Yue Ban

• Doctor of Philosophy
• Ramón y Cajal researcher at Instituto de Ciencia de Materiales de Madrid (ICMM-CSIC)

The complete knowledge of the global and marginal second-order moments of a quantum state is in general insufficient for entanglement detection. By deriving the conditions on local-unitary (LU) transformations through the second-order moments, we construct pairs of separable and entangled states that are not LU equivalent, contain different amount...

A bstract 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...

We consider subspace transfer within the time-dependent one-dimensional quantum transverse Ising model, with random nearest-neighbor interactions and a transverse field. We run numerical simulations using a variational approach and the numerical GRAPE (gradient-ascent pulse engineering) and dCRAB (dressed chopped random basis) quantum control algor...

Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable the loading of matrices into quantum computers. A representative example of these methods is the Llo...

The bin packing problem, a classical NP-hard combinatorial optimization challenge, has emerged as a promising candidate for quantum computing applications. In this work, we address the one-dimensional bin packing problem (1dBPP) using a digitized counter-diabatic quantum algorithm (DC-QAOA), which incorporates counter-diabatic (CD) driving to reduc...

Achieving practical applications of quantum machine learning (QML) for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation industry, using QML techniques. Specifically, we focus on classifying...

We investigate the lateral displacements for ballistic electron beams in a two-dimensional electron gas modulated by metallic ferromagnetic (FM) stripes with parallel and antiparallel (AP) magnetization configurations. It is shown that the displacements are negative as well as positive, which can be controlled by adjusting the electric potential in...

Considering a universal deep neural network organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads we analyze its expressivity. This ability to approximate continuous functions in regression tasks is quantified making use of a partial Fourier decomposition of the generated output and systematically benchmarked w...

Quantum information transfer is fundamental for scalable quantum computing in any potential platform and architecture. Hole spin qubits, owing to their intrinsic spin-orbit interaction (SOI), promise fast quantum operations which are fundamental for the implementation of quantum gates. Yet, the influence of SOI in quantum transfer protocols remains...

One of the key applications of near-term quantum computers has been the development of quantum optimization algorithms. However, these algorithms have largely been focused on qubit-based technologies. Here, we propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing c...

A practical application of quantum machine learning in real-world scenarios in the short term remains elusive, despite significant theoretical efforts. Image classification, a common task for classical models, has been used to benchmark quantum algorithms with simple datasets, but only few studies have tackled complex real-data classification chall...

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 Letter investigates the role of quantum scrambling in quantum batteries and its effect on optimal power and charging times. We ado...

Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out of reach for the state-of-art quantum technologies. In this context, a promising strategy to find p...

Quantum algorithms are prominent in the pursuit of achieving quantum advantage in various computational tasks. However, addressing challenges, such as limited qubit coherence and high error rate in near-term devices, requires extensive efforts. In this paper, we present a substantial stride in quantum chemistry by integrating shortcuts-to-adiabatic...

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 throu...

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...

Quantum sensors leverage matter’s quantum properties to enable measurements with unprecedented spatial and spectral resolution. Among these sensors, those utilizing nitrogen-vacancy (NV) centers in diamond offer the distinct advantage of operating at room temperature. Nevertheless, signals received from NV centers are often complex, making interpre...

The Su-Schrieffer-Heeger (SSH) chain, which serves as a paradigmatic model for comprehending topological phases and their associated edge states, plays an essential role in advancing our understanding of quantum materials and quantum information processing and technology. In this paper, we introduce a hybrid analog-digital protocol designed for the...

We investigate the behavior of relativistic electrons encountering a potential step through analogies with optical phenomena. By accounting for the conservation of the Dirac current, we elucidate that the Goos-Hänchen shift can be understood as a combination of two components: one arising from the current entering the transmission region and the ot...

The Su-Schrieffer-Heeger (SSH) chain, which serves as a paradigmatic model for comprehending topological phases and their associated edge states, plays an essential role in advancing our understanding of quantum materials and quantum information processing and technology. In this paper, we introduce a hybrid analog-digital protocol designed for the...

