arXiv subjects

Ivan Zhukov

Publications and source records attributed to Ivan Zhukov.

4 recordsLinked to original sources

arXiv2026

Parahydrogen Cooling of Nuclear Spin Chains at Hypogeomagnetic Fields

Solution-state molecular nuclear spin networks are promising quantum simulators because their scalar-coupling Hamiltonians are chemically programmable, precisely measurable, and coherent at room temperature. Their main limitation for quantum information science is initialization: thermal Boltzmann polarization produces highly mixed, high-entropy states. Here, we use parahydrogen-based Signal Amplification by Reversible Exchange (SABRE) at hypogeomagnetic fields (i.e., magnetic fields below Earth field) to hyperpolarize the chemically engineered 12-spin chain [U-13C,15N]-butyronitrile. SABRE generates percent-level 13C and 15N polarization and prepares non-equilibrium multi-spin orders across the network. A von Neumann entropy analysis of such a hyperpolarized system shows that, at the optimal transfer field of 0.52 uT, the full spin system could reach S/k = 8.274, compared with S/k = 8.318 for the unpolarized reference, giving (S-Sth)/k = -0.043. Experimentally, nuclear spin temperatures of 52 mK and 257 mK are achieved for 15N and 13C subensembles, respectively. The larger entropy deficit of the full network than of individual subsystems indicates correlated multi-spin order beyond single-spin polarizations. Rapid field cycling to 9.4 T enables site-resolved NMR readout, while the precisely determined coupling network provides an experimentally benchmarked Hamiltonian for testing quantum-simulation, quantum-control, and Hamiltonian-learning protocols.

quant-ph↗

arXiv2026

High-Field NMR Characterization and Indirect $J$-Spectroscopy of a Nuclear Spin Chain [U-$^{13}$C,$^{15}$N]-butyronitrile

One-dimensional chains of coupled spins are minimal models of strongly correlated quantum matter, and have been proposed as wires for transporting quantum information. In liquids, rapid molecular tumbling averages anisotropic dipolar couplings and leaves effective isotropic scalar $J$-coupling Hamiltonians. At zero- to ultralow-field (ZULF) conditions, differences in frequency between nuclear spins of different types are quenched and the internal Hamiltonians can be closely approximated by an isotropic Heisenberg model. In this work, we present [U-$^{13}$C,$^{15}$N]-butyronitrile as a chemically engineered nuclear spin chain whose full spin-spin coupling network can be determined and validated by combining high-field NMR detection with evolution at ultralow fields. Starting from high-field (16.4 T) NMR spectra of $^1$H, $^{13}$C, and $^{15}$N nuclei, we extract all relevant $J$-couplings within a 12-spin network (four $^{13}$C, one $^{15}$N, and seven $^1$H). We then employ a mechanical field-cycling apparatus to prepolarize the spins at high field, shuttle them into a magnetically shielded region for evolution at <50 nT, and detect signals after returning to high field. Fourier analysis of the ultralow-field evolution yields indirect $J$-spectra that are conceptually analogous to ZULF NMR spectra but measured by a high-field NMR spectrometer. We observe clear spectral features at $J$, 1.5$J$, and 2$J$, in good agreement with simulations using the extracted coupling matrix. Finally, we demonstrate 2D experiments that correlate high-field chemical shifts and, thus, fully map interactions within the molecular spin chain. Our results establish [U-$^{13}$C,$^{15}$N]-butyronitrile as an extremely well-characterized spin chain model system and provide a quantitative Hamiltonian benchmark for future hyperpolarization and quantum-control studies.

quant-ph↗

arXiv2022

Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing

We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in autonomous driving and to help advance the state of the art on a realistic benchmark. Analogous to racing being used to test cutting-edge vehicles, we envision autonomous racing to serve as a particularly challenging proving ground for autonomous agents as: (i) they need to make sub-second, safety-critical decisions in a complex, fast-changing environment; and (ii) both perception and control must be robust to distribution shifts, novel road features, and unseen obstacles. Thus, the main goal of the challenge is to evaluate the joint safety, performance, and generalisation capabilities of reinforcement learning agents on multi-modal perception, through a two-stage process. In the first stage of the challenge, we evaluate an autonomous agent's ability to drive as fast as possible, while adhering to safety constraints. In the second stage, we additionally require the agent to adapt to an unseen racetrack through safe exploration. In this paper, we describe the new L2R Task 2.0 benchmark, with refined metrics and baseline approaches. We also provide an overview of deployment, evaluation, and rankings for the inaugural instance of the L2R Autonomous Racing Virtual Challenge (supported by Carnegie Mellon University, Arrival Ltd., AICrowd, Amazon Web Services, and Honda Research), which officially used the new L2R Task 2.0 benchmark and received over 20,100 views, 437 active participants, 46 teams, and 733 model submissions -- from 88+ unique institutions, in 58+ different countries. Finally, we release leaderboard results from the challenge and provide description of the two top-ranking approaches in cross-domain model transfer, across multiple sensor configurations and simulated races.

cs.RO↗

arXiv2021

Learn-to-Race: A Multimodal Control Environment for Autonomous Racing

Existing research on autonomous driving primarily focuses on urban driving, which is insufficient for characterising the complex driving behaviour underlying high-speed racing. At the same time, existing racing simulation frameworks struggle in capturing realism, with respect to visual rendering, vehicular dynamics, and task objectives, inhibiting the transfer of learning agents to real-world contexts. We introduce a new environment, where agents Learn-to-Race (L2R) in simulated competition-style racing, using multimodal information--from virtual cameras to a comprehensive array of inertial measurement sensors. Our environment, which includes a simulator and an interfacing training framework, accurately models vehicle dynamics and racing conditions. In this paper, we release the Arrival simulator for autonomous racing. Next, we propose the L2R task with challenging metrics, inspired by learning-to-drive challenges, Formula-style racing, and multimodal trajectory prediction for autonomous driving. Additionally, we provide the L2R framework suite, facilitating simulated racing on high-precision models of real-world tracks. Finally, we provide an official L2R task dataset of expert demonstrations, as well as a series of baseline experiments and reference implementations. We make all code available: https://github.com/learn-to-race/l2r.

cs.RO↗