In the context of a “bitstring guessing game,” Daniel Lidar, Director of the USC Center for Quantum Information Science & Technology and Viterbi Professor of Engineering at USC, and Dr. Bibek Pokharel, a Research Scientist at IBM Quantum, achieved this quantum speedup advantage. By effectively suppressing errors that are typically seen at this scale, they were able to manage strings up to 26 bits long, which was significantly more than was previously possible. ( Bits are binary numbers that can be either one or zero.
Quantum computers say they can solve some problems with a benefit that grows as the problems get more complex. However, they are also extremely susceptible to noise and errors. “To obtain an advantage in the real world where today’s quantum computers are still ‘noisy,'” according to Lidar, is the obstacle. The “NISQ” (Noisy Intermediate-Scale Quantum) era is the current state of noise-prone quantum computing. This term comes from the RISC architecture, which is used to describe classical computing devices. Therefore, reducing noise is required for any current demonstration of a quantum speed advantage.
The difficulty of a problem for a computer to solve typically increases with the number of unknown variables. By playing a game with a computer, scholars can see how quickly an algorithm can guess hidden information and how well it performs. Consider a television version of the game Jeopardy, in which contestants guess a known-length secret word one whole word at a time. Before revealing the secret word at random, the host only reveals one correct letter for each guessed word.
Bitstrings were used in place of words in the study. To correctly identify a 26-bit string, a traditional computer would need approximately 33 million guesses on average. In contrast, presenting guesses in quantum superposition, a perfectly functioning quantum computer could determine the correct answer with just one guess. A quantum algorithm developed more than 25 years ago by computer scientists Umesh Vazirani and Ethan Bernstein yields this efficiency. However, this exponential quantum advantage can be significantly hampered by noise.
By adapting dynamical decoupling, a noise suppression strategy, Lidar and Pokharel were able to achieve their quantum speedup. Pokharel worked as a doctoral candidate under Lidar at USC during the year of experiments. At first, applying dynamical decoupling appeared to debase execution. The quantum algorithm, on the other hand, worked as intended after going through numerous iterations. The quantum advantage became increasingly apparent as the problems became more complex, as the time required to solve them decreased more slowly than it did with any classical computer at the time.
That’s what lidar noticed “at present, traditional PCs can in any case take care of the issue quicker in outright terms.” All in all, the announced benefit is estimated as far as the time-scaling it takes to track down the arrangement, not the outright time. This intends that for adequately lengthy bitstrings, the quantum arrangement will ultimately be speedier.
The concentrate definitively shows the way that with appropriate mistake control, quantum PCs can execute total calculations with better scaling of the time it takes to track down the arrangement than regular PCs, even in the NISQ period.