arXiv subjects

Takahiko Satoh

Publications and source records attributed to Takahiko Satoh.

At least 19 recordsLinked to original sources

arXiv2026

Digital Annealer-Assisted CNOT Oriented Quantum Circuit Transpilation with Integrated QUBO Mapping and Routing

Limited qubit counts and two-qubit gate errors motivate reducing CNOT overhead in noisy intermediate-scale quantum (NISQ) transpilation. In circuit-partitioning workflows, repeated hardware sampling can motivate additional classical search, although this study does not evaluate end-to-end runtime or hardware fidelity. We evaluate two Digital Annealer (DA) strategies on seven fixed standalone benchmark instances and a 64-node 8 x 8 square-grid. Hybrid combines DA-based global initial mapping with Qiskit routing, whereas Full DA applies DA to both initial mapping and iterative routing. Hybrid achieved lower mean equivalent CNOT counts than both configured baselines on four benchmarks and identical means on the remaining three. The unweighted macro-average reductions were 6.14% relative to Qiskit and 17.9% relative to $t|ket\rangle$. Among the six benchmarks with feasible Full DA outputs, Full DA achieved lower means than ISAAQ on five, with a success-conditioned macro-average reduction of 48.9%; however, it produced no feasible BV output and did not outperform Hybrid on any benchmark. These results identify DA-generated global placement followed by heuristic routing as the primary positive result and characterize the limitations of the evaluated short-horizon DA routing workflow. Equivalent CNOT count is a structural proxy and does not directly measure execution fidelity.

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arXiv2026

Qubit Loss Inference with Stabilizer Codes without Leakage Detection Units

Qubit loss occurs when the physical carrier of a qubit leaves the computational system without directly revealing the event's location. Such errors are a major obstacle to fault-tolerant quantum computation on platforms including photonic, neutral-atom, and trapped-ion systems. Loss locations are commonly identified using additional hardware operations such as leakage-detection units (LDUs), which introduce space-time overhead and may themselves become a source of error. We investigate whether qubit loss on stabilizer codes can instead be inferred from syndrome data obtained through standard repeated stabilizer measurements. Under a non-entangling model for gates involving a lost qubit, we derive a sufficient condition for loss detectability in general stabilizer codes. The condition is based on the emergence of anticommutation between stabilizer checks after their support on the lost qubits is removed. By using that condition, we formulate the exact loss-inference problem using the observed set of non-deterministic checks together with its maximum-likelihood formulation. We then relax the problem to the minimum set cover problem with a greedy heuristic algorithm. We evaluate the resulting inference and loss-correction protocols on the rotated surface code via circuit-level noise simulations for trapped-ion and neutral-atom platforms. On both platforms, inference-based and adaptive protocols reduce the logical error rate relative to a noisy-LDU baseline in the low-to-moderate loss-rate regime relevant to near-term hardware, while requiring fewer space-time overheads.

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arXiv2026

Treewidth-Aware Gate Cut Selection for Reducing Transpilation Overhead on Superconducting Quantum Devices

On superconducting quantum devices with sparse qubit connectivity, transpilation of long-range two-qubit interactions inserts additional SWAP gates, increasing hardware cost and execution error. Gate cutting via quasi-probability decomposition (QPD) can remove a selected two-qubit gate and thereby reduce routing overhead, but its sampling cost makes cut placement critical. We propose TW2S, a graph-only two-stage gate-cut selection method that operates on the circuit interaction graph without backend-specific transpilation at selection time. Stage 1 analyzes a min-fill elimination trace and scores edges by their contribution to a treewidth upper bound. Stage 2 ranks the resulting candidates by edge betweenness centrality with a degree penalty to identify routing bottlenecks. Across grid, Watts-Strogatz, barbell, and stochastic block model benchmarks transpiled to IBM's FakeSherbrooke backend, TW2S consistently outperforms random cut selection when the interaction graph contains identifiable sparse cuts. The advantage is governed not by absolute graph density but by moderate community structure and accessible inter-community edges. We further derive a mean-squared-error breakeven condition showing that, under a shared total shot budget, QPD is beneficial only when the ECR reduction is large enough and the signal strength is sufficient. Under an expanded per-subcircuit budget the signal-strength requirement is substantially relaxed. In noisy simulations of the J1-J2 transverse-field Ising model, TW2S achieves $Δ$ECR = 47 for n = 8, compared with approximately 9 for random selection, and yields lower estimation error than the uncut baseline in the tested strong-signal regime, with larger gains at increased shot budgets. These results position graph-structural cut selection as a practical compiler-side tool for turning circuit cutting into a targeted routing-reduction strategy.

