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Shin Nishio

Publications and source records attributed to Shin Nishio.

At least 19 recordsLinked to original sources

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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Anticipating Decoder Side-channel Attacks in Fault-tolerant Quantum Computers

As quantum computing emerges as an applied technology, there is a growing need to protect quantum computers against information security attacks. This work identifies a new class of side-channel attacks against fault-tolerant quantum computers, in which the syndrome data that is sent to the decoder system is used to infer which computation (logical circuit) is taking place on the quantum computer. Our work introduces the concept of gate fingerprints, which describes those patterns present in syndrome data that indicate which logical operation took place on the quantum computer. We show different effects by which logical operations produce gate fingerprints by focusing on Clifford+T computation in the surface code. We then explore how gate fingerprint information can be used to make inferences about the circuits or algorithms run on a quantum computer. Our findings indicate that decoder systems can be a vector for side-channel attacks and thus to prevent this, decoder systems should either be secured or built by a trusted party.

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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 $\Delta$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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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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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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Efficient Preparation of Graph States using the Quotient-Augmented Strong Split Tree

Graph states are a key resource for measurement-based quantum computation and quantum networking, but state-preparation costs limit their practical use. Graph states related by local complement (LC) operations are equivalent up to single-qubit Clifford gates; one may reduce entangling resources by preparing a favorable LC-equivalent representative. However, exhaustive optimization over the LC orbit is not scalable. We address this problem using the split decomposition and its quotient-augmented strong split tree (QASST). For several families of distance-hereditary (DH) graphs, we use the QASST to characterize LC orbits and identify representatives with reduced controlled-Z count or preparation circuit depth. We also introduce a split-fuse construction for arbitrary DH graph states, achieving linear scaling with respect to entangling gates, time steps, and auxiliary qubits. Beyond the DH setting, we discuss a generalized divide-and-conquer split-fuse strategy and a simple greedy heuristic for generic graphs based on triangle enumeration. Together, these methods outperform direct implementations on sufficiently large graphs, providing a scalable alternative to brute-force optimization.

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Local Equivalence Classes of Distance-Hereditary Graphs using Split Decompositions

Local complement is a graph operation formalized by Bouchet which replaces the neighborhood of a chosen vertex with its edge-complement. This operation induces an equivalence relation on graphs; determining the size of the resulting equivalence classes is a challenging problem in general. Bouchet obtained formulas only for paths and cycles, and brute-force methods are limited to very small graphs. In this work, we extend these results by deriving explicit formulas for several broad families of distance-hereditary graphs, including complete multipartite graphs, clique-stars, and repeater graphs. Our approach uses a technique known as split decomposition to establish upper bounds on equivalence class sizes, and we prove these bounds are tight through a combinatorial enumeration of the graphs' decomposed structure up to symmetry.

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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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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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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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Online Job Scheduler for Fault-tolerant Quantum Multiprogramming

Fault-tolerant quantum computers are expected to be offered as cloud services due to their significant resource and infrastructure requirements. Quantum multiprogramming, which runs multiple quantum jobs in parallel, is a promising approach to maximize the utilization of such systems. A key challenge in this setting is the need for an online scheduler capable of handling jobs submitted dynamically while other programs are already running. In this study, we formulate the online job scheduling problem for fault-tolerant quantum computing systems based on lattice surgery and propose an efficient scheduler to address it. To meet the responsiveness required in an online environment, our scheduler approximates lattice surgery programs, originally represented as polycubes, by using simpler cuboid representations. This approximation enables efficient scheduling while improving overall throughput. In addition, we incorporate a defragmentation mechanism into the scheduling process, demonstrating that it can further enhance QPU utilization.

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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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Use of Faulty States in Cat-Code Error Correction

Bosonic codes have seen a resurgence in interest for applications as varied as fault tolerant quantum architectures, quantum enhanced sensing, and entanglement distribution. Cat codes have been proposed as low-level elements in larger architectures, and the theory of rotationally symmetric codes more generally has been significantly expanded in the recent literature. The fault-tolerant preparation and maintenance of cat code states as a stand-alone quantum error correction scheme remains however limited by the need for robust state preparation and strong inter-mode interactions. In this work, we consider the teleportation-based correction circuit for cat code quantum error correction. We show that the class of acceptable ancillary states is broader than is typically acknowledged, and exploit this to propose the use of many-component ``bridge'' states which, though not themselves in the cat code space, are nonetheless capable of syndrome extraction in the regime where non-linear interactions are a limiting factor.

