Light polarized at other angles has components of both H and V, representing 0 and 1 simultaneously. A classical binary digit could be represented by encoding 0 as horizontally ( H) polarized light, and 1 as vertically ( V) polarized light.
Polarized light is an example of superposition. Abstract though it may seem, information always involves a physical representation, and the physics matters. This was the mantra of the distinguished IBM researcher Rolf Landauer. But before describing how we think such error correction can be made practical, we need to first review what makes a quantum computer tick. The two of us, along with many other researchers involved in quantum computing, are trying to move definitively beyond these preliminary demos of QEC so that it can be employed to build useful, large-scale quantum computers. But these experiments still have not reached the level of quality and sophistication needed to reduce the overall error rate in a system. A mature body of theory built up over the past quarter century now provides a solid theoretical foundation, and experimentalists have demonstrated dozens of proof-of-principleĮxamples of QEC. This tremendous susceptibility to errors is the single biggest problem holding back quantum computing from realizing its great promise.įortunately, an approach known as quantum error correction (QEC) can remedy this problem, at least in principle. The situation inside a quantum computer is far different: The information itself has its own idiosyncratic properties, and compared with standard digital microelectronics, state-of-the-art quantum-computer hardware is more than a billion trillion times as likely to suffer a fault.
They both involve conventional, classical information, carried by hardware that is relatively immune to errors. Dates chiseled into an ancient tombstone have more in common with the data in your phone or laptop than you may realize.
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