• A quantum computer, utilizing a D-Wave Advantage processor, outperformed classical supercomputers in approximate optimization tasks.
• The study achieved a quantum scaling advantage through Quantum Annealing Correction (QAC) which suppressed errors on over 1,300 logical qubits.
• This research signifies a potential for quantum computers to accelerate scientific simulations and solve complex real-world optimization problems more efficiently.

Researchers have demonstrated that a quantum computer can outperform classical supercomputers in solving approximate optimization problems. The study, led by the University of Southern California (USC) and involving collaboration with D-Wave Quantum Inc. and Oak Ridge National Laboratory (ORNL), showcased a quantum scaling advantage using a D-Wave Advantage quantum annealing processor. By implementing Quantum Annealing Correction (QAC) to mitigate noise, the quantum computer was able to find near-optimal solutions faster than classical algorithms, opening new avenues for quantum algorithms in various optimization tasks.

• What: A quantum computer outperformed classical supercomputers in solving approximate optimization problems, specifically in finding near-optimal solutions to complex tasks.
• Who: Researchers from the University of Southern California (USC), D-Wave Quantum Inc., and Oak Ridge National Laboratory (ORNL), including Daniel Lidar, Gonzalo Alvarez, Travis Humble, Stephen Jordan, and Eddie Farhi.
• When: The study was published recently, with some related work dating back to 2023. The Wired article references a paper posted on arxiv.org last year.
• Where: The experiments were conducted using a D-Wave Advantage quantum annealing processor installed at USC’s Information Sciences Institute and ORNL's Frontier supercomputer.

The achievement of quantum speedup in approximate optimization marks a significant milestone in quantum computing. The successful implementation of Quantum Annealing Correction (QAC) to mitigate noise, a major challenge in quantum computing, is particularly noteworthy. This development suggests that quantum computers are becoming increasingly viable for tackling real-world problems where near-optimal solutions are acceptable. While the WIREDSave article acknowledges the ongoing competition between quantum and classical algorithms, this study provides concrete evidence of quantum advantage in a specific domain. Further research is needed to explore the applicability of these findings to broader classes of optimization problems and to continue improving quantum hardware and error correction techniques.

Researchers in the Quantum Science Center are using these new paradigms in computation for simulating the behavior of models of materials, such as frustrated magnets, to understand their potential for making new sensing and computing technologies.

The way quantum annealing works is by finding low-energy states in quantum systems, which correspond to optimal or near-optimal solutions to the problems being solved.

While quantum computers demonstrate a speedup in approximate optimization through quantum annealing and error correction, surpassing classical supercomputers in specific tasks, significant hurdles remain. Scaling these quantum systems, managing inherent noise, and generalizing findings across diverse problem sets are key challenges. Current research focuses on hybrid classical-quantum algorithms and novel error mitigation techniques, like those based on ParityQC architectures, to improve accuracy on noisy intermediate-scale quantum (NISQ) devices. Further optimization of quantum error correction codes, such as surface codes, and exploration of algorithms like QAOA and VQE are crucial. Ultimately, overcoming hardware limitations and achieving fault-tolerant quantum computation will pave the way for quantum computing to revolutionize complex optimization across logistics, finance, energy, and materials science, potentially working alongside classical methods to redefine solutions for intractable problems.