Category Archives: Computational physics

“Supercomputers are awesome and why I love what I do!!!”

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This essay by Charles Day first appeared on page 88 of the January/February 2012 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society:

Tiananmen_275 My title comes from a comment made on Physics Today‘s Facebook page by Fernanda Foertter, a physicist who programs high-performance computers for a biotechnology company.

Although Foertter’s computational science background lies mostly in molecular dynamics simulations of polymers, her comment was about this post I wrote on colliding galaxies:

Here’s a great example of using computer simulation to help interpret observations. Jennifer Lotz of Space Telescope Science Institute and her colleagues modeled pairs of galaxies merging into each other. Stills from her movies were then compared with Hubble images of galaxies that looked as though they had just merged or were about to merge. The comparison yielded a new, more accurate estimate of the galaxy merger rate.

Until I encountered Foertter’s enthusiastic outburst, I hadn’t thought of supercomputers as being inspirational. As a science writer, I’ve seen plenty of stunning simulations of exploding supernovae, wiggling proteins, and other phenomena. I’ve written about climate calculations that gobbled up weeks of supercomputer time. Several Nobel Prizes, I know, have been awarded for work that required the services of high-performance computers.

But now I’ve come to realize that supercomputers are not just useful, they’re glamorous, too. What’s more, their awesome power could be used to encourage schoolchildren to think about careers in computational science.

To see what I mean, consider what is perhaps the most ambitious, most glamorous field of physics: particle physics. When I was in high school, I read Nigel Calder’s The Key to the Universe: A Report on the New Physics (Viking Press, 1977), which I found in my local library. There within its pages, in accessible prose accompanied by photos and diagrams, was the quest to discover the ultimate constituents of matter and the laws that govern their behavior.

Back in 1977, the world’s most powerful particle accelerator was Fermilab’s Main Ring, whose circumference and maximum collision energy were 6.4 km and 400 gigaelectronvolts. The current record holder, CERN’s Large Hadron Collider, is 27 km in circumference and is designed to reach 7 teraelectronvolts. When the LHC ended its latest science run in October, it had smashed together 7 × 1014 protons and antiprotons.

To me, supercomputing—or high-performance computing, if you prefer—is the particle physics of computational science. The world’s fastest computer, K, consumes 10 megawatts of electricity to carry out 8 × 1015 floating-point operations per second. The problems that K and other supercomputers are programmed to tackle are among the toughest and most important in all of science, such as understanding Earth’s changing climate and figuring out how 1011 interconnected neurons form a thinking human brain.

As I write this column, Supercomputing 2011 is being held at the Washington State Convention Center in Seattle. I was glad to see that the meeting’s education track has 19 talks altogether, including one entitled “Parallel: HPC Overview” by Charlie Peck and his colleagues.

Attending a lecture or class is still work to a student, no matter how interesting the topic. But reading a captivating book is play, and therefore more likely to fire a student’s imagination. I’ve just looked on Amazon for an inspiring book on supercomputing. I couldn’t find one.

Monte Carlo, colloids, and public health

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C&Edec012_275 My first professional encounter with the Monte Carlo method came not during my long-abandoned career as an astronomer when I might have used the computational technique, but years later when I ran Physics Today‘s Search and Discovery department.

In 2004, I faced the task of describing a new Monte Carlo algorithm. Devised by Erik Luijten (while taking a shower, he told me), the new algorithm could do what the standard one, the Metropolis algorithm, couldn’t: efficiently simulate a colloid whose suspended particles had widely different sizes.

Suspecting that some of my readers might be unfamiliar with Metropolis, I included a short tutorial. I pointed out that using an alternative, more direct simulation method—molecular dynamics (MD)—was impractical: It’s possible to calculate the forces acting on all the colloid’s particles, but only for a modest number of consecutive time steps. The movie-like simulation that MD produces would be too brief to provide physical insight.

But the Metropolis algorithm, I told my readers, doesn’t follow every particle all the time. Rather, it calculates snapshots of the system and uses statistical mechanics to combine them. Comparing the two methods, I wrote:

So, if MD is like a movie, the Metropolis algorithm is like a sparse set of shuffled snapshots. If you simulated a cocktail party with the Metropolis algorithm, you wouldn’t see dynamical events, such as guests arriving and departing, or rare events, such as a waiter refilling a punchbowl. But, taken together, the Metropolis snapshots would fairly represent the party in full swing. From them, you could deduce whether, on average, people had enjoyed themselves.

