1,145 research outputs found
Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures
Reading a qubit is a fundamental operation in quantum computing. It
translates quantum information into classical information enabling subsequent
classification to assign the qubit states `0' or `1'. Unfortunately, qubit
readout is one of the most error-prone and slowest operations on a
superconducting quantum processor. On state-of-the-art superconducting quantum
processors, readout errors can range from 1-10%. High readout accuracy is
essential for enabling high fidelity for near-term noisy quantum computers and
error-corrected quantum computers of the future.
Prior works have used machine-learning-assisted single-shot qubit-state
classification, where a deep neural network was used for more robust
discrimination by compensating for crosstalk errors. However, the neural
network size can limit the scalability of systems, especially if fast hardware
discrimination is required. This state-of-the-art baseline design cannot be
implemented on off-the-shelf FPGAs used for the control and readout of
superconducting qubits in most systems, which increases the overall readout
latency as discrimination has to be performed in software.
In this work, we propose HERQULES, a scalable approach to improve qubit-state
discrimination by using a hierarchy of matched filters in conjunction with a
significantly smaller and scalable neural network for qubit-state
discrimination. We achieve substantially higher readout accuracies (16.4%
relative improvement) than the baseline with a scalable design that can be
readily implemented on off-the-shelf FPGAs. We also show that HERQULES is more
versatile and can support shorter readout durations than the baseline design
without additional training overheads
Distributed Maximum Likelihood Sensor Network Localization
We propose a class of convex relaxations to solve the sensor network
localization problem, based on a maximum likelihood (ML) formulation. This
class, as well as the tightness of the relaxations, depends on the noise
probability density function (PDF) of the collected measurements. We derive a
computational efficient edge-based version of this ML convex relaxation class
and we design a distributed algorithm that enables the sensor nodes to solve
these edge-based convex programs locally by communicating only with their close
neighbors. This algorithm relies on the alternating direction method of
multipliers (ADMM), it converges to the centralized solution, it can run
asynchronously, and it is computation error-resilient. Finally, we compare our
proposed distributed scheme with other available methods, both analytically and
numerically, and we argue the added value of ADMM, especially for large-scale
networks
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and
generalizability of deep neural networks. Here, we further demonstrate that
always-on multiplicative stochasticity combined with simple threshold neurons
are sufficient operations for deep neural networks. We call such models Neural
Sampling Machines (NSM). We find that the probability of activation of the NSM
exhibits a self-normalizing property that mirrors Weight Normalization, a
previously studied mechanism that fulfills many of the features of Batch
Normalization in an online fashion. The normalization of activities during
training speeds up convergence by preventing internal covariate shift caused by
changes in the input distribution. The always-on stochasticity of the NSM
confers the following advantages: the network is identical in the inference and
learning phases, making the NSM suitable for online learning, it can exploit
stochasticity inherent to a physical substrate such as analog non-volatile
memories for in-memory computing, and it is suitable for Monte Carlo sampling,
while requiring almost exclusively addition and comparison operations. We
demonstrate NSMs on standard classification benchmarks (MNIST and CIFAR) and
event-based classification benchmarks (N-MNIST and DVS Gestures). Our results
show that NSMs perform comparably or better than conventional artificial neural
networks with the same architecture
Neurosymbolic Programming for Science
Neurosymbolic Programming (NP) techniques have the potential to accelerate
scientific discovery. These models combine neural and symbolic components to
learn complex patterns and representations from data, using high-level concepts
or known constraints. NP techniques can interface with symbolic domain
knowledge from scientists, such as prior knowledge and experimental context, to
produce interpretable outputs. We identify opportunities and challenges between
current NP models and scientific workflows, with real-world examples from
behavior analysis in science: to enable the use of NP broadly for workflows
across the natural and social sciences.Comment: Neural Information Processing Systems 2022 - AI for science worksho
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