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arXiv2026

A Neutral-Atom Quantum Compiler with Application-Specific Layout and Hub-Assisted Shuttling

Compiling arbitrary-connectivity NISQ circuits onto monolithic single-zone neutral-atom devices is constrained by a finite interaction range and a minimum separation between simultaneously addressable sites. Under the minimum-separation constraint, the SWAP-only configuration of our pipeline does not return a schedule within a practical time budget on a range of circuits, including circuits as small as nine qubits. We address this with hub traps, a small number of dynamically placed empty traps that serve as transit waypoints, together with a per-gate rule that chooses between SWAP-based routing and hub-mediated shuttling. We evaluate the compiler on seventeen benchmarks using analytic estimates of execution time and a per-layer fidelity proxy, comparing against a placement-matched baseline and against ablations of our own pipeline. Hub traps make these otherwise-unsolved circuits compile in seconds to minutes and remove SWAP gates entirely on every completed circuit, so their role is to enable routing rather than only to optimize fidelity. The benefit is concentrated on routing-dominated circuits and is absent on routing-free ones, which we separate by the structure of the interaction graph. On the most routing-dominated circuit the fidelity proxy improves by up to three orders of magnitude over the placement-matched baseline. The gain comes primarily from eliminating SWAP overhead, as the absolute fidelities there remain low.

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arXiv2026

Dense packing of the surface code: code deformation procedures and hook-error-avoiding gate scheduling

The surface code is one of the leading quantum error correction codes for realizing large-scale fault-tolerant quantum computing (FTQC). One major challenge in realizing surface-code-based FTQC is the extremely large number of qubits required. To mitigate this problem, fusing multiple codewords of the surface code into a densely packed configuration has been proposed. It is known that by using dense packing, the number of physical qubits required per logical qubit can be reduced to approximately three-fourths compared to simply placing surface-code patches side by side. Despite its potential, concrete deformation procedures and quantitative error-rate analyses have remained largely unexplored. In this work, we present a detailed code-deformation procedure that transforms multiple standard surface code patches into a densely packed, connected configuration, along with a conceptual microarchitecture to utilize this dense packing. We also propose a CNOT gate-scheduling for stabilizer measurement circuits that suppresses hook errors in the densely packed surface code. We performed circuit-level Monte Carlo noise simulation of densely packed surface codes using this gate scheduling. The numerical results demonstrate that as the code distance of the densely packed surface code increases and the physical error rate decreases, the logical error rate of the densely packed surface code becomes lower than that of the standard surface code. Furthermore, we find that only when employing hook-error-avoiding syndrome extraction can the densely packed surface code achieve a lower logical error rate than the standard surface code, while simultaneously reducing the space overhead.

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arXiv2026

Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience

AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from $3\,TNMP$, the cost of pure Centralized QFL, to $\{3t + 2(T - t)\}\,NMP$ over $T$ rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.

