arXiv subjects

Hsi-Sheng Goan

Publications and source records attributed to Hsi-Sheng Goan.

At least 37 records · Page 2Linked to original sources

arXiv2025

Signatures of broken symmetries in the excitations of a periodic 2DEG coupled to a cylindrical photon cavity

In a two-dimensional electron gas (2DEG) in a periodic lateral superlattice subjected to an external homogeneous magnetic field and in a cylindrical far-infrared photon cavity we search for effects of broken symmetries: Static ones, stemming from the unit cell of the system, and the external magnetic field together with the dynamic ones caused by the vector potential of the cavity promoting magnetic types of transitions, and the chirality of the excitation pulse. The Coulomb interaction of the electrons is described within density functional theory, but the electron-photon interactions are handled by a configuration interaction formalism within each step of the density functional approach, both for the static and the dynamic system. In the dynamical calculations we observe weak chiral effects that change character as the strength of the electron-photon interaction and the external magnetic field are increased. From the analysis of the chiral effects we identify an important connection of the para- and diamagnetic electron-photon interactions that promotes the diamagnetic interaction in the present system when the interaction strength is increased. Furthermore, the asymmetric potential in the unit cell of the square array activates collective oscillation modes that are not present in the system when the unit cell has a higher symmetry.

cond-mat.mes-hall↗

arXiv2025

Operational simultaneous correlations in complementary bases of bipartite states via one-sided semi-device-independent steering

Recently, a different form of quantum steering, i.e., certification of quantum steering in a one-sided semi-device-independent way, has been formulated [Jebarathinam \etal Phys. Rev. A 108, 042211 (2023)]. In this work, we use this phenomenon to provide operational simultaneous correlations in mutually unbiased bases as quantified by the measures in [Wu \etal Scientific Reports 4, 4036 (2014)]. First, we show that for any bipartite state, such measure of simultaneous correlations in two mutually unbiased bases can be operationally identified as exhibiting one-sided semi-device-independent steering in a two-setting scenario. Next, we demonstrate that for two-qubit Bell-diagonal states, quantifying one-sided semi-device-independent steerability provides an operational quantification of information-theoretic quantification of simultaneous correlations in mutually unbiased bases. Then, we provide a different classification of two-qubit separable states with the above-mentioned information-theoretic quantification of simultaneous correlations in mutually unbiased bases and the quantification of one-sided semi-device-independent steerability. Finally, we invoke quantum steering ellipsoid formalism to shed intuitions on the operational characterization of simultaneous correlations in complementary bases of two-qubit states via one-sided semi-device-independent steerability. This provides us with a geometric visualization of the results.

quant-ph↗

arXiv2025

A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning

Quantum-centric supercomputing presents a compelling framework for large-scale hybrid quantum-classical tasks. Although quantum machine learning (QML) offers theoretical benefits in various applications, challenges such as large-size data encoding in the input stage and the reliance on quantum resources in the inference stage limit its practicality for tasks like fine-tuning large language models (LLMs). Quantum parameter generation, a novel approach of QML, addresses these limitations by using quantum neural networks (QNNs) to generate classical model weights (parameters) exclusively during training, thereby decoupling inference from quantum hardware. In this work, we introduce Quantum Parameter Adaptation (QPA) in the framework of quantum parameter generation, which integrates QNNs with a classical multi-layer perceptron mapping model to generate parameters for fine-tuning methods. Using Gemma-2 and GPT-2 as case studies, QPA demonstrates significant parameter reduction for parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), while maintaining comparable or improved performance in text generation tasks. Specifically, QPA reduces the number of parameters to $52.06\%$ of the original LoRA for GPT-2 with a slight performance gain of $0.75\%$, and to $16.84\%$ for Gemma-2, with a marginal performance improvement of $0.07\%$. These results highlight QPA's ability to achieve efficient parameter reduction without sacrificing performance in the quantum parameter generation framework. This work showcases the potential of quantum-enhanced parameter reduction, offering a scalable quantum-classical solution for fine-tuning LLMs while preserving the feasibility of inference on classical hardware.

