiv:1611.05998.
arXiv:1611.05998

[7] Vijay Bhattiprolu, Mrinalkanti Ghosh, Venkatesan Guruswami, Euiwoong Lee, and Madhur Tulsiani. Weak decoupling, polynomial folds and approximate optimization over the sphere. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, oct 2017. doi:10.1109/​focs.2017.97.
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[8] R.F. Werner. Large deviations and mean-field quantum systems. In Quantum Probability and Related Topics, pages 349–381. WORLD SCIENTIFIC, jul 1992. doi:10.1142/​9789814354783_0024.
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[9] Christina V. Kraus, Maciej Lewenstein, and J. Ignacio Cirac. Ground states of fermionic lattice hamiltonians with permutation symmetry. Physical Review A, 88(2), aug 2013. doi:10.1103/​physreva.88.022335.
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[10] Fernando G. S. L. Brandão and Aram W. Harrow. Product-state approximations to quantum states. Communications in Mathematical Physics, 342(1):47–80, jan 2016. doi:10.1007/​s00220-016-2575-1.
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[11] Scott Aaronson and Lijie Chen. Complexity-theoretic foundations of quantum supremacy experiments. In 32nd Computational Complexity Conference (CCC 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017. arXiv:1612.05903.
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[12] Madan Lal Mehta. Random matrices, volume 142. Elsevier, 2004.

[13] James Demmel, Ioana Dumitriu, and Olga Holtz. Fast linear algebra is stable. Numerische Mathematik, 108(1):59–91, oct 2007. doi:10.1007/​s00211-007-0114-x.
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[14] Guang Hao Low and Isaac L. Chuang. Optimal Hamiltonian simulation by quantum signal processing. Phys. Rev. Lett., 118:010501, 2017. arXiv:1606.02685v2, doi:10.1103/​PhysRevLett.118.010501.
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[15] Guang Hao Low and Isaac L Chuang. Hamiltonian simulation by qubitization. 2016. arXiv:1610.06546 https:/​/​doi.org/​10.22331/​q-2019-07-12-163.
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[16] Dominic W. Berry, Andrew M. Childs, Richard Cleve, Robin Kothari, and Rolando D. Somma. Exponential improvement in precision for simulating sparse Hamiltonians. pages 283–292, 2014. arXiv:1312.1414, doi:10.1145/​2591796.2591854.
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[17] Dominic W. Berry, Andrew M. Childs, Richard Cleve, Robin Kothari, and Rolando D. Somma. Simulating Hamiltonian dynamics with a truncated Taylor series. Phys. Rev. Lett., 114:090502, 2015. arXiv:1412.4687, doi:10.1103/​PhysRevLett.114.090502.
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[18] D. W. Berry, A. M. Childs, and R. Kothari. Hamiltonian simulation with nearly optimal dependence on all parameters. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, pages 792–809, Oct 2015. arXiv:1501.01715, doi:10.1109/​FOCS.2015.54.
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[19] Alexei Yu Kitaev, Alexander Shen, and Mikhail N Vyalyi. Classical and quantum computation, volume 47. American Mathematical Society Providence, 2002. doi:10.1090/​gsm/​047.
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[20] Gilles Brassard, Peter Hoyer, Michele Mosca, and Alain Tapp. Quantum amplitude amplification and estimation. Contemporary Mathematics, 305:53–74, 2002. doi:10.1090/​conm/​305.
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[21] Anthony Carbery and James Wright. Distributional and ${L^q}$ norm inequalities for polynomials over convex bodies in ${R^n}$. Mathematical Research Letters, 8(3):233–248, 2001. doi:10.4310/​mrl.2001.v8.n3.a1.
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[22] Dave Wecker, Matthew B. Hastings, Nathan Wiebe, Bryan K. Clark, Chetan Nayak, and Matthias Troyer. Solving strongly correlated electron models on a quantum computer. Physical Review A, 92(6), dec 2015. doi:10.1103/​physreva.92.062318.
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[23] Matthew B. Hastings. The asymptotics of quantum max-flow min-cut. Communications in Mathematical Physics, 351(1):387–418, nov 2016. doi:10.1007/​s00220-016-2791-8.
https:/​/​doi.org/​10.1007/​s00220-016-2791-8

Cited by

[1] Alexander S. Wein, Ahmed El Alaoui, and Cristopher Moore, “The Kikuchi Hierarchy and Tensor PCA”, arXiv:1904.03858.

The above citations are from SAO/NASA ADS (last updated successfully 2020-02-28 01:28:52). 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 2020-02-28 01:28:50).

Source: https://quantum-journal.org/papers/q-2020-02-27-237/

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Quantum

Analysing causal structures in generalised probabilistic theories

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Mirjam Weilenmann1,2 and Roger Colbeck2

1Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Boltzmanngasse 3, 1090 Vienna, AT
2Department of Mathematics, University of York, Heslington, York, YO10 5DD, UK

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Abstract

Causal structures give us a way to understand the origin of observed correlations. These were developed for classical scenarios, but quantum mechanical experiments necessitate their generalisation. Here we study causal structures in a broad range of theories, which include both quantum and classical theory as special cases. We propose a method for analysing differences between such theories based on the so-called measurement entropy. We apply this method to several causal structures, deriving new relations that separate classical, quantum and more general theories within these causal structures. The constraints we derive for the most general theories are in a sense minimal requirements of any causal explanation in these scenarios. In addition, we make several technical contributions that give insight for the entropic analysis of quantum causal structures. In particular, we prove that for any causal structure and for any generalised probabilistic theory, the set of achievable entropy vectors form a convex cone.

Quantum theory has many strange features that set it apart from the classical theory before it. Perhaps the most striking is the ‘spooky action at a distance’, where a quantum measurement on one particle leads to a state update on another that appears to go against the spirit of relativity theory, although without allowing superluminal signalling. In the 1960s Bell pinned this down more precisely by showing that describing quantum correlations in a causal model requires either dropping the notion of locality, or modifying the usual definition of causation.

In this work we consider modified notions of causation within a broad class of theories (generalised probabilistic theories) that include classical and quantum theory as special cases but also theories whose correlations are more ‘spooky’ than those in quantum mechanics. We show how to derive causal constraints within such theories, and apply our technique to a variety of causal structures. By studying causation in the most general post-quantum theory, we derive constraints that must hold for any causal explanations of correlations, and so can be understood as minimal requirements of causation itself. Furthermore, by comparing general constraints to those in quantum theory provides a way to investigate what is special about quantum mechanics.

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Cited by

[1] V. Vilasini and Roger Colbeck, “Analyzing causal structures using Tsallis entropies”, Physical Review A 100 6, 062108 (2019).

The above citations are from SAO/NASA ADS (last updated successfully 2020-02-27 12:15:38). The list may be incomplete as not all publishers provide suitable and complete citation data.

Could not fetch Crossref cited-by data during last attempt 2020-02-27 12:15:36: Could not fetch cited-by data for 10.22331/q-2020-02-27-236 from Crossref. This is normal if the DOI was registered recently.

Source: https://quantum-journal.org/papers/q-2020-02-27-236/

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