Scientists from the Universities Space Research Association (USRA), Rigetti Computing, and NASA Ames Research Center have made significant progress in the field of quantum computing, specifically in the area of combinatorial optimization. Supported by the DARPA Optimization with Noisy Intermediate Scale Quantum (ONISQ) program, the research team has developed a groundbreaking quantum algorithm that addresses the persistent challenge of noise in quantum hardware.
Noise in quantum hardware has long been a hindrance in achieving optimal quantum computing performance. To overcome this obstacle, the researchers developed an innovative quantum algorithm that builds upon recent advancements in quantum hybrid optimization. This algorithm is designed to outperform classical “greedy” algorithms even in the presence of strong hardware noise.
The team utilized the state-of-the-art Rigetti Aspen™-M-3 system, a programmable superconducting quantum computer boasting up to 72 qubits. This research is a major milestone in our understanding of the requirements for achieving quantum advantage. The results of the study were recently published in the prestigious journal Science Advances, under the title “Quantum-Enhanced Greedy Combinatorial Optimization Solver.”
Dr. Davide Venturelli, Associate Director of USRA’s Research Institute for Advanced Computer Science and Principal Investigator of the ONISQ project “Scheduling Applications with Advanced Mixers” (SAAM), emphasizes the importance of developing sophisticated algorithms that fully utilize the resources of current quantum hardware. He encourages the quantum computing community to embrace the challenge of overcoming quantum noise, no matter how daunting it may seem.
Lead author of the research paper, Dr. Maxime Dupont, highlights the groundbreaking nature of their work. He explains that their experiments demonstrate the capability of noisy superconducting quantum computers to solve combinatorial optimization problems at scale, bridging the gap towards achieving quantum advantage. As quantum hardware continues to improve in terms of qubit count and fidelity, the performance of these quantum algorithms will only improve.
The successful demonstration of their algorithm on 72 qubits opens up new possibilities for the development of quantum optimization algorithms. Future projects will explore error-mitigation techniques to further enhance performance and mitigate the effects of noise.
This research has significant implications, particularly in the U.S. military sphere, as quantum optimization algorithms have the potential to revolutionize military capabilities. As this field continues to advance, quantum computing stands to drive transformative changes across various industries and scientific disciplines.
Frequently Asked Questions (FAQ)
1. What is combinatorial optimization?
Combinatorial optimization is a branch of mathematics and computer science that involves finding the best possible solution among a vast number of possible combinations or permutations. It is a challenging problem with applications in various fields, including logistics, scheduling, and network optimization.
2. What is quantum advantage?
Quantum advantage refers to the point at which a quantum computer outperforms classical computers in solving certain problems. It is a significant milestone in the development of quantum technologies and has the potential to revolutionize industries by enabling the efficient solution of complex problems that are currently intractable using classical methods.
3. How does noise affect quantum computing?
Noise in quantum computing refers to unwanted disturbances or errors that arise during the computation process. These errors can corrupt the quantum information encoded in qubits and reduce the accuracy of the computation. Mitigating noise is crucial for achieving reliable and accurate results in quantum computing.
4. What are some potential applications of quantum optimization algorithms?
Quantum optimization algorithms have the potential to revolutionize various fields, including logistics and supply chain management, drug discovery, financial modeling, and vehicle route optimization. These algorithms can provide more efficient solutions to complex optimization problems, leading to better decision making and resource allocation.