684 research outputs found
The state of quantum computing applications in health and medicine
Quantum computing hardware and software have made enormous strides over the
last years. Questions around quantum computing's impact on research and society
have changed from "if" to "when/how". The 2020s have been described as the
"quantum decade", and the first production solutions that drive scientific and
business value are expected to become available over the next years. Medicine,
including fields in healthcare and life sciences, has seen a flurry of
quantum-related activities and experiments in the last few years (although
medicine and quantum theory have arguably been entangled ever since
Schr\"odinger's cat). The initial focus was on biochemical and computational
biology problems; recently, however, clinical and medical quantum solutions
have drawn increasing interest. The rapid emergence of quantum computing in
health and medicine necessitates a mapping of the landscape. In this review,
clinical and medical proof-of-concept quantum computing applications are
outlined and put into perspective. These consist of over 40 experimental and
theoretical studies from the last few years. The use case areas span genomics,
clinical research and discovery, diagnostics, and treatments and interventions.
Quantum machine learning (QML) in particular has rapidly evolved and shown to
be competitive with classical benchmarks in recent medical research. Near-term
QML algorithms, for instance, quantum support vector classifiers and quantum
neural networks, have been trained with diverse clinical and real-world data
sets. This includes studies in generating new molecular entities as drug
candidates, diagnosing based on medical image classification, predicting
patient persistence, forecasting treatment effectiveness, and tailoring
radiotherapy. The use cases and algorithms are summarized and an outlook on
medicine in the quantum era, including technical and ethical challenges, is
provided
Co-regularized Alignment for Unsupervised Domain Adaptation
Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a \emph{target domain} whose
distribution differs from the training data distribution, referred as the
\emph{source domain}. It can be expensive or even infeasible to obtain required
amount of labeled data in all possible domains. Unsupervised domain adaptation
sets out to address this problem, aiming to learn a good predictive model for
the target domain using labeled examples from the source domain but only
unlabeled examples from the target domain. Domain alignment approaches this
problem by matching the source and target feature distributions, and has been
used as a key component in many state-of-the-art domain adaptation methods.
However, matching the marginal feature distributions does not guarantee that
the corresponding class conditional distributions will be aligned across the
two domains. We propose co-regularized domain alignment for unsupervised domain
adaptation, which constructs multiple diverse feature spaces and aligns source
and target distributions in each of them individually, while encouraging that
alignments agree with each other with regard to the class predictions on the
unlabeled target examples. The proposed method is generic and can be used to
improve any domain adaptation method which uses domain alignment. We
instantiate it in the context of a recent state-of-the-art method and observe
that it provides significant performance improvements on several domain
adaptation benchmarks.Comment: NIPS 2018 accepted versio
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