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Younghun Kwon

Publications and source records attributed to Younghun Kwon.

At least 19 recordsLinked to original sources

Accelerated Quantum-Assisted Selected Configuration Interaction via Fast-Annealing-Based Determinant Selection

Full configuration interaction (FCI) provides exact electronic structure within a given atomic basis, but its computational cost grows exponentially with the number of spin orbitals. Selected configuration interaction (SCI) methods alleviate this limitation by retaining only the most important Slater determinants. However, the repeated identification of important determinants remains a major computational bottleneck. We present a quantum assisted selected configuration interaction (QASCI) method that combines SCI with graph based block diagonalization (GBBD) of FCI Hamiltonian. The GBBD method partitions FCI Hamiltonian into independent blocks, within which determinant selection problem is formulated as a quadratic unconstrained binary optimization (QUBO) problem. The QUBO problems for selecting determinants to construct SCI space are iteratively solved using a fast annealing approach. We benchmark method on H8-H18 hydrogen chains and Li2S in STO3G basis. For Hn chains, chemical accuracy is achieved while retaining only a small fraction of Slater determinants, and this fraction decreases with increasing n, despite the exponential growth of the FCI Hilbert space. For Li2S, QASCI results remain within chemical accuracy while retaining substantially fewer determinants than the full FCI space. We apply QASCI to N2 using the 631G basis, considering both active orbital and full orbital treatments. The full orbital QASCI calculation, using 50000 determinants, yields a lower ground state energy than an FCI calculation within an active space comprising 12 spin orbitals and 12 electrons. These results demonstrate that the combination of QASCI and the GBBD approach can substantially reduce computational cost of determinant selection while maintaining the accuracy of FCI based electronic structure calculations, thereby enabling accurate calculations in larger orbital spaces.

physics.chem-ph

Lattice patch structure for fixed-frequency transmon quantum computer with high-fidelity CNOT gates

Superconducting transmon processors represent a leading platform for large-scale quantum computing due to their high gate fidelities and scalability. However, conventional qubit-coupler-qubit (QCQ) architectures face critical physical and structural bottlenecks, notably frequency crowding [spectator qubit collisions] during system scaling and inefficient mapping onto the standard surface code.To overcome these limitations, we propose a novel lattice-patch architecture that couples four fixed-frequency transmons to a single fixed-frequency coupler.This design enhances qubit connectivity and maps directly onto the surface-code lattice unit [plaquette], thereby minimizing the compilation overhead associated with logical qubit implementation. Furthermore, utilizing an entirely fixed-frequency design intrinsically eliminates susceptibility to external flux noise, ensuring robust operational stability.Multi-level numerical simulations demonstrate CNOT gate fidelities exceeding 0.98 across all six connectivity directions within the patch. Nevertheless, the complex interaction network of the four-qubit architecture induces unintended residual phase accumulation during cross-resonance driving. This parasitic effect necessitates precise calibration, achievable via virtual $R_z$ gates [software phase updates]. Ultimately, our results establish the lattice-patch architecture as an efficient, robust building block for future fault-tolerant quantum computers.

quant-ph

Effectiveness of the syndrome extraction circuit with flag qubits on IBM quantum hardware

Large scale quantum circuits are required to exploit the advantages of quantum computers. Despite significant advancements in quantum hardware, scalability remains a challenge, with errors accumulating as more qubits and gates are added. To overcome this limitation, quantum error-correction codes have been introduced. Although the success of quantum error correction codes has been demonstrated on superconducting quantum processors and neutral atom-based systems, there have been no experimental reports of error suppression using flag qubits on a quantum processor. IBM's quantum hardware features a non-topological coupling map, and past developments of quantum error correction codes on this platform have primarily explored the use of flag qubits. Here, we report the successful implementation of a syndrome extraction circuit with flag qubits on IBM quantum computers. Moreover, we demonstrate its effectiveness by considering the repetition code as a test code among the quantum error-correcting codes. Even though the data qubit is not adjacent to the syndrome qubit, logical error rates diminish as the distance of the repetition code increases from three to nine. Even when two flag qubits exist between the data and syndrome qubits, the logical error rates decrease as the distance increases similarly. This confirms the successful implementation of the syndrome extraction circuit with flag qubits on the IBM quantum computer.

