Edward Conard

Top Ten New York Times Bestselling Author

  • “Unintended Consequences should be read by anyone who takes for granted the superiority of progressive taxation and has not thought carefully about the trade-offs involved.” - The New Republic
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “…reminds us that inequality sends a signal of what society lacks most, in America’s case, entrepreneurship and risk taking.” - Lawrence Lindsey, CEO, The Lindsey Group, former Director of the National Economic Council
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
  • “Unintended Consequences provides a provocative interpretation of the causes of the global financial crisis and the policies needed to return to rapid growth. Whether you agree or not, this analysis is well worth reading.” - Nouriel Roubini, New York University; Chairman, Roubini Global Economics
  • “There are an amazing number of good ideas and interesting points made in Unintended Consequences. The thinking underlying it, and the obvious depth of understanding of the author, are very impressive.” - Steven Levitt, coauthor of Freakonomics; 2004 John Bates Clark Medal
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
Upside of Inequality Oxford Unintended Consequences

AI Summary. Researchers developed a recurrent, transformer-based neural network that decodes the surface code, the leading quantum error-correction code, and report it outperforms other state-of-the-art decoders on data from Google's Sycamore quantum processor and on simulated data with realistic noise. The authors argue machine learning can surpass human-designed decoding algorithms by learning directly from data.

Johannes Bausch, Andrew Senior, Francisco Heras et al. Nature
Date Posted:

Google DeepMind announces AlphaQubit, an AI-based decoder that identifies quantum computing errors with state-of-the art accuracy, addressing one of the biggest challenges in scaling quantum computing.

Can machine learning decode quantum errors better than human-designed algorithms?

Core argument: A recurrent, transformer-based neural network decoder outperforms other state-of-the-art decoders on real-world data from Google's Sycamore quantum processor for distance 3 and distance 5 surface codes, the leading quantum error-correction code.

Building a large-scale quantum computer requires effective strategies to correct errors that inevitably arise in physical quantum systems. Quantum error-correction codes present a way to reach this goal by encoding logical information redundantly into many physical qubits. Here we develop a recurrent, transformer-based neural network that learns to decode the surface code, the leading quantum error-correction code. Our decoder outperforms other state-of-the-art decoders on real-world data from Google’s Sycamore quantum processor for distance 3 and distance 5 surface codes. On distances up to 11, the decoder maintains its advantage on simulated data with realistic noise including cross-talk and leakage, utilizing soft readouts and leakage information. Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers.

Takeaways by Macro Roundup® AI

  1. A recurrent, transformer-based neural network decoder outperforms other state-of-the-art decoders on real-world data from Google's Sycamore quantum processor for distance 3 and distance 5 surface codes, the leading quantum error-correction code.
  2. On simulated data with realistic noise including cross-talk and leakage, the neural network decoder maintains its advantage at code distances up to 11 by using soft readouts and leakage information.
  3. The authors argue that machine learning can go beyond human-designed algorithms by learning from data directly, making it a strong contender for decoding in quantum computers.

Related Articles:

  • Quantum Computing Advance Begins New Era, IBM Says — IBM researchers report that a 127-qubit quantum processor simulated the magnetic behavior of 127 atom-scale bar magnets, a problem too complex for an exact answer on the largest supercomputers. Because quantum noise made each sub-millisecond calculation unreliable, the computation was repeated 600,000 times to converge on an answer.
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