arXiv subjects

Lukas Bödeker

Publications and source records attributed to Lukas Bödeker.

8 recordsLinked to original sources

arXiv2026

Reducing Decoding Latency in Quantum Error Correction by Early Starting Clustering

In quantum error correction, fast low-latency decoding is essential for fault-tolerant quantum computation, as delays in processing syndrome data can lead to the backlog problem. Existing decoders, including parallelizable approaches such as Union-Find, begin decoding only after all stabilizer measurement outcomes from an error-correction cycle have been received, inherently introducing a delay before decoding begins. We introduce Cluster-As-You-Go (CAYG), a modification of the Union-Find decoder that processes syndrome information during stabilizer measurements by clustering and correcting errors as they appear. This approach reduces the size of the remaining decoding problem at the end of the quantum error correction cycle and, consequently, the time required to complete the decoding process. While this early start of clustering incurs a modest reduction in decoding accuracy, it preserves the decoder's scalability. Surface-code simulations show that the resulting reduction in post-measurement idling can outweigh the accuracy loss, yielding an improved speed-accuracy trade-off. These results demonstrate that real-time, early-starting decoding during QEC cycles is both feasible and can be advantageous for quantum error correction. Demonstrated here for the surface code, CAYG is broadly applicable to other quantum error-correcting codes, which allow for clustering-based decoding approaches, and is extensible to dedicated real-time decoding hardware.

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arXiv2026

Towards logical entanglement creation in trivalent planar architectures

Low-overhead quantum error-correction schemes are essential for enabling quantum computation on registers containing multiple logical qubits. For planar architectures with limited nearest-neighbor qubit connectivity, the surface code has emerged as the leading paradigm. Recent theoretical and experimental work has shown that a physical-qubit connectivity of degree three is sufficient to implement fault-tolerant quantum error correction. In this work, we study lattice surgery in the context of such trivalent architectures and introduce scalable circuit constructions to implement it. Compared with the four-valent measurement scheme, the trivalent lattice-surgery protocol reduces the required resources by $\mathcal{O}(d)$ qubits out of a total qubit count of $\mathcal{O}(d^2)$ and by $\mathcal{O}(d)$ two-qubit gates out of a total two-qubit gate count of $\mathcal{O}(d^3)$. We benchmark the logical fidelity of both lattice-surgery schemes in terms of experimentally realistic simulations targeting an implementation with a fluxonium qubit based architecture and find a potential improvement of