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Multiplexed Quantum Communication with Surface and Hypergraph Product Codes

Connecting multiple processors via quantum interconnect technologies could help overcome scalability issues in single-processor quantum computers. Transmission via these interconnects can be performed more efficiently using quantum multiplexing, where information is encoded in high-dimensional photonic degrees of freedom. We explore the effects of multiplexing on logical error rates in surface codes and hypergraph product codes. We show that, although multiplexing makes loss errors more damaging, assigning qubits to photons in an intelligent manner can minimize these effects, and the ability to encode higher-distance codes in a smaller number of photons can result in overall lower logical error rates. This multiplexing technique can also be adapted to quantum communication and multimode quantum memory with high-dimensional qudit systems.

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InQuIR: Intermediate Representation for Interconnected Quantum Computers

Various physical constraints limit the number of qubits that can be implemented in a single quantum processor, and thus it is necessary to connect multiple quantum processors via quantum interconnects. While several compiler implementations for interconnected quantum computers have been proposed, there is no suitable representation as their compilation target. The lack of such representation impairs the reusability of compiled programs and makes it difficult to reason formally about the complicated behavior of distributed quantum programs. We propose InQuIR, an intermediate representation that can express communication and computation on distributed quantum systems. InQuIR has formal semantics that allows us to describe precisely the behaviors of distributed quantum programs. We give examples written in InQuIR to illustrate the problems arising in distributed programs, such as deadlock. We present a roadmap for static verification using type systems to deal with such a problem. We also provide software tools for InQuIR and evaluate the computational costs of quantum circuits under various conditions. Our tools are available at https://github.com/team-InQuIR/InQuIR.

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Hardness of braided quantum circuit optimization in the surface code

Large-scale quantum information processing requires the use of quantum error correcting codes to mitigate the effects of noise in quantum devices. Topological error-correcting codes, such as surface codes, are promising candidates as they can be implemented using only local interactions in a two-dimensional array of physical qubits. Procedures such as defect braiding and lattice surgery can then be used to realize a fault-tolerant universal set of gates on the logical space of such topological codes. However, error correction also introduces a significant overhead in computation time, the number of physical qubits, and the number of physical gates. While optimizing fault-tolerant circuits to minimize this overhead is critical, the computational complexity of such optimization problems remains unknown. This ambiguity leaves room for doubt surrounding the most effective methods for compiling fault-tolerant circuits for a large-scale quantum computer. In this paper, we show that the optimization of a special subset of braided quantum circuits is NP-hard by a polynomial-time reduction of the optimization problem into a specific problem called Planar Rectilinear 3SAT.

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Impact of the form of weighted networks on the quantum extreme reservoir computation

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driven system Hamiltonian dynamics was employed as the quantum feature map. In this work we capture how the quantum feature map is generated as the number of time-steps of the dynamics increases by a method to characterize unitary matrices in the form of weighted networks. Furthermore, to identify the key properties of the feature map that has sufficiently grown, we evaluate it with various weighted network models that could be used for the quantum reservoir in image classification situations. At last, we show how a simple Hamiltonian model based on a disordered discrete time crystal with its simple implementation route provides nearly-optimal performance while removing the necessity of programming of the quantum processor gate by gate.

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Resource Reduction in Multiplexed High-Dimensional Quantum Reed-Solomon Codes

Quantum communication technologies will play an important role in quantum information processing in the near future as we network devices together. However, their implementation is still a challenging task due to both loss and gate errors. Quantum error correction codes are one important technique to address this issue. In particular, the Quantum Reed-Solomon codes are known to be quite efficient for quantum communication tasks. The high degree of physical resources required, however, makes such a code difficult to use in practice. A recent technique called quantum multiplexing has been shown to reduce resources by using multiple degrees of freedom of a photon. In this work, we propose a method to decompose multi-controlled gates using fewer $\rm{CX}$ gates via this quantum multiplexing technique. We show that our method can significantly reduce the required number of $\rm{CX}$ gates needed in the encoding circuits for the quantum Reed-Solomon code. Our approach is also applicable to many other quantum error correction codes and quantum algorithms, including Grovers and quantum walks.

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