Aug. 20, 2013 — Researchers at the Johns Hopkins University Applied Physics Laboratory have devised a quantum algorithm for solving big linear systems of equations. Furthermore, they say the algorithm could be used to calculate complex measurements such as radar cross sections, an ability integral to the development of radar stealth technology, among many other applications.
Their research is reported in the June 18 issue of Physical Review Letters.
The field of quantum computing is still relatively young. First proposed in the 1980s, a quantum computer harnesses the principles of quantum mechanics (the physics of very small things like electrons and photons) to process information significantly faster than traditional computers. A classical computer has a memory made up of bits (units of information), where each bit represents either a one or a zero. A quantum computer maintains a sequence of qubits. Similar to a bit, a single qubit can represent a one or a zero, but it can also represent any quantum superposition of these two states, meaning it can be both a one and a zero simultaneously.
While several few-qubit systems have been built, a full-scale quantum computer is still years away. Qubits are difficult to manipulate, since any disturbance causes them to fall out of their quantum state or "decohere," and their behavior can no longer be explained by quantum mechanics. Other larger scale non-universal computers have been built -- including the much-heralded D-Wave computer, purchased by NASA and Google last month -- but none of them currently have the power to replace classical computers.
Theoretical breakthroughs in quantum algorithm design are few and far between. In 1994 Peter Shor introduced a method for finding the prime factors of large numbers -- a capability that would render modern cryptography vulnerable. Fifteen years later, MIT researchers presented the Quantum Linear Systems Algorithm (QLSA), that promised to bring the same type of efficiency to systems of linear equations -- whose solution is crucial to image processing, video processing, signal processing, robot control, weather modeling, genetic analysis and population analysis, to name just a few applications.
"But it didn't quite deliver; based on their process, no one could figure out how to get a useful answer out of the computer," explains APL's David Clader, who along with Bryan Jacobs, and Chad Sprouse wrote, "Preconditioned Quantum Linear System Algorithm."
As presented, the algorithm had three features that made it dif
Their research is reported in the June 18 issue of Physical Review Letters.
The field of quantum computing is still relatively young. First proposed in the 1980s, a quantum computer harnesses the principles of quantum mechanics (the physics of very small things like electrons and photons) to process information significantly faster than traditional computers. A classical computer has a memory made up of bits (units of information), where each bit represents either a one or a zero. A quantum computer maintains a sequence of qubits. Similar to a bit, a single qubit can represent a one or a zero, but it can also represent any quantum superposition of these two states, meaning it can be both a one and a zero simultaneously.
While several few-qubit systems have been built, a full-scale quantum computer is still years away. Qubits are difficult to manipulate, since any disturbance causes them to fall out of their quantum state or "decohere," and their behavior can no longer be explained by quantum mechanics. Other larger scale non-universal computers have been built -- including the much-heralded D-Wave computer, purchased by NASA and Google last month -- but none of them currently have the power to replace classical computers.
Theoretical breakthroughs in quantum algorithm design are few and far between. In 1994 Peter Shor introduced a method for finding the prime factors of large numbers -- a capability that would render modern cryptography vulnerable. Fifteen years later, MIT researchers presented the Quantum Linear Systems Algorithm (QLSA), that promised to bring the same type of efficiency to systems of linear equations -- whose solution is crucial to image processing, video processing, signal processing, robot control, weather modeling, genetic analysis and population analysis, to name just a few applications.
"But it didn't quite deliver; based on their process, no one could figure out how to get a useful answer out of the computer," explains APL's David Clader, who along with Bryan Jacobs, and Chad Sprouse wrote, "Preconditioned Quantum Linear System Algorithm."
As presented, the algorithm had three features that made it dif