Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Izaac Josh"'
Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems. For many ground states of gapped Hamiltonians, generative models can learn from measurements of a single quantum state to reconstruct the
Externí odkaz:
http://arxiv.org/abs/2211.16943
Autor:
Di Matteo, Olivia, Izaac, Josh, Bromley, Tom, Hayes, Anthony, Lee, Christina, Schuld, Maria, Száva, Antal, Roberts, Chase, Killoran, Nathan
We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples acro
Externí odkaz:
http://arxiv.org/abs/2202.13414
Autor:
Arrazola, Juan Miguel, Jahangiri, Soran, Delgado, Alain, Ceroni, Jack, Izaac, Josh, Száva, Antal, Azad, Utkarsh, Lang, Robert A., Niu, Zeyue, Di Matteo, Olivia, Moyard, Romain, Soni, Jay, Schuld, Maria, Vargas-Hernández, Rodrigo A., Tamayo-Mendoza, Teresa, Lin, Cedric Yen-Yu, Aspuru-Guzik, Alán, Killoran, Nathan
This work describes the theoretical foundation for all quantum chemistry functionality in PennyLane, a quantum computing software library specializing in quantum differentiable programming. We provide an overview of fundamental concepts in quantum ch
Externí odkaz:
http://arxiv.org/abs/2111.09967
Publikováno v:
Quantum 6, 677 (2022)
Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale quantum computers. Due to the structure of conventional parametrized quantum gates, the evaluated functions typically are finite Fourier series of the input par
Externí odkaz:
http://arxiv.org/abs/2107.12390
Autor:
Delgado, Alain, Arrazola, Juan Miguel, Jahangiri, Soran, Niu, Zeyue, Izaac, Josh, Roberts, Chase, Killoran, Nathan
Classical algorithms for predicting the equilibrium geometry of strongly correlated molecules require expensive wave function methods that become impractical already for few-atom systems. In this work, we introduce a variational quantum algorithm for
Externí odkaz:
http://arxiv.org/abs/2106.13840
Autor:
Bourassa, J. Eli, Quesada, Nicolás, Tzitrin, Ilan, Száva, Antal, Isacsson, Theodor, Izaac, Josh, Sabapathy, Krishna Kumar, Dauphinais, Guillaume, Dhand, Ish
Publikováno v:
Phys. Rev. X Quantum 2, 040315 (2021)
Bosonic qubits are a promising route to building fault-tolerant quantum computers on a variety of physical platforms. Studying the performance of bosonic qubits under realistic gates and measurements is challenging with existing analytical and numeri
Externí odkaz:
http://arxiv.org/abs/2103.05530
Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a high-dimensional Hilbert spa
Externí odkaz:
http://arxiv.org/abs/2001.03622
Publikováno v:
Quantum 4, 340 (2020)
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer le
Externí odkaz:
http://arxiv.org/abs/1912.08278
Autor:
Bromley, Thomas R., Arrazola, Juan Miguel, Jahangiri, Soran, Izaac, Josh, Quesada, Nicolás, Gran, Alain Delgado, Schuld, Maria, Swinarton, Jeremy, Zabaneh, Zeid, Killoran, Nathan
Publikováno v:
Quantum Sci. Technol. 5, 034010 (2020)
Gaussian Boson Sampling (GBS) is a near-term platform for photonic quantum computing. Recent efforts have led to the discovery of GBS algorithms with applications to graph-based problems, point processes, and molecular vibronic spectra in chemistry.
Externí odkaz:
http://arxiv.org/abs/1912.07634
Publikováno v:
Quantum 4, 269 (2020)
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is interpreted as moving in the steepest descent direction with respect
Externí odkaz:
http://arxiv.org/abs/1909.02108