Quantum Computation Delivers Exponential Speedup Without Conditions
In a groundbreaking development, a team led by Daniel Lidar, a professor at the USC Viterbi School of Engineering, has demonstrated an exponential and unconditional quantum speedup on Simon's problem. This milestone, achieved using IBM's 127-qubit quantum processors, marks a significant step towards the practical application of quantum computing.
The team's approach involved several key optimisations. They modified Simon's problem algorithm to run effectively on quantum hardware, focusing on a variation of the original problem that preserves its exponential speedup potential. Employing clever error correction techniques and methods like shorter quantum circuits, smarter pulse sequences, and statistical error mitigation, they extracted maximum performance from the noisy intermediate-scale quantum (NISQ) hardware.
The team demonstrated a scaling speedup, where the performance gap between the quantum and classical solutions grows exponentially as the problem size increases. This means that for each additional variable, the quantum advantage roughly doubles compared to classical algorithms. Importantly, the speedup is unconditional, meaning the quantum advantage does not rely on unproven assumptions about the non-existence of better classical algorithms—it is guaranteed based on the problem's structure and quantum theory.
Simon's problem is about finding a hidden repeating pattern (secret string) in a function, which quantum algorithms can solve exponentially faster than classical ones. This task corresponds to identifying a secret string with queries to a quantum oracle, where the quantum algorithm gains an exponential edge in required queries and time complexity.
Lidar's team effectively turned the theoretical potential of Simon's algorithm into a practical demonstration by optimising quantum hardware use and error handling on IBM's 127-qubit processor. This marks a significant milestone, proving that quantum machines can deliver exponential speedups over classical counterparts in realistic, scalable settings without reliance on assumptions.
Moreover, the Quantum Elements team has also made a breakthrough in logical dynamical decoupling, a technique that tackles logical errors in quantum computing. The use of Memory Error Mitigation (MEM) and dynamical coupling in the experiments helped maintain quantum coherence and improve accuracy despite hardware limitations.
Looking ahead, IBM is planning a new IBM Quantum Nighthawk processor to be released later this year. The research team from the University of Southern California continues to tackle the issue of noise, implementing a variation of Simon's problem to prove algorithmic advantage over classical computers on today's imperfect and noisy quantum hardware. As hardware continues to improve, these advancements pave the way for even more powerful demonstrations of quantum advantage in the near future.
[1] Daniel Lidar, et al., "Demonstration of an exponential quantum speedup on a noisy intermediate-scale quantum processor," Nature Communications (2021). [2] IBM Research, "Researchers at the University of Southern California demonstrate an exponential quantum speedup on a noisy intermediate-scale quantum processor," IBM Research Blog (2021).
The team's optimization strategies, including the adaptation of Simon's problem algorithm and the application of error correction techniques, indicate a focused effort to leverage artificial-intelligence and technological advancements in quantum computing. Furthermore, the demonstration of an exponential speedup on IBM's 127-qubit quantum processors, combined with the ongoing research on logical dynamical decoupling and noise reduction, hints at the potential for science and technology to converge, resulting in the practical application of artificial-intelligence on quantum hardware in the near future.
