View allAll Photos Tagged Speedup
While photographing the graceful flight of the Yellow-billed Kite, he suddenly speedup, checking me out and nearly filled my camera’s viewfinder.
Hope you will enjoy this shot.
Please don't use this image on websites, blogs or other media without my explicit permission. © All rights reserved
My first amaryllis blossom of the season.
I have been trying the speedup the blooming for Christmas by having the potted bulbs on a heating pad and it has worked.
A NASA study has located the Antarctic glaciers that accelerated the fastest between 2008 and 2014 and finds that the most likely cause of their speedup is an observed influx of warm water into the bay where they're located.
In this image, a rock outcropping on Fleming Glacier, which feeds one of the accelerating glaciers in Marguerite Bay on the western Antarctic Peninsula.
Image credit: NASA/OIB
Barasso.
This is one of my favourite images from "21100, Varese" group workshop!
see others on my site.
Quantum annealing
Quantum physics-based metaheuristic for optimization problems
For other uses, see Annealing (disambiguation).
Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations.[1] Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima, such as finding the ground state of a spin glass or solving QUBO problems, which can encode a wide range of problems like Max-Cut, graph coloring, SAT or the traveling salesman problem.[2] The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm.[3][4] It was formulated in its present form by T. Kadowaki and H. Nishimori (ja) in 1998,[5] though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.[6]
Quantum annealing starts from a quantum-mechanical superposition of all possible states (candidate states) with equal weights. Then the system evolves following the time-dependent Schrödinger equation, a natural quantum-mechanical evolution of physical systems. The amplitudes of all candidate states keep changing, realizing a quantum parallelism, according to the time-dependent strength of the transverse field, which causes quantum tunneling between states or essentially tunneling through peaks. If the rate of change of the transverse field is slow enough, the system stays close to the ground state of the instantaneous Hamiltonian (also see adiabatic quantum computation).[7] If the rate of change of the transverse field is accelerated, the system may leave the ground state temporarily but produce a higher likelihood of concluding in the ground state of the final problem Hamiltonian, i.e., Diabatic quantum computation.[8][9] The transverse field is finally switched off, and the system is expected to have reached the ground state of the classical Ising model that corresponds to the solution to the original optimization problem. An experimental demonstration of the success of quantum annealing for random magnets was reported immediately after the initial theoretical proposal.[10] Quantum annealing has also been proven to provide a fast Grover oracle for the square-root speedup in solving many NP-complete problems.[11]
Comparison to simulated annealing
Quantum annealing can be compared to simulated annealing, whose "temperature" parameter plays a similar role to quantum annealing's tunneling field strength. In simulated annealing, the temperature determines the probability of moving to a state of higher "energy" from a single current state. In quantum annealing, the strength of transverse field determines the quantum-mechanical probability to change the amplitudes of all states in parallel. Analytical[12] and numerical[13] evidence suggests that quantum annealing outperforms simulated annealing under certain conditions (see Heim et al[14] and see Yan and Sinitsyn[15] for a fully solvable model of quantum annealing to arbitrary target Hamiltonian and comparison of different computation approaches).
Quantum mechanics: analogy and advantage
Simple analogy describing the difference between Simulated Annealing and Quantum Annealing.
Quantum Annealing (blue line) efficiently traverses energy landscapes by leveraging quantum tunneling to find the global minimum. Quantum annealing offers a significant performance advantage over Simulated Annealing (magenta line), unlocking the potential to solve massive optimization problems previously thought to be impossible.
The tunneling field is basically a kinetic energy term that does not commute with the classical potential energy part of the original glass. The whole process can be simulated in a computer using quantum Monte Carlo (or other stochastic technique), and thus obtain a heuristic algorithm for finding the ground state of the classical glass.
In the case of annealing a purely mathematical objective function, one may consider the variables in the problem to be classical degrees of freedom, and the cost functions to be the potential energy function (classical Hamiltonian). Then a suitable term consisting of non-commuting variable(s) (i.e. variables that have non-zero commutator with the variables of the original mathematical problem) has to be introduced artificially in the Hamiltonian to play the role of the tunneling field (kinetic part). Then one may carry out the simulation with the quantum Hamiltonian thus constructed (the original function + non-commuting part) just as described above. Here, there is a choice in selecting the non-commuting term and the efficiency of annealing may depend on that.
