6 research outputs found
Quantum Policy Gradient Algorithms
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments. Yet, the applicability of quantum algorithms in this setting remains very limited, notably in environments with large state and action spaces. In this work, we design quantum algorithms to train state-of-the-art reinforcement learning policies by exploiting quantum interactions with an environment. However, these algorithms only offer full quadratic speed-ups in sample complexity over their classical analogs when the trained policies satisfy some regularity conditions. Interestingly, we find that reinforcement learning policies derived from parametrized quantum circuits are well-behaved with respect to these conditions, which showcases the benefit of a fully-quantum reinforcement learning framework
Shadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising
uses for a quantum computer to solve practical problems. However, a major
obstacle to the widespread use of quantum machine learning models in practice
is that these models, even once trained, still require access to a quantum
computer in order to be evaluated on new data. To solve this issue, we suggest
that following the training phase of a quantum model, a quantum computer could
be used to generate what we call a classical shadow of this model, i.e., a
classically computable approximation of the learned function. While recent
works already explore this idea and suggest approaches to construct such shadow
models, they also raise the possibility that a completely classical model could
be trained instead, thus circumventing the need for a quantum computer in the
first place. In this work, we take a novel approach to define shadow models
based on the frameworks of quantum linear models and classical shadow
tomography. This approach allows us to show that there exist shadow models
which can solve certain learning tasks that are intractable for fully classical
models, based on widely-believed cryptography assumptions. We also discuss the
(un)likeliness that all quantum models could be shadowfiable, based on common
assumptions in complexity theory.Comment: 7 + 16 pages, 5 figure
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of
physical systems that meaningfully encode information about the systems. The
representations learnt by most current machine learning techniques reflect
statistical structure present in the training data; however, these methods do
not allow us to specify explicit and operationally meaningful requirements on
the representation. Here, we present a neural network architecture based on the
notion that agents dealing with different aspects of a physical system should
be able to communicate relevant information as efficiently as possible to one
another. This produces representations that separate different parameters which
are useful for making statements about the physical system in different
experimental settings. We present examples involving both classical and quantum
physics. For instance, our architecture finds a compact representation of an
arbitrary two-qubit system that separates local parameters from parameters
describing quantum correlations. We further show that this method can be
combined with reinforcement learning to enable representation learning within
interactive scenarios where agents need to explore experimental settings to
identify relevant variables.Comment: 24 pages, 13 figure
A large cardiac hydatid cyst in the interventricular septum: A case report
Isolated cardiac location is an uncommon presentation of echinococcosis (0.5-2%), and involvement of the interventricular septum is even rarer. It may lead to various complications because of rupture and embolization. We report the case of a 26 - year- old man who was diagnosed to have a large inter-ventricular hydatid cyst complicated by both cerebral and coronary embolism. Presentation, management and follow-up of the patient is discussed. This case is of particular interest because of the rarity of septal localization of a hydatid cyst, and the conflict between the severity of the complications that occurred and the absence of correlated symptoms. Keywords: Large hydatid cyst, Interventricular septum, Acute coronary syndrome, Cerebral 19 embolization, Echocardiography, MRI, Surger
Operationally meaningful representations of physical systems in neural networks
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. Such representations ignore redundant features and treat parameters such as velocity and position separately because they can be useful for making statements about different experimental settings. Here, we capture this notion by formally defining the concept of operationally meaningful representations. We present an autoencoder architecture with attention mechanism that can generate such representations and demonstrate it on examples involving both classical and quantum physics. For instance, our architecture finds a compact representation of an arbitrary two-qubit system that separates local parameters from parameters describing quantum correlations.ISSN:2632-215