Publications

Order by: publication date number of citations

• A large-$N$ approach to magnetic impurities in superconductors

  Chen-How Huang, Alejandro M. Lobos, Miguel A. Cazalilla

  SciPost Phys. 18, 087 (2025) · published 10 March 2025 | Toggle abstract · pdf

  Quantum spin impurities coupled to superconductors are under intense investigation for their relevance to fundamental research as well as the prospects to engineer novel quantum phases of matter. Here we develop a large-$N$ mean-field theory of a strongly coupled spin-$\tfrac{1}{2}$ quantum impurity in a conventional $s$-wave superconductor. The approach is benchmarked against Wilson's numerical renormalization group (NRG). While the large-$N$ method is not applicable in the weak-coupling regime where the Kondo temperature $T_K$ is smaller than the superconducting gap $\Delta$, it performs very well in the strong coupling regime where $T_K \gtrsim \Delta$, thus allowing us to obtain a reasonably accurate description of experimentally relevant quantities. The latter include the energy of the Yu-Shiba-Rusinov subgap states, their spectral weight, as well as the local density of continuum states. The method provides a reliable analytical tool that complements other perturbative and non-perturbative methods, and can be extended to more complex impurity models for which NRG may not be easily applicable.

• Identifying quantum phase transitions with minimal prior knowledge by unsupervised learning

  Mohamad Ali Marashli, Ho Lai Henry Lam, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki, Ho-Kin Tang, Wing Chi Yu

  SciPost Phys. Core 8, 029 (2025) · published 6 March 2025 | Toggle abstract · pdf

  In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.

  1 citation

• Two-particle self-consistent approach for broken symmetry phases

  Lorenzo Del Re

  SciPost Phys. 18, 077 (2025) · published 3 March 2025 | Toggle abstract · pdf

  Spontaneous symmetry breaking of interacting fermion systems constitutes a major challenge for many-body theory due to the proliferation of new independent scattering channels once absent or degenerate in the symmetric phase. One example is given by the ferro/antiferromagnetic broken symmetry phase (BSP) of the Hubbard model, where vertices in the spin-transverse and spin-longitudinal channels become independent with a consequent increase in the computational power for their calculation. We generalise the non-perturbative Two-Particle-Self-Consistent method (TPSC) to address broken SU(2) magnetic phases in the Hubbard model, offering an efficient approach that incorporates strong correlations. We show that in the BSP, the sum-rule enforcement of susceptibilities must be accompanied by a modified gap equation resulting in a renormalisation of the order parameter, vertex corrections and the preservation of the gap-less feature of the Goldstone modes. We then apply the theory to the antiferromagnetic phase of the Hubbard model in the cubic lattice at half-filling. We compare our results of double occupancies and staggered magnetisation to the ones obtained using Diagrammatic Monte Carlo showing excellent quantitative agreement. We demonstrate how vertex corrections play a central role in lowering the Higgs resonance with respect to the quasi-particle excitation gap in the spin-longitudinal susceptibility, yielding a well visible Higgs-mode.

  3 citations

• Codebase release 1.2 for YASTN

  Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, Juraj Hasik

  SciPost Phys. Codebases 52-r1.2 (2025) · published 26 February 2025 | Toggle abstract · src

  We present an open-source tensor network Python library for quantum many-body simulations. At its core is an Abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multidimensional array backend. This serves as the basis for higher-level tensor network algorithms operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.

  4 citations

• YASTN: Yet another symmetric tensor networks; A Python library for Abelian symmetric tensor network calculations

  Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, Juraj Hasik

  SciPost Phys. Codebases 52 (2025) · published 26 February 2025 | Toggle abstract · pdf

  We present an open-source tensor network Python library for quantum many-body simulations. At its core is an Abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multidimensional array backend. This serves as the basis for higher-level tensor network algorithms operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.

