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Found 12 papers, 5 papers with code
Variational measurement-based quantum computation for generative modeling
Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms.
Potential and limitations of random Fourier features for dequantizing quantum machine learning
We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.
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.
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quantum systems.
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.
Reinforcement Learning Assisted Recursive QAOA
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems.
Near-Optimal Quantum Algorithms for Multivariate Mean Estimation
We propose the first near-optimal quantum algorithm for estimating in Euclidean norm the mean of a vector-valued random variable with finite mean and covariance.
Quantum machine learning beyond kernel methods
In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.
Parametrized quantum policies for reinforcement learning
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
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.
Quantum enhancements for deep reinforcement learning in large spaces
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.
