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Saeed S. Jahromi

Saeed S. Jahromi appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.str-elquant-phcond-mat.mtrl-scicond-mat.stat-mechArtificial IntelligenceMachine Learningcond-mat.mes-hallhep-lathep-thnucl-exphysics.comp-ph

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Published work

14 published item(s)

preprint2026arXiv

Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters

Large language models (LLMs) have transformed artificial intelligence, yet classical architectures impose a fundamental constraint: every trainable parameter demands classical memory that scales unfavourably with model size. Quantum computing offers a qualitatively different pathway, but practical demonstrations on real hardware have remained elusive for models of practical relevance. Here we show that Cayley-parameterised unitary adapters -- quantum circuit blocks inserted into the frozen projection layers of pre-trained LLMs and executed on a 156-qubit IBM Quantum System Two superconducting processor -- improve the perplexity of Llama 3.1 8B, an 8-billion-parameter model in widespread use, by 1.4% with only 6,000 additional parameters and end-to-end inference validated on real Quantum Processing Unit (QPU). A systematic study on SmolLM2 (135M parameters), chosen for its tractability, reveals monotonically improving perplexity with unitary block dimension, 83% recovery of compression-induced degradation, and correct answers to questions that both classical baselines fail -- with a sharp noise-expressivity phase transition identifying the concrete path to quantum utility at larger qubit scales.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantum-Inspired Tensor Neural Networks for Partial Differential Equations

Partial Differential Equations (PDEs) are used to model a variety of dynamical systems in science and engineering. Recent advances in deep learning have enabled us to solve them in a higher dimension by addressing the curse of dimensionality in new ways. However, deep learning methods are constrained by training time and memory. To tackle these shortcomings, we implement Tensor Neural Networks (TNN), a quantum-inspired neural network architecture that leverages Tensor Network ideas to improve upon deep learning approaches. We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. We benchmark TNN by applying them to solve parabolic PDEs, specifically the Black-Scholes-Barenblatt equation, widely used in financial pricing theory, empirically showing the advantages of TNN over DNN. Further examples, such as the Hamilton-Jacobi-Bellman equation, are also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fine-Grained Tensor Network Methods

We develop a strategy for tensor network algorithms that allows to deal very efficiently with lattices of high connectivity. The basic idea is to fine-grain the physical degrees of freedom, i.e., decompose them into more fundamental units which, after a suitable coarse-graining, provide the original ones. Thanks to this procedure, the original lattice with high connectivity is transformed by an isometry into a simpler structure, which is easier to simulate via usual tensor network methods. In particular this enables the use of standard schemes to contract infinite 2d tensor networks - such as Corner Transfer Matrix Renormalization schemes - which are more involved on complex lattice structures. We prove the validity of our approach by numerically computing the ground-state properties of the ferromagnetic spin-1 transverse-field Ising model on the 2d triangular and 3d stacked triangular lattice, as well as of the hard-core and soft-core Bose-Hubbard models on the triangular lattice. Our results are benchmarked against those obtained with other techniques, such as perturbative continuous unitary transformations and graph projected entangled pair states, showing excellent agreement and also improved performance in several regimes.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Spin-$\frac{1}{2}$ kagome Heisenberg antiferromagnet with strong breathing anisotropy

We study the zero-temperature phase diagram of the spin-$\frac{1}{2}$ Heisenberg model with breathing anisotropy (i.e., with different coupling strength on the upward and downward triangles) on the kagome lattice. Our study relies on large scale tensor network simulations based on infinite projected entangled-pair state and infinite projected entangled-simplex state methods adapted to the kagome lattice. Our energy analysis suggests that the U(1) algebraic quantum spin-liquid (QSL) ground-state of the isotropic Heisenberg model is stable up to very large breathing anisotropy until it breaks down to a critical lattice-nematic phase that breaks rotational symmetry in real space through a first-order quantum phase transition. Our results also provide further insight into the recent experiment on vanadium oxyfluoride compounds which has been shown to be relevant platforms for realizing QSL in the presence of breathing anisotropy.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Topological $\mathbb{Z}_2$ RVB quantum spin liquid on the ruby lattice