  8 citations

• InP/GaSb core-shell nanowires: A novel hole-based platform with strong spin-orbit coupling for full-shell hybrid devices

  Andrea Vezzosi, Carlos Payá, Paweł Wójcik, Andrea Bertoni, Guido Goldoni, Elsa Prada, Samuel D. Escribano

  SciPost Phys. 18, 069 (2025) · published 25 February 2025 | Toggle abstract · pdf

  Full-shell hybrid nanowires (NWs), structures comprising a superconductor shell that encapsulates a semiconductor (SM) core, have attracted considerable attention in the search for Majorana zero modes (MZMs). However, the predicted Rashba spin-orbit coupling (SOC) in the SM is too small to achieve substantial topological minigaps. In addition, the SM wavefunction spreads all across the section of the nanowire, leading typically to a finite background of trivial subgap states with which MZMs may coexist. To overcome both problems, we explore the advantages of utilizing core-shell hole-band NWs as the SM part of a full-shell hybrid, with an insulating core and an active SM shell. In particular, we consider InP/GaSb core-shell NWs, which allow to exploit the unique characteristics of the III-V compound SM valence bands. We demonstrate that they exhibit a robust hole SOC that emerges from the combination of the intrinsic spin-orbit interaction of the SM active shell and the confinement effects of the nanostructure, thus depending mainly on SM and geometrical parameters. In other words, the SOC is intrinsic and does not rely on red electric fields, which are non-tunable in a full-shell hybrid geometry. As a result, core-shell SM hole-band NWs are found to be a promising candidate to explore Majorana physics in full-hell hybrid devices, addressing several challenges in the field.

  3 citations

• Spin-lattice couplings and effect of displacements on magnetic interactions of a skyrmion system PdFe/Ir(111)

  Banasree Sadhukhan, Anders Bergman, Johan Hellsvik, Patrik Thunström, Anna Delin

  SciPost Phys. 18, 064 (2025) · published 21 February 2025 | Toggle abstract · pdf

  PdFe/Ir(111) has attracted tremendous attention for next-generation spintronics devices due to existence of magnetic skyrmions with the external magnetic field. Our density functional theoretical calculations in combination with spin dynamics simulation suggest that the spin spiral phase in fcc stacked PdFe/Ir(111) flips into the skyrmion lattice phase around B$_{ext} \sim$ 6 T. This leads to the microscopic understanding of the thermodynamic and kinetic behaviours affected by the intrinsic spin-lattice couplings (SLCs) in this skyrmion material for magneto-mechanical properties. Here we calculate fully relativistic SLC parameters from first principle computations and investigate the effect of SLC on dynamical magnetic interactions in skyrmion multilayers PdFe/Ir(111). The exchange interactions arising from next nearest-neighbors (NN) in this material are highly frustrated and responsible for enhancing skyrmion stability. We report the larger spin-lattice effect on both dynamical Heisenberg exchanges and Dzyaloshinskii-Moriya interactions for next NN compared to NN which is in contrast with recently observed spin-lattice effect in bulk bcc Fe and CrI$_3$ monolayer. Based on our analysis, we find that the effective measures of SLCs in fcc (hcp) stacking of PdFe/Ir(111) are $\sim 2.71 ( \sim 2.36)$ and $\sim 14.71 ( \sim21.89)$ times stronger for NN and next NN respectively, compared to bcc Fe. The linear regime of displacement for SLC parameters is $≤$ 0.02 Å which is 0.72% of the lattice constant for PdFe/Ir(111). The microscopic understanding of SLCs provided by our current study could help in designing spintronic devices based on thermodynamic properties of skyrmion multilayers.

  1 citation

• Probing valley phenomena with gate-defined valley splitters

  Juan Daniel Torres Luna, Kostas Vilkelis, Antonio L. R. Manesco

  SciPost Phys. 18, 062 (2025) · published 21 February 2025 | Toggle abstract · pdf

  Despite many reports of valley-related phenomena in graphene and its multilayers, current transport experiments cannot probe valley phenomena without the application of external fields. Here we propose a gate-defined valley splitter as a direct transport probe for valley phenomenon in graphene multilayers. First, we show how the device works, its magnetotransport response, and its robustness against fabrication errors. Secondly, we present two applications for valley splitters: (i) resonant tunneling of quantum dots probed by a valley splitter shows the valley polarization of dot levels; (ii) a combination of two valley splitters resolves the nature of order parameters in mesoscopic samples.

  2 citations

• Codebase release 1.0 for SOLAX

  Louis Thirion, Philipp Hansmann, Pavlo Bilous

  SciPost Phys. Codebases 51-r1.0 (2025) · published 20 February 2025 | Toggle abstract · src

  Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.

  1 citation

• SOLAX: A Python solver for fermionic quantum systems with neural network support

  Louis Thirion, Philipp Hansmann, Pavlo Bilous

  SciPost Phys. Codebases 51 (2025) · published 20 February 2025 | Toggle abstract · pdf

  Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.

  2 citations