Usc Study Demonstrates Unconditional Exponential Quantum Scaling Advan

Conducted on IBM quantum processors, study demonstrates “a promise of quantum computing previously articulated only on paper.” Interior shot of a quantum computer with an IBM Eagle processor/Photo credit: IBM Quantum computers have the potential to speed up computation, help design new medicines, break codes, and discover exotic new materials—but that’s only when they are truly functional. One key thing that gets in the way: noise or the errors that are produced during computations on a quantum machine–which in fact makes them less powerful than classical computers – until recently. Daniel Lidar, holder of the Viterbi Professorship in Engineering and Professor of Electrical & Computing Engineering at the USC Viterbi School of Engineering, has been iterating on quantum error correction, and in a new... The paper, “Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem,” was published in APS flagship journal Physical Review X.

Quantum computers have the potential to speed up computation, help design new medicines, break codes, and discover exotic new materials -- but that's only when they are truly functional. One key thing that gets in the way: noise or the errors that are produced during computations on a quantum machine -- which in fact makes them less powerful than classical computers - until... Daniel Lidar, holder of the Viterbi Professorship in Engineering and Professor of Electrical & Computing Engineering at the USC Viterbi School of Engineering, has been iterating on quantum error correction, and in a new... The paper, "Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem," was published in APS flagship journal Physical Review X. "There have previously been demonstrations of more modest types of speedups like a polynomial speedup, says Lidar, who is also the cofounder of Quantum Elements, Inc. "But an exponential speedup is the most dramatic type of speed up that we expect to see from quantum computers."

The key milestone for quantum computing, Lidar says, has always been to demonstrate that we can execute entire algorithms with a scaling speedup relative to ordinary "classical" computers. A new study confirms algorithmic speedup for an oracle-based mathematical problem, showing the potential for algorithm development to bring us closer to quantum advantage. Algorithm discovery has entered its own belle époque. With the latest performance gains of IBM quantum hardware and software, researchers are better positioned than ever before to develop and improve algorithms that will bring us closer to quantum advantage. Recently, researchers led by Dr. Daniel Lidar’s team at the University of Southern California (USC) published results in Physical Review X that demonstrate exponential algorithmic speedup for a modified version of Simon’s problem using IBM quantum computers.

This paper offers one of the first proofs of quantum scaling speedup not dependent on unproven assumptions regarding the limitations of classical methods. Put simply, the team ran circuits on noisy quantum hardware up to 126 qubits, demonstrating that as the problem increased in size, the speedup scaled exponentially for quantum. However, past 58 qubits, noise inherent to today’s quantum computers allowed classical to win. A key part of algorithmic development is identifying the problems for which quantum will provide a speedup over classical methods, as well as determining what kind of speedup is expected. This is an effort that requires novel strategies on today’s noisy devices. Conducted on IBM quantum processors, study demonstrates “a promise of quantum computing previously articulated only on paper”

Interior shot of a quantum computer with an IBM Eagle processor. Quantum computers have the potential to speed up computation, help design new medicines, break codes, and discover exotic new materials—but that’s only when they are truly functional. One key thing that gets in the way: noise or the errors that are produced during computations on a quantum machine--which in fact makes them less powerful than classical computers – until recently. Daniel Lidar, holder of the Viterbi Professorship in Engineering and Professor of Electrical & Computing Engineering at the USC Viterbi School of Engineering, has been iterating on quantum error correction, and in a new... The paper, “Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem,” was published in APS flagship journal Physical Review X. Quantum computing holds the promise to revolutionize how we perform calculations, enabling breakthroughs in medicine design, codebreaking, and the discovery of new materials.

However, the technology has long been hindered by a fundamental challenge: noise and errors during quantum computations have made quantum devices less effective than classical computers—until now. The core difficulty in building practical quantum computers has been managing the errors caused by interactions between delicate quantum bits (qubits) and their noisy surroundings. This noise introduces faults that can derail calculations, limiting the machines' usefulness compared to classical counterparts. Daniel Lidar, a professor at the University of Southern California (USC) Viterbi School of Engineering and an expert in quantum error correction, has been leading efforts to push past this barrier. In collaboration with researchers at USC and Johns Hopkins University, Lidar's team has demonstrated a significant and unconditional exponential scaling advantage in quantum computing performance by running algorithms on two IBM Quantum Eagle processors,... Their groundbreaking results were published in the journal Physical Review X in a paper titled "Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem."

Many previous quantum speedup demonstrations have shown only modest improvements, often polynomial speedups, over classical algorithms. Lidar explains, "An exponential speedup is the most dramatic and sought-after type, meaning that as problem sizes grow, the quantum advantage increases at an exponential rate rather than a linear or polynomial one." Glad to be a part of this project that demonstrates the first unconditional exponential quantum speedup on real hardware from IBM Quantum! The peer-reviewed paper is now published in Physical Review X. Paper link: https://lnkd.in/g6z6itXk Quantum Speedup, Demonstrated We’re excited to share a major breakthrough by our Co-Founder, Prof.

Daniel Lidar whose new study—published in Physical Review X—marks the first unconditional demonstration of exponential quantum speedup using real quantum hardware. Working with collaborators at USC and Johns Hopkins, Prof. Lidar and the team used two IBM 127-qubit Eagle processors to solve a version of Simon’s problem—showing that today’s quantum machines can outperform classical computers not just in theory, but in practice. What makes this result especially significant: the speedup is unconditional—it doesn’t rely on any unproven assumptions. It demonstrates that as problem size increases, quantum performance scales exponentially beyond classical capabilities. The achievement was made possible by pushing the limits of current quantum technology: tighter circuit optimization, dynamical decoupling to suppress noise, and advanced error mitigation—all working together to realize a long-predicted quantum milestone.

While this result is academic and not yet tied to direct applications, it represents a turning point: quantum computing’s theoretical promise is now being confirmed by real-world execution. At Quantum Elements Inc., we’re proud to be building tools and platforms that help move quantum computing from possibility to practice. Congratulations to Prof. Lidar and the team for this landmark result. DM me if you are interested to see how this ground breaking technology will improve your quantum algorithm #QuantumAdvantage #DanielLidar #QuantumElements #QuantumComputing #DeepTech #ScientificBreakthrough #USC #IBMQ https://lnkd.in/gcVxsqUu Glad to have you on this project Victor Zhou

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