My latest brush with Monte Carlo happened last week. Looking for research to write about, I came across a paper by Luis Zamora and his colleagues entitled “A Monte Carlo tool to study the mortality reduction due to breast screening programs.”

Screening for breast cancer is difficult and controversial. It’s difficult because the principal method, x-ray mammography, cannot by itself determine whether a lesion is malignant. Because of that limitation, follow-up biopsies are essential, but most lesions—roughly 4 in 5—turn out to be benign.

Controversy surrounds the question of when to start screening. Not only is the disease harder to detect in young women, it’s also less prevalent. Definitive evidence in favor of screening women aged between 40 and 49 years is lacking. Yet doctors—who treat individuals, not populations—are reluctant to tell patients under 49 that they don’t need a mammogram yet. Why take even a small risk?

The tool that Zamora and his colleagues have built simulates the fate of a population of women who enter a screening program. You can adjust the program’s age range and participation rate. Clinically derived parameters, such as the probability of detecting a tumor, are incorporated into the tool.

Zamora and his colleagues present their results in graphs and tables, which are hard to summarize in a short column. They predict, for example, that breast cancer mortality can be reduced by 29% if 100% of women aged 50–70 are screened every two years.

But they did discover what appears to be a critical parameter. For a screening program to be effective, its participation rate must be at least 50%. In the US, where 16.3% of the population lacks health insurance, that target is unfortunately ambitious.

This essay by Charles Day first appeared on page 88 of the March/April 2013 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

Computing hell

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SFChas “Let us always keep before our mind’s eye an overheated and glowing stove and inside a naked man, supine, who will never be released from such pain. Does not his pain appear unbearable to us for even a single moment?”

Thus wrote the 15th-century theologian and mystic Denis the Carthusian in his tract about the Last Judgment, De quatuor hominis novissimus. When I encountered the passage in Johan Huizinga’s The Waning of the Middle Ages (1919), another, more recent book came to mind: Iain M. Banks’s science fiction novel, Surface Detail (2010).

The novel’s action takes place in our galaxy in AD 2970. By then, technology has reached the point that a person’s consciousness can be recorded and inserted into virtual, simulated worlds—including hells of such fiendishly imaginative gruesomeness that I’ll refrain from quoting a description. Some of the galaxy’s species support the hells as an effective means to discourage bad behavior; others decry them as a moral outrage. To settle the hells’ disputed existence, the various interested species have agreed to abide by the outcome of a vast simulated war game.

Virtual, simulated worlds have been featured in science fiction for some time. My first encounter with them—and perhaps yours, too—was in William Gibson’s Neuromancer (1984). The novel’s complex, thrilling plot involves two powerful and resourceful artificial intelligences and a cast of drug-addicted hackers, former special operations soldiers, plutocratic industrialists, and cyberpunk ninjas.

permutation-city

Gibson favored a mostly metaphorical description of computed reality. In Permutation City (1994), Greg Egan delves in more technical detail into the philosophical questions of simulated afterlives. Presciently, in Egan’s near-future world, computing power is available in abundance via the cloud. With such resources, Paul Durham, a computer scientist and entrepreneur, proposes to create a self-sustaining virtual world where scanned consciousnesses can live for eternity.

In reality, though, how likely is the prospect of scanning a consciousness and uploading it into a virtual world? Human brains contain 1011 neurons that form 1015 interconnections. Storing a static map of something that big isn’t beyond current technology. CERN has already amassed 2 × 1017 bytes of data from the Large Hadron Collider.

The bigger technological challenge, I think, lies in generating the map in the first place. Conceivably, neuroscientists could discover a modest set of principles that embody how our brains are networked, obviating the task of mapping individual neurons. But if they can’t, every neuron and synapse would have to be located. Super-resolution techniques such as Stochastic Optical Reconstruction Microscopy (STORM) and Photoactivation Localization Microscopy (PALM) can already map fluorescently tagged molecules with a spatial resolution of a few tens of microns, but only—so far—in samples just a few millimeters thick.

Although it’s not physically impossible, like faster-than-light travel, or physically impractical, like Star Trek–style teleportation, brain mapping remains scientifically out of reach, but comfortably within the realm of science fiction. As for Denis the Carthusian, he reported making mental excursions into purgatory, during which he received revelations and conversed with souls. That experience is not unlike a Neuromancer hacker “jacking into” cyberspace and meeting avatars.

This essay by Charles Day first appeared on page 104 of the January/February 2013 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

Gaming in meatspace

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C&E275 One evening earlier this summer, I was enjoying a martini at a hotel bar in San Francisco’s SoMa district. Although I’d brought an engrossing book to read—Tokyo Year Zero by David Peace—I looked up now and then at the bar’s TV to watch the Miami Heat strive to nullify the Boston Celtics’ large early lead in game four of the NBA East finals.

During a commercial break, I became transfixed by a trailer for what seemed like an exciting new horror movie. Humans and zombies were fighting each other in a dark, empty New York and a bright, crowded Hong Kong. To catch the scenes of mayhem, the camera swooped, panned, and zoomed with unnatural agility and speed, greatly intensifying the thrills.

It turned out the camera work was unnatural. The trailer was not promoting a Hollywood movie, but an Xbox and PlayStation video game, Resident Evil 6. My long-held disdain for video games had been challenged!

The first video game I encountered was Space Invaders, which appeared in one of my hometown pubs around 1980. In case you’ve forgotten or never knew, the game’s object is to shoot down an armada of alien spacecraft, each depicted within a 16- by 16-pixel grid. But crude graphics weren’t what put me off Space Invaders and its descendants. Rather, I couldn’t see the point of acquiring the skill needed to win: the ability to press the controller’s buttons quickly and accurately. I still don’t—even to play Resident Evil 6 on a 1920- by 1080-pixel monitor.

Besides making me reconsider video games, my chance encounter with computer-animated zombies made me wonder why I’ve recently come to enjoy playing board games, despite the gulf between the games’ typically rich scenarios and their manifestly artificial boards. In Railways of England and Wales, for example, players vie to build the most profitable rail network between a limited number of major towns and cities, just as their historical counterparts did in the early years of Queen Victoria’s reign.

The state of play during a game of Railways of England and Wales. The image comes from BoardGameGeek, where you can find a description and review of the game.

The state of play during a game of Railways of England and Wales. The image comes from BoardGameGeek, where you can find a description and review of the game.

Although the board and accoutrements of Railways of England and Wales are somewhat cartoonish, I and my fellow players Jan, Kate, Kevin, Stacy, and Ty borrowed money, laid down track, bought rolling stock, and transported goods with gusto. The locally brewed beer and home-pulled pork served by our hosts, Stacy and Ty, added to the enjoyment.

So why do I prefer playing at railway barony on a cartoonish board to shooting zombies on a realistically rendered street? Paradoxically, when it comes to human behavior, Railways of England and Wales seems more realistic than Resident Evil 6. Whereas real railway barons schemed while sitting in chairs and looking at maps, “real” zombie hunters should be running outside and shooting weapons. The first activity resembles its corresponding game; the second doesn’t.

But even if I don’t succumb to the attraction of playing video games, I’m affected by their popularity. You are, too. In an article published last year, Martin Hilbert and Priscila López determined that video games consumed 42% of the world’s capacity to store information in 2007, up from 5% in 2000. Video games’ share of total CPUs grew at a similar rate, from 5% in 2000 to 25% in 2007. Your next home computer could be optimized for Resident Evil 6, whether you play the game or not.

This essay by Charles Day first appeared on page 88 of the September/October 2012 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

Mind-reading computers

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EchoCharles275 Last year, the website of Britain’s Daily Mail newspaper became the world’s most-visited English-language news source. Although the Mail‘s website owes its popularity to a menu rich in celebrities, crime, and royals, it offers readers something that my stuffier hometown newspaper, the Washington Post, lacks: a top-level section devoted to science.

Granted, the Mail’s science coverage tends toward the sensational, but it does encompass superluminal neutrinos, the Higgs boson, and other weighty topics. The story that led the science section on 1 February 2012 was both sensational and important, as you can tell from the headline:

Mind-boggling! Science creates computer that can decode your thoughts and put them into words.