The use of two-level atomic systems in quantum optics allows for the design of highly efficient and broadband achromatic retarders through the application of adiabatic passage and composite pulse techniques. In this work, we propose shortcuts to adiabaticity to improve broadband polarization retarders with shorter lengths. We achieve this by invers...

We investigate the behavior of relativistic electrons encountering a potential step through analogies with optical phenomena. By accounting for the conservation of Dirac current, we elucidate that the Goos-H\"anchen shift can be understood as a combination of two components: one arising from the current entering the transmission region and the othe...

We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantly reduces the required quantum operations for optimization as compared to adiabatic protocols. This...

The use of variational quantum algorithms for optimization tasks has emerged as a crucial application for the current noisy intermediate-scale quantum computers. However, these algorithms face significant difficulties in finding suitable ansatz and appropriate initial parameters. In this paper, we employ meta-learning using recurrent neural network...

Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world...

The simulation of quantum dynamics on a digital quantum computer with parametrized 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...

We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multi-qubit potentials in the quantum perceptrons enables more efficient information processing tasks such as XOR gate implementation and prime...

Efficient packing of items into bins is a common daily task. Known as Bin Packing Problem, it has been intensively studied in the field of artificial intelligence, thanks to the wide interest from industry and logistics. Since decades, many variants have been proposed, with the three-dimensional Bin Packing Problem as the closest one to real-world...

A versatile magnetometer must deliver a readable response when exposed to target fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of ¹⁷¹ Yb ⁺ atomic sensors with adequately trained neural networks enables us to investigate target fields in distinct challenging scenarios. In particular, we charac...

Entangled quantum many-body systems can be used as sensors that enable the estimation of parameters with a precision larger than that achievable with ensembles of individual quantum detectors. Typically, the parameter estimation strategy requires the microscopic modelling of the quantum many-body system, as well as a an accurate description of its...

Hole spin qubits in semiconductor quantum dots (QDs) are promising candidates for quantum information processing due to their weak hyperfine coupling to nuclear spins, and to the strong spin-orbit coupling, which allows for rapid operation time. We propose a coherent control on two heavy-hole spin qubits in a double QD by a fast adiabatic driving p...

We propose the combination of digital quantum simulation and variational quantum algorithms as an alternative approach to numerical methods for solving quantum control problems. As a hybrid quantum–classical framework, it provides an efficient simulation of quantum dynamics compared to classical algorithms, exploiting the previous achievements in d...

Robust and efficient manipulation of electron spin qubits in quantum dots is of great significance for the reliable realization of quantum computers and execution of quantum algorithms. In this paper, we study the robust control on a singlet-triplet qubit based on inverse engineering, one technique of shortcuts to adiabaticity (STA), in a nanowire...

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 pu...

Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning,and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder op...

Coherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder o...

Solving optimization tasks using variational quantum algorithms has emerged as a crucial application of the current noisy intermediate-scale quantum devices. However, these algorithms face several difficulties like finding suitable ansatz and appropriate initial parameters, among others. In this work, we tackle the problem of finding suitable initi...

We experimentally investigate deep reinforcement learning (DRL) as an artificial intelligence approach to control a quantum system. We verify that DRL explores fast and robust digital quantum controls with operation time analytically hinted by shortcuts to adiabaticity. In particular, the protocol’s robustness against both over-rotations and off-re...

Hole spin qubits in semiconductor quantum dots (QDs) are promising candidates for quantum information processing due to their weak hyperfine coupling to nuclear spins and rapid operation time due to the strong spin orbit coupling. We study the coherent control on two heavy hole spin qubits in a double quantum dot, and construct one qubit and two qu...

A versatile magnetometer must deliver a readable response when exposed to targets fields in a wide range of parameters. In this work, we experimentally demonstrate that the combination of a $^{171}$Yb$^{+}$ atomic sensor with adequately trained neural networks enables the characterisation of target fields in distinct challenging scenarios. In parti...

Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of the concept and analyze neural network-based machine learning algorithm for achieving feasible high-fidelity qua...

Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of the concept and analyze a neural-network-based machine-learning algorithm for achieving feasible high-fidelity q...

Quantum sensors typically translate external fields into a periodic response whose frequency is then determined by analyses performed in Fourier space. This allows for a linear inference of the parameters that characterize external signals. In practice, however, quantum sensors are able to detect fields only in a narrow range of amplitudes and freq...

Artificial neural networks (NNs) 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, NNs find a natural play...

Quantum machine learning emerges from the symbiosis of quantum mechanics and machine learning. In particular, the latter gets displayed in quantum sciences as: (i) the use of classical machine learning as a tool applied to quantum physics problems, (ii) or the use of quantum resources such as superposition, entanglement, or quantum optimization pro...

We propose quantum neural networks that include multi-qubit interactions in the neural potential leading to a reduction of the network depth without losing approximative power. We show that the presence of multi-qubit potentials in the quantum perceptrons enables more efficient information processing tasks such as XOR gate implementation and prime...

In the noisy intermediate-scale quantum era, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speedup by pulse control. Here,...

The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptr...

Robust and high-precision quantum control is crucial but challenging for scalable quantum computation and quantum information processing. Traditional adiabatic control suffers severe limitations on gate performance imposed by environmentally induced noise because of a quantum system's limited coherence time. In this work, we experimentally demonstr...

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 natura...

We present a method relying on shortcuts to adiabaticity to achieve quantum detection of high-frequency signals at the nanoscale in a robust manner. More specifically, our protocol delivers tailored amplitudes and frequencies for control fields that, firstly, enable the coupling of the sensor with high-frequency signals and, secondly, minimize erro...

In the era of digital quantum computing, optimal digitized pulses are requisite for efficient quantum control. This goal is translated into dynamic programming, in which a deep reinforcement learning (DRL) agent is gifted. As a reference, shortcuts to adiabaticity (STA) provide analytical approaches to adiabatic speed up by pulse control. Here, we...

We present a method relying on shortcuts to adiabaticity to achieve quantum detection of high frequency signals at the nanoscale in a robust manner. More specifically, our protocol delivers tailored amplitudes and frequencies for control fields that, firstly, enable the coupling of the sensor with high-frequency signals and, secondly, minimise erro...

The quantum perceptron is a fundamental building block in the area of quantum machine learning. This is a multidisciplinary field that incorporates properties of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quan...

Quantum sensors typically translate external fields into a periodic response whose frequency is then determined by analyses performed in Fourier space. This allows for a linear inference of the parameters that characterize external signals. In practice, however, quantum sensors are able to detect fields only in a narrow range of amplitudes and freq...

Long‐distance transfer of quantum states is an indispensable part of large‐scale quantum information processing. A novel scheme for the transfer of two‐electron entangled states from one edge of a quantum dot array to the other by coherent adiabatic passage is proposed. This protocol is mediated by pulsed tunneling barriers. In a second step, a spe...

Long-distance transfer of quantum states is an indispensable part of large-scale quantum information processing. We propose a novel scheme for the transfer of two-electron entangled states, from one edge of a quantum dot array to the other by coherent adiabatic passage. This protocol is mediated by pulsed tunneling barriers. In a second step, we se...

Motivated by the progress on shortcuts to adiabaticity, we propose three schemes for speeding up (fractional) stimulated Raman adiabatic passage, and achieving rapid and non-adiabatic creation and transfer of maximal coherence in a triple-quantum-dot system. These different but relevant protocols, designed from counter-diabatic driving, dress-state...

Charge, spin and quantum states transfer in solid state devices is an important issue in quantum information. Adiabatic protocols, such as coherent transfer by adiabatic passage have been proposed for the direct charge transfer, also denoted as long range transfer, between the outer dots in a QD array without occupying the intermediate ones. Howeve...

Rapid preparation, manipulation, and correction of spin states with high fidelity are requisite for quantum information processing and quantum computing. In this paper, we propose a fast and robust approach for controlling two spins with Heisenberg and Ising interactions. By using the concept of shortcuts to adiabaticity, we first inverse design th...

Rapid preparation, manipulation, and correction of spin states with high fidelity are requisite for quantum information processing and quantum computing. In this paper, we propose a fast and robust approach for controlling two spins with Heisenberg and Ising interactions. By using the concept of shortcuts to adiabaticity, we first inverse design th...

Rapid and efficient preparation, manipulation and transfer of quantum states through an array of quantum dots (QDs) is a demanding requisite task for quantum information processing and quantum computation in solid-state physics. Conventional adiabatic protocols, as coherent transfer by adiabatic passage (CTAP) and its variations, provide slow trans...

We investigate fast transport and spin manipulation of tunable spin-orbit-coupled Bose-Einstein condensates in a moving harmonic trap. Motivated by the concept of "shortcuts to adiabaticity", we design inversely the time-dependent trap position and spin-orbit coupling strength. By choosing appropriate boundary conditions we obtain fast transport an...

We investigate fast transport and spin manipulation of tunable spin-orbit-coupled Bose-Einstein condensates in a moving harmonic trap. Motivated by the concept of "shortcuts to adiabaticity", we design inversely the time-dependent trap position and spin-orbit coupling strength. By choosing appropriate boundary conditions we obtain fast transport an...

We put forward a method for achieving fast and robust for magnetization reversal in a nanomagnet, by combining the inverse engineering and composite pulses. The magnetic fields, generated by microwave with time-dependent frequency, are first designed inversely within short operation time, and composite pulses are further incorporated to improve the...

Graphene-assisted resonant transmission and enhanced Goos–Hänchen shift are investigated in a two-prism frustrated total internal reflection configuration. Due to the excitation of surface plasmons induced by graphene in a low terahertz frequency range, there exist the resonant transmission and anomalous Goos–Hänchen shifts in such an optical tunne...

Nonlinear two-level Landau-Zener type equations for systems with relevance for Bose-Einstein condensates and nonlinear optics are considered and the minimal time Tmin to drive an initial state to a given target state is investigated. Surprisingly, the nonlinearity may be canceled by a time-optimal unconstrained driving and Tmin becomes independent...

Electronic transport is investigated in the asymmetric graphene superlattice consisting of a periodic potential structure and a wide potential barrier, which are separated by an internal potential well. Our results show that under a certain condition a pronounced peak occurs in the original transmission gap region, and reveal that such an asymmetri...

Tunable group delay and Hartman effect have been investigated for massless Dirac electrons in graphene magnetic barriers. In the presence of magnetic field, dwell time is found to be equal to net group delay plus the group delay contributing from the lateral shifts. The group delay times are discussed in both cases of normal and oblique incidence,...

A finite-size quasi two-dimensional Bose-Einstein condensate collapses if the attraction between atoms is sufficiently strong. Here we present a theory of collapse for condensates with the interatomic attraction and spin-orbit coupling. We consider two realizations of spin-orbit coupling: the axial Rashba coupling and balanced, effectively one-dime...

Achieving full control of a Bose-Einstein condensate can have valuable applications in metrology, quantum information processing, and quantum condensed matter physics. We propose protocols to simultaneously control the internal (related to its pseudospin-1/2) and motional (position-related) states of a spin-orbit-coupled Bose-Einstein condensate co...

Conventional narrowband spectrum polarization devices are short but not robust, based on quasi-phase matching (QPM) technique, in periodically poled lithium niobate (PPLN) crystal. In this paper, we propose short-length and robust polarization rotators by using shortcuts to adiabaticity. Beyond the QPM condition, the electric field and period of PP...

The techniques of shortcuts to adiabaticity have been proposed to accelerate the “slow” adiabatic processes in various quantum systems with the applications in quantum information processing. In this paper, we study the counter-diabatic driving for fast adiabatic spin manipulation in a two-electron double quantum dot by designing time-dependent ele...

Tunneling times, including group delay and dwell time, are studied for massless Dirac electrons transmitting through a one-dimensional barrier in strain-engineered graphene. The Hartman effect, the independence of group delay on barrier length, is induced by the strain effect, and associated with the transmission gap and the evanescent mode. The in...

Inverse engineering of electric fields has been recently proposed to achieve fast and robust spin control in a single-electron quantum dot with spin-orbit coupling. In this paper we design, by inverse engineering based on Lewis-Riesenfeld invariants, time-dependent electric fields to realize fast transitions in the selected singlet-triplet subspace...

The analogies between optical and electronic Goos–Hänchen effects are established based on electron wave optics in semiconductor or graphene-based nanostructures. In this paper, we give a brief overview of the progress achieved so far in the field of electronic Goos–Hänchen shifts, and show the relevant optical analogies. In particular, we present...

We study electron transport in nanosized semiconductor waveguides of different shapes. The spin-dependent transport through these nonuniform nanostructures is investigated in the presence of spin-orbit coupling of the Rashba and Dresselhaus types. The resulting spin rotation strongly depends on the shape of the waveguide. The crossover from the cla...

We apply the transitionless quantum driving method to control the electron spin of a two-electron double quantum dot with spin-orbit coupling by time-dependent electric fields. The $x$ and $y$ components of applied electric fields in each dot are designed to achieve fast adiabatic-like passage in the nanosecond timescale. To simplify the setup, we...

We analyze spin dynamics in the tunneling decay of a metastable localized state in the presence of spin-orbit coupling. We find that the spin polarization at short time scales is affected by the initial state while at long time scales both the probability- and the spin density exhibit diffraction-in-time phenomenon. We find that in addition to the...

DOI:https://doi.org/10.1103/PhysRevLett.109.249901

We apply an invariant-based inverse engineering method to control, by time-dependent electric fields, the spin dynamics in a quantum dot with spin-orbit coupling in a weak magnetic field. The designed electric fields provide a shortcut to adiabatic processes that flip the spin rapidly, thus avoiding decoherence effects. This approach, being robust...

The results for joint effects of tunneling and spin-orbit coupling on spin dynamics in nanostructures are presented for systems with discrete and continuous spectra. We demonstrate that tunneling plays the crucial role in the spin dynamics and the abilities of spin manipulation by external electric field. This result can be important for design of...

We apply an invariant-based inverse engineering method to control by time-dependent electric fields electron spin dynamics in a quantum dot with spin-orbit coupling in a weak magnetic field. The designed electric fields provide a shortcut to adiabatic processes that flips the spin rapidly, thus avoiding decoherence effects. This approach, being rob...

We study effects of the shape of a two-dimensional waveguide on the spin-dependent electron transport in the presence of spin-orbit coupling. The transition from classical motion to the tunneling regime can be controlled there by modulating the strength of spin-orbit coupling if the waveguide has a constriction. The spin precession strongly depends...

We study effects of the shape of a two-dimensional waveguide on the spin-dependent electron transport in the presence of spin-orbit coupling. The transition from classical motion to the tunneling regime can be controlled there by modulating the strength of spin-orbit coupling if the waveguide has a constriction. The spin precession strongly depends...

The quantum Goos-Hänchen effect in graphene is found to be the lateral shift of Dirac fermions on the total reflection at a single p-n interface. In this paper, we investigate the lateral shifts of Dirac fermions in transmission through a monolayer graphene barrier. Compared to the smallness of the lateral shifts in total reflection, the lateral sh...

Motivated by recent time-domain experiments on ultrafast atom ionization, we analyze the transients and time scales that characterize, aside from the relatively long lifetime, the decay of a localized state by tunneling. While the tunneling starts immediately, some time is required for the outgoing flux to develop. This short-term behavior depends...

Diffraction in time (DIT) is a fundamental phenomenon in quantum dynamics due to time-dependent obstacles and slits. It is formally analogous to diffraction of light, and is expected to play an increasing role to design coherent matter wave sources, as in the atom laser, to analyze time-of-flight information and emission from ultrafast pulsed excit...

It is investigated that the lateral shifts of the ballistic electrons transmitted through semiconductor quantum slabs can be negative as well as positive, which are analogous to the anomalous lateral shifts of the transmitted light beam through a dielectric slab. The necessary condition for the shift to be negative is advanced. It is shown that the...

It is investigated that the lateral shifts of the ballistic electrons transmitted through a semiconductor quantum slabs can be negative as well as positive, which are analogous to the anomalous lateral shifts of the transmitted light beam through a dielectric slab. The necessary condition for the shift to be negative is advanced. It is shown that t...

It is shown that the group delay of an electron wave packet through a potential well can be either negative or positive and is tunable by finite electric fields. The group delay depends not only on the width of potential well and the incident energy, but also on the electric field strength. It is also proved that the transmission time and reflectio...