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arXiv2024

Spin-phase transition in an array of quantum rings controlled by cavity photons

We model a spin-phase transition in a two-dimensional square array, or a lateral superlattice, of quantum rings in an external perpendicular homogeneous magnetic field. The electron system is placed in a circular cylindrical far-infrared photon cavity with a single circularly symmetric photon mode. Our numerical results reveal that the spin ordering of the two-dimensional electron gas in each quantum ring can be influenced or controlled by the electron-photon coupling strength and the energy of the photons. The Coulomb interaction between the electrons is described by a spin-density functional approach, but the para- and the diamagnetic electron-photon interactions are modeled via a configuration interaction formalism in a truncated many-body Fock-space, which is updated in each iteration step of the density functional approach. In the absence of external electromagnetic pulses this spin-phase transition is replicated in the orbital magnetization of the rings. The spin-phase transition can be suppressed by a strong electron-photon interaction. In addition, fluctuations in the spin configuration are found in dynamical calculations, where the system is excited by a time-dependent scheme specially fit for emphasizing the diamagnetic electron-photon interaction.

cond-mat.mes-hall↗

arXiv2024

Test-Time Training with Quantum Auto-Encoder: From Distribution Shift to Noisy Quantum Circuits

In this paper, we propose test-time training with the quantum auto-encoder (QTTT). QTTT adapts to (1) data distribution shifts between training and testing data and (2) quantum circuit error by minimizing the self-supervised loss of the quantum auto-encoder. Empirically, we show that QTTT is robust against data distribution shifts and effective in mitigating random unitary noise in the quantum circuits during the inference. Additionally, we establish the theoretical performance guarantee of the QTTT architecture. Our novel framework presents a significant advancement in developing quantum neural networks for future real-world applications and functions as a plug-and-play extension for quantum machine learning models.

quant-ph↗

arXiv2024

The tuning of para- and diamagnetic cavity photon excitations in a square array of quantum dots in a magnetic field

We employ a ``real-time'' excitation scheme to calculate the excitation spectra of a two-dimensional electron system in a square array of quantum dots placed in a circular cylindrical far-infrared photon cavity subjected to a perpendicular homogeneous external magnetic field. The Coulomb interaction of the electrons is handled via spin density functional theory and the para- and the diamagnetic parts of the electron-photon coupling are updated according to a configuration interaction method in each iteration of the density functional calculation. The results show that an excitation scheme built on using the symmetry of the lateral square superlattice of the dots and the cylindrical cavity produces both para- and diamagnetic resonance peaks with oscillator strengths that can be steered by the excitation pulse parameters. The excitation method breaks the conditions for the generalized Kohn theorem and allows for insight into the subband structure of the electron system and can be used both in and outside the linear response regime.

cond-mat.mes-hall↗

arXiv2024

L2O-$g^{\dagger}$: Learning to Optimize Parameterized Quantum Circuits with Fubini-Study Metric Tensor

Before the advent of fault-tolerant quantum computers, variational quantum algorithms (VQAs) play a crucial role in noisy intermediate-scale quantum (NISQ) machines. Conventionally, the optimization of VQAs predominantly relies on manually designed optimizers. However, learning to optimize (L2O) demonstrates impressive performance by training small neural networks to replace handcrafted optimizers. In our work, we propose L2O-$g^{\dagger}$, a $\textit{quantum-aware}$ learned optimizer that leverages the Fubini-Study metric tensor ($g^{\dagger}$) and long short-term memory networks. We theoretically derive the update equation inspired by the lookahead optimizer and incorporate the quantum geometry of the optimization landscape in the learned optimizer to balance fast convergence and generalization. Empirically, we conduct comprehensive experiments across a range of VQA problems. Our results demonstrate that L2O-$g^{\dagger}$ not only outperforms the current SOTA hand-designed optimizer without any hyperparameter tuning but also shows strong out-of-distribution generalization compared to previous L2O optimizers. We achieve this by training L2O-$g^{\dagger}$ on just a single generic PQC instance. Our novel $\textit{quantum-aware}$ learned optimizer, L2O-$g^{\dagger}$, presents an advancement in addressing the challenges of VQAs, making it a valuable tool in the NISQ era.

quant-ph↗

arXiv2024

BiSHop: Bi-Directional Cellular Learning for Tabular Data with Generalized Sparse Modern Hopfield Model