The story’s origin lies in an article published in PLoS Biology by Brian Pasley of the University of California, Berkeley, and his collaborators. Fifteen patients who suffered either epilepsy or brain cancer agreed to let Pasley’s team attach an array of electrodes to their brains while their skulls were opened for surgery. The electrodes recorded signals from neurons located in a part of the brain, the auditory cortex, that interprets spoken language.

Before the patients underwent surgery, they listened to single words and whole sentences. Pasley and his collaborators correlated the electrical recordings with the words’ acoustic spectra. A machine-learning algorithm then derived a mapping that could reproduce an acoustic spectrum from a neural recording.

Predicting what someone hears based on his or her brain activity is impressive, but it hardly qualifies as mind reading. However, it turns out that the auditory cortex is also responsible for encoding speech. When Pasley’s team asked each patient to think of words without uttering them, the algorithm accurately predicted what those unspoken words were. In that sense, the algorithm really did read the patients’ minds.

Pasley’s algorithm occupies one front in a broad campaign to understand how the human brain works. On another front, biophysicists are developing ways to map the topography of the brain’s interconnected neurons. Given that the human brain contains on the order of 1011 neurons, each of which is connected to up to 1000 other neurons, assembling a complete neuronal map could turn out to be infeasible—and perhaps unnecessary.

A detailed map of a single, characteristic neighborhood of the brain might yield enough information to identify the physical features that underlie thought and memory. But knowledge of those features alone might fall short of demonstrating that someone understands the brain. If that turns out to be the case, then a convincing demonstration might entail building a simulated brain.

The anatomy and physiology of such a brain wouldn’t necessarily resemble those of our own. Indeed, the first prototype could turn out to consist of a building-sized stack of optical tables where pulsed beams of light—the information-carrying signals—bounce off mirrors and pass through prisms. Provided that the simulated brain’s topology and interconnections are described using the same mathematical equations that apply to a human brain, such a demonstration would be valid.

And if that fantasy becomes a reality, simulation would have attained a new and higher status in science. Rather than providing a way to calculate a theory’s validation, the simulation would be the validation.

This essay by Charles Day first appeared on page 104 of the July/August 2012 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

Standards rule OK

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My title comes from the chorus of a song on the Jam’s second album, This Is the Modern World (1977). Written by the band’s singer and guitarist Paul Weller, the song is a bombastically ironic attack on the enforcers of social conformity.

But if Weller were not a socially conscious rock musician and instead were a computational scientist, he might have still chanted, “Standards rule OK!” For without standards in hardware, software, and data formats, our work would be less efficient and less effective.

I first appreciated the importance of computer standards when I worked at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, in the early 1990s. My field, x-ray astronomy, was just three decades old at the time. The first pioneering missions could detect only a handful of bright objects. But their successors—among them the European Space Agency’s European X-ray Observatory Satellite (EXOSAT; 1983–86) and NASA’s Einstein Observatory (1978−82)—observed thousands of x-ray emitting stars, galaxies, and other cosmic objects. Then came Germany’s Röntgen Satellite (ROSAT; 1990−99) and Japan’s Ginga (1987−91), which added to that swelling collection.

Because spacecraft telemetry is limited by bandwidth, the data gathered and beamed to Earth by satellite observatories are packaged in efficient, instrument-specific formats—15 altogether for the instruments carried by the four spacecraft listed above. In contrast with the diversity of telemetry formats, the figures that embody the data’s scientific content (and ultimately appear in research papers) typically come in a smaller set of generic flavors: images, spectra, and light curves.

Creating those figures entails background subtraction, binning, filtering, and other generic tasks. In principle, the software that, say, Fourier-transforms a data stream from EXOSAT‘s Medium Energy instrument could do the same for a data stream from Ginga‘s Large-Area Counter. But the raw formats are as different as Dutch and Japanese. If the same software is to work with data from those and other missions, the data must be translated into a common format. And that format must be flexible enough to accommodate new instruments.

My former colleagues at GSFC duly picked such a format: flexible image transport system (FITS). Originally developed for optical and radio data, FITS makes extensive use of headers and keywords. Like XML, FITS is extensible. Whenever a new detector technology comes online, new keywords and data structures are defined within the FITS framework. Granted, someone has to write an instrument-specific program that translates telemetry into FITS, but no one has to take on the more onerous job of rewriting data analysis software.