quant-ph

Local Temporal Feature Enhanced Transformer with ROI-rank Based Masking for Diagnosis of ADHD

In modern society, Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the common mental diseases discovered not only in children but also in adults. In this context, we propose a ADHD diagnosis transformer model that can effectively simultaneously find important brain spatiotemporal biomarkers from resting-state functional magnetic resonance (rs-fMRI). This model not only learns spatiotemporal individual features but also learns the correlation with full attention structures specialized in ADHD diagnosis. In particular, it focuses on learning local blood oxygenation level dependent (BOLD) signals and distinguishing important regions of interest (ROI) in the brain. Specifically, the three proposed methods for ADHD diagnosis transformer are as follows. First, we design a CNN-based embedding block to obtain more expressive embedding features in brain region attention. It is reconstructed based on the previously CNN-based ADHD diagnosis models for the transformer. Next, for individual spatiotemporal feature attention, we change the attention method to local temporal attention and ROI-rank based masking. For the temporal features of fMRI, the local temporal attention enables to learn local BOLD signal features with only simple window masking. For the spatial feature of fMRI, ROI-rank based masking can distinguish ROIs with high correlation in ROI relationships based on attention scores, thereby providing a more specific biomarker for ADHD diagnosis. The experiment was conducted with various types of transformer models. To evaluate these models, we collected the data from 939 individuals from all sites provided by the ADHD-200 competition. Through this, the spatiotemporal enhanced transformer for ADHD diagnosis outperforms the performance of other different types of transformer variants. (77.78ACC 76.60SPE 79.22SEN 79.30AUC)

eess.IV

Construction of new type of CNOT gate using cross-resonance pulse in the transmon-PPQ system

The transmon, known for its fast operation time and the coherence time of tens of microseconds, is the most commonly used qubit for superconducting quantum processors. However, it is still necessary to enhance the coherence time and the gate fidelity of superconducting quantum processors for the practical implementation of fault-tolerant quantum computing. Meanwhile, a novel superconducting qubit, which has the ability to protect the Cooper-pair parity on the superconducting island, has been proposed. This new qubit shows better coherence performance than the transmon, but it does not yet have an efficient method for realizing a superconducting hybrid system that harnesses it. In this work, we show how to implement a new type of CNOT gate in a superconducting hybrid system composed of tunable transmon and parity-protected qubit by applying a cross-resonance pulse. First, we provide hardware specifications and pulse parameters to construct a successful two-qubit gate in the hybrid system. Second, we show that our method can supply a CNOT gate of average fidelity with more than 0.998. Therefore, our work implies that the hybrid system may provide a new platform for quantum computers.

quant-ph

Implementation of Magic State Injection within Heavy-Hexagon Architecture

The magic state injection process is a critical component of fault-tolerant quantum computing, and numerous studies have been conducted on this topic. Many existing studies have focused on square-lattice structures, where each qubit connects directly to four other qubits via two-qubit gates. However, hardware that does not follow a lattice structure, such as IBM's heavy-hexagon structure, is also under development. In these non-lattice structures, many quantum error correction (QEC) codes designed for lattice-based system cannot be directly applied. Adapting these codes often requires incorporating additional qubits, such as flag qubits. This alters the properties of the QEC code and introduces new variables into the magic state injection process. In this study, we implemented and compared the magic state injection process on a heavy-hexagon structure with flag qubits and a lattice structure without flag qubits. Additionally, we considered biased errors in superconducting hardware and investigated the impact of flag qubits under these conditions. Our analysis reveals that the inclusion of flag qubits introduces distinct characteristics into the magic state injection process, which are absent in systems without flag qubits. Based on these findings, we identify several critical considerations for performing magic state injection on heavy-hexagon systems incorporating flag qubits. Furthermore, we propose an optimized approach to maximize the efficacy of this process in such systems.

quant-ph

New Design of three-qubit system with three transmons and a single fixed-frequency resonator coupler