We introduce the \textbf{B}i-Directional \textbf{S}parse \textbf{Hop}field Network (\textbf{BiSHop}), a novel end-to-end framework for deep tabular learning. BiSHop handles the two major challenges of deep tabular learning: non-rotationally invariant data structure and feature sparsity in tabular data. Our key motivation comes from the recent established connection between associative memory and attention mechanisms. Consequently, BiSHop uses a dual-component approach, sequentially processing data both column-wise and row-wise through two interconnected directional learning modules. Computationally, these modules house layers of generalized sparse modern Hopfield layers, a sparse extension of the modern Hopfield model with adaptable sparsity. Methodologically, BiSHop facilitates multi-scale representation learning, capturing both intra-feature and inter-feature interactions, with adaptive sparsity at each scale. Empirically, through experiments on diverse real-world datasets, we demonstrate that BiSHop surpasses current SOTA methods with significantly less HPO runs, marking it a robust solution for deep tabular learning.

cs.LG↗

arXiv2024

Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective

We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications.

quant-ph↗

arXiv2024

Magneto-optical properties of a quantum dot array interacting with a far-infrared photon mode of a cylindrical cavity

We model the equilibrium properties of a two-dimensional electron gas in a square lateral superlattice of quantum dots in a GaAs heterostructure subject to an external homogeneous perpendicular magnetic field and a far-infrared circular cylindrical photon cavity with one quantized mode, the TE011 mode. In a truncated linear basis constructed by a tensor product of the single-electron states of the noninteracting system and the eigenstates of the photon number operator, a local spin density approximation of density functional theory is used to compute the electron-photon states of the two-dimensional electron gas in the cavity. The common spatial symmetry of the vector fields for the external magnetic field and the cavity photon field in the long wavelength approximation enhances higher order magnetic single- and multi-photon processes for both the para- and the diamagnetic electron-photon interactions. The electron-photon coupling introduces explicit photon replicas into the bandstructure and all subbands gain a photon content, constant for each subband, that can deviate from an integer value as the coupling is increased or the photon energy is varied. The subbands show a complex Rabi anticrossing behavior when the photon energy and the coupling bring subbands into resonances. The complicated energy subband structure leads to photon density variations in reciprocal space when resonances occur in the spectrum. The electron-photon coupling polarizes the charge density and tends to reduce the Coulomb exchange effects as the coupling strength increases.

cond-mat.mes-hall↗

arXiv2023

Efficient Postprocessing Procedure for Evaluating Hamiltonian Expectation Values in Variational Quantum Eigensolver

We proposed a simple strategy to improve the postprocessing overhead of evaluating Hamiltonian expectation values in Variational quantum eigensolvers (VQEs). Observing the fact that for a mutually commuting observable group G in a given Hamiltonian, is fixed for a measurement outcome bit string $b$ in the corresponding basis, we create a measurement memory (MM) dictionary for every commuting operator group G in a Hamiltonian. Once a measurement outcome bit string $b$ appears, we store $b$ and as key and value, and the next time the same bit string appears, we can find from the memory, rather than evaluate it once again. We further analyze the complexity of MM and compare it with commonly employed post-processing procedure, finding that MM is always more efficient in terms of time complexity. We implement this procedure on the task of minimizing a fully connected Ising Hamiltonians up to 20 qubits, and $H_2$, $H_4$, $LiH$, and $H_2O$ molecular Hamiltonians with different grouping methods. For Ising Hamiltonian, where all $O(N^2)$ terms commute, our method offers an $O(N^2)$ speedup in terms of the percentage of time saved. In the case of molecular Hamiltonians, we achieved over $O(N)$ percentage time saved, depending on the grouping method.

quant-ph↗

arXiv2023

Accurate harmonic vibrational frequencies for diatomic molecules via quantum computing