We construct a short-range resonating valence-bond state (RVB) on the ruby lattice, using projected entangled-pair states (PEPS) with bond dimension $D=3$. By introducing non-local moves to the dimer patterns on the torus, we distinguish four distinct sectors in the space of dimer coverings, which is a signature of the topological nature of the RVB wave function. Furthermore, by calculating the reduced density matrix of a bipartition of the RVB state on an infinite cylinder and exploring its entanglement entropy, we confirm the topological nature of the RVB wave function by obtaining non-zero topological contribution, $γ=-\rm{ln}\ 2$, consistent with that of a $\mathbb{Z}_2$ topological quantum spin liquid. We also calculate the ground-state energy of the spin-$\frac{1}{2}$ antiferromagnetic Heisenberg model on the ruby lattice and compare it with the RVB energy. Finally, we construct a quantum-dimer model for the ruby lattice and discuss it as a possible parent Hamiltonian for the RVB wave function.

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preprint2016arXiv

Topological spin liquids in the ruby lattice with anisotropic Kitaev interactions

The ruby lattice is a four-valent lattice interpolating between honeycomb and triangular lattices. In this work we investigate the topological spin-liquid phases of a spin Hamiltonian with Kitaev interactions on the ruby lattice using exact diagonalization and perturbative methods. The latter interactions combined with the structure of the lattice yield a model with $\mathbb{Z}_2 \times \mathbb{Z}_2$ gauge symmetry. We mapped out the phase digram of the model and found gapped and gapless spin-liquid phases. While the low energy sector of the gapped phase corresponds to the well-known topological color code model on a honeycomb lattice, the low-energy sector of the gapless phases is described by an effective spin model with three-body interactions on a triangular lattice. A gap is opened in the spectrum in a small magnetic field. We argue that the latter phases could be possibly described by exotic excitations, whose their spectrum is richer than the Ising phase of the Kitaev model.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Baxter-Wu model in a transverse magnetic field

We investigate the low-energy properties as well as quantum and thermal phase transitions of the Baxter-Wu model in a transverse magnetic field. Our study relies on stochastic series expansion quantum Monte Carlo and on series expansions about the low- and high-field limits at zero temperature using the quantum finite-lattice method on the triangular lattice. The phase boundary consists of a second-order critical line in the 4-state Potts model universality class starting from the pure Baxter-Wu limit meeting a first-order line connected to the zero-temperature transition point ($h\approx2.4$, $T=0$). Both lines merge at a tricritical point approximatively located at ($h\approx 2.3J$, $T\approx J$).

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Quantum phase transitions out of a Z2 x Z2 topological phase

We investigate the low-energy spectral properties and robustness of the topological phase of color code, which is a quantum spin model for the aim of fault-tolerant quantum computation, in the presence of a uniform magnetic field or Ising interactions, using high-order series expansion and exact diagonalization. In a uniform magnetic field, we find 1st-order phase transitions in all field directions. In contrast, our results for the Ising interactions unveil that for strong enough Ising couplings, the Z2 x Z2 topological phase of color code breaks down to symmetry broken phases by 1st- or 2nd-order phase transitions.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Robustness of a topological phase: Topological color code in parallel magnetic field

The robustness of the topological color code, which is a class of error correcting quantum codes, is investigated under the influence of an uniform magnetic field on the honeycomb lattice. Our study relies on two high-order series expansions using perturbative continuous unitary transformations in the limit of low and high fields, exact diagonalization and a classical approximation. We show that the topological color code in a single parallel field is isospectral to the Baxter-Wu model in a transverse field on the triangular lattice. It is found that the topological phase is stable up to a critical field beyond which it breaks down to the polarized phase by a first-order phase transition. The results also suggest that the topological color code is more robust than the toric code, in the parallel magnetic field.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

One directional Polarized Neutron Reflectometry with optimized reference layer method

