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ScienceWise - Autumn 2013

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Dr Syed Assad, Dr Thomas Symul and Professor Ping Koy Lam

The quantum science of chance

 

When you toss a dice you might think the outcome is random, but it’s not, it’s merely complex.  If it were possible to throw it again keeping everything, including the air currents, exactly the same it would inevitably come up with the same number.  In a classically complex system like a dice roll, tiny changes in the starting conditions lead to very different outcomes but it’s not really random.

So if you want to create the perfect casino, or more importantly deliver good random data to things like climate models, what do you do? One thing you can and indeed many people do, is to visit the website of the ANU Quantum Optics Group. 

http://qrng.anu.edu.au

Professor Ping Koy Lam, Dr Thomas Symul and Dr Syed Assad lead a group of scientists who have adapted their expertise in quantum systems to create one of the world’s first sources of large quantities of truly random data.

“We knew there was some demand amongst the scientific community for random data streams to feed encryption systems and various computer models, but we had no idea of just how popular this service would become.” Dr Symul says. “In the first year of operation we’ve had ten million hits!”

But what’s so special about this system that scientists flock to use it? “Our numbers are truly random and unbiased because we extract them directly from quantum fluctuations in vacuum,” Professor Lam explains, “So it’s physically impossible for them to form a predictable sequence.”

For some applications it doesn’t matter if a data stream is predictable so long as the numbers statistically average out to random, but for others it’s out of the question.

“We’re heavily involved in quantum encryption systems that ensure secure communications,” Professor Lam says, “And one of the corner stones of such systems is that the encryption key is truly random. If you get those keys from the type of pseudo-random number generators found in most computers then you introduce a potential weakness into an otherwise impregnable system.”  

Another issue that arises with pseudo random generators is that eventually the numbers repeat. It’s not a problem if you just take a few thousand or even million, but sophisticated computer models often require trillions and trillions of such numbers. Hidden repeating sequences can easily corrupt such models leading to incorrect conclusions and wasting vast amounts of human and computer time. However with the quantum vacuum data, there is no possibility of any such problems arising.

So how exactly do you extract quantum noise from a vacuum? The key lies in the fact that there’s really no such thing as a true vacuum. “Even if you remove every last molecule of air from a chamber, it’s not actually empty. Virtual particles are constantly being spontaneously created by the quantum field before they’re quickly re-absorbed,” Dr Symul says. “And it’s these virtual photons of light that we measure to generate our random numbers.”

One of the most difficult parts of this is that the fleeting virtual photons can’t be seen directly using normal photo-detectors. “What we have to do is essentially mix this fleeting quantum vacuum noise with a classical oscillator and use what’s known as homodyne detection to extract the data.” Dr Symul says.

The quantum side of the experiment is a rather inconspicuous group of components bolted to the corner of one of the optical tables in the lab. However its small size doesn’t prevent it generating about five thousand million bits per second. Ironically the bottleneck in data delivery is the server. “When we began we hadn’t imagined the demand,” Dr Symul says, “So we simply used one of the spare lab computers as our web server. Now it’s looking like we going to have to make some significant upgrades!”

 

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