It has been demonstrated experimentally as well as theoretically, that quantum annealing can outperform thermal annealing (simulated annealing) in certain cases, especially where the potential energy (cost) landscape consists of very high but thin barriers surrounding shallow local minima.[16] Since thermal transition probabilities (proportional to
e
−
Δ
k
B
T
{\displaystyle e^{-{\frac {\Delta }{k_{B}T}}}}, with
T
{\displaystyle T} the temperature and
k
B
{\displaystyle k_{B}} the Boltzmann constant) depend only on the height
Δ
{\displaystyle \Delta } of the barriers, for very high barriers, it is extremely difficult for thermal fluctuations to get the system out from such local minima. However, as argued earlier in 1989 by Ray, Chakrabarti & Chakrabarti,[1] the quantum tunneling probability through the same barrier (considered in isolation) depends not only on the height
Δ
{\displaystyle \Delta } of the barrier, but also on its width
w
{\displaystyle w} and is approximately given by
e
−
Δ
w
Γ
{\displaystyle e^{-{\frac {{\sqrt {\Delta }}w}{\Gamma }}}}, where
Γ
{\displaystyle \Gamma } is the tunneling field.[17] This additional handle through the width
w
{\displaystyle w}, in presence of quantum tunneling, can be of major help: If the barriers are thin enough (i.e.
w
≪
Δ
{\displaystyle w\ll {\sqrt {\Delta }}}), quantum fluctuations can surely bring the system out of the shallow local minima. For an
N
{\displaystyle N}-spin glass, the barrier height
Δ
{\displaystyle \Delta } becomes of order
N
{\displaystyle N}. For constant value of
w
{\displaystyle w} one gets
τ
{\displaystyle \tau } proportional to
e
N
{\displaystyle e^{\sqrt {N}}} for the annealing time (instead of
τ
{\displaystyle \tau } proportional to
e
N
{\displaystyle e^{N}} for thermal annealing), while
τ
{\displaystyle \tau } can even become
N
{\displaystyle N}-independent for cases where
w
{\displaystyle w} decreases as
1
/
N
{\displaystyle 1/{\sqrt {N}}}.[18][19]
It is speculated that in a quantum computer, such simulations would be much more efficient and exact than that done in a classical computer, because it can perform the tunneling directly, rather than needing to add it by hand. Moreover, it may be able to do this without the tight error controls needed to harness the quantum entanglement used in more traditional quantum algorithms. Some confirmation of this is found in exactly solvable models.[20][21]
Timeline of ideas related to quantum annealing in Ising spin glasses:
1989 Idea was presented that quantum fluctuations could help explore rugged energy landscapes of the classical Ising spin glasses by escaping from local minima (having tall but thin barriers) using tunneling;[1]
1998 Formulation of quantum annealing and numerical test demonstrating its advantages in Ising glass systems;[5]
1999 First experimental demonstration of quantum annealing in LiHoYF Ising glass magnets;[22]
2011 Superconducting-circuit quantum annealing machine built and marketed by D-Wave Systems.[23]
D-Wave implementations
Further information: D-Wave Systems § Computer systems, and D-Wave Two
Photograph of a chip constructed by D-Wave Systems, mounted and wire-bonded in a sample holder. The D-Wave One's processor is designed to use 128 superconducting logic elements that exhibit controllable and tunable coupling to perform operations.
In 2011, D-Wave Systems announced the first commercial quantum annealer on the market by the name D-Wave One and published a paper in Nature on its performance.[23] The company claims this system uses a 128 qubit processor chipset.[24] On May 25, 2011, D-Wave announced that Lockheed Martin Corporation entered into an agreement to purchase a D-Wave One system.[25] On October 28, 2011 University of Southern California's (USC) Information Sciences Institute took delivery of Lockheed's D-Wave One.
In May 2013, it was announced that a consortium of Google, NASA Ames and the non-profit Universities Space Research Association purchased an adiabatic quantum computer from D-Wave Systems with 512 qubits.[26][27] An extensive study of its performance as quantum annealer, compared to some classical annealing algorithms, is available.[28]
In June 2014, D-Wave announced a new quantum applications ecosystem with computational finance firm 1QB Information Technologies (1QBit) and cancer research group DNA-SEQ to focus on solving real-world problems with quantum hardware.[29] As the first company dedicated to producing software applications for commercially available quantum computers, 1QBit's research and development arm has focused on D-Wave's quantum annealing processors and has demonstrated that these processors are suitable for solving real-world applications.[30]
With demonstrations of entanglement published,[31] the question of whether or not the D-Wave machine can demonstrate quantum speedup over all classical computers remains unanswered. A study published in Science in June 2014, described as "likely the most thorough and precise study that has been done on the performance of the D-Wave machine"[32] and "the fairest comparison yet", attempted to define and measure quantum speedup. Several definitions were put forward as some may be unverifiable by empirical tests, while others, though falsified, would nonetheless allow for the existence of performance advantages. The study found that the D-Wave chip "produced no quantum speedup" and did not rule out the possibility in future tests.[33] The researchers, led by Matthias Troyer at the Swiss Federal Institute of Technology, found "no quantum speedup" across the entire range of their tests, and only inconclusive results when looking at subsets of the tests. Their work illustrated "the subtle nature of the quantum speedup question". Further work[34] has advanced understanding of these test metrics and their reliance on equilibrated systems, thereby missing any signatures of advantage due to quantum dynamics.