During the noisy intermediate-scale quantum (NISQ) era, quantum computational approaches refined to overcome the challenge of limited quantum resources are highly valuable. However, the accuracy of the molecular properties predicted by most of the quantum computations nowadays is still far off (not within chemical accuracy) compared to their corresponding experimental data. Here, we propose a promising qubit-efficient quantum computational approach to calculate the harmonic vibrational frequencies of a large set of neutral closed-shell diatomic molecules with results in great agreement with their experimental data. To this end, we construct the accurate Hamiltonian using molecular orbitals, derived from density functional theory to account for the electron correlation and expanded in the Daubechies wavelet basis set to allow an accurate representation in real space grid points, where an optimized compact active space is further selected so that only a reduced small number of qubits is sufficient to yield an accurate result. To justify the approach, we benchmark the performance of the Hamiltonians spanned by the selected molecular orbitals by first transforming the molecular Hamiltonians into qubit Hamiltonians and then using the exact diagonalization method to calculate the results, regarded as the best results achievable by quantum computation. Furthermore, we show that the variational quantum circuit with the chemistry-inspired UCCSD ansatz can achieve the same accuracy as the exact diagonalization method except for systems whose Mayer bond order indices are larger than 2. For those systems, we demonstrate that the heuristic hardware-efficient RealAmplitudes ansatz, even with a shorter circuit depth, can provide a significant improvement over the UCCSD ansatz, verifying that the harmonic vibrational frequencies could be calculated accurately by quantum computation in the NISQ era.

quant-ph↗

arXiv2023

Black-Litterman Portfolio Optimization with Noisy Intermediate-Scale Quantum Computers

In this work, we demonstrate a practical application of noisy intermediate-scale quantum (NISQ) algorithms to enhance subroutines in the Black-Litterman (BL) portfolio optimization model. As a proof of concept, we implement a 12-qubit example for selecting 6 assets out of a 12-asset pool. Our approach involves predicting investor views with quantum machine learning (QML) and addressing the subsequent optimization problem using the variational quantum eigensolver (VQE). The solutions obtained from VQE exhibit a high approximation ratio behavior, and consistently outperform several common portfolio models in backtesting over a long period of time. A unique aspect of our VQE scheme is that after the quantum circuit is optimized, only a minimal number of samplings is required to give a high approximation ratio result since the probability distribution should be concentrated on high-quality solutions. We further emphasize the importance of employing only a small number of final samplings in our scheme by comparing the cost with those obtained from an exhaustive search and random sampling. The power of quantum computing can be anticipated when dealing with a larger-size problem due to the linear growth of the required qubit resources with the problem size. This is in contrast to classical computing where the search space grows exponentially with the problem size and would quickly reach the limit of classical computers.

quant-ph↗

arXiv2023

Existence of Pauli-like stabilizers for every quantum error-correcting code

The Pauli stabilizer formalism is perhaps the most thoroughly studied means of procuring quantum error-correcting codes, whereby the code is obtained through commutative Pauli operators and ``stabilized'' by them. In this work we will show that every quantum error-correcting code, including Pauli stabilizer codes and subsystem codes, has a similar structure, in that the code can be stabilized by commutative ``Paulian'' operators which share many features with Pauli operators and which form a \textbf{Paulian stabilizer group}. By facilitating a controlled gate we can measure these Paulian operators to acquire the error syndrome. Examples concerning codeword stabilized codes and bosonic codes will be presented; specifically, one of the examples has been demonstrated experimentally and the observable for detecting the error turns out to be Paulian, thereby showing the potential utility of this approach. This work provides a possible approach to implement error-correcting codes and to find new codes.

quant-ph↗

arXiv2023

Hamiltonian Phase Error in Resonantly Driven CNOT Gate Above the Fault-Tolerant Threshold

Because of their long coherence time and compatibility with industrial foundry processes, electron spin qubits are a promising platform for scalable quantum processors. A full-fledged quantum computer will need quantum error correction, which requires high-fidelity quantum gates. Analyzing and mitigating the gate errors are useful to improve the gate fidelity. Here, we demonstrate a simple yet reliable calibration procedure for a high-fidelity controlled-rotation gate in an exchange-always-on Silicon quantum processor allowing operation above the fault-tolerance threshold of quantum error correction. We find that the fidelity of our uncalibrated controlled-rotation gate is limited by coherent errors in the form of controlled-phases and present a method to measure and correct these phase errors. We then verify the improvement in our gate fidelities by randomized benchmark and gate-set tomography protocols. Finally, we use our phase correction protocol to implement a virtual, high-fidelity controlled-phase gate.