The transmon, which has a short gate time and remarkable scalability, is the most commonly utilized superconducting qubit, based on the Cooper pair box as a qubit or coupler in superconducting quantum computers. Lattice and heavy-hexagon structures are well-known large-scale configurations for transmon-based quantum computers that classical computers cannot simulate. These structures share a common feature: a resonator coupler that connects two transmon qubits. Although significant progress has been made in implementing quantum error correction and quantum computing using quantum error mitigation, fault-tolerant quantum computing remains unachieved due to the inherent vulnerability of these structures. This raises the question of whether the transmon-resonator-transmon structure is the best option for constructing a transmon-based quantum computer. To address this, we demonstrate that the average fidelity of CNOT gates can exceed 0.98 in a structure where a resonator coupler mediates the coupling of three transmon qubits. This result suggests that our novel structure could be a key method for increasing the number of connections among qubits while preserving gate performance in a transmon-based quantum computer.

quant-ph

Searching for Effective Preprocessing Method and CNN-based Architecture with Efficient Channel Attention on Speech Emotion Recognition

Speech emotion recognition (SER) classifies human emotions in speech with a computer model. Recently, performance in SER has steadily increased as deep learning techniques have adapted. However, unlike many domains that use speech data, data for training in the SER model is insufficient. This causes overfitting of training of the neural network, resulting in performance degradation. In fact, successful emotion recognition requires an effective preprocessing method and a model structure that efficiently uses the number of weight parameters. In this study, we propose using eight dataset versions with different frequency-time resolutions to search for an effective emotional speech preprocessing method. We propose a 6-layer convolutional neural network (CNN) model with efficient channel attention (ECA) to pursue an efficient model structure. In particular, the well-positioned ECA blocks can improve channel feature representation with only a few parameters. With the interactive emotional dyadic motion capture (IEMOCAP) dataset, increasing the frequency resolution in preprocessing emotional speech can improve emotion recognition performance. Also, ECA after the deep convolution layer can effectively increase channel feature representation. Consequently, the best result (79.37UA 79.68WA) can be obtained, exceeding the performance of previous SER models. Furthermore, to compensate for the lack of emotional speech data, we experiment with multiple preprocessing data methods that augment trainable data preprocessed with all different settings from one sample. In the experiment, we can achieve the highest result (80.28UA 80.46WA).

cs.SD

Complete security analysis of {quantum key distribution} based on unified model of sequential discrimination strategy

The quantum key distribution for multiparty is one of the essential subjects of study. Especially, without using entangled states, performing the quantum key distribution for multiparty is a critical area of research. For this purpose, sequential discrimination, which provides multiparty quantum communication and quantum key distribution for {multiple receivers}, has recently been introduced. However, since there is a possibility of eavesdropping on the measurement result of a receiver by an intruder using quantum entanglement, a security analysis for {quantum key distribution} should be performed. {However,} no one has provided the security analysis for {quantum key distribution in view of the sequential scheme} yet. In this work, by proposing a unified model of sequential discrimination including an eavesdropper, we provide the security analysis of {quantum key distribution based on the unified model of sequential discrimination strategy.} In this model, the success probability of eavesdropping and the secret key rate can be used as a figure of merit. Then, we obtain a non-zero secret key rate between the sender and receiver, which implies that the sender and receiver can share a secret key despite eavesdropping. Further, we propose a realistic quantum optical experiment for the proposed model. We observe that the secret key between the sender and receiver can be non-zero, even with imperfections. As opposed to common belief, we further observe that the success probability of eavesdropping is smaller in the case of colored noise than in the case of white noise.

quant-ph

Design of Quantum error correcting code for biased error on heavy-hexagon structure

Surface code is an error-correcting method that can be applied to the implementation of a usable quantum computer. At present, a promising candidate for a usable quantum computer is based on superconductor-specifically transmon. Because errors in transmon-based quantum computers appear biasedly as Z type errors, tailored surface and XZZX codes have been developed to deal with the type errors. Even though these surface codes have been suggested for lattice structures, since transmons-based quantum computers, developed by IBM, have a heavy-hexagon structure, it is natural to ask how tailored surface code and XZZX code can be implemented on the heavy-hexagon structure. In this study, we provide a method for implementing tailored surface code and XZZX code on a heavy-hexagon structure. Even when there is no bias, we obtain $ 0.231779 \%$ as the threshold of the tailored surface code, which is much better than $ 0.210064 \%$ and $ 0.209214 \%$ as the thresholds of the surface code and XZZX code, respectively. Furthermore, we can see that even though a decoder, which is not the best of the syndromes, is used, the thresholds of the tailored surface code and XZZX code increase as the bias of the Z error increases. Finally, we show that in the case of infinite bias, the threshold of the surface code is $ 0.264852\%$, but the thresholds of the tailored surface code and XZZX code are $ 0.296157 \% $ and $ 0.328127 \%$ respectively.

