Oscar Higgott
@oscarhiggott
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Research Scientist @GoogleQuantumAI working on quantum computing and quantum error correction | PhD from @UCLQuantum
London, England
Joined August 2010
Are you a PhD student in quantum computing and interested in working with our team in 2026 as a student researcher (i.e., an internship that's ~3 months and in-person at our office)? If so, apply here: https://t.co/tlhv8Yp0xt
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Using time-dynamics, the surface code can be embedded on a hexagonal lattice rather than a square lattice. In our new paper, we show that these hex-grid circuits can still be adapted to broken qubits and couplers, despite their sparser connectivity: https://t.co/HuN95nktWi.
arxiv.org
Recent work has shown that a hexagonal grid qubit layout, with only three couplers per qubit, is sufficient to implement the surface code with performance comparable to that of a traditional...
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I'm often asked if I'll redo the 2019 quantum factoring estimate. Denser storage by yokes, smaller magic factories by cultivation, slimmer approx arithmetic by Chevignard et al… surely the cost is lower now? Yes, it's lower now. https://t.co/yZGrKETInF
https://t.co/1OROIX3kab
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Happy to share that some previous work with @krishnanirudh and @preskill that we call hierarchical codes (2303.04798) was recently published in Quantum! 1/10
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Introducing Tesseract, a search-based decoder for quantum error correction at APS Global Physics Summit today. Try it out now on Github ↓
github.com
Search-based decoder for quantum error correction (QEC) written in C++ and Python. - quantumlib/tesseract-decoder
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New construction of Floquet quantum error-correction codes derived from the tiling of hyperbolic surfaces can be up to 100 times more efficient than surface codes. @BristolUni @ucl
https://t.co/dfUevKn2Ny
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Check out our latest preprint @GoogleQuantumAI pushing the surface code to the next level! - Below threshold (Λ > 2) - Distance-7 logical qubit, 0.0014 error per cycle, >2x better than physical qubits - Explore 10^-10 error regime with repetition codes https://t.co/tGLKKg51jS
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Houston, we are below the quantum error correction threshold! 🚀 In “Quantum error correction below the surface code threshold” ( https://t.co/sScOACIe7u), we implement a 101-qubit surface code. Each time we increase the distance by two, the logical error rate is cut in half!
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🚨 Deadline approaching! Less than a month left to submit your papers to QCTIP24 🚀. February 5th is the last day! We're excited to see submissions across a wide range of quantum computing topics.Submit here: https://t.co/MKKkMrFrYf
#QuantumComputing #QCTIP24
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We also construct small examples amenable to near-term experiments, including a Floquet code derived from the Bolza surface that encodes four logical qubits into 16 physical qubits.
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For standard circuit-level depolarising noise, our constructions are 30x more efficient than planar honeycomb codes and 5.6x more efficient than surface codes.
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For a noise model that assumes direct two-qubit measurements, our semi-hyperbolic Floquet codes are 48x more efficient than planar honeycomb codes and therefore over 100x more efficient than other compilations of the surface code to pair measurements.
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For a given code distance and system size, the hyperbolic and semi-hyperbolic Floquet codes we constructed can encode over an order of magnitude more logical qubits than planar honeycomb codes.
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New paper on arXiv today with @nikobreu: https://t.co/BIYnK2EdAQ We construct families of Floquet codes derived from closed hyperbolic surfaces and show that they outperform honeycomb codes and surface codes even at high physical error rates. @UCLQuantum @BristolUniMaths
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This has now been published in PRX: https://t.co/7I3rDS8gCS I've also now released an open-source implementation of belief-matching, which integrates with stim and sinter:
pypi.org
A package for decoding quantum error correcting codes using belief-matching.
Phys. Rev. X 13, 031007 (2023) - Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes
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New, efficient, classical software for controlling quantum error-correcting codes exploits the structure of relevant noise models to outperform the state-of-the-art in terms of accuracy. Read https://t.co/CwzrJqSgmW
#PRXqst #PRXjustpublished #openaccess
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Phys. Rev. X 13, 031007 (2023) - Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes
journals.aps.org
New, efficient, classical software for controlling quantum error-correcting codes exploits the structure of relevant noise models to outperform the state-of-the-art in terms of accuracy.
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This has now been published in PRX: https://t.co/7I3rDS8gCS I've also now released an open-source implementation of belief-matching, which integrates with stim and sinter:
pypi.org
A package for decoding quantum error correcting codes using belief-matching.
Phys. Rev. X 13, 031007 (2023) - Improved Decoding of Circuit Noise and Fragile Boundaries of Tailored Surface Codes
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The single-shot performance of higher-dimensional hypergraph product codes is studied, revealing strategies that greatly exceed all previously reported thresholds. @oscarhiggott, @nikobreu, @UCLQuantum
https://t.co/GIGYeOwspC
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Our Fusion Blossom paper is finally out!!! https://t.co/VTpFZPCEA3 The very first parallel MWPM decoder! It's fast, exact, and even supports stream decoding! Check out our tutorial if you want to use it in Python
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