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.

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 difﬁcult to apply to generic problem speciﬁcations and achieve the promised exponential speedup, they wrote. Technical details with setting up the problem on a quantum computer made it unclear how one would apply it to a real-world calculation. In addition, the promise of exponential speedup was only true for a very restricted set of linear systems that typically don’t exist in real-world problems. Finally, getting a useful answer from the calculation proved to be quite difficult due to intricacies with the inherently probabilistic nature of quantum measurement.

In their paper, the authors describe however they were able to solve every of those problems and extract helpful info from the answer. what is more, they incontestable the relevance of the algorithmic program by showing the way to code the matter of scheming the magnetic attraction scattering crosswise, additionally referred to as measuring instrument cross section (RCS).

RCS measurements became progressively vital to the military. It refers to the facility that may be came back by Associate in Nursing object once lit with measuring instrument. the facility indicates however well the microwave radarion and ranging|radiolocation|measuring instrument|measuring system|measuring device} will detect or track that focus on, thus there ar current efforts to cut back the RCS of such objects as missiles, ships, tanks and craft. With a quantum pc, APL researchers have currently shown that these calculations will be done a lot of quicker and model rather more complicated objects than would be potential victimization even on the foremost powerful classical supercomputers.

The work was funded by the Intelligence Advanced analysis comes Activity beneath its Quantum applied science program, that explores queries regarding the procedure resources needed to run quantum algorithms on realistic quantum computers.

source : sciencedaily.com