cond-mat.mes-hall↗

arXiv2023

Counting stabiliser codes for arbitrary dimension

In this work, we compute the number of $[[n,k]]_d$ stabilizer codes made up of $d$-dimensional qudits, for arbitrary positive integers $d$. In a seminal work by Gross (Ref. [23]) the number of $[[n,k]]_d$ stabilizer codes was computed for the case when $d$ is a prime (or the power of a prime, i.e., $d=p^m$, but when the qudits are Galois-qudits). The proof in Ref. Ref. [23] is inapplicable to the non-prime case. For our proof, we introduce a group structure to $[[n,k]]_d$ codes, and use this in conjunction with the Chinese remainder theorem to count the number of $[[n,k]]_d$ codes. Our work overlaps with Ref. Ref. [23] when $d$ is a prime and in this case our results match exactly, but the results differ for the more generic case. Despite that, the overall order of magnitude of the number of stabilizer codes scales agnostic of whether the dimension is prime or non-prime. This is surprising since the method employed to count the number of stabilizer states (or more generally stabilizer codes) depends on whether $d$ is prime or not. The cardinality of stabilizer states, which was so far known only for the prime-dimensional case (and the Galois qudit prime-power dimensional case) plays an important role as a quantifier in many topics in quantum computing. Salient among these are the resource theory of magic, design theory, de Finetti theorem for stabilizer states, the study and optimisation of the classical simulability of Clifford circuits, the study of quantum contextuality of small-dimensional systems and the study of Wigner-functions. Our work makes available this quantifier for the generic case, and thus is an important step needed to place results for quantum computing with non-prime dimensional quantum systems on the same pedestal as prime-dimensional systems.

quant-ph↗

arXiv2023

Reinforcement Learning Quantum Local Search

Quantum Local Search (QLS) is a promising approach that employs small-scale quantum computers to tackle large combinatorial optimization problems through local search on quantum hardware, starting from an initial point. However, the random selection of the sub-problem to solve in QLS may not be efficient. In this study, we propose a reinforcement learning (RL) based approach to train an agent for improved subproblem selection in QLS, beyond random selection. Our results demonstrate that the RL agent effectively enhances the average approximation ratio of QLS on fully-connected random Ising problems, indicating the potential of combining RL techniques with Noisy Intermediate-scale Quantum (NISQ) algorithms. This research opens a promising direction for integrating RL into quantum computing to enhance the performance of optimization tasks.

quant-ph↗

arXiv2022

Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems

One of the major problems of most quantum computing applications is that the required number of qubits to solve a practical problem is much larger than that of today's quantum hardware. We propose an algorithm, called large-system sampling approximation (LSSA), to solve Ising problems with sizes up to $N_{\rm{gb}}2^{N_{\rm{gb}}}$ by an $N_{\rm{gb}}$-qubit gate-based quantum computer, and with sizes up to $N_{\rm{an}}2^{N_{\rm{gb}}}$ by a hybrid computational architecture of an $N_{\rm{an}}$-qubit quantum annealer and an $N_{\rm{gb}}$-qubit gate-based quantum computer. By dividing the full-system problem into smaller subsystem problems, the LSSA algorithm then solves the subsystem problems by either gate-based quantum computers or quantum annealers, optimizes the amplitude contributions of the solutions of the different subsystems with the full-problem Hamiltonian by the variational quantum eigensolver (VQE) on a gate-based quantum computer, and determines the approximated ground-state configuration. We apply the level-1 approximation of LSSA to solving fully-connected random Ising problems up to 160 variables using a 5-qubit gate-based quantum computer, and solving portfolio optimization problems up to 4096 variables using a 100-qubit quantum annealer and a 7-qubit gate-based quantum computer. We demonstrate the use of the level-2 approximation of LSSA to solve the portfolio optimization problems up to 5120 ($N_{\rm{gb}}2^{2N_{\rm{gb}}}$) variables with pretty good performance by using just a 5-qubit ($N_{\rm{gb}}$-qubit) gate-based quantum computer. The completely new computational concept of the hybrid gate-based and annealing quantum computing architecture opens a promising possibility to investigate large-size Ising problems and combinatorial optimization problems, making practical applications by quantum computing possible in the near future.

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