arXiv subjects

Ryu Sawada

Publications and source records attributed to Ryu Sawada.

15 recordsLinked to original sources

arXiv2026

Improving initial-state-dependent quantum circuit optimization by introducing state labels

While the capabilities of quantum hardware have significantly advanced in recent years, executing quantum algorithms as quantum circuits at the lowest possible cost remains crucial, regardless of the hardware progress. We are developing a quantum-state-dependent circuit optimizer called AQCEL. Our guiding principle, implemented as the AQCEL optimization protocol, is to optimize quantum circuits by measuring the states of the control qubits to identify and eliminate unnecessary control operations. In this paper, we introduce two key improvements: the state label manager that reduces unnecessary state measurements and the $CX$-pair removal process that eliminates redundant gate pairs. These enhancements significantly reduce the number of two-qubit gates, improving the fidelity of quantum circuits executed on quantum hardware. To demonstrate the effectiveness of our method, we apply AQCEL to quantum circuits for the quantum parton shower algorithm. Experimental results using the IBM quantum computer show a substantial reduction in gate counts and an improvement in fidelity compared to the conventional optimization technique as well as the original AQCEL protocol. Our findings highlight the potential of state-dependent circuit optimization for enhancing the performance of quantum algorithms on near-term quantum devices.

quant-ph↗

arXiv2026

Symmetries and overparametrization properties of Hamiltonian variational ansatzes for the $(1+1)$d $\mathbb{Z}_2$ lattice gauge theory

We perform detailed studies of five Hamiltonian variational ansatzes (HVA) based on the Hamiltonian of the $(1+1)$d $\mathbb{Z}_2$ lattice gauge theory. The ansatzes are designed to respect local and global symmetries of the original Hamiltonian and therefore act on a finely segmented state Hilbert space. Following Larocca et al. (2023), we numerically study the dimension of the dynamical Lie algebra (DLA) and the rank of the quantum Fisher information matrix (QFIM) of the ansatzes within specific invariant subspaces. The ansatzes all involve sums of weight-three Paulis in their generators, which is a feature that have so far been underexplored in this context. We also perform numerical experiments to determine the ground state energy of the original Hamiltonian via variational quantum eigensolver (VQE), and observe that overparametrization of the ansatzes coincides with the apparent disappearance of local minima in the loss function, in line with the finding in the reference. Finally, the decay rate of the VQE loss function under gradient descent optimization is revealed to scale linearly with the number of parameters in the ansatz. These results help to enrich the theory of overparameterization of quantum circuits and inform the design of scalable variational ansatzes.

quant-ph↗

arXiv2026

Comprehensive Numerical Studies of Barren Plateau and Overparametrization in Variational Quantum Algorithm

The variational quantum algorithm (VQA) with a parametrized quantum circuit is widely applicable to near-term quantum computing, but its fundamental issues that limit optimization performance have been reported in the literature. For example, VQA optimization often suffers from vanishing gradients called barren plateau (BP) and the presence of local minima in the landscape of the cost function. Numerical studies have shown that the trap in local minima is significantly reduced when the circuit is overparametrized (OP), where the number of parameters exceeds a certain threshold. Theoretical understanding of the BP and OP phenomena has advanced over the past years, however, comprehensive studies of both effects in the same setting are not fully covered in the literature. In this paper, we perform a comprehensive numerical study in VQA, quantitatively evaluating the impacts of BP and OP and their interplay on the optimization of a variational quantum circuit, using concrete implementations of one-dimensional transverse and longitudinal field quantum Ising model. The numerical results are compared with the theoretical diagnostics of BP and OP phenomena. The framework presented in this paper will provide a guiding principle for designing VQA algorithms and ansatzes with theoretical support for behaviors of parameter optimization in practical settings.

quant-ph↗

arXiv2026

Search for Dark Photon Dark Matter with a Mass around 36.1 μeV Using a Frequency-tunable Cavity Controlled through a Coupled Superconducting Qubit

We report the results of a search for dark photon dark matter using a cavity that employs a transmon qubit as a frequency tuner. The tuning mechanism utilizes the energy level shift arising from the mode mixing between the qubit and the cavity mode. This method is advantageous as it avoids the frictional heating and electromagnetic leakage associated with mechanical tuning. We searched for a dark matter signal in the mass range $36.132 - 36.179$ $μ$eV and found no significant evidence. As a result, we set the exclusion limit on the kinetic mixing parameter down to approximately $5 \times 10^{-13}$, surpassing the existing limit set by cosmology.

