Google’s Quantum AI team has unveiled a major milestone in quantum computing with its new Willow processor — a 105-qubit superconducting chip that reportedly runs certain calculations 13,000 times faster than today’s most powerful supercomputers. The breakthrough, published in Nature, marks one of the clearest demonstrations yet of quantum advantage in a practical, verifiable experiment.

The Willow chip successfully completed the Quantum Echoes algorithm — a test designed to measure quantum chaos. Unlike the company’s 2019 Sycamore experiment, which showed quantum “supremacy” only on a highly specific random-sampling test, Willow’s performance has broader scientific value, simulating molecular structures and electronic behaviors with far greater accuracy.

Michel Devoret, one of the project’s lead physicists, described Willow as evidence that electrical circuits can behave like “artificial atoms,” allowing researchers to explore atomic interactions without relying on traditional approximations.

Each qubit of the chip can exist in multiple states simultaneously and become entangled with others, enabling computations across an enormous range of possibilities at once. Its single-qubit gate fidelity reaches 99.97%, while entangling operations hit 99.88%, both critical for reducing cumulative errors in larger systems.

The performance jump could have practical consequences beyond academic benchmarks. Simulating molecular bonds and chemical reactions could accelerate drug discovery and materials research, while future applications might help design efficient batteries or superconductors. Quantum methods may even aid AI training by handling complex optimization tasks with less energy consumption.

Still, researchers caution that the field is far from fully realized. Scaling to stable, error-corrected logical qubits remains the hardest problem to solve. But Willow represents a meaningful step forward — not just a flashy demo, but a testbed showing that quantum hardware can now produce results worth verifying and building on.

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(Source | Via)