quant-ph

Qubit State Discrimination using Post-measurement Information

We consider the optimal discrimination of nonorthogonal qubit states with post-measurement information and provide an analytic structure of the optimal measurements. We also show that there is always a null optimal measurement when post-measurement information is given. Further, in discriminating four states using post-measurement information, we analytically provide the optimal probability of correct guessing and show that the uniqueness of optimal measurement is equivalent to the non-existence of non-null optimal measurement with post-measurement information.

quant-ph

Complete analysis to minimum-error discrimination of mixed four qubit states with arbitrary prior probabilities

In this work, we provide a complete analysis to minimum-error discrimination of mixed four qubit states with arbitrary prior probabilities. For the complete anaysis, the most important work to do is to find the necessary and sufficient conditions for the existence of null measurement operator. From the geometric structure of qubit states, we obtain the analytic condition for deciding the existence of a null operator in minimum-error measurement for mixed four qubit states, which also gives the necessary and sufficient conditions for every optimal POVM to have non-zero elements. Using the condition, we completely analyze minimum-error discrimination of mixed four qubit states with arbitrary prior probabilities.

quant-ph

Finding essential parts of the brain in rs-fMRI can improve diagnosing ADHD by Deep Learning

Attention Deficit\Hyperactivity Disorder(ADHD) is considered a very common psychiatric disorder, but it is difficult to establish an accurate diagnostic method for ADHD. Recently, with the development of computing resources and machine learning methods, studies have been conducted to classify ADHD using resting-state functional magnetic resonance(rsfMRI) imaging data. However, most of them utilized all areas of the brain for training the models. In this study, as a different way from this approach, we conducted a study to classify ADHD by selecting areas that are essential for using a deep learning model. For the experiment, rsfMRI data provided by ADHD 200 global competition was used. To obtain an integrated result from the multiple sites, each region of the brain was evaluated with Leave one site out cross-validation. As a result, when we only used 15 important region of interest(ROIs) for training, an accuracy of 70.6% was obtained, significantly exceeding the existing results of 68.6% from all ROIs. In addition, to explore the new structure based on SCCNN-RNN, we performed the same experiment with three modified models: (1) Separate Channel CNN RNN with Attention (ASCRNN), (2) Separate Channel dilate CNN RNN with Attention (ASDRNN), (3) Separate Channel CNN slicing RNN with Attention (ASSRNN). As a result, the ASSRNN model provided a high accuracy of 70.46% when training with only 13 important region of interest (ROI). These results show that finding and using the crucial parts of the brain in diagnosing ADHD by Deep Learning can get better results than using all areas.

eess.IV

An optimal discrimination of two mixed qubit states with a fixed rate of inconclusive results

In this paper we consider the optimal discrimination of two mixed qubit states for a measurement that allows a fixed rate of inconclusive results(FRIR). Our strategy for the problem is to transform the FRIR of two qubit states into a minimum error discrimination for three qubit states by adding a specific quantum state $ρ_{0}$ and a prior probability $q_{0}$(which we will call an inconclusive degree), which we name the modified FRIR problem. First, we investigate special inconclusive degrees $q_{0}^{(0)}$ and $q_{0}^{(1)}$, which appear naturally in the modified FRIR problem and are the beginning and the end of practical interval of inconclusive degree, and find the analytic form of them. Next, we show that the modified FRIR problem can be classified into two cases $q_{0}=q_{0}^{(0)}$(or $q_{0}=q_{0}^{(1)}$) and $q_{0}^{(0)}\!<\!q_{0}\!<\!q_{0}^{(1)}$. In fact, by maximum confidences of two qubit states and non-diagonal element of $ρ_{0}$, the modified FRIR problem is completely understood. Then, we provide an analytic solution of the FRIR problem when $q_{0}=q_{0}^{(0)}$(or $q_{0}=q_{0}^{(1)}$). However, when $q_{0}^{(0)}\!<\!q_{0}\!<\!q_{0}^{(1)}$, we rather supply the numerical method to find the solution, because of the complex relation between inconclusive degree and corresponding failure probability. Finally we confirm our results using previously known examples.