When I left GSFC in 1997, astronomers there and elsewhere used three software programs to analyze their data: Xspec (for spectra), Xronos (for light curves), and Ximage (for images). Now, 14 years later, they’re still using the same three programs for data from observatories that launched years after my departure.

FITS made its public debut in 1981 in a paper in Astronomy and Astrophysics. On 30 November of that same year, the Swedish pop group ABBA’s eighth and final album The Visitors became the first recording available on a new format, the compact disc. Although CD sales are waning, it remains a durable standard—at least I hope so. I have six Jam CDs.

This essay by Charles Day first appeared on page 96 of the September/October 2011 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

The computation of poetry

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My nadir as a programmer happened in grad school. I’d written a Fortran program to calculate the column density of a stellar atmosphere—at least, that’s what the program was supposed to do, but it had a bug. Fortunately, the VAX I was using had a chatty, helpful compiler that told me where the bug was and what was wrong—sort of.

The trouble was that even when I printed the code and stared at the recalcitrant line, the bug remained hidden. In the end, I finally saw my error: I’d spelled “GOTO” with two zeros instead of two Os. The characters are next to each other on the keyboard, and when printed, they looked almost identical. The error was trivial—even funny—but it soured my view of programming forever. Now, as a writer and editor, I reassure myself that writing is so much more forgiving than coding. If a word doesn’t fit, I can pick another. English is much more flexible than Fortran.

But one form of literary endeavor, poetry, shares some properties with programming. “Predict” has several synonyms, for example—including foretell, prophesy, forecast—Shakespeare picked “prognosticate” for his 14th sonnet:

Or else of thee this I prognosticate:
Thy end is truth’s and beauty’s doom and date.

Unlike the alternatives, “prognosticate” fits the rhyme and meter. It also echoes a four-syllable word that appears near the beginning of the poem (“astronomy”).

The English sonnet is nicely balanced between form and function. Its 14 lines, typically in iambic pentameter, can have any one of several rhyme schemes with strictures light enough to inspire poets to artful, not forced, contrivances.

But some poetic forms in other languages are difficult to follow in English, the top of my list being cynghanedd. This centuries-old Welsh form stipulates the order in which consonants should appear within lines. Gerard Manley Hopkins, an English poet who learned Welsh, wrote cynghanedd-like lines, but not in full accordance to the rules.

Which makes me wonder: Could a computer be programmed to write cynghanedd in English (or German, for that matter)? What would such a feat mean? If a reader couldn’t tell the difference—that is, if our computer bard passed a Turing test—would it matter?

And then there’s the authorship. Whoever programmed the poetry generator must know and appreciate the rules and subtleties of cynghanedd, so whatever art and skill a computer cynghanedd might show lies as much in the program as in the poem itself. Freed by the power of computation, you could conceivably invent poetic forms so complex that only a computer could write in them.

These speculations aren’t wholly whimsical. Just as a particular poetic form might stipulate rhyme scheme, meter, length of lines, length of poem, and, yes, the pattern of consonants, so, too, might an accurate, complete model of human consciousness. What would it mean if only a computer could follow those rules?

Cynghanedd example

One form of cynghanedd requires repeating the order of consonants in the first and second halves of a line, as in this example from Tudur Aled (c. 1465–1525):

Os marw bun, oes mwy o’r byd?
Mae’r haf wedi marw hefyd.

If the girl dies, what’s left in the world?
Summer has died as well.

Some leeway is allowed for rhyming.

This essay by Charles Day first appeared on page 96 of the November/December 2007 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

When bits bite

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As the 1995 movie Species begins, space aliens have beamed to Earth information about a new, seemingly endless source of energy. The same transmission includes the codons for alien DNA and instructions for splicing them into the human genome.

The energy source proves bountiful, but the splicing, played out over the movie’s next 100 or so minutes, proves disastrous. Scientists construct the alien DNA, transfect it into a human egg cell, and watch as the egg develops with unnatural speed into a girl they name Sil. Alarmed by Sil’s rapid growth, the scientists plan to kill her, but she escapes and matures into a half-alien/half-human whose drive to mate and lack of inhibition combine in a gory rampage of sex and murder.

