arXiv subjects

Yu-Chun Wu

Publications and source records attributed to Yu-Chun Wu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Accelerating Fermionic System Simulation on Quantum Computers

A potential approach for demonstrating quantum advantage is using quantum computers to simulate fermionic systems. Quantum algorithms for fermionic system simulation usually involve the Hamiltonian evolution and measurements. However, in the second quantization representation, the number of terms in many fermion-system Hamiltonians, such as molecular Hamiltonians, is substantial, approximately $\mathcal{O}(N^4)$, where $N$ is the number of molecular orbitals. Due to this, the computational resources required for Hamiltonian evolution and expectation value measurements could be excessively large. To address this, we introduce a grouping strategy that partitions these $\mathcal{O}(N^4)$ Hamiltonian terms into $\mathcal{O}(N^2)$ groups, with the terms in each group mutually commuting. Based on this grouping method, we propose a parallel Hamiltonian evolution scheme that reduces the circuit depth of Hamiltonian evolution by a factor of $N$. Moreover, our grouping measurement strategy reduces the number of measurements needed to $\mathcal{O}(N^2)$, whereas the current best grouping measurement schemes require $\mathcal{O}(N^3)$ measurements. Additionally, we find that measuring the expectation value of a group of Hamiltonian terms requires fewer repetitions than measuring a single term individually, thereby reducing the number of quantum circuit executions. Our approach saves a factor of $N^3$ in the overall time for Hamiltonian evolution and measurements, significantly decreasing the time required for quantum computers to simulate fermionic systems.

quant-ph↗

arXiv2025

PolyQROM: Orthogonal-Polynomial-Based Quantum Reduced-Order Model for Flow Field Analysis

Quantum computing promises exponential acceleration for fluid flow simulations, yet the measurement overhead required to extract flow features from quantum-encoded flow field data fundamentally undermines this advantage--a critical challenge termed the ``output problem''. To address this, we propose an orthogonal-polynomial-based quantum reduced-order model (PolyQROM) that integrates orthogonal polynomial basis transformations with variational quantum circuits (VQCs). PolyQROM employs optimized polynomial-based quantum operations to compress flow field data into low-dimensional representations while preserving essential features, enabling efficient quantum or classical post-processing for tasks like reconstruction and classification. By leveraging the mathematical properties of orthogonal polynomials, the framework enhances circuit expressivity and stabilizes training compared to conventional hardware-efficient VQCs. Numerical experiments demonstrate PolyQROM's effectiveness in reconstructing flow fields with high fidelity and classifying flow patterns with accuracy surpassing classical methods and quantum benchmarks, all while reducing computational complexity and parameter counts. The work bridges quantum simulation outputs with practical fluid analysis, addressing the ``output problem'' through efficient reduced-order modeling tailored for quantum-encoded flow data, offering a scalable pathway to exploit quantum advantages in computational fluid dynamics.

quant-ph↗

arXiv2025

Realizing Scalable Conditional Operations through Auxiliary Energy Levels

In the noisy intermediate-scale quantum (NISQ) era, flexible quantum operations are essential for advancing large-scale quantum computing, as they enable shorter circuits that mitigate decoherence and reduce gate errors. However, the complex control of quantum interactions poses significant experimental challenges that limit scalability. Here, we propose a transition composite gate scheme based on transition pathway engineering, which digitally implements conditional operations with reduced complexity by leveraging auxiliary energy levels. Experimentally, we demonstrate the controlled-unitary (CU) family and its applications. In entangled state preparation, our CU gate reduces the circuit depth for three-qubit Greenberger-Horne-Zeilinger (GHZ) and W states by approximately 40-44% compared to circuits using only CZ gates, leading to fidelity improvements of 1.5% and 4.2%, respectively. Furthermore, with a 72% reduction in circuit depth, we successfully implement a quantum comparator-a fundamental building block for quantum algorithms requiring conditional logic, which has remained experimentally challenging due to its inherent circuit complexity. These results demonstrate the scalability and practicality of our scheme, laying a solid foundation for the implementation of large-scale quantum algorithms in future quantum processors.

quant-ph↗

arXiv2025

SparQSim: Simulating Scalable Quantum Algorithms via Sparse Quantum State Representations