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arXiv2026

QuBridge: Layer-wise Fidelity Decomposition in Quantum Computation Pipeline

Running a quantum circuit on current hardware involves a sequence of engineering decisions, each with tunable parameters and distinct error characteristics. Existing tools optimize each decision in isolation, leaving practitioners unable to determine how much each decision contributes to final output quality. We present QuBridge, a pipeline analysis tool that decomposes quantum computation into three decision layers and measures each layer's fidelity contribution through progressive ablation and isolation experiments. Applied to quantum teleportation under IBM-calibrated noise models, the framework surfaces three phenomena that end-to-end measurement obscures. Qubit selection narrows the worst-case fidelity band from 11.8% to under 2% with downstream layers held fixed, without changing the peak. Per-gate pulse-shape assignment adds a +0.9% residual gain whose attributed magnitude depends on upstream layout. Error-detection encoding is not uniformly advantageous, and its conditional benefit emerges for input states whose dominant error channel is detectable by the chosen code. QuBridge operates on cached calibration data without requiring live hardware access.

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arXiv2026

Applicability and Limitations of Quantum Circuit Cutting in Classical State-Vector Simulation

Circuit cutting partitions a large quantum circuit into smaller subcircuits that can be executed independently and recombined by classical post-processing. In classical state-vector simulation with full-state reconstruction, the runtime is governed by a trade-off between reduced subcircuit size and the overheads of exponentially many subcircuits and full-state reconstruction. For equal partitioning, we derive threshold conditions on the number of cuts below which cutting reduces the wall-clock time. State-vector experiments validate the predicted speedup boundary up to 24 qubits, and a runtime breakdown up to 30 qubits identifies crossovers at $q \approx 18$ and $q \approx 22$ where merging overtakes first preprocessing and then subcircuit simulation. As a practical guideline, we show that under a 10-minute wall-clock budget, two-way cutting extends the maximum feasible qubit count by 4 to 6 qubits relative to simulation without cutting.

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arXiv2026

Telemetry-Based Server Selection in the Quantum Internet via Cross-Layer Runtime Estimation

The Quantum Internet will allow clients to delegate quantum workloads to remote servers over heterogeneous networks, but choosing the server that minimizes end-to-end execution time is difficult because server processing, feedforward classical communication, and entanglement distribution can overlap in protocol-dependent ways and shift the runtime bottleneck. We propose $T_{\max}$, a lightweight runtime score that sums coarse telemetry from multiple layers to obtain a conservative ranking for online server selection without calibrating weights for each deployment. Using NetSquid discrete-event simulations of a modified parameter-blind VQE (PB-VQE) workload, we evaluate $T_{\max}$ on pools of 10,000 heterogeneous candidates (selecting among up to 100 per decision) across crossover and bottleneck-dominated regimes, including temporal jitter scenarios and jobs with multiple shots. $T_{\max}$ achieves single-digit mean regret normalized by the oracle (below 10%) in both regimes and remains in the single-digit range under classical communication latency jitter for multi-shot jobs, while performance degrades for single-shot jobs under severe jitter. To connect performance to deployment planning, we derive an operating map based on requirements relating distance and entanglement rate requirements to protocol level counts, quantify how simple multiuser contention shifts the crossover, and use Sobol global sensitivity analysis to identify regime-dependent bottlenecks. These findings suggest that simple cross-layer telemetry can enable practical server selection while providing actionable provisioning guidance for emerging Quantum Internet services.

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arXiv2025

RuleSet Generation Framework for Application Layer Integration in Quantum Internet

Layered architectures for the Quantum Internet have been proposed, inspired by that of the classical Internet, which has demonstrated high maintainability even in large-scale systems. While lower layers in the Quantum Internet, such as entanglement generation and distribution, have been extensively studied, the application layer - responsible for translating user requests into executable quantum-network operations - remains largely unexplored. A significant challenge is translating application-level requests into the concrete instructions executable at lower layers. In this work, we introduce a RuleSet-based framework that explicitly incorporates the application layer into the layered architecture of the Quantum Internet. Our framework builds on a RuleSet-based protocol, clarifying communication procedures, organizing application request information, and introducing new Rules for application execution by embedding application specifications into RuleSets. To evaluate feasibility, we constructed state machines from the generated RuleSets. This approach enables a transparent integration from the application layer down to the physical layer, thereby lowering barriers to deploying new applications on the Quantum Internet.