In the past decade, several neutron reflectometry methods for determining the modulus and phase of the complex reflection coefficient of an unknown multilayer thin film have been worked out among which the method of variation of surroundings and reference layers are of highest interest. These methods were later modified for measurement of the polarization of the reflected beam instead of the measurement of the intensities. In their new architecture, these methods not only suffered from the necessity of change of experimental setup, but also another difficulty was added to their experimental implementations. This deficiency was related to the limitations of the technology of the neutron reflectometers that could only measure the polarization of the reflected neutrons in the same direction as the polarization of the incident beam. As the instruments are limited, the theory has to be optimized so that the experiment could be performed. In a recent work, we developed the method of variation of surroundings for one directional polarization analysis. In this new work, the method of reference layer with polarization analysis has been optimized to determine the phase and modulus of the unknown film with measurement of the polarization of the reflected neutrons in the same direction as the polarization of the incident beam.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Thin film growth by using random shape cluster deposition

The growth of a rough and porous thin surface by deposition of randomly shaped clusters with different sizes over an initially flat linear substrate is simulated, using Monte Carlo technique. Unlike the ordinary Random Deposition, our approach results in aggregation of clusters which produces a porous bulk with correlation along the surface and the surface saturation occurs in long enough deposition times. The scaling exponents; the growth, roughness, and dynamic exponents are calculated based on the time scale. Moreover, the porosity and its dependency to the time and clusters size are also calculated. We also study the influence of clusters size on the scaling exponent, as well as on the global porosity.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Detection of nano scale thin films with polarized neutron reflectometry at the presence of smooth and rough interfaces

By knowing the phase and modules of the reflection coefficient in neutron reflectometry problems, a unique result for the scattering length density (SLD) of a thin film can be determined which will lead to the exact determination of type and thickness of the film. In the past decade, several methods have been worked out to resolve the phase problem such as dwell time method, reference layer method and variation of surroundings, among which the reference method and variation of surroundings by using a magnetic substrate and polarized neutrons is of the most applicability. All of these methods are based on the solution of Schrodinger equation for a discontinuous and step-like potential at each interface. As in real sample there are some smearing and roughness at boundaries, consideration of smoothness and roughness of interfaces would affect the final output result. In this paper, we have investigated the effects of smoothness of interfaces on determination of the phase of reflection as well as the retrieval process of the SLD, by using a smooth varying function (Eckart potential). The effects of roughness of interfaces on the same parameters, have also been investigated by random variation of the interface around it mean position.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Polarized Neutron Reflectometry at the Presence of Smooth Interfacial Potential

Polarized neutron reflectometry (PNR) have developed theoretically and experimentally in the past decades. In order to resolve the phase problem in neutron reflectometry, several simulation methods have been proposed such as reference layer and the variation of surroundings. By considering some factors such as smoothness or roughness in simulations, it is tried to gain more compatible results with experiment. In this paper, by using four different functions "Linear, Airy, Eckart and Error function", we investigate the effects of the smoothness of the interfacial potential on reflectivity, polarization of reflected neutrons and determination of the SLD of the sample, using reference layer method with polarized neutrons. We have also proposed a solution in order to gain better results with less noise in output reflectivity data, at the presence of smoothness.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Retrieval of depth profile of nano scale thin films by one directional polarization analysis in neutron specular reflectometry

Recently it has been shown that the modules and phase of complex reflection coefficient can be determined by using a magnetic substrate and polarized neutrons. Several other methods have also been worked out based on measurement of polarizations of reflected neutrons from magnetic reference layers and magnetic substrate. However, due to the fact that available reflectometers are limited in the choice of polarization of reflected beam in the same direction as the polarization of the incident beam, neither of the methods which are based on polarization analysis, has been proven to be experimentally practical. In this paper, we have proposed a new method for determining the phase of reflection coefficient which is based on two measurements of polarization which correspond to two magnetic fields with the same magnitudes and different orientations. The polarization analysis is performed in the same direction as the polarization of the incident beam and is well suited for available reflectometers. The problems envisaged in implementation of the method are also discussed.

0 citations0 reviewsNo code linkedOpen access