Quantum Advantage Proven

In a significant scientific breakthrough, researchers at USC have demonstrated that quantum computers can outperform even the most powerful supercomputers when tackling certain complex problems.

Figure 1. Quantum Computers Surpass Supercomputers: A New Milestone Unlocked

This milestone, known as quantum advantage, was achieved using a method called quantum annealing. This technique works like a highly efficient strategy for finding good—though not always perfect—solutions to difficult challenges that traditional computers find hard to solve. The findings were recently published in Physical Review Letters. Figure 1 shows Quantum Computers Surpass Supercomputers: A New Milestone Unlocked

“Quantum annealing identifies low-energy states in quantum systems, which represent optimal or near-optimal solutions to the problems at hand,” explained Daniel Lidar, the study’s corresponding author and a professor at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences.

Embracing Approximate Optimization

For years, researchers have worked to show that quantum computers can maintain their edge over classical systems as problem sizes grow. This study takes a new angle by prioritizing near-optimal solutions over perfect ones—an approach that often proves more practical in real-world applications.

This strategy, known as approximate optimization, is especially valuable in fields like finance, logistics, and machine learning, where reaching a good-enough answer quickly often outweighs the need for absolute precision.

By leveraging quantum annealing, the researchers were able to outperform leading classical algorithms in speed and solution quality—representing a significant leap toward practical quantum computing.

Quantum annealing, a specialized form of quantum computing, applies the principles of quantum mechanics to efficiently tackle complex optimization problems. Instead of insisting on exact results, this study targeted solutions within a small margin (at least 1%) of the best possible outcome.

Practical Impact of Approximate Solutions

In many real-world scenarios, exact answers aren’t necessary—making the pursuit of approximate solutions both efficient and relevant. For instance, when building a mutual fund, the goal is often just to outperform a major market index, not to outdo every possible portfolio combination.

To showcase quantum scaling advantage in action, the researchers utilized a D-Wave Advantage quantum annealing processor, a specialized quantum device housed at USC’s Information Sciences Institute. Like all current quantum systems, this hardware faces significant challenges from noise, which can interfere with performance and limit quantum advantage.

To address this, the team applied a method known as quantum annealing correction (QAC) on the D-Wave system. This allowed them to construct over 1,300 error-suppressed logical qubits. The suppression of errors proved crucial in surpassing the performance of parallel tempering with isoenergetic cluster moves (PT-ICM)—the most advanced classical algorithm currently available for solving similar optimization problems.

Benchmarking with Spin-Glass Models

To demonstrate quantum advantage, the researchers employed a variety of methods focused on a specific class of problems: two-dimensional spin-glass models with high-precision interactions. “Spin-glass problems are complex optimization tasks rooted in statistical physics and based on disordered magnetic systems,” explained Lidar. Rather than aiming for exact solutions, the team measured time-to-epsilon performance—tracking how quickly each method could find answers within a defined margin of the optimal result.

Advancing Toward Practical Quantum Optimization

Looking ahead, the researchers plan to expand their work to more complex, denser, and higher-dimensional optimization problems, with a goal of applying these advances in real-world scenarios. Lidar noted that continued progress in quantum hardware and error-correction techniques could further enhance the quantum advantage. “This paves the way for new quantum algorithms aimed at solving practical optimization tasks where near-optimal solutions are not only sufficient but preferred.”

Source: SciTECHDaily

Cite this article:

Priyadharshini S (2025), Quantum Computers Just Outperformed Supercomputers – Here's What They Achieved, AnaTechMaz, pp. 284