quant-ph

Distilling Entanglement with Noisy Operations

Entanglement distillation is a fundamental task in quantum information processing. It not only extracts entanglement out of corrupted systems but also leads to protecting systems of interest against intervention with environment. In this work, we consider a realistic scenario of entanglement distillation where noisy quantum operations are applied. In particular, the two-way distillation protocol that tolerates the highest error rate is considered. We show that among all types of noise there are only four equivalence classes according to the distillability condition. Since the four classes are connected by local unitary transformations, our results can be used to improve entanglement distillability in practice when entanglement distillation is performed in a realistic setting.

quant-ph

Quantum Fisher information of fermionic cavity modes in an accelerated motion

We investigate the effect of the inertial and non-inertial segments of relativistic motion on the quantum Fisher information of (1+1) Dirac field modes confined to cavities. %For this purpose, a bipartite system comprising of Alice's and Rob's cavities with appropriate boundary conditions is prepared. For the purpose, we consider the situation that Rob's cavity, initially inertial, accelerates uniformly with respect to its proper time and then again becomes inertial while Alice's cavity remains inertial. The acceleration is assumed to be very small and its effects were analyzed in a perturbative regime. For analysis, we consider $θ$ parameterized two-qubit pure entangled state and a Werner state. In contrast to the degradation of entanglement due to the relativistic motion between the cavities, the quantum Fisher information of the pure composite system $\mathcal{F}_θ$ with respect to parameter $θ$ is found to be invariant under the same conditions. However, in the case of the Werner state, the quantum Fisher information displays periodic degradation, due to the inertial and non-inertial segments of motion. Further, we investigate how this evolution process affects the quantum Fisher information distribution over the subsystems of Alice's and Rob's cavities. We find that the quantum Fisher information over the Rob's cavity shows the periodic degradation behavior depending upon the parameter $θ$ as well as the uniform acceleration for both the two qubit pure state and Werner state. The quantum Fisher information over Alice's cavity remains invariant throughout the motion of Rob's cavity for the two qubit pure state whereas for Werner state it is affected by the mixing parameter of the Werner state.

quant-ph

Analytic Model of Variable Characteristic of Coefficient of Restitution and Its Application to Soccer Ball Trajectory Planning

In this article, we investigate the behavior of the coefficient of restitution (COR) which is an important parameter in many impact-related fields. In many cases, the COR is considered as a constant value, but it varies according to many variables. In this paper, we introduce an analytical variable COR model considering aero dynamics along with its verification through experiment. To introduce and analyze the variable characteristic of the COR model, the collision phenomenon between a pendulum and two kinds of ball is employed as an example and aerodynamics such as drag force is considered for analyzing the after-effect of the collision. Collision velocity of the pendulum, dynamic parameters of colliding bodies, contact time, drag coefficient, the air density, and the cross-sectional area of the ball are found as the typical variables of analytical COR model. This observation generalizes the result in previous researches. To verify new COR model, the travel distances for the curve-fitted constant COR model and the curve-fitted variable COR model are compared through simulation and experiment. Moreover, comparison between constant COR and variable COR is presented in several point of views. Finally, using the variable COR model, the travel distance of the ball for collision velocity, which is beyond the curve-fitted range, is estimated.

physics.class-ph

Revisiting Quantum discord for two-qubit X states: Error bound to Analytical formula

In this article, we investigate the error bound of quantum discord, obtained by the analytic formula of Ali et al.[Phys. Rev. A 81(2010), 042105] in case of general X states and by the analytic formula of Fanchini et al.[Phys. Rev. A 81(2010), 052107] in case of symmetric X states. We show that results of Ali et. al. to general X states and Fanchini et al. to symmetric X states may have worst-case error of 0.004565 and 0.0009 respectively.

quant-ph