Outputs (21)

Scalable simulation of nonequilibrium quantum dynamics via classically optimized unitary circuits (2024)
Journal Article
Causer, L., Jung, F., Mitra, A., Pollmann, F., & Gammon-Smith, A. (2024). Scalable simulation of nonequilibrium quantum dynamics via classically optimized unitary circuits. Physical Review Research, 6(3), Article 033062. https://doi.org/10.1103/physrevresearch.6.033062

The advent of near-term digital quantum computers could offer us an exciting opportunity to investigate quantum many-body phenomena beyond that of classical computing. To make the best use of the hardware available, it is paramount that we have metho... Read More about Scalable simulation of nonequilibrium quantum dynamics via classically optimized unitary circuits.

Quench dynamics in lattices above one dimension: The free fermionic case (2024)
Journal Article
Gibbins, M., Jafarizadeh, A., Gammon-Smith, A., & Bertini, B. (2024). Quench dynamics in lattices above one dimension: The free fermionic case. Physical Review B, 109(22), Article 224310. https://doi.org/10.1103/physrevb.109.224310

We begin a systematic investigation of quench dynamics in higher-dimensional lattice systems considering the case of noninteracting fermions with conserved particle number. We prepare the system in a translational-invariant nonequilibrium initial sta... Read More about Quench dynamics in lattices above one dimension: The free fermionic case.

Time evolution of uniform sequential circuits (2023)
Journal Article
Astrakhantsev, N., Lin, S.-H., Pollmann, F., & Smith, A. (2023). Time evolution of uniform sequential circuits. Physical Review Research, 5(3), Article 033187. https://doi.org/10.1103/physrevresearch.5.033187

Simulating time evolution of generic quantum many-body systems using classical numerical approaches has an exponentially growing cost either with evolution time or with the system size. In this work we present a polynomially scaling hybrid quantum-cl... Read More about Time evolution of uniform sequential circuits.

Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks (2023)
Journal Article
Liu, Y.-J., Smith, A., Knap, M., & Pollmann, F. (2023). Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks. Physical Review Letters, 130(22), Article 220603. https://doi.org/10.1103/physrevlett.130.220603

Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preser... Read More about Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks.

Numerical simulation of non-Abelian anyons (2023)
Journal Article
Kirchner, N., Millar, D., Ayeni, B. M., Smith, A., Slingerland, J. K., & Pollmann, F. (2023). Numerical simulation of non-Abelian anyons. Physical Review B, 107(19), Article 195129. https://doi.org/10.1103/physrevb.107.195129

Two-dimensional systems such as quantum spin liquids or fractional quantum Hall systems exhibit anyonic excitations that possess more general statistics than bosons or fermions. This exotic statistics makes it challenging to solve even a many-body sy... Read More about Numerical simulation of non-Abelian anyons.

Methods for Simulating String-Net States and Anyons on a Digital Quantum Computer (2022)
Journal Article
Liu, Y.-J., Shtengel, K., Smith, A., & Pollmann, F. (2022). Methods for Simulating String-Net States and Anyons on a Digital Quantum Computer. PRX Quantum, 3(4), Article 040315. https://doi.org/10.1103/prxquantum.3.040315

The finding of physical realizations of topologically ordered states in experimental settings, from condensed matter to artificial quantum systems, has been the main challenge en route to utilizing their unconventional properties. We show how to real... Read More about Methods for Simulating String-Net States and Anyons on a Digital Quantum Computer.

Data compression for quantum machine learning (2022)
Journal Article
Dilip, R., Liu, Y. J., Smith, A., & Pollmann, F. (2022). Data compression for quantum machine learning. Physical Review Research, 4(4), Article 043007. https://doi.org/10.1103/PhysRevResearch.4.043007

The advent of noisy-intermediate scale quantum computers has introduced the exciting possibility of achieving quantum speedups in machine learning tasks. These devices, however, are composed of a small number of qubits and can faithfully run only sho... Read More about Data compression for quantum machine learning.

Finite-depth scaling of infinite quantum circuits for quantum critical points (2022)
Journal Article
Jobst, B., Smith, A., & Pollmann, F. (2022). Finite-depth scaling of infinite quantum circuits for quantum critical points. Physical Review Research, 4(3), Article 033118. https://doi.org/10.1103/PhysRevResearch.4.033118

The scaling of the entanglement entropy at a quantum critical point allows us to extract universal properties of the state, e.g., the central charge of a conformal field theory. With the rapid improvement of noisy intermediate-scale quantum (NISQ) de... Read More about Finite-depth scaling of infinite quantum circuits for quantum critical points.

Crossing a topological phase transition with a quantum computer (2022)
Journal Article
Smith, A., Jobst, B., Green, A. G., & Pollmann, F. (2022). Crossing a topological phase transition with a quantum computer. Physical Review Research, 4(2), Article L022020. https://doi.org/10.1103/PhysRevResearch.4.L022020

Quantum computers promise to perform computations beyond the reach of modern computers with profound implications for scientific research. Due to remarkable technological advances, small scale devices are now becoming available for use. One of the mo... Read More about Crossing a topological phase transition with a quantum computer.

Identifying correlation clusters in many-body localized systems (2022)
Journal Article
Hémery, K., Pollmann, F., & Smith, A. (2022). Identifying correlation clusters in many-body localized systems. Physical Review B, 105(6), Article 064202. https://doi.org/10.1103/physrevb.105.064202

We introduce techniques for analyzing the structure of quantum states of many-body localized (MBL) spin chains by identifying correlation clusters from pairwise correlations. These techniques proceed by interpreting pairwise correlations in the state... Read More about Identifying correlation clusters in many-body localized systems.