The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes, but shines by combining the best features of these two methods.
For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state.
We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix.
no code implementations • 27 Sep 2019 • Shreyas Fadnavis, Hamza Farooq, Maryam Afzali, Christoph Lenglet, Tryphon Georgiou, Hu Cheng, Sharlene Newman, Shahnawaz Ahmed, Rafael Neto Henriques, Eric Peterson, Serge Koudoro, Ariel Rokem, Eleftherios Garyfallidis
Fitting multi-exponential models to Diffusion MRI (dMRI) data has always been challenging due to various underlying complexities.
Here we show how, in the ultra-strongly-coupled spin-boson model, apparently unphysical "Matsubara modes" are required not only to regulate detailed balance, but also to arrive at a correct and physical description of the non-perturbative dynamics and steady-state.
26 code implementations • 12 Nov 2018 • Ville Bergholm, Josh Izaac, Maria Schuld, Christian Gogolin, Shahnawaz Ahmed, Vishnu Ajith, M. Sohaib Alam, Guillermo Alonso-Linaje, B. AkashNarayanan, Ali Asadi, Juan Miguel Arrazola, Utkarsh Azad, Sam Banning, Carsten Blank, Thomas R Bromley, Benjamin A. Cordier, Jack Ceroni, Alain Delgado, Olivia Di Matteo, Amintor Dusko, Tanya Garg, Diego Guala, Anthony Hayes, Ryan Hill, Aroosa Ijaz, Theodor Isacsson, David Ittah, Soran Jahangiri, Prateek Jain, Edward Jiang, Ankit Khandelwal, Korbinian Kottmann, Robert A. Lang, Christina Lee, Thomas Loke, Angus Lowe, Keri McKiernan, Johannes Jakob Meyer, J. A. Montañez-Barrera, Romain Moyard, Zeyue Niu, Lee James O'Riordan, Steven Oud, Ashish Panigrahi, Chae-Yeun Park, Daniel Polatajko, Nicolás Quesada, Chase Roberts, Nahum Sá, Isidor Schoch, Borun Shi, Shuli Shu, Sukin Sim, Arshpreet Singh, Ingrid Strandberg, Jay Soni, Antal Száva, Slimane Thabet, Rodrigo A. Vargas-Hernández, Trevor Vincent, Nicola Vitucci, Maurice Weber, David Wierichs, Roeland Wiersema, Moritz Willmann, Vincent Wong, Shaoming Zhang, Nathan Killoran
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.
Assessing the robustness of collective effects against local dissipation is paramount to predict their presence in different physical implementations.