Reflecting on the movie one morning on my way to work, I thought, “That’s amazing—malevolent aliens could invade us with pure information!” Just the bits needed to encode DNA are enough to threaten human civilization. But a sequence of alien DNA, expressed as binary signal, is just that: a binary signal. It took unsuspecting humans to make the monster itself.

The idea that information is physical is hardly new. Although we can’t know for sure, marks pressed on wet clay or knots tied in string would have seemed more physical than not to ancient Babylonians and Incas. What’s more recent is the notion that information is intrinsically and inextricably physical. AT&T’s Claude Shannon, IBM’s Rolf Landauer, and others pioneered this viewpoint in the 1940s.

One of Landauer’s successors has shown that the physics of information has theological implications. Carlo Beenakker at the University of Leiden in the Netherlands usually theorizes about electron transport in structures that are just small enough for quantum coherence to play a role. But in a recent article, he tackled a problem raised 40 years ago by Carl Gustav Hempel: Where does physics stop and metaphysics begin?[1]

Beenakker’s analysis tools are limits derived by physicists. Information can’t be transferred faster than the speed of light (Albert Einstein), erased without generating kT log 2 of heat per bit (Landauer), or processed with available energy E faster than 4E/h operations per second (Norman Margolus and Lev Levitin). Beenakker asks three questions, among them, is the immortal soul physical or metaphysical? He answers,

In order to be physical, the immortal soul should contain and process information beyond death, which is the erasure of most information in the organism. Estimates of the amount of information lost upon death are in the order of 1032 per human. Mankind as a whole has lost some 1043 bits of information over the course of 50,000 years. We know of no mechanism by which this amount of information could have survived by physical means, leaving the immortal soul in the metaphysical domain.

Beenakker’s estimate for the duration of mankind comes from science: the age of our most recent chromosomal ancestor “Adam.” But his estimate of a human’s information content comes, indirectly, from science fiction: Lawrence Krauss derived it to conclude that the Star Trek transporters are infeasible.

Which is a relief. Otherwise, malevolent aliens could beam themselves to Earth, rather than just send us information about their DNA.

Reference

  1. C. Beenakker, “Hempel’s dilemma and the physics of computation,” Knowledge in Ferment: Dilemmas in Science, Scholarship and Society, A. Groen et al., eds., Leiden U. Press, Leiden, the Netherlands (2007), p. 65.

This essay by Charles Day first appeared on page 96 of the July/August 2007 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

Quantum computing is exciting and important—really!

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Quantum computing, say its champions, promises prodigious power. Its basic currency, the qubit, exists in an on/off limbo until it’s read out, so if you could operate on k qubits, a potentially vast space of 2k values opens up for computation. The fundamental operation on qubits is a rotation. Combine the rotations, and you have logic gates. Combine the logic gates, and you have algorithms. In principle, these algorithms can perform calculations far beyond classical computing’s conceivable reach.

But to wield that power, you need an actual quantum computer, and building one has proved impossible. Qubits live in small, cold enclaves within the classical macroworld. When heat and other environmental disturbances inevitably intrude, they rob a quantum system of its coherence, its entanglement, and its ability to compute.

So beguiling is the potential of quantum computers that rather than putting people off, the difficulty of building one has assumed the qualities of a mythical quest. Like Jason’s for the Golden Fleece, the quest for a quantum computer is hard and long. To sustain it, the champions of quantum computing appeal not to Olympian gods but to terrestrial funding agencies. Not surprisingly, quantum computing has acquired an aura of hope—and hype. Researchers have made steady progress, though. Physicists have fashioned qubits from superconducting Josephson junctions, trapped ions, semiconducting quantum dots, and other systems. They’ve even built working logic gates.

Still, scaling up a handful of logic gates, whose physical embodiments could require a roomful of lasers, cryopumps, and other finicky equipment, to an actual computer remains out of reach. Rolf Landauer, the IBM physicist who pioneered the notion that information is intrinsically physical, was famously skeptical of quantum computing. All papers on the topic, he said, should come with a disclaimer, and if you didn’t have one, he was happy to offer his own:

This proposal, like all proposals for quantum computation, relies on speculative technology, does not in current form take into account all possible sources of noise, unreliability and manufacturing error, and probably will not work.

