747 citations · 1.2k across the 24 of their papers we have counts for
26 papers
Towards Surrogate Based Dequantization of Quantum Reinforcement Learning
Pablo Rodriguez-Grasa, Sofiene Jerbi, Mikel Sanz +1
In recent years, the utility of parameterized quantum circuits as function approximators has been widely studied. In the context of reinforcement learning, this approach has led to…
Instantiating Microcrypt: Obstacles and opportunities via tailored state certification
Jose Carrasco, Jens Eisert, Soumik Ghosh +3
Recent work has introduced the Hamiltonian phase state (HPS) assumptions, which postulate that Hamiltonian phase states can be used to instantiate pseudorandom and one-way state ge…
Distributed Quantum Property Testing with Quantum Carrier Pigeons
Kenny Chen, Mina Doosti, Ryan Sweke +1
We introduce a framework for distributed quantum inference under communication constraints. In our model, distributed nodes each receive one copy of an unknown -dimensional…
Distributed Quantum Property Testing with Communication Constraints
Mina Doosti, Ryan Sweke, Chirag Wadhwa
We introduce a framework for distributed quantum inference under communication constraints. In our model, distributed nodes each receive one copy of an unknown -dimensional…
Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models
David Layden, Ryan Sweke, Vojtěch Havlíček +2
Flow models are a cornerstone of modern machine learning. They are generative models that progressively transform probability distributions according to learned dynamics. Specifica…
Efficient Quantum Gibbs Sampling with Local Circuits
Dominik Hahn, Ryan Sweke, Abhinav Deshpande +1
The problem of simulating the thermal behavior of quantum systems remains a central open challenge in quantum computing. Unlike well-established quantum algorithms for unitary dyna…
Kernel-based dequantization of variational QML without Random Fourier Features
Ryan Sweke, Seongwook Shin, Elies Gil-Fuster
There is currently a huge effort to understand the potential and limitations of variational quantum machine learning (QML) based on the optimization of parameterized quantum circui…
New perspectives on quantum kernels through the lens of entangled tensor kernels
Seongwook Shin, Ryan Sweke, Hyunseok Jeong
Quantum kernel methods are one of the most explored approaches to quantum machine learning. However, the structural properties and inductive bias of quantum kernels are not fully u…
Dynamic parameterized quantum circuits: expressive and barren-plateau free
Abhinav Deshpande, Marcel Hinsche, Khadijeh Najafi +3
Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning…
Interactive proofs for verifying (quantum) learning and testing
Matthias C. Caro, Jens Eisert, Marcel Hinsche +3
We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place limitations on the effici…
Learning topological states from randomized measurements using variational tensor network tomography
Yanting Teng, Rhine Samajdar, Katherine Van Kirk +5
Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic meth…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…
Classical Verification of Quantum Learning
Matthias C. Caro, Marcel Hinsche, Marios Ioannou +2
Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in…
On the average-case complexity of learning output distributions of quantum circuits
Alexander Nietner, Marios Ioannou, Ryan Sweke +4
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely…
A super-polynomial quantum-classical separation for density modelling
Niklas Pirnay, Ryan Sweke, Jens Eisert +1
Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, w…
Scalably learning quantum many-body Hamiltonians from dynamical data
Frederik Wilde, Augustine Kshetrimayum, Ingo Roth +3
The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all…
Transparent reporting of research-related greenhouse gas emissions through the scientific COnduct initiative
Ryan Sweke, Paul Boes, Nelly H. Y. Ng +3
Estimating the greenhouse gas emissions of research-related activities is a critical first step towards the design of mitigation policies and actions. Here we propose and motivate…
Learnability of the output distributions of local quantum circuits
Marcel Hinsche, Marios Ioannou, Alexander Nietner +6
There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two differe…
Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer +2
A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical…
The effect of data encoding on the expressive power of variational quantum machine learning models
Maria Schuld, Ryan Sweke, Johannes Jakob Meyer
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to…
On the Quantum versus Classical Learnability of Discrete Distributions
Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter +1
Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consi…
Tensor network approaches for learning non-linear dynamical laws
A. Goeßmann, M. Götte, I. Roth +3
Given observations of a physical system, identifying the underlying non-linear governing equation is a fundamental task, necessary both for gaining understanding and generating det…
Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Meyer +4
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…
Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning
Ivan Glasser, Ryan Sweke, Nicola Pancotti +2
Tensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning…