arXiv subjects

Yongcheng Ding

Publications and source records attributed to Yongcheng Ding.

At least 19 recordsLinked to original sources

arXiv2026

Phase-Programmable Free Electron Quantum States in Synthetic Momentum Space

Light-electron interactions generate synthetic momentum-space dynamics that can be used to engineer free electron quantum states. Here we develop coherent control protocols in which the optical phase acts as the controllable hopping phase of a Floquet-Bloch momentum lattice. Pontryagin optimization designs phase-only waveforms that prepare selected momentum populations and coherent few-sideband superpositions with programmable relative phases. In a complementary Bragg regime protocol, dynamical phase matching selectively couples neighboring sidebands and enables deterministic sequential state synthesis. Full wave-packet simulations based on the minimal-coupling Hamiltonian identify the tolerance window set by phase noise, detuning, and finite momentum spread. The two protocols expose a speed-selectivity tradeoff between ultrafast multilevel interference control and slower resonant engineering, establishing programmable free electron sidebands as a platform for ultrafast quantum state synthesis.

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arXiv2025

Ultrafast quantum gates with fully quantized free-electron quantum optics

Free-electron quantum optics provides a versatile platform for manipulating electrons at the quantum level with potential applications in quantum information technologies. We propose a grating-based architecture for fully quantized free-electron quantum optics, in which photon-electron interactions map onto Jaynes-Cummings and Tavis-Cummings models via Bloch-Floquet analysis. Within this framework, we design ultrafast single- and two-qubit gates with cavity-free flying electrons, enabling universal quantum computing in experimentally accessible setups. More broadly, this framework establishes a platform for probing free-electron quantum optics and advancing quantum technologies in simulation, sensing, and information processing.

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arXiv2025

Ultrafast Stern-Gerlach and Anomalous Bragg Diffraction Regimes of Low-energy Free Electron Interaction with Light

Recent advances in photon-induced near-field electron microscopy (PINEM) have significantly impacted allied disciplines such as laser-driven accelerators and free electron radiations, collectively fostering the emergence of free-electron quantum optics (FEQO). A central objective of FEQO is to achieve coherent optical control of free electrons, analogous to light manipulation of atoms in atom optics. Motivated by this analogy, we propose an ultrafast Stern-Gerlach (USG) regime for low-energy quantum electron wavepacket (QEW), which crucially incorporates the effects of second-order dispersion inherent to slow electrons. We demonstrate that the USG diffraction induces spectral splitting and shifting of the QEW via a longitudinal electric field gradient, with the two dominant truncated sidebands forming a pseudospin degree of freedom for an effective "two-level" electron. Furthermore, by examining the wave-particle duality of the QEW during light interaction, we identify a dispersion-induced anomalous Bragg diffraction regime. This regime exhibits a distinct spectral pattern, differentiating it from these reported PINEM (Raman-Nath), dielectric laser accelerators (DLA), anomalous PINEM, and Bragg diffraction regimes. Our study provides a comprehensive classification for light-induced diffraction regimes for both swift and slow electrons. These findings underscore the pivotal role of slow-electron dispersion and duality nature in free-electron optics, offering promising avenues for electron wavefunction quantum engineering ultrafast interferometers.

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arXiv2025

Superoscillating Quantum Control Induced By Sequential Selections

Superoscillation is a counterintuitive phenomenon for its mathematical feature of ``faster-than-Fourier", which has allowed novel optical imaging beyond the diffraction limit. In this article, we introduce a superoscillating quantum control protocol realized by sequential selections within the framework of weak measurement, which steers the apparatus (target) by repeatedly applying optimal pre- and post-selections to the system (controller). Our protocol accelerates theadiabatic transport of trapped ions and adiabatic quantum search algorithm at a finite energy cost. We demonstrate the accuracy and robustness of the protocol in the presence of decoherence and fluctuating noise, and elucidate the trade-off between fidelity and rounds of selections. Our findings open new avenues for quantum state manipulation and wave-packet control using superoscillation across a range of quantum platforms.

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arXiv2025

Scrambling in the Charging of Quantum Batteries

Exponentially fast scrambling of an initial state characterizes quantum chaotic systems. Given the importance of quickly populating higher energy levels from low-energy states in quantum battery charging protocols, this work investigates the role of quantum scrambling in quantum batteries and its effect on optimal power and charging times by means of the Sachdev-Ye-Kitaev model, a maximally-chaotic black hole physics model that has been recently proposed as a quantum battery. We adopt a bare representation with normalized bandwidths to suppress system energy dependence. To our knowledge, this is the first in-depth exploration of quantum scrambling in the context of quantum batteries. By analyzing the dynamics of out-of-time-order correlators, our findings indicate that quantum scrambling does not necessarily lead to faster charging, despite its potential for accelerating the process.

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arXiv2024

Free electron topological bound state induced by light beam with a twisted wavefront

Recent advances in ultrafast electron emission, microscopy, and diffraction have demonstrated a remarkable ability to manipulate free electrons with quantum coherence using light beams. Here, we present a framework for exploring free electron quantum number in ultrafast electron-light interactions. We derive an explicit Jackiw-Rebbi solution for a low-energy free electron wavefunction subjected to a spatiotemporally twisted laser field, resulting in a flying topologically protected bound state with a quantum number of e/2 - termed a "half-electron". This flying bound state is dispersion-free due to its topological nature. We demonstrate the topological confinement and pair generation mechanism of half-electrons in free space, expanding their domain beyond the topological states typically found in solids and photonics. This advancement enhances our understanding of emulating exotic quantum and topological effects with low-energy free electrons.

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arXiv2024

Pulse-based variational quantum optimization and metalearning in superconducting circuits

Solving optimization problems using variational algorithms stands out as a crucial application for noisy intermediate-scale devices. Instead of constructing gate-based quantum computers, our focus centers on designing variational quantum algorithms within the analog paradigm. This involves optimizing parameters that directly control pulses, driving quantum states towards target states without the necessity of compiling a quantum circuit. In this work, we introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external fluxes on superconducting quantum interference devices, effectively driving the wave function of this specific quantum architecture to the ground state of an encoded problem Hamiltonian. Given that the performance of variational algorithms heavily relies on appropriate initial parameters, we introduce a global optimizer as a meta-learning technique to tackle a simple problem. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.

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arXiv2024

Quantum Active Learning

Quantum machine learning, as an extension of classical machine learning that harnesses quantum mechanics, facilitates effiient learning from data encoded in quantum states. Training a quantum neural network typically demands a substantial labeled training set for supervised learning. Human annotators, often experts, provide labels for samples through additional experiments, adding to the training cost. To mitigate this expense, there is a quest for methods that maintain model performance over fully labeled datasets while requiring fewer labeled samples in practice, thereby extending few-shot learning to the quantum realm. Quantum active learning estimates the uncertainty of quantum data to select the most informative samples from a pool for labeling. Consequently, a QML model is supposed to accumulate maximal knowledge as the training set comprises labeled samples selected via sampling strategies. Notably, the QML models trained within the QAL framework are not restricted to specific types, enabling performance enhancement from the model architecture's perspective towards few-shot learning. Recognizing symmetry as a fundamental concept in physics ubiquitous across various domains, we leverage the symmetry inherent in quantum states induced by the embedding of classical data for model design. We employ an equivariant QNN capable of generalizing from fewer data with geometric priors. We benchmark the performance of QAL on two classification problems, observing both positive and negative results. QAL effectively trains the model, achieving performance comparable to that on fully labeled datasets by labeling less than 7\% of the samples in the pool with unbiased sampling behavior. Furthermore, we elucidate the negative result of QAL being overtaken by random sampling baseline through miscellaneous numerical experiments. (character count limit, see the main text)

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arXiv2024

Exploring Ground States of Fermi-Hubbard Model on Honeycomb Lattices with Counterdiabaticity

Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing or adiabatic quantum computation. Shortcuts to adiabaticity by counter-diabatic driving serve to accelerate these processes by suppressing energy excitations. Motivated by this, we develop variational quantum algorithms incorporating the auxiliary counterdiabatic interactions, comparing them with digitized adiabatic algorithms. These algorithms are then implemented on gate-based quantum circuits to explore the ground states of the Fermi-Hubbard model on honeycomb lattices, utilizing systems with up to 26 qubits. The comparison reveals that the counter-diabatic inspired ansatz is superior to traditional Hamiltonian variational ansatz. Furthermore, the number and duration of Trotter steps are analyzed to understand and mitigate errors. Given the model's relevance to materials in condensed matter, our study paves the way for using variational quantum algorithms with counterdiabaticity to explore quantum materials in the noisy intermediate-scale quantum era.

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arXiv2024

Weak-valued correlation functions: Insights and precise readout strategies

The correlation function in quantum systems plays a vital role in decoding their properties and gaining insights into physical phenomena. Its interpretation corresponds to the propagation of particle excitations between space-time, similar in spirit to the idea of quantum weak measurement in terms of recording the system information by interaction. By defining weak-valued correlation function, we propose the basic insights and the universal methods for recording them on the apparatus through weak measurement. To demonstrate the feasibility of our approach, we perform numerical experiments of perturbed quantum harmonic oscillators, addressing the intricate interplay between the coupling strength and the number of ensemble copies. Additionally, we extend our protocol to the domain of quantum field theory, where joint weak values encode crucial information about the correlation function. Hopefully, this comprehensive investigation can advance our understanding of the fundamental nature of the correlation function and weak measurement in quantum theories.

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arXiv2023

Dropout is all you need: robust two-qubit gate with reinforcement learning

In the realm of quantum control, reinforcement learning, a prominent branch of machine learning, emerges as a competitive candidate for computer-assisted optimal design for experiments. This study investigates the extent to which guidance from human experts is necessary for the effective implementation of reinforcement learning in designing quantum control protocols. Specifically, we focus on the engineering of a robust two-qubit gate within a nuclear magnetic resonance system, utilizing a combination of analytical solutions as prior knowledge and techniques from the field of computer science. Through extensive benchmarking of different models, we identify dropout, a widely-used method for mitigating overfitting in machine learning, as an especially robust approach. Our findings demonstrate the potential of incorporating computer science concepts to propel the development of advanced quantum technologies.

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arXiv2023

Active Learning in Physics: From 101, to Progress, and Perspective

Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.

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arXiv2023

Towards Prediction of Financial Crashes with a D-Wave Quantum Computer

Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can guarantee to find optimal solutions efficiently. We experimentally explore a novel approach to this problem by using a D-Wave quantum computer, benchmarking its performance for attaining financial equilibrium. To be specific, the equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed to a spin-$1/2$ Hamiltonian with at most two-qubit interactions. The problem is thus equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The size of the simulation is mainly constrained by the necessity of a large quantity of physical qubits representing a logical qubit with the correct connectivity. Our experiment paves the way to codify this quantitative macroeconomics problem in quantum computers.

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arXiv2022

Time-Optimal Quantum Driving by Variational Circuit Learning

The simulation of quantum dynamics on a digital quantum computer with parameterized circuits has widespread applications in fundamental and applied physics and chemistry. In this context, using the hybrid quantum-classical algorithm, combining classical optimizers and quantum computers, is a competitive strategy for solving specific problems. We put forward its use for optimal quantum control. We simulate the wave-packet expansion of a trapped quantum particle on a quantum device with a finite number of qubits. We then use circuit learning based on gradient descent to work out the intrinsic connection between the control phase transition and the quantum speed limit imposed by unitary dynamics. We further discuss the robustness of our method against errors and demonstrate the absence of barren plateaus in the circuit. The combination of digital quantum simulation and hybrid circuit learning opens up new prospects for quantum optimal control.

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arXiv2021

Quantum Stream Learning

The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.

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arXiv2021

Active Learning for the Optimal Design of Multinomial Classification in Physics

Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze its capability as an assistant for the design of experiments, extracting maximum information for learning with the minimal cost in fidelity loss, or reducing total operation costs of labeling in the laboratory. We present two typical applications as quantum information retrieval in qutrits and phase boundary prediction in many-body physics. For an equivalent multinomial classification problem, we achieve the correct rate of 99% with less than 2% samples labeled. We reckon that active-learning-inspired physics experiments will remarkably save budget without loss of accuracy.

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arXiv2021

Neural-network-based parameter estimation for quantum detection

Artificial neural networks bridge input data into output results by approximately encoding the function that relates them. This is achieved after training the network with a collection of known inputs and results leading to an adjustment of the neuron connections and biases. In the context of quantum detection schemes, neural networks find a natural playground. In particular, in the presence of a target, a quantum sensor delivers a response, i.e., the input data, which can be subsequently processed by a neural network that outputs the target features. We demonstrate that adequately trained neural networks enable to characterize a target with minimal knowledge of the underlying physical model, in regimes where the quantum sensor presents complex responses, and under a significant shot noise due to a reduced number of measurements. We exemplify the method with a development for $^{171}$Yb$^{+}$ atomic sensors. However, our protocol is general, thus applicable to arbitrary quantum detection scenarios.

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arXiv2021

Implementation of a Hybrid Classical-Quantum Annealing Algorithm for Logistic Network Design

The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the optimal configuration of the supply chain's infrastructures and facilities based on customer demand. Key economic decisions are taken about the location, number, and size of manufacturing facilities and warehouses based on the optimal solution. Therefore, improvements in the methods to address this question, which is known to be in the NP-hard complexity class, would have relevant financial consequences. Here, we implement in the D-Wave quantum annealer a hybrid classical-quantum annealing algorithm. The cost function with constraints is translated to a spin Hamiltonian, whose ground state encodes the searched result. As a benchmark, we measure the accuracy of results for a set of paradigmatic problems against the optimal published solutions (the error is on average below $1\%$), and the performance is compared against the classical algorithm, showing a remarkable reduction in the number of iterations. This work shows that state-of-the-art quantum annealers may codify and solve relevant supply-chain problems even still far from useful quantum supremacy.

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