Contributed editorial appearing in
Scientific Computing 24:10, September 2007, pg. 20.
Our course in Laboratory Informatics continues on from a discussion of transducers, response models and calibration to a look at the systems and methods utilized for the collection of values from the many samples and disparate instruments throughout the laboratory and across the entire organization. The workhorse technology used to enable this ability is the laboratory network. The time spent generating a table of contents and cross-index for a paper notebook, producing photocopies and delivering them to an archive room to be filed in a dusty cabinet are thankfully joining the bygone days of pipetting by mouth. Electronic records, online data analysis, relational databases and other life-blood technologies routinely used by the business side of the organization are finding standardization and acceptance in the laboratory. Additionally, the networking and information technologies benefit from advances in the physical laboratory as well. This symbiotic relationship involves the integration of advances in energy, material and information into high-level systems that provide innovative solutions toward the common goals of bigger, faster, safer, cheaper, and the elusive – more accurate. An initial link between laboratories at the University of California Los Angeles (UCLA) and the Stanford Research Institute of Stanford University in 1969 evolved into the modern Internet through the addition of nodes, new technologies and standards developed and adopted by the growing number of members within the network. While the speed and size of the digital Internet continues to grow, laboratories have been working diligently on new systems based on research into quantum computing and quantum information science with the promise of increased performance and new capabilities.A January 2005 SC article looked at emerging experimental research into quantum computing and recent major advances continue to demonstrate practical uses of the technology. Two primary differences between quantum computing and “classical” computing involve the quantum concepts of superposition and entanglement. In a classical system, a single value is represented by a large collection of individual components through the use of statistics. For example, let’s consider the average result of flipping 10 unbiased coins. We assign “heads” a value of 1 and “tails” a value of 0, flip each coin into the air and then record the outcome of the flip after it comes to rest. The statistical value of the arithmetic mean for the entire process can be obtained by summing all ten results and dividing the sum by the “degrees of freedom” (DOF) of the measurement. In this case, the DOF is 10 (generically, n, the number of flips) as each coin was “free” to produce a result of 1 or 0 during its flip. The probability of producing a 1 or 0 is “superpositioned” or spread across all ten coins by dividing the sum by the DOF. We can calculate the average difference or “variance” of the measurement by comparing the difference between each of the ten results and our calculated mean (we will need to square each difference to prevent negative values from destructively interfering with our calculation). As before, we calculate the average by summing the squared differences and dividing by the DOF. However, in this case the DOF is down to 9 (generically, n-1). Since we need to know the arithmetic mean to calculate each difference, we already have information about all ten flips. After we know the results of the first nine, we can use this data and the mean to determine the value of the tenth flip – it does not have any freedom to be 1 or 0; it must have the value dictated by the mean. The tenth flip is entirely correlated or “entangled” with the mean and it must assume a specific value based on the values of the other nine flips and the mean.
Classical measurements use these statistical tools to obtain information from large collections of items. But what happens when the number of items is reduced down to one or two? In the case of a single coin flip, the equal probability of being heads or tails “collapses” into a single value when measured. Before it is measured, the superposition of heads and tails can be visualized as the single coin is flipping though the air. Classical intuition says it can be thought of as rapidly oscillating between 1 and 0 or having both values simultaneously. A classical example of entanglement, however, would involve flipping two coins simultaneously. After the first coin has stopped and is measured, the second coin must always land with the opposite value to maintain the probability of 0.5. Our statistic calculation of variance also predicts this two-element system has one Degree of Freedom (n-1) and the second coin has no chance of having the same value as the first coin. Albert Einstein famously derided this classical analogy as “spooky action at a distance” and along with Boris Podolsky and Nathan Rosen posed what is known as the EPR Paradox in a 1935 paper that argued that something was missing from quantum theory.
Several theoretical approaches have been proposed to dispel the EPR Paradox and the 2001 approach described by collaborating researchers Luming Duan at the University of Science and Technology of China, Mikhail Lukin at Harvard University, and Juan Ignacio Cirac and Peter Zoller of the Universität Innsbruck (known as the DLCZ Protocol) has been demonstrated experimentally by Professor H. Jeff Kimble and his research group at the California Institute of Technology (Caltech) this past Spring. Like the pair of spinning coins described above, the DLCZ Protocol uses a pair of Cesium (Cs) atoms. Since it is experimentally difficult to work with single atoms, a pair of ensembles (each containing around 100,000 cooled Cs atoms) is used to increase the probability of interacting with a single Cs atom. The pair is first conditioned by ensuring all of the Cs atoms are in their ground state. Then a single photon is introduced into each ensemble with a chance it will collide with one of the Cs atoms through inelastic scattering (i.e., non-resonant Raman Scattering) producing a single Cs atom in a different ground state that can be designated as a digital “1”. This “writing” event can be confirmed by detecting the single Raman-scattered photon. There is also a chance the write photon will not interact with the Cs atoms, leaving the ensemble as a digital “0”. An entangled pair of ensembles is produced when one ensemble scatters the write photon and the other does not, yielding a “1” and “0” pair referred to as a “quantum node”. We can’t know which of the pairs has scattered the photon or that would constitute as a measurement and the entanglement would collapse. To keep this information unknown, the output of each ensemble is directed to a 50/50 beam splitter that is observed by two, single-photon detectors. If neither ensemble scatters a photon, nothing is detected and the pair is measured to be 0-0. If two scattered photons are detected, the pair is measured to be 1-1. If only one scattered photon is detected, then we have measured 1-0 or 0-1, depending on which ensemble did the scattering. Because of the 50/50 beam splitter, each single-photon detector is observing both ensembles simultaneously, so there is no way of knowing which ensemble scattered the photon and the pair remains superpositioned and entangled. Professor Kimbles’ group also prepared a second quantum node separated 3 meters from the first. Using photons having the same frequency as the Raman-scattered photons produced by the write pulse, the ensembles can be read by detecting the anti-stokes Raman-scattered photons produced by the Cs atom as it returns to it’s initial ground state. With the use of additional 50/50 beam splitters, the 1-0/0-1 superpositioned state of the first quantum node can be written to the second node without knowing which node is which. While there are several major technological hurdles to overcome before quantum networks become practical, this project demonstrates the ability to use classical data acquisition methods for the creation of functional quantum systems.
Image: First two nodes of the elementary quantum network at Caltech. The values of the initial quantum node (blue) are transferred to the remote quantum node (green). Photo by Nara Cavalcanti.
Related Web Resources
January 2005 Scientific Computing article “Change for the Better”
http://www.scientificcomputing.com/PRArchivebyIssue.aspx?RELTYPE=FE&YEAR=2005&MONTH=01&CommonCount=0
Quantiki – Wiki for Quantum Information Science
http://www.quantiki.org/wiki/index.php/Main_Page
Quantum Optics Group at California Institute of Technology
http://www.its.caltech.edu/~qoptics/home.html
Caltech Press Release on Entanglement Distribution
http://www.its.caltech.edu/~qoptics/Ensemble/index.html
The EPR Paradox in Quantum Theory
http://plato.stanford.edu/entries/qt-epr/
Quantum Optics and Quantum Information Group at University of Michigan
http://www-personal.umich.edu/~lmduan/
Quantum Optics Group at Harvard University
http://lukin.physics.harvard.edu/
Theory Group at Max Plank Institute for Quantum Optics
http://www.mpq.mpg.de/Theorygroup/CIRAC/wiki/index.php/Theory_Division.html
http://www.uibk.ac.at/th-physik/qo/index.html
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