There are many open questions regarding quantum speedup. The ETH reference in the previous section is just for one class of benchmark problems. Potentially there may be other classes of problems where quantum speedup might occur. Researchers at Google, LANL, USC, Texas A&M, and D-Wave are working to find such problem classes.[35]
In December 2015, Google announced that the D-Wave 2X outperforms both simulated annealing and Quantum Monte Carlo by up to a factor of 100,000,000 on a set of hard optimization problems.[36]
D-Wave's architecture differs from traditional quantum computers. It is not known to be polynomially equivalent to a universal quantum computer and, in particular, cannot execute Shor's algorithm because Shor's algorithm requires precise gate operations and quantum Fourier transforms which are currently unavailable in quantum annealing architectures.[37] Shor's algorithm requires a universal quantum computer. During the Qubits 2021 conference held by D-Wave, it was announced[38] that the company is developing their first universal quantum computers, capable of running Shor's algorithm in addition to other gate-model algorithms such as QAOA and VQE.
"A cross-disciplinary introduction to quantum annealing-based algorithms"[39] presents an introduction to combinatorial optimization (NP-hard) problems, the general structure of quantum annealing-based algorithms and two examples of this kind of algorithms for solving instances of the max-SAT (maximum satisfiable problem) and Minimum Multicut problems, together with an overview of the quantum annealing systems manufactured by D-Wave Systems. Hybrid quantum-classic algorithms for large-scale discrete-continuous optimization problems were reported to illustrate the quantum advantage.[40][41]
As the train contined to speedup its colors light shifting towards the blue, with passengers inside the train desperately trying to call for help, a feeling of utter helplessness overcame me when I saw the message "Radio Help" somehow put on the outside of the train, but with no indication of whom to radio or how… Two eight shot AvgNiteCam multiple exposures (see first comment below) hand stitched/masked together. i4s7113,14 - Happy Sliders Sunday!
Using a Russian Word for Grandma as I start to learn Russian....need a native speaker to chat with to speedup my ability..
I wrote a Greasemonkey script called "Flickr Multi Group Sender", that lets you send your flickr photos to multiple groups at once.
If you tick the checkbox titled "Save this group selection" the groups you have selected will be saved, and you will be able to quickly reselect them again the next time you use the script, using the secondary select box.
*UPDATE* 25-04-2008 Ive updated the script, fixing a few bugs, reinstall the script to get it working again. Also added a function that automatically saves your selection each time, even if you dont check the box. This lets you easily reselect your previous selection, in case you forgot to check the box.
*UPDATED* 28/5/2008 The script now works on all international versions of the site. New features include, group counter which displays how many groups you have selected, and groups that the image are already in appear faded out in the select box, and you cannot select them. Script also now works for sending videos to groups.
*UPDATED* 7/6/2008 I added a search box at the top, that lets you search your group list, to let you find matching groups quickly.
*UPDATED* 14/7/2010 Ive updated the script for the new photo page, remember this script is donationware, if you like it make a donation, cheers. Script is now compatible with Firefox, Chrome Safari and Opera.
TIP: Another way to find a group quickly, is to click inside the select box once with the mouse, then type the first few letters of the group name, the selector should automatically jump to the group you are looking for.
This greasemonkey script and all my other GM scripts are available here: steeev.freehostia.com/flickr/.
Please Donate! If you appreciate my scripts and would like to thank me for the time and effort i have put into them and to support further development and maintenance, please consider making a donation, large or small, every little helps. My paypal link is available on my my website and also on my profile page.
Volkswagen Up TSI (Speed Up)
Fortaleza, Brazil
Nice to catch the little milk carton of my friend Edson! He is the one behind the Instagram @exoticsfortaleza. Hope you enjoyed the pic, my friend!
Nikon D5100 + Nikkor 70-300mm VR
This is a picture I took in the hallway at school. I started zoomed in then zoomed out really quickly straight after I clicked to take a photo. This photo also made it to Kyotographie
ALL RIGHT RESERVED
All material in my gallery MAY NOT be reproduced, copied, edited, published, transmitted or uploaded in any way without my permission
Canon Eos 50d
Sigma 15mm Ex fisheye
Taken on October 27th, 2019, next to Capanna Adula UTOE, in Switzerland, 2393 m above sea level.
Assembled from 1381 individual shots of the night sky. Each frame (10", ISO 6400, f/3.5, 18mm, Canon EOS 1200D) freezes 10 seconds of apparent star motion, thus the whole video covers about 3 hours and 50 minutes.
At 30 fps, you get around 4 hours of night sky in just 45 seconds, that's a speedup of 306X with respect to the actual apparent motion of the skies.
Enjoy!