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Experimental Phase Estimation Enhanced By Machine Learning
Alessandro Lumino, Emanuele Polino, Adil S. Rab +4
Phase estimation protocols provide a fundamental benchmark for the field of quantum metrology. The latter represents one of the most relevant applications of quantum theory, potent…
Pattern recognition techniques for Boson Sampling validation
Iris Agresti, Niko Viggianiello, Fulvio Flamini +5
The difficulty of validating large-scale quantum devices, such as Boson Samplers, poses a major challenge for any research program that aims to show quantum advantages over classic…
Hardening Quantum Machine Learning Against Adversaries
Nathan Wiebe, Ram Shankar Siva Kumar
Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help o…
Improved Techniques for Preparing Eigenstates of Fermionic Hamiltonians
Dominic W. Berry, Mária Kieferová, Artur Scherer +5
Modeling low energy eigenstates of fermionic systems can provide insight into chemical reactions and material properties and is one of the most anticipated applications of quantum…
Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
Ian D. Kivlichan, Jarrod McClean, Nathan Wiebe +4
As physical implementations of quantum architectures emerge, it is increasingly important to consider the cost of algorithms for practical connectivities between qubits. We show th…
Optimizing quantum optimization algorithms via faster quantum gradient computation
András Gilyén, Srinivasan Arunachalam, Nathan Wiebe
We consider a generic framework of optimization algorithms based on gradient descent. We develop a quantum algorithm that computes the gradient of a multi-variate real-valued funct…