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arXiv2025

Quantitative Evaluation of Quantum/Classical Neural Network Using a Game Solver Metric

To evaluate the performance of quantum computing systems relative to classical counterparts and explore the potential, we propose a game-solving benchmark based on Elo ratings in the game of tic-tac-toe. We compare classical convolutional neural networks (CCNNs), quantum or quantum convolutional neural networks (QNNs, QCNNs), and hybrid classical-quantum neural networks (Hybrid NNs) by assessing their performance based on round-robin matches. Our results show that the Hybrid NNs engines achieve Elo ratings comparable to those of CCNNs engines, while the quantum engines underperform under current hardware constraints. Additionally, we implement a QNN integrated with quantum communication and evaluate its performance to quantify the overhead introduced by noisy quantum channels, and the communication overhead was found to be modest. These results demonstrate the viability of using game-based benchmarks for evaluating quantum computing systems and suggest that quantum communication can be incorporated with limited impact on performance, providing a foundation for future hybrid quantum applications.

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arXiv2025

Improved sampling bounds and scalable partitioning for quantum circuit cutting beyond bipartitions

We propose a new method for identifying cutting locations for quantum circuit cutting, with a primary focus on partitioning circuits into three or more parts. Under the assumption that the classical postprocessing function is decomposable, we derive a new upper bound on the sampling overhead resulting from both time-like and space-like cuts. We show that this bound improves upon the previously known bound by orders of magnitude in cases of three or more partitions. Based on this bound, we formulate an objective function, $L_Q^{}$, and present a method to determine cutting locations that minimize it. Our method is shown to outperform a previous approach in terms of computation time. Moreover, the quality of the obtained partitioning is found to be comparable to or better than that of the baseline in all but a few cases, as measured by $L_Q^{}$. These results are obtained by identifying cutting locations in a number of benchmark circuits of the size and type expected in quantum computations that outperform classical computers.

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arXiv2025

Enhancing the Dynamic Range of Quantum Sensing via Quantum Circuit Learning

Quantum metrology is a promising application of quantum technologies, enabling the precise measurement of weak external fields at a local scale. In typical quantum sensing protocols, a qubit interacts with an external field, and the amplitude of the field is estimated by analyzing the expectation value of a measured observable. Sensitivity can, in principle, be enhanced by increasing the number of qubits within a fixed volume, thereby maintaining spatial resolution. However, at high qubit densities, inter-qubit interactions induce complex many-body dynamics, resulting in multiple oscillations in the expectation value of the observable even for small field amplitudes. This ambiguity reduces the dynamic range of the sensing protocol. We propose a method to overcome the limitation in quantum metrology by adopting a quantum circuit learning framework using a parameterized quantum circuit to approximate a target function by optimizing the circuit parameters. In our method, after the qubits interact with the external field, we apply a sequence of parameterized quantum gates and measure a suitable observable. By optimizing the gate parameters, the expectation value is trained to exhibit a monotonic response within a target range of field amplitudes, thereby eliminating multiple oscillations and enhancing the dynamic range. This method offers a strategy for improving quantum sensing performance in dense qubit systems.

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arXiv2025

SU(4) gate design via unitary process tomography: its application to cross-resonance based superconducting quantum devices

We present a novel approach for implementing pulse-efficient SU(4) gates on cross resonance (CR)-based superconducting quantum devices. Our method introduces a parameterized unitary derived from the CR-Hamiltonian propagator, which accounts for static-$ZZ$ interactions. Leveraging the Weyl chamber's geometric structure, we successfully realize a continuous 2-qubit basis gate, $R_{ZZ}(θ)$, as an echo-free pulse schedule on the IBM Quantum device ibm_kawasaki. We evaluate the average fidelity and gate time of various SU(4) gates generated using the $R_{ZZ}(θ)$ to confirm the advantages of our implementation.

