462 citations · 4.5k across the 108 of their papers we have counts for
12 papers · 1 filter
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Limitations of measure-first protocols in quantum machine learning
Casper Gyurik, Riccardo Molteni, Vedran Dunjko
In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomograph…
Compilation of product-formula Hamiltonian simulation via reinforcement learning
Lea M. Trenkwalder, Eleanor Scerri, Thomas E. O'Brien +1
Hamiltonian simulation is believed to be one of the first tasks where quantum computers can yield a quantum advantage. One of the most popular methods of Hamiltonian simulation is…
On the expressivity of embedding quantum kernels
Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are…
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
Alberto Di Meglio, Karl Jansen, Ivano Tavernelli +43
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage…
Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces
Alberto Manzano, David Dechant, Jordi Tura +1
Parametrized quantum circuits (PQC) are quantum circuits which consist of both fixed and parametrized gates. In recent approaches to quantum machine learning (QML), PQCs are essent…
Exponential separations between classical and quantum learners
Casper Gyurik, Vedran Dunjko
Despite significant effort, the quantum machine learning community has only demonstrated quantum learning advantages for artificial cryptography-inspired datasets when dealing with…
Enhancing variational quantum state diagonalization using reinforcement learning techniques
Akash Kundu, Przemysław Bedełek, Mateusz Ostaszewski +4
The variational quantum algorithms are crucial for the application of NISQ computers. Such algorithms require short quantum circuits, which are more amenable to implementation on n…
Shadows of quantum machine learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a ma…
Application of quantum-inspired generative models to small molecular datasets
C. Moussa, H. Wang, M. Araya-Polo +2
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-t…
All this for one qubit? Bounds on local circuit cutting schemes
Simon C. Marshall, Jordi Tura, Vedran Dunjko
Small numbers of qubits are one of the primary constraints on the near-term deployment of advantageous quantum computing. To mitigate this constraint, techniques have been develope…
Bloch Sphere Binary Trees: A method for the visualization of sets of multi-qubit systems pure states
Alice Barthe, Michele Grossi, Jordi Tura +1
Understanding the evolution of a multi-qubit quantum system, or elucidating what portion of the Hilbert space is occupied by a quantum dataset becomes increasingly hard with the nu…