Efficient simulation of large-scale quantum algorithms is pivotal yet challenging due to the exponential growth of the state space inherent in both Schödinger-based and Feynman-based methods. While Feynman-based simulators can be highly efficient when the quantum state is sparse, these simulators often do not fully support the simulation of large-scale, complex quantum algorithms which rely on QRAM and other oracle-based operations. In this work, we present SparQSim, a quantum simulator implemented in C++ and inspired by the Feynman-based method. SparQSim operates at the register level by storing only the nonzero components of the quantum state, enabling flexible and resource-efficient simulation of basic quantum operations and integrated QRAM for advanced applications such as quantum linear system solvers. In particular, numerical experiments on benchmarks from QASMBench and MQTBench demonstrate that SparQSim outperforms conventional Schrödinger-based simulators in both execution time and memory usage for circuits with high sparsity. Moreover, full-process simulations of quantum linear system solvers based on a discrete adiabatic method yield results that are consistent with theoretical predictions. This work establishes SparQSim as a promising platform for the efficient simulation of scalable quantum algorithms.

quant-ph↗

arXiv2025

Scalable Constant-Time Logical Gates for Large-Scale Quantum Computation Using Window-Based Correlated Decoding

Large-scale quantum computation requires to be performed in the fault-tolerant manner. One crucial challenge of fault-tolerant quantum computing (FTQC) is reducing the overhead of implementing logical gates. Recently work proposed correlated decoding and ``algorithmic fault tolerance" to achieve constant-time logical gates that enables universal quantum computation. However, for circuits involving mid-circuit measurements and feedback, the previous scheme for constant-time logical gates is incompatible with window-based decoding, which is a scalable approach for handling large-scale circuits. In this work, we propose an architecture that employs delayed fixup circuits and window-based correlated decoding, realizing scalable constant-time logical gates. This design significantly reduces both the frequency and duration of decoding, while maintaining support for constant-time and universal logical gates across a broad class of quantum codes. More importantly, by spatial parallelism of windows, this architecture well adapts to time-optimal FTQC, making it particularly useful for large-scale quantum computation. Using Shor's algorithm as an example, we explore the application of our architecture and reveals the promising potential of using constant-time logical gates to perform large-scale quantum computation with acceptable overhead on physical systems like ion traps.

quant-ph↗

arXiv2024

Neural Network-Based Frequency Optimization for Superconducting Quantum Chips

Optimizing the frequency configuration of qubits and quantum gates in superconducting quantum chips presents a complex NP-complete optimization challenge. This process is critical for enabling practical control while minimizing decoherence and suppressing significant crosstalk. In this paper, we propose a neural network-based frequency configuration approach. A trained neural network model estimates frequency configuration errors, and an intermediate optimization strategy identifies optimal configurations within localized regions of the chip. The effectiveness of our method is validated through randomized benchmarking and cross-entropy benchmarking. Furthermore, we design a crosstalk-aware hardware-efficient ansatz for variational quantum eigensolvers, achieving improved energy computations.

quant-ph↗

arXiv2024

Demonstrating a universal logical gate set in error-detecting surface codes on a superconducting quantum processor

Fault-tolerant quantum computing (FTQC) is essential for achieving large-scale practical quantum computation. Implementing arbitrary FTQC requires the execution of a universal gate set on logical qubits, which is highly challenging. Particularly, in the superconducting system, two-qubit gates on surface code logical qubits have not been realized. Here, we experimentally implement a logical CNOT gate along with arbitrary single-qubit rotation gates on distance-2 surface codes using the superconducting quantum processor \textit{Wukong}, thereby demonstrating a universal logical gate set. In the experiment, we demonstrate the transversal CNOT gate on a two-dimensional topological processor based on a tailored encoding circuit, at the cost of removing the ancilla qubits required for stabilizer measurements. Furthermore, we fault-tolerantly prepare logical Bell states and observe a violation of CHSH inequality, confirming the entanglement between logical qubits. Using the logical CNOT gate and an ancilla logical state, arbitrary single-qubit rotation gates are realized through gate teleportation. All logical gates are characterized on a complete state set and their fidelities are evaluated by logical Pauli transfer matrices. The demonstration of a universal logical gate set and the entangled logical states highlights significant aspects of FTQC on superconducting quantum processors.

quant-ph↗

arXiv2024

Hardware-Efficient Quantum Random Access Memory Design with a Native Gate Set on Superconducting Platforms

Quantum Random Access Memory (QRAM) is a critical component for enabling data queries in superposition, which is the cornerstone of quantum algorithms. Among various QRAM architectures, the bucket-brigade model stands out due to its noise resilience. This paper presents a hardware-efficient native gate set {iSCZ, C-iSCZ} for implementing bucket-brigade QRAM on superconducting platforms. The experimental feasibility of the proposed gate set is demonstrated, showing high fidelity and reduced complexity. By leveraging the complementary control property in QRAM, our approach directly substitutes the conventional {SWAP, CSWAP} gates with the new gate set, eliminating decomposition overhead and significantly reducing circuit depth and gate count.