hep-ex↗

arXiv2024

Qudit-Generalization of the Qubit Echo and Its Application to a Qutrit-Based Toffoli Gate

The fidelity of certain gates on noisy quantum computers may be improved when they are implemented using more than two levels of the involved transmons. The main impediments to achieving this potential are the dynamic gate phase errors that cannot be corrected via calibration. The standard tool for countering such phase errors in two-level qubits is the echo protocol, often referred to as the dynamical decoupling sequence, where the evolution of a qubit is punctuated by an even number of X gates. We introduce basis cycling, which is a direct generalization of the qubit echo to general qudits, and provide an analytic framework for designing gate sequences to produce desired effects using this technique. We then apply basis cycling to a Toffoli gate decomposition incorporating a qutrit and obtain CCZ gate fidelity values up to 93.8$\pm$0.1%, measured by quantum process tomography, on IBM quantum computers. The gate fidelity remains stable without recalibration even while the resonant frequency of the qutrit fluctuates, highlighting the dynamical nature of phase error cancellation through basis cycling. Our results demonstrate that one of the biggest difficulties in implementing qudit-based gate decompositions on superconducting quantum computers can be systematically overcome when certain conditions are met, and thus open a path toward fulfilling the promise of qudits as circuit optimization agents.

quant-ph↗

arXiv2022

Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We introduce a new circuit optimizer called AQCEL, which aims to remove redundant controlled operations from controlled gates, depending on initial states of the circuit. Especially, the AQCEL can remove unnecessary qubit controls from multi-controlled gates in polynomial computational resources, even when all the relevant qubits are entangled, by identifying zero-amplitude computational basis states using a quantum computer. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. For this benchmark, we have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates. Moreover, when deploying AQCEL with a noisy intermediate scale quantum computer, it efficiently produces a quantum circuit that approximates the original circuit with high fidelity by truncating low-amplitude computational basis states below certain thresholds. Our technique is useful for a wide variety of quantum algorithms, opening up new possibilities to further simplify quantum circuits to be more effective for real devices.

quant-ph↗

arXiv2022

Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning

Super-resolution is a machine-learning technique in image processing which generates high-resolution images from low-resolution images. Inspired by this approach, we perform a numerical experiment of quantum machine learning, which takes low-resolution (low plane-wave energy cutoff) one-particle molecular wavefunctions in plane-wave basis as input and generates high-resolution (high plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems, and study the performance of different learning models. We show that the trained models can generate wavefunctions having higher fidelity values with respect to the ground-truth wavefunctions than a simple linear interpolation, and the results can be improved both qualitatively and quantitatively by including data-dependent information in the ansatz. On the other hand, the accuracy of the current approach deteriorates for wavefunctions calculated in electronic configurations not included in the training dataset. We also discuss the generalization of this approach to many-body electron wavefunctions.

quant-ph↗

arXiv2021

Measurement-Free Ultrafast Quantum Error Correction by Using Multi-Controlled Gates in Higher-Dimensional State Space

Quantum error correction is a crucial step beyond the current noisy-intermediate-scale quantum device towards fault-tolerant quantum computing. However, most of the error corrections ever demonstrated rely on post-selection of events or post-correction of states, based on measurement results repeatedly recorded during circuit execution. On the other hand, real-time error correction is supposed to be performed through classical feedforward of the measurement results to data qubits. It provides unavoidable latency from conditional electronics that would limit the scalability of the next-generation quantum processors. Here we propose a new approach to real-time error correction that is free from measurement and realized by using multi-controlled gates based on higher-dimensional state space. Specifically, we provide a series of novel decompositions of a Toffoli gate by using the lowest three energy levels of a transmon that significantly reduce the number of two-qubit gates and discuss their essential features, such as extendability to an arbitrary number of control qubits, the necessity of exclusively controlled NOT gates, and usefulness of their incomplete variants. Combined with the recently demonstrated schemes of fast two-qubit gates and all-microwave qubit reset, it would substantially shorten the time required for error correction and resetting ancilla qubits compared to a measurement-based approach and provide an error correction rate of $\gtrsim1$~MHz with high accuracy for three-qubit bit- and phase-flip errors.

quant-ph↗

arXiv2021

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of near-term quantum devices. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware co-optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and efficient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.

