Christopher Chamberland

Christopher Chamberlandchristopher-chamberland

Apr 18 2018 04:57 UTC
Apr 18 2018 04:57 UTC
Christopher Chamberland scited Hyperbolic quantum color codes
Apr 13 2018 09:42 UTC
Apr 12 2018 15:41 UTC
Apr 10 2018 19:04 UTC
Apr 05 2018 13:17 UTC
Apr 05 2018 13:16 UTC
Mar 28 2018 02:00 UTC
Flag qubits have recently been proposed in syndrome extraction circuits to detect high-weight errors arising from fewer faults. The use of flag qubits allows the construction of fault-tolerant protocols with the fewest number of ancillas known to-date. In this work, we prove some critical properties of CSS codes constructed from classical cyclic codes that enable the construction of a flag fault-tolerant error correction scheme. We then develop a fault-tolerant protocol as well as a family of circuits for flag fault-tolerant error correction requiring only four ancilla qubits and applicable to distance-three CSS codes constructed from classical cyclic codes.
Feb 21 2018 21:03 UTC
Christopher Chamberland scited Universal qudit Hamiltonians
Feb 20 2018 02:00 UTC
Finding efficient decoders for quantum error correcting codes adapted to realistic experimental noise in fault-tolerant devices represents a significant challenge. In this paper we introduce several decoding algorithms complemented by deep neural decoders and apply them to analyze several fault-tolerant error correction protocols such as the surface code as well as Steane and Knill error correction. Our methods require no knowledge of the underlying noise model afflicting the quantum device making them appealing for real-world experiments. Our analysis is based on a full circuit-level noise model. It considers both distance-three and five codes, and is performed near the codes pseudo-threshold regime. Training deep neural decoders in low noise rate regimes appears to be a challenging machine learning endeavour. We provide a detailed description of our neural network architectures and training methodology. We then discuss both the advantages and limitations of deep neural decoders. Lastly, we provide a rigorous analysis of the decoding runtime of trained deep neural decoders and compare our methods with anticipated gate times in future quantum devices. Given the broad applications of our decoding schemes, we believe that the methods presented in this paper could have practical applications for near term fault-tolerant experiments.
Feb 19 2018 09:31 UTC
Feb 16 2018 15:13 UTC
Christopher Chamberland scited Approximate quantum Markov chains
Feb 06 2018 06:41 UTC
Jan 27 2018 17:11 UTC
Christopher Chamberland scited Quantum Computing with Majorana Fermion Codes
Jan 19 2018 16:34 UTC
Christopher Chamberland scited Real Randomized Benchmarking
Jan 18 2018 16:11 UTC
Jan 15 2018 07:30 UTC
Jan 13 2018 22:53 UTC
Jan 11 2018 02:07 UTC
Jan 09 2018 06:19 UTC
Dec 30 2017 14:24 UTC
Christopher Chamberland scited Quantum codes on a lattice with boundary