Abstract The storage and processing of quantum information are susceptible to external noise, resulting in computational errors. A powerful method to suppress these effects is quantum error correction. Typically, quantum error correction is executed in discrete rounds, using entangling gates and projective measurement on ancillary qubits to complete each round of error correction. Here we use direct parity measurements to implement a continuous quantum bit-flip correction code in a resource-efficient manner, eliminating entangling gates, ancillary qubits, and their associated errors. An FPGA controller actively corrects errors as they are detected, achieving an average bit-flip detection efficiency of up to 91%. Furthermore, the protocol increases the relaxation time of the protected logical qubit by a factor of 2.7 over the relaxation times of the bare comprising qubits. Our results showcase resource-efficient stabilizer measurements in a multi-qubit architecture and demonstrate how continuous error correction codes can address challenges in realizing a fault-tolerant system.
more »
« less
ReiNN: Efficient error resilience in artificial neural networks using encoded consistency checks
In this research, a low cost error detection and
correction approach is developed for multilayer perceptron
networks, where checker neurons are used to encode hidden
layer functions using independent training experiments. Error
detection and correction is predicated on validating consistency
properties of the encoded checks and shows that high coverage of
injected errors can be achieved with extremely low computational
overhead.
more »
« less
- Award ID(s):
- 1723997
- PAR ID:
- 10098272
- Date Published:
- Journal Name:
- European Test Symposium
- Page Range / eLocation ID:
- 1 to 2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Deep learning techniques have been widely adopted in daily life with applications ranging from face recognition to recommender systems. The substantial overhead of conventional error tolerance techniques precludes their widespread use, while approaches involving median filtering and invariant generation rely on alterations to DNN training that may be difficult to achieve for larger networks on larger datasets. To address this issue, this paper presents a novel approach taking advantage of the statistics of neuron output gradients to identify and suppress erroneous neuron values. By using the statistics of neurons’ gradients with respect to their neighbors, tighter statistical thresholds are obtained compared to the use of neuron output values alone. This approach is modular and is combined with accurate, low-overhead error detection methods to ensure it is used only when needed, further reducing its cost. Deep learning models can be trained using standard methods and our error correction module is fit to a trained DNN, achieving comparable or superior performance compared to baseline error correction methods while incurring comparable hardware overhead without needing to modify DNN training or utilize specialized hardware architectures.more » « less
-
Deep learning techniques have been widely adopted in daily life with applications ranging from face recognition to recommender systems. The substantial overhead of conventional error tolerance techniques precludes their widespread use, while approaches involving median filtering and invariant generation rely on alterations to DNN training that may be difficult to achieve for larger networks on larger datasets. To address this issue, this paper presents a novel approach taking advantage of the statistics of neuron output gradients to identify and suppress erroneous neuron values. By using the statistics of neurons’ gradients with respect to their neighbors, tighter statistical thresholds are obtained compared to the use of neuron output values alone. This approach is modular and is combined with accurate, low-overhead error detection methods to ensure it is used only when needed, further reducing its cost. Deep learning models can be trained using standard methods and our error correction module is fit to a trained DNN, achieving comparable or superior performance compared to baseline error correction methods while incurring comparable hardware overhead without needing to modify DNN training or utilize specialized hardware architectures.more » « less
-
more » « less
Abstract Large-scale quantum computers will inevitably need quantum error correction to protect information against decoherence. Traditional error correction typically requires many qubits, along with high-efficiency error syndrome measurement and real-time feedback. Autonomous quantum error correction instead uses steady-state bath engineering to perform the correction in a hardware-efficient manner. In this work, we develop a new autonomous quantum error correction scheme that actively corrects single-photon loss and passively suppresses low-frequency dephasing, and we demonstrate an important experimental step towards its full implementation with transmons. Compared to uncorrected encoding, improvements are experimentally witnessed for the logical zero, one, and superposition states. Our results show the potential of implementing hardware-efficient autonomous quantum error correction to enhance the reliability of a transmon-based quantum information processor.
-
more » « less
Abstract Long nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ETS.2018.8400706
