786 citations · 5.7k across the 70 of their papers we have counts for
10 papers · 1 filter
Interferometric Readout of Momentum-Space Topology in a Programmable Dissipative Photonic Circuit
Andrea Cataldo, Emil J. Bergholtz, Daniel Leykam +2
Topology under non-Hermitian dynamics is encoded in the phase of the bulk evolution, yet strong dissipation suppresses the amplitudes carrying it. We resolve this using a programma…
Unsupervised learning of quantum many-body scars using intrinsic dimension
Harvey Cao, Dimitris G. Angelakis, Daniel Leykam
Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share hi…
Landscape approximation of low energy solutions to binary optimization problems
Benjamin Y. L. Tan, Beng Yee Gan, Daniel Leykam +1
We show how the localization landscape, originally introduced to bound low energy eigenstates of disordered wave media and many-body quantum systems, can form the basis for hardwar…
Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer
Chong Hian Chee, Daniel Leykam, Adrian M. Mak +1
Fermionic ansatz state preparation is a critical subroutine in many quantum algorithms such as Variational Quantum Eigensolver for quantum chemistry and condensed matter applicatio…
Unravelling quantum chaos using persistent homology
Harvey Cao, Daniel Leykam, Dimitris G. Angelakis
Topological data analysis is a powerful framework for extracting useful topological information from complex datasets. Recent work has shown its application for the dynamical analy…
Computing Electronic Correlation Energies using Linear Depth Quantum Circuits
Chong Hian Chee, Adrian M. Mak, Daniel Leykam +2
Efficient computation of molecular energies is an exciting application of quantum computing for quantum chemistry, but current noisy intermediate-scale quantum (NISQ) devices can o…
Fock State-enhanced Expressivity of Quantum Machine Learning Models
Beng Yee Gan, Daniel Leykam, Dimitris G. Angelakis
The data-embedding process is one of the bottlenecks of quantum machine learning, potentially negating any quantum speedups. In light of this, more effective data-encoding strategi…
Quantum transient heat transport in the hyper-parametric oscillator
JungYun Han, Daniel Leykam, Dimitris G. Angelakis +1
We explore nonequilibrium quantum heat transport in nonlinear bosonic systems in the presence of a non-Kerr-type interaction governed by hyper-parametric oscillation due to two-pho…
Disorder-Protected Quantum State Transmission through Helical Coupled-Resonator Waveguides
JungYun Han, Andrey A. Sukhorukov, Daniel Leykam
We predict the preservation of temporal indistinguishability of photons propagating through helical coupled-resonator optical waveguides (H-CROWs). H-CROWs exhibit a pseudospin-mom…
Disorder-robust entanglement transport
Clemens Gneiting, Daniel Leykam, Franco Nori
We study the disorder-perturbed transport of two entangled particles in the absence of backscattering. This situation is, for instance, realized along edges of topological insulato…