Disputed Quantum-Computing Paper Retracted by Microsoft-Led Team
A team of Microsoft researchers has formally retracted a much-discussed paper that claimed to demonstrate the feasibility of designing quantum computers. The move follows an investigation by the editors of Physical Review A, in which the authors conceded that certain statements and approximations in the paper were incorrect, and removed those statements from their final version of the work. The paper’s retraction was announced on the journal’s Web site today, with editor Ted Jacobson saying that he had concluded that the calculations reported in the article were performed correctly, but their interpretation was incorrect.
The new finding
Researchers of the University of New South Wales, with collaborators from Microsoft, have retracted a paper detailing how they were able to manipulate quantum systems. The contested paper was retracted due to an error in some formulas that is unrelated to their main results. While the retraction process is embarrassing and time-consuming, this also shows how rigorously research papers are reviewed before they are published. In a statement released alongside the retraction notice, the researcher of UNSW confirmed that the main results remain valid.
The paper in question, titled Experimental Decoy State Quantum Key Distribution, was published earlier in March. It claimed that researchers had developed a more secure way to encode quantum key distribution (QKD) systems using decoy states. These systems are based on light particles and rely on quantum mechanics to encrypt data into secure communications networks. QKD is typically done with single photons, but as you can imagine from reading that description, it requires very precise equipment and a lot of trial and error to make it work correctly. The researchers believed they found a way around that requirement through what is called an imperfection source.
The original claim
A collaborative effort from the Software Engineering Institute (SEI) at Carnegie Mellon University, Google, and the quantum software company QuTech has resulted in a nine-qubit machine that could serve as a prototype for future quantum computers. The team used feedback error-correcting codes to increase the stability of their results. Errors arising due to decoherence caused problems with their first-generation machine, but this new model had a completely different architecture that is said to have reduced decoherence errors by almost 100%. This system is based on a 2×2 array of superconducting flux qubits manufactured using CMOS technology. The average coherent coupling factor is also improved significantly, which translates into faster computation rates on average – compared to previous generations of hardware.
The paper originally cited an algorithm that could find correlations between data in the quantum system using a fixed number of queries. If the paper had stood, it would have been a major step forward for quantum computing. In a blog post, Scott Aaronson, a professor at MIT and one of the original critics of the paper, applauded this decision to retract because doing research well is as important as doing research fast. After seeing problems with how author Nicolas Lioar presented some aspects of their work to the public, Microsoft agreed with Aaronson and other researchers on the panel that no retraction was necessary if there were changes made to the paper’s structure and conclusions.
The paper has now been retracted, but there’s a lot more to the story. The first publication of this story in Science was rife with errors and mistakes. The following is just a brief timeline to bring everyone up to speed:
In 2013, Geordie Rose (CEO) and his team at D-Wave published their latest breakthrough – evidence of adiabatic quantum computing in one machine. Geordie Rose has since presented his research at TEDxCambridge where he details how his theory on quantum computing builds off work from predecessors such as John Von Neumann and Richard Feynman.
Final thoughts on scientific rigor
It is true that when it comes to scientific rigor and reproducibility, the system should be fixed so that the incentives align with researchers. The mistakes made in this case are not a reflection of flawed systems or inherent biases in research, but rather the consequence of people having too much trust in their own assumptions and forgetting to be critical. These are only a few of many common issues found in every single field. It’s important to remember that as long as science still retains its intellectual honesty, as it’s done for hundreds of years, it will never stop striving for progress.