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arXiv2023

Digital quantum simulator for the time-dependent Dirac equation using discrete-time quantum walks

We introduce a quantum algorithm for simulating the time-dependent Dirac equation in 3+1 dimensions using discrete-time quantum walks. Thus far, promising quantum algorithms have been proposed to simulate quantum dynamics in non-relativistic regimes efficiently. However, only some studies have attempted to simulate relativistic dynamics due to its theoretical and computational difficulty. By leveraging the convergence of discrete-time quantum walks to the Dirac equation, we develop a quantum spectral method that approximates smooth solutions with exponential convergence. This mitigates errors in implementing potential functions and reduces the overall gate complexity that depends on errors. We demonstrate that our approach does not require additional operations compared to the asymptotic gate complexity of non-relativistic real-space algorithms. Our findings indicate that simulating relativistic dynamics is achievable with quantum computers and can provide insights into relativistic quantum physics and chemistry.

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arXiv2023

Leveraging hardware-control imperfections for error mitigation via generalized quantum subspace

In the era of quantum computing without full fault-tolerance, it is essential to suppress noise effects via the quantum error mitigation techniques to enhance the computational power of the quantum devices. One of the most effective noise-agnostic error mitigation schemes is the generalized quantum subspace expansion (GSE) method, which unifies various mitigation algorithms under the framework of the quantum subspace expansion. Specifically, the fault-subspace method, a subclass of GSE method, constructs an error-mitigated quantum state with copies of quantum states with different noise levels. However, from the experimental aspect, it is nontrivial to determine how to reliably amplify the noise so that the error in the simulation result is efficiently suppressed. In this work, we explore the potential of the fault-subspace method by leveraging the hardware-oriented noise: intentional amplification of the decoherence, noise boost by insertion of identity, making use of crosstalk, and probabilistic implementation of noise channel. We demonstrate the validity of our proposals via both numerical simulations with the noise parameters reflecting those in quantum devices available via IBM Quantum, and also experiments performed therein.

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arXiv2022

Amplitude Amplification for Optimization via Subdivided Phase Oracle

We propose an algorithm using a modified variant of amplitude amplification to solve combinatorial optimization problems via the use of a subdivided phase oracle. Instead of dividing input states into two groups and shifting the phase equally for all states within the same group, the subdivided phase oracle changes the phase of each input state uniquely in proportion to their objective value. We provide visualization of how amplitudes change after each iteration of applying the subdivided phase oracle followed by conventional Grover diffusion in the complex plane. We then show via numerical simulation that for normal, skew normal, and exponential distribution of objective values, the algorithm can be used to amplify the probability of measuring the optimal solution to a significant degree independent of the search space size. In the case of skew normal and exponential distributions, this probability can be amplified to be close to unity, making our algorithm near deterministic. We then modify our algorithm in order to demonstrate how it can be extended to a broader set of objective value distributions. Finally, we discuss the speedup compared to classical schemes using the query complexity model, and show that our algorithm offers a significant advantage over these classical approaches.

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arXiv2022

Pulse-engineered Controlled-V gate and its applications on superconducting quantum device

In this paper, we demonstrate that, by employing OpenPulse design kit for IBM superconducting quantum devices, the controlled-V gate (CV gate) can be implemented in about half the gate time to the controlled-X (CX or CNOT gate) and consequently 65.5\% reduced gate time compared to the CX-based implementation of CV. Then, based on the theory of Cartan decomposition, we characterize the set of all two-qubit gates implemented with only two or three CV gates; using pulse-engineered CV gates enables us to implement these gates with shorter gate time and possibly better gate fidelity than the CX-based one, as actually demonstrated in two examples. Moreover, we showcase the improvement of linearly-coupled three-qubit Toffoli gate, by implementing it with the pulse-engineered CV gate, both in gate time and the averaged output-state fidelity. These results imply the importance of our CV gate implementation technique, which, as an additional option for the basis gate set design, may shorten the overall computation time and consequently improve the precision of several quantum algorithms executed on a real device.

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