arXiv subjects

Ilia A. Iakovlev

Publications and source records attributed to Ilia A. Iakovlev.

8 recordsLinked to original sources

arXiv2026

Quantum-optical reset with classical memory

Dynamic quantum circuits generate states that depend on the measurement results obtained during circuit execution. To date such a quantum computing model has mainly been implemented with qubit-based superconducting hardware utilizing reset operations and classical logic. Here we develop a model of optical reset by using time-bin self-looped interferometers demonstrated in recent experiments. Synchronizing the optical reset with a simple classical device storing history of measurement results allows one to decrease uncertainty of future measurements, which suggests new possibilities for constructing dynamical circuits on optical platforms. Information flow with significant multi-time correlations and memory depth is identified through distinct information-theoretic measures. We discuss potential applications of the proposed reset model, including the realization of boson sampling and experimental tests of the quantum-mechanical formulation of Landauer's principle.

quant-ph↗

arXiv2025

Emergence of global receptive fields capturing multipartite quantum correlations

In quantum physics, even simple data with a well-defined structure at the wave function level can be characterized by extremely complex correlations between its constituent elements. The inherent non-locality of the quantum correlations generally prevents one from providing their simple and transparent interpretation, which also remains a challenging problem for advanced classical techniques that approximate quantum states with neural networks. Here we show that monitoring the neural network weight space while learning quantum statistics from measurements allows to develop physical intuition about complex multipartite patterns and thus helps to construct more effective classical representations of the wave functions. Particularly, we observe the formation of distinct global convolutional structures, receptive fields in the hidden layer of the Restricted Boltzmann Machine (RBM) within the neural quantum tomography of the highly-entangled Dicke states. On this basis we propose an exact two-parameter classical representation not only for a specific quantum wave function, but for the whole family of the N-qubit Dicke states of different entanglement. Our findings suggest a fresh look at constructing convolutional neural networks for processing data with non-local patterns and pave the way for developing exact learning-based representations of entangled quantum states.

quant-ph↗

arXiv2024

Achieving the volume-law entropy regime with random-sign Dicke states

Manipulating entanglement, which reflects non-local correlations in a quantum system and defines the complexity of describing its wave function, represents the extremely tough challenge in the fields of quantum computing, quantum information, and condensed matter physics. In this work, by the example of the well-structured Dicke states we demonstrate that the complexity of these real-valued wave functions can be accurately tuned by introducing a random-sign structure, which allows us to explore the regime of the volume-law entanglement. Importantly, setting nontrivial sign structure one can increase the entanglement entropy of the Dicke state to the values that are close to Page's estimates for Haar-random states. The practical realization of these random-sign Dicke states is possible on different physical platforms with shallow quantum circuits. On the level of the measurements the change in the quantum state complexity due to sign structure can be traced out with the dissimilarity measure that estimates multi-scale variety of patterns in bit-string arrays.

quant-ph↗

arXiv2023

An effective spin model on the honeycomb lattice for the description of magnetic properties in two-dimensional Fe$_3$GeTe$_2$

Fe$_3$GeTe$_2$ attracts significant attention due to technological perspectives of realizing room temperature ferromagnetism in two-dimensional materials. Here we show that due to structural peculiarities of the Fe$_3$GeTe$_2$ monolayer, short distance between the neighboring iron atoms induces a strong exchange coupling. This strong coupling allows us to consider them as an effective cluster with a magnetic moment $\sim$5 $μ_B$, giving rise to a simplified spin model on a bipartite honeycomb lattice with the reduced number of long-range interactions. The simplified model perfectly reproduces the results of the conventional spin model, but allows for a more tractable description of the magnetic properties of Fe$_3$GeTe$_2$, which is important, e.g., for large-scale simulations. Also, we discuss the role of biaxial strain in the stabilization of ferromagnetic ordering in Fe$_3$GeTe$_2$.

cond-mat.mtrl-sci↗

arXiv2023

Benchmarking a boson sampler with Hamming nets

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to required amount of measurements. A special attention to this problem appears within the context of characterizing outcomes of noisy intermediate-scale quantum devices, which produce quantum states with specific properties so that it is expected to be hard to simulate such states using classical resources. In this work, we address the problem of characterization of a boson sampling device, which uses interference of input photons to produce samples of non-trivial probability distributions that at certain condition are hard to obtain classically. For realistic experimental conditions the problem is to probe multi-photon interference with a limited number of the measurement outcomes without collisions and repetitions. By constructing networks on the measurements outcomes, we demonstrate a possibility to discriminate between regimes of indistinguishable and distinguishable bosons by quantifying the structures of the corresponding networks. Based on this we propose a machine-learning-based protocol to benchmark a boson sampler with unknown scattering matrix. Notably, the protocol works in the most challenging regimes of having a very limited number of bitstrings without collisions and repetitions. As we expect, our framework can be directly applied for characterizing boson sampling devices that are currently available in experiments.

quant-ph↗

arXiv2023

Estimating Patterns of Classical and Quantum Skyrmion States

In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protected magnetic textures originated from the Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange in ferromagnets. We show that for classical spin systems there is a whole pool of machine approaches allowing their accurate phase classification and quantitative description on the basis of few magnetization snapshots. In turn, investigation of the quantum skyrmions is a less explored issue, since there are fundamental limitations on the simulation of such wave functions with classical supercomputers. One needs to find the ways to imitate quantum skyrmions on near-term quantum computers. In this respect, we discuss implementation of the method for estimating structural complexity of classical objects for characterization of the quantum skyrmion state on the basis of limited number of bitstrings obtained from the projective measurements.

cond-mat.str-el↗

arXiv2020

Multi-scale structural complexity of natural patterns

Complexity of patterns is a key information for human brain to differ objects of about the same size and shape. Like other innate human senses, the complexity perception cannot be easily quantified. We propose a transparent and universal machine method for estimating structural (effective) complexity of two- and three-dimensional patterns that can be straightforwardly generalized onto other classes of objects. It is based on multi-step renormalization of the pattern of interest and computing the overlap between neighboring renormalized layers. This way, we can define a single number characterizing the structural complexity of an object. We apply this definition to quantify complexity of various magnetic patterns and demonstrate that not only does it reflect the intuitive feeling of what is "complex" and what is "simple", but also can be used to accurately detect different phase transitions and gain information about dynamics of non-equilibrium systems. When employed for that, the proposed scheme is much simpler and numerically cheaper than the standard methods based on computing correlation functions or using machine learning techniques.

nlin.PS↗

arXiv2020

Field evolution of the spin-liquid candidate YbMgGaO$_4$

We report magnetization, heat capacity, thermal expansion, and magnetostriction measurements down to millikelvin temperatures on the triangular antiferromagnet YbMgGaO$_4$. Our data exclude the formation of the distinct $\frac13$ plateau phase observed in other triangular antiferromagnets, but reveal plateaulike features in second derivatives of the free energy, magnetic susceptibility and specific heat, at $μ_0H$ = 1.0 - 2.5 T for $H\parallel{}c$ and 2 - 5 T for $H\perp{}c$. Using Monte-Carlo simulations of a realistic spin Hamiltonian, we ascribe these features to nonmonotonic changes in the magnetization and the $\frac12$ plateau that is smeared out by the random distribution of exchange couplings in YbMgGaO$_4$.

cond-mat.str-el↗