Landauer’s skepticism could prove justified in the end, but it would be a pity if research in quantum computing stopped now. Much of it continues to be worthwhile. At NIST’s lab in Boulder, Colorado, for example, David Wineland and his collaborators have applied the techniques they developed for atomic clocks to build logic gates based on trapped ions. Thanks to their work on logic gates, they developed new, entanglement-based clocks of unprecedented precision.

In making qubits out of gallium arsenide quantum dots, Jason Petta, who is now at Princeton University, and his collaborators at Harvard measured the tiny fluctuating magnetic field of 106 gallium and arsenic nuclei inside a quantum dot—a remarkable feat.

Results have been just as impressive on the theoretical front. The work of Microsoft’s Alexei Kitaev and others on topological quantum computation has spawned rich and fruitful explorations of the mathematical similarities of field theory, knots, and the fractional quantum Hall effect. Princeton’s Robert Calderbank has applied the theory of quantum error correction to understand radar polarimetry, and Ignacio Cirac and Frank Verstraete of the Max Planck Institute for Quantum Optics outside Munich have used the entangled states that crop up in quantum information theory to analyze networks of coupled spins.

Do all these advances, and others, represent milestones on a longer, ultimately successful journey or the ends of truncated trips? I don’t know. But they’re exciting and important—really.

This essay by Charles Day first appeared on page 104 of the March/April 2007 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.

My computational education

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I peaked as a computational scientist in 1986. At that time, two years from finishing my PhD, I was trying to account for an aspect of the x-ray emission from a pulsar known as Hercules X-1, the first x-ray source discovered in the constellation Hercules. Hercules X-1 consists of a normal star and a rapidly spinning neutron star. So close are the two stars to each other that the neutron star’s gravity grabs material from the normal star’s outer atmosphere. By the time the purloined material reaches the neutron star, it’s a million-degree, x-ray-emitting plasma.

Every 1.7 days—a number I can’t forget!—the two stars orbit their mutual center of mass, and, if you’re watching from Earth, the neutron star is eclipsed by its companion. But just before and just after total eclipse, the neutron star’s x rays pass through the companion’s atmosphere. From a plot of x-ray emission versus time, you can probe and explain the atmosphere’s vertical structure—with the help of a computer model, that is.

Artist’s impression of an x-ray binary system from NASA’s Imagine the Universe!

And so, over several months, I created my longest-ever computer program. Excluding comments, it ran to more than 120 lines of Digital Equipment’s proprietary flavor of Fortran 77. Fortunately, I didn’t have to create a data analysis program from scratch—a postdoc had done that before me—but I did have to incorporate my model into his code. (See Dianne O’Leary’s “Computational software: Writing your legacy,” Computing in Science & Engineering, January/February 2006, page 78, to learn why that exercise builds character.)

How did I become a programmer? When I started graduate school I had no programming—or even computer—experience. To me, computers were toys for the uncool boys. But then my university offered its graduate students a course in scientific programming. And I took it.

The teacher, whose research was in the field of artificial intelligence, displayed impressive loyalty to a single ancient T-shirt. In class and around town, he’d pad about without shoes or socks like a hobbit. His course briefly touched on programming techniques, but only in the abstract. My real teachers were the postdocs in my group. They taught me a sprinkling of practical tricks and, perhaps more useful, some sound principles of programming practice.

Those learning experiences popped into my head when I read Francis Sullivan’s column about Sudoku (“Born to compute,” Computing in Science & Engineering, July/August 2006, page 88). Lacking Francis’s “compute gene,” I have no attraction whatsoever for Sudoku or any other mathematical puzzle. But I do have the physics gene, and its expression, in graduate school, was what compelled me to program.

Having solved the physics for my model, I had to implement it on a computer—which meant I had to read arcane computing manuals, swat the bugs I’d introduced into my program, and struggle with fussy compilers. Every now and again, amid the toil and frustration, would come flashes of inspiration and progress. In the end, I grew to like programming.

So if you teach computational science and your class includes compute-free students like me, give them an interesting science or engineering problem to solve. The poor things might be driven to program despite their genetic inheritance.

This essay by Charles Day first appeared on page 104 of the September/October 2006 issue of Computing in Science & Engineering, a bimonthly magazine published jointly by the American Institute of Physics and IEEE Computer Society.