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Circuit for the BV algorithm, including dynamic decoupling (DD) pulses. The oracle shown encodes the unknown bit string b = 111,000 for the ssBV-6 problem. Credit: Physical Review Letters (2023). DOI: 10.1103/PhysRevLett.130.210602

Daniel Lidar, professor of engineering at USC in Viterbi and director of the USC Center for Quantum Information Science & Technology, and Dr. Bibek Pokharel, a researcher at IBM Quantum, obtained a quantum acceleration advantage in the context of a “bitstring hypothesis game.” They handled strings up to 26 bits long, significantly larger than previously possible, effectively suppressing errors typically seen at this scale. (A bit is a binary number that is zero or one). Their article is published in the magazine Physical Review Letters.

Quantum computers promise to solve certain problems with an edge that increases as problems increase in complexity. However, they are also very prone to errors or noise. The challenge, says Lidar, is “to get an edge in the real world where today’s quantum computers are still ‘noisy’.” , a term adapted from the RISC architecture used to describe classical computing devices. Therefore, any current demonstration of the quantum speed advantage requires noise reduction.

The more unknown variables a problem has, the more difficult it is usually for a computer to solve. Scholars can evaluate a computer’s performance by playing one type of game with it to see how fast an algorithm can guess hidden information. For example, imagine a version of the television game show Jeopardy, where contestants take turns guessing a secret word of known length, one whole word at a time. The host reveals only one correct letter for each guessed word before randomly changing the secret word.

In their study, the researchers replaced words with strings of bits. A classical computer would require, on average, about 33 million guesses to correctly identify a 26-bit string. Conversely, a fully functional quantum computer, presenting hypotheses in quantum superposition, could identify the correct answer in only one hypothesis. This efficiency comes from running a quantum algorithm developed more than 25 years ago by computer scientists Ethan Bernstein and Umesh Vazirani. However, noise can significantly impede this exponential quantum advantage.

Lidar and Pokharel achieved their quantum acceleration by adapting a noise suppression technique called dynamic decoupling. They spent a year experimenting, with Pokharel working as a graduate student under Lidar at USC. Initially, applying dynamic decoupling seemed to degrade performance. However, after several refinements, the quantum algorithm worked as expected. The time to solve problems thus grew more slowly than with any classical computer, with the quantum advantage becoming increasingly apparent as problems became more complex.

Lidar notes that “currently, classical computers can still solve the problem faster in absolute terms.” In other words, reported benefit is measured in terms of the time scale required to find the solution, not absolute time. This means that for long enough bitstrings, the quantum solution will ultimately be faster.

The study conclusively demonstrates that with proper error control, quantum computers can run complete algorithms with better scaling of the time to solution finding than conventional computers, even in the NISQ era.

More information:
Bibek Pokharel et al, Demonstration of algorithmic quantum acceleration, Physical Review Letters (2023). DOI: 10.1103/PhysRevLett.130.210602

About the magazine:
Physical Review Letters

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