8 papers
A Tensor-Network Chebyshev-Galerkin Method for Boundary-Value Problems in Computational Mechanics
Juan José Rodríguez-Aldavero, Ángel de la Rosa Velasco, Juan José García-Ripoll
Quantum-inspired numerical methods for partial differential equations represent discretized fields and operators as quantics tensor trains (QTT), so that their cost depends on how…
Emission and Absorption of Microwave Photons in Orthogonal Temporal Modes across a 30-Meter Two-Node Network
Alonso Hernández-Antón, Josua D. Schär, Aleksandr Grigorev +6
The tunable interaction between stationary quantum bits and propagating modes of light allows for the encoding of quantum information in the state of itinerant photons. This abilit…
Decoherence-Free Qubit and Chiral Emission from a Giant Molecule in Waveguide QED
Yang Wang, Juan José García-Ripoll, Alan C. Santos
Combining decoherence protection with directional photon emission in a single waveguide quantum electrodynamics (QED) device remains an open challenge. Here we show that an artific…
High-Resolution Tensor-Network Fourier Methods for Exponentially Compressed Non-Gaussian Aggregate Distributions
Juan José Rodríguez-Aldavero, Juan José García-Ripoll
Characteristic functions of weighted sums of independent random variables exhibit low-rank structure in the quantized tensor train (QTT) representation, also known as matrix produc…
Photon-echo synchronization and quantum state transfer in short quantum links
Hong Jiang, Carlos Barahona-Pascual, Juan José García-Ripoll
The short quantum link regime, where the photon travel time is comparable to the emitter lifetime , is experimentally relevant but theoretically underexplored: existing fe…
Resource-Efficient Digitized Adiabatic Quantum Factorization
Felip Pellicer, Juan José García-Ripoll, Alan C. Santos
Digitized adiabatic quantum factorization is a hybrid algorithm that exploits the advantage of digitized quantum computers to implement efficient adiabatic algorithms for factoriza…
SeeMPS: A Python-based Matrix Product State and Tensor Train Library
Paula García-Molina, Juan José Rodríguez-Aldavero, Jorge Gidi +1
We introduce SeeMPS, a Python library dedicated to implementing tensor network algorithms based on the well-known Matrix Product States (MPS) and Quantized Tensor Train (QTT) forma…
Efficient quantum state preparation of multivariate functions using tensor networks
Marco Ballarin, Juan José García-Ripoll, David Hayes +1
For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits c…