quant-ph↗

arXiv2024

HiMA: Hierarchical Quantum Microarchitecture for Qubit-Scaling and Quantum Process-Level Parallelism

Quantum computing holds immense potential for addressing a myriad of intricate challenges, which is significantly amplified when scaled to thousands of qubits. However, a major challenge lies in developing an efficient and scalable quantum control system. To address this, we propose a novel Hierarchical MicroArchitecture (HiMA) designed to facilitate qubit scaling and exploit quantum process-level parallelism. This microarchitecture is based on three core elements: (i) discrete qubit-level drive and readout, (ii) a process-based hierarchical trigger mechanism, and (iii) multiprocessing with a staggered triggering technique to enable efficient quantum process-level parallelism. We implement HiMA as a control system for a 72-qubit tunable superconducting quantum processing unit, serving a public quantum cloud computing platform, which is capable of expanding to 6144 qubits through three-layer cascading. In our benchmarking tests, HiMA achieves up to a 4.89x speedup under a 5-process parallel configuration. Consequently, to the best of our knowledge, we have achieved the highest CLOPS (Circuit Layer Operations Per Second), reaching up to 43,680, across all publicly available platforms.

cs.AR↗

arXiv2024

Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers

Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science.

physics.comp-ph↗

arXiv2024

Statistics-Informed Parameterized Quantum Circuit via Maximum Entropy Principle for Data Science and Finance

Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge.

quant-ph↗

arXiv2024

Facilitating Practical Fault-tolerant Quantum Computing Based on Color Codes

Color code is a promising topological code for fault-tolerant quantum computing. Insufficient research on the color code has delayed its practical application. In this work, we address several key issues to facilitate practical fault-tolerant quantum computing based on color codes. First, by introducing decoding graphs with error-rate-related weights, we obtained the threshold of $0.47\%$ of the 6,6,6 triangular color code under the standard circuit-level noise model, narrowing the gap to that of the surface code. Second, our work firstly investigates the circuit-level decoding of color code lattice surgery, and gives an efficient decoding algorithm, which is crucial for performing logical operations in a quantum computer with two-dimensional architectures. Lastly, a new state injection protocol of the triangular color code is proposed, reducing the output magic state error rate in one round of 15 to 1 distillation by two orders of magnitude compared to a previous rough protocol. We have also proven that our protocol offers the lowest logical error rates for state injection among all possible CSS codes.

quant-ph↗

arXiv2024

Variational quantum eigensolver with linear depth problem-inspired ansatz for solving portfolio optimization in finance

Great efforts have been dedicated in recent years to explore practical applications for noisy intermediate-scale quantum (NISQ) computers, which is a fundamental and challenging problem in quantum computing. As one of the most promising methods, the variational quantum eigensolver (VQE) has been extensively studied. In this paper, VQE is applied to solve portfolio optimization problems in finance by designing two hardware-efficient Dicke state ansatze that reach a maximum of 2n two-qubit gate depth and n^2/4 parameters, with n being the number of qubits used. Both ansatze are partitioning-friendly, allowing for the proposal of a highly scalable quantum/classical hybrid distributed computing (HDC) scheme. Combining simultaneous sampling, problem-specific measurement error mitigation, and fragment reuse techniques, we successfully implement the HDC experiments on the superconducting quantum computer Wu Kong with up to 55 qubits. The simulation and experimental results illustrate that the restricted expressibility of the ansatze, induced by the small number of parameters and limited entanglement, is advantageous for solving classical optimization problems with the cost function of the conditional value-at-risk (CVaR) for the NISQ era and beyond. Furthermore, the HDC scheme shows great potential for achieving quantum advantage in the NISQ era. We hope that the heuristic idea presented in this paper can motivate fruitful investigations in current and future quantum computing paradigms.

quant-ph↗

arXiv2024

Unveiling NPT bound problem: From Distillability Sets to Inequalities and Multivariable Insights

Equivalence between Positive Partial Transpose (PPT) entanglement and bound entanglement is a long-standing open problem in quantum information theory. So far limited progress has been made, even on the seemingly simple case of Werner states bound entanglement. The primary challenge is to give a concise mathematical representation of undistillability. To this end, we propose a decomposition of the N-undistillability verification into $log(N)$ repeated steps of 1-undistillability verification. For Werner state N-undistillability verification, a bound for N-undistillability is given, which is independent of the dimensionality of Werner states. Equivalent forms of inequalities for both rank one and two matrices are presented, before transforming the two-undistillability case into a matrix analysis problem. A new perspective is also attempted by seeing it as a non-convex multi-variable function, proving its critical points and conjecturing Hessian positivity, which would make them local minimums.

