New Quantum Algorithm Breakthroughs Push the Boundaries of Optimization

A recent joint research effort by the Universities Space Research Association (USRA), Rigetti Computing, and NASA Ames Research Center has made significant strides in the field of quantum computing. The team has successfully developed advanced quantum optimization algorithms, bringing us closer to harnessing the true power of quantum computers for combinatorial optimization tasks. These breakthroughs have the potential to revolutionize multiple industries, including the U.S. military.

One of the main challenges faced by quantum hardware is noise. To address this issue, the researchers introduced an innovative quantum algorithm inspired by recent advancements in quantum hybrid optimization. This algorithm has proven to outperform classical “greedy” algorithms, even in the presence of strong hardware noise.

The team conducted their research using the Rigetti Aspen™-M-3 system, a state-of-the-art programmable superconducting quantum computer with up to 72 qubits. Their work represents a crucial milestone in understanding the requirements for achieving quantum advantage. The results have been published in the prestigious journal Science Advances under the title “Quantum-Enhanced Greedy Combinatorial Optimization Solver.”

According to Dr. Davide Venturelli, Associate Director of USRA’s Research Institute for Advanced Computer Science and Principal Investigator of the project, it is essential for the quantum computing community to develop sophisticated algorithms that fully utilize the resources of current quantum hardware. Overcoming the effects of noise on quantum systems is a daunting task, but it is crucial for unlocking the true potential of quantum computing.

Lead author Dr. Maxime Dupont emphasized that their work demonstrates the efficacy of noisy superconducting quantum computers in solving combinatorial optimization problems at scale. As more qubits with improved fidelities become available, the researchers believe they are closing in on achieving a true quantum advantage.

The success of this research opens up exciting possibilities for the development of quantum algorithms. Future projects will focus on investigating error-mitigation techniques to further improve the performance of the quantum optimization solver.


What is quantum optimization?

Quantum optimization involves using quantum computers to solve complex optimization problems more efficiently than classical computers. It aims to find the best solution among a vast number of possible combinations quickly.

What is the main challenge in quantum computing?

One of the main challenges in quantum computing is noise. Noise interferes with the stability and accuracy of quantum systems, affecting the reliability of computations. Researchers are continuously working on developing methods to mitigate the effects of noise.

What is a quantum advantage?

Quantum advantage refers to the point at which a quantum computer can solve a problem significantly faster or more efficiently than a classical computer. It is the threshold where quantum computing surpasses classical computing capabilities.

What are quantum hybrid optimization algorithms?

Quantum hybrid optimization algorithms combine classical computing resources with quantum computing capabilities. They leverage the strengths of both classical and quantum systems to solve optimization problems efficiently.