Preparing projected entangled pair states on a quantum computer.

Phys Rev Lett

Vienna Center for Quantum Science and Technology, Faculty of Physics, University of Vienna, Vienna, Austria.

Published: March 2012

We present a quantum algorithm to prepare injective projected entangled pair states (PEPS) on a quantum computer, a class of open tensor networks representing quantum states. The run time of our algorithm scales polynomially with the inverse of the minimum condition number of the PEPS projectors and, essentially, with the inverse of the spectral gap of the PEPS's parent Hamiltonian.

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevLett.108.110502DOI Listing

Publication Analysis

Top Keywords

projected entangled
8
entangled pair
8
pair states
8
quantum computer
8
preparing projected
4
quantum
4
states quantum
4
computer quantum
4
quantum algorithm
4
algorithm prepare
4

Similar Publications

Predictive Complexity of Quantum Subsystems.

Entropy (Basel)

December 2024

Department of Physics, University of Maryland, College Park, MD 20742-4111, USA.

We define predictive states and predictive complexity for quantum systems composed of distinct subsystems. This complexity is a generalization of entanglement entropy. It is inspired by the statistical or forecasting complexity of predictive state analysis of stochastic and complex systems theory but is intrinsically quantum.

View Article and Find Full Text PDF

We report on a class of gapped projected entangled pair states (PEPS) with non-trivial Euler topology motivated by recent progress in band geometry. In the non-interacting limit, these systems have optimal conditions relating to saturation of quantum geometrical bounds, allowing for parent Hamiltonians whose lowest bands are completely flat and which have the PEPS as unique ground states. Protected by crystalline symmetries, these states evade restrictions on capturing tenfold-way topological features with gapped PEPS.

View Article and Find Full Text PDF
Article Synopsis
  • A new machine learning method, called SU-VAE, allows scientists to separate brain connectome data shared between humans and macaques from species-specific traits.
  • This method was validated by linking unique human features to cognitive abilities, while shared features aligned more with sensorimotor skills.
  • The results suggest that human-specific brain traits may make networks more efficient and are associated with certain genes related to axon guidance.
View Article and Find Full Text PDF

In the aftermath of unparalleled disruptive technologies, the quantum realm has become a fundamental field of research due to unrivaled computational power and super-secure communication. In addition to conventional networks, a new word in the quantum domain is quantum network. The quantum network uses quantum communication (QC) to send quantum information bits known as qubits, to predetermined destination nodes.

View Article and Find Full Text PDF

Efficient characterization of higher dimensional many-body physical states presents significant challenges. In this Letter, we propose a new class of projected entangled pair states (PEPS) that incorporates two isometric conditions. This new class facilitates the efficient calculation of general local observables and certain two-point correlation functions, which have been previously shown to be intractable for general PEPS, or PEPS with only a single isometric constraint.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!