quant-ph↗

arXiv2023

An efficient quantum algorithm for independent component analysis

Independent component analysis (ICA) is a fundamental data processing technique to decompose the captured signals into as independent as possible components. Computing the contrast function, which serves as a measure of independence of signals, is vital in the separation process using ICA. This paper presents a quantum ICA algorithm which focuses on computing a specified contrast function on a quantum computer. Using the quantum acceleration in matrix operations, we efficiently deal with Gram matrices and estimate the contrast function with the complexity of $O(ε_1^{-2}\mbox{poly}\log(N/ε_1))$. This estimation subprogram, combined with the classical optimization framework, enables our quantum ICA algorithm, which exponentially reduces the complexity dependence on the data scale compared with classical algorithms. The outperformance is further supported by numerical experiments, while a source separation of a transcriptomic dataset is shown as an example of application.

quant-ph↗

arXiv2023

Quantum Encoding and Analysis on Continuous Time Stochastic Process with Financial Applications

The continuous time stochastic process is a mainstream mathematical instrument modeling the random world with a wide range of applications involving finance, statistics, physics, and time series analysis, while the simulation and analysis of the continuous time stochastic process is a challenging problem for classical computers. In this work, a general framework is established to prepare the path of a continuous time stochastic process in a quantum computer efficiently. The storage and computation resource is exponentially reduced on the key parameter of holding time, as the qubit number and the circuit depth are both optimized via our compressed state preparation method. The desired information, including the path-dependent and history-sensitive information that is essential for financial problems, can be extracted efficiently from the compressed sampling path, and admits a further quadratic speed-up. Moreover, this extraction method is more sensitive to those discontinuous jumps capturing extreme market events. Two applications of option pricing in Merton jump diffusion model and ruin probability computing in the collective risk model are given.

quant-ph↗

arXiv2023

Can Variational Quantum Algorithms Demonstrate Quantum Advantages? Time Really Matters

Applying low-depth quantum neural networks (QNNs), variational quantum algorithms (VQAs) are both promising and challenging in the noisy intermediate-scale quantum (NISQ) era: Despite its remarkable progress, criticisms on the efficiency and feasibility issues never stopped. However, whether VQAs can demonstrate quantum advantages is still undetermined till now, which will be investigated in this paper. First, we will prove that there exists a dependency between the parameter number and the gradient-evaluation cost when training QNNs. Noticing there is no such direct dependency when training classical neural networks with the backpropagation algorithm, we argue that such a dependency limits the scalability of VQAs. Second, we estimate the time for running VQAs in ideal cases, i.e., without considering realistic limitations like noise and reachability. We will show that the ideal time cost easily reaches the order of a 1-year wall time. Third, by comparing with the time cost using classical simulation of quantum circuits, we will show that VQAs can only outperform the classical simulation case when the time cost reaches the scaling of $10^0$-$10^2$ years. Finally, based on the above results, we argue that it would be difficult for VQAs to outperform classical cases in view of time scaling, and therefore, demonstrate quantum advantages, with the current workflow. Since VQAs as well as quantum computing are developing rapidly, this work does not aim to deny the potential of VQAs. The analysis in this paper provides directions for optimizing VQAs, and in the long run, seeking more natural hybrid quantum-classical algorithms would be meaningful.

quant-ph↗

arXiv2023

Classical-Assisted Quantum Ground State Preparation with Tensor Network States and Monte Carlo Sampling

Quantum computing offers potential solutions for finding ground states in condensed-matter physics and chemistry. However, achieving effective ground state preparation is also computationally hard for arbitrary Hamiltonians. It is necessary to propose certain assumptions to make this problem efficiently solvable, including preparing a trial state of a non-trivial overlap with the genuine ground state. Here, we propose a classical-assisted quantum ground state preparation method for quantum many-body systems, combining Tensor Network States (TNS) and Monte Carlo (MC) sampling as a heuristic method to prepare a trial state with a non-trivial overlap with the genuine ground state. We extract a sparse trial state by sampling from TNS, which can be efficiently prepared by a quantum algorithm on early fault-tolerant quantum computers. Our method demonstrates a polynomial improvement in scaling of overlap between the trial state and genuine ground state compared to random trial states, as evidenced by numerical tests on the spin-$1/2$ $J_1$-$J_2$ Heisenberg model. Furthermore, our method is a novel approach to hybridize a classical numerical method and a quantum algorithm and brings inspiration to ground state preparation in other fields.

quant-ph↗