quant-ph↗

arXiv2021

Event Classification with Multi-step Machine Learning

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of an ML model from several small ML model candidates for each sub-task has been performed by using the idea based on Neural Architecture Search (NAS). In this paper, Differentiable Architecture Search (DARTS) and Single Path One-Shot NAS (SPOS-NAS) are tested, where the construction of loss functions is improved to keep all ML models smoothly learning. Using DARTS and SPOS-NAS as an optimization and selection as well as the connections for multi-step machine learning systems, we find that (1) such a system can quickly and successfully select highly performant model combinations, and (2) the selected models are consistent with baseline algorithms, such as grid search, and their outputs are well controlled.

cs.LG↗

arXiv2021

An Improvement of Object Detection Performance using Multi-step Machine Learnings

Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific component model of the pipeline, the overall solution can be made accurate and explainable. This paper describes an enhancement of object detection based on this multi-step concept, where a post-processing step called the calibration model is introduced. The calibration model consists of a convolutional neural network, and utilizes rich contextual information based on the domain knowledge of the input. Improvements of object detection performance by 0.8-1.9 in average precision metric over existing object detectors have been observed using the new model.

cs.CV↗

arXiv2021

Event Classification with Quantum Machine Learning in High-Energy Physics

We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.

physics.comp-ph↗

arXiv2019

Discovery reach for wino and higgsino dark matter with a disappearing track signature at a 100 TeV $pp$ collider

Within the theory of supersymmetry, the lightest neutralino is a dark matter candidate and is often assumed to be the lightest supersymmetric particle (LSP) as well. If the neutral wino or higgsino is dark matter, the upper limit of the LSP mass is determined by the observed relic density of dark matter. If the LSP is a nearly-pure neutral state of the wino or higgsino, the lightest chargino state is expected to have a significant lifetime due to a tiny mass difference between the LSP and the chargino. This article presents discovery potential of the 100 TeV future circular hadron collider (FCC) for the wino and higgsino dark matter using a disappearing-track signature. The search strategy to extend the discovery reach to the thermal limits of wino/higgsino dark matter is discussed with detailed studies on the background rate and the reference design of the FCC-hadron detector under possible running scenarios of the FCC-hadron machine. A proposal of modifying the detector layout and several ideas to improve the sensitivity further are also discussed.

hep-ph↗

arXiv2019

Studying gaugino masses in supersymmetric model at future 100 TeV $pp$ collider

We discuss prospects of studying supersymmetric model at future $pp$ circular collider (FCC) with its centre-of-mass energy of $\sim 100\ {\rm TeV}$. We pay particular attention to the model in which Wino is lighter than other supersymmetric particles and all the gauginos are within the kinematical reach of the FCC, which is the case in a large class of so-called pure gravity mediation model based on anomaly mediated supersymmetry breaking. In such a class of model, charged Wino becomes long-lived with its decay length of $\sim 6\ {\rm cm}$, and the charged Wino tracks may be identified in particular by the inner pixel detector; the charged Wino tracks can be used not only for the discrimination of standard model backgrounds but also for the event reconstructions. We show that precise determinations of the Bino, Wino, and gluino masses are possible at the FCC. For such measurements, information about the charged Wino tracks, including the one about the velocity of the charged Wino using the time of the hit at the pixel detector, is crucial. With the measurements of the gaugino masses in the pure gravity mediation model, we have an access to more fundamental parameters like the gravitino mass.

hep-ph↗

arXiv2011

The Architecture of MEG Simulation and Analysis Software

MEG (Mu to Electron Gamma) is an experiment dedicated to search for the $μ^+ \rightarrow e^+γ$ decay that is strongly suppressed in the Standard Model but predicted in several Super Symmetric extensions of it at an accessible rate. MEG is a small-size experiment ($\approx 50-60$ physicists at any time) with a life span of about 10 years. The limited human resource available, in particular in the core offline group, emphasized the importance of reusing software and exploiting existing expertise. Great care has been devoted to provide a simple system that hides implementation details to the average programmer. That allowed many members of the collaboration to contribute to the development of the software of the experiment with limited programming skill. The offline software is based on two frameworks: {\bf REM} in FORTRAN 77 used for the event generation and detector simulation package {\bf GEM}, based on GEANT 3, and {\bf ROME} in C++ used in the readout simulation {\bf Bartender} and in the reconstruction and analysis program {\bf Analyzer}. Event display in the simulation is based on GEANT 3 graphic libraries and in the reconstruction on ROOT graphic libraries. Data are stored in different formats in various stage of the processing. The frameworks include utilities for input/output, database handling and format conversion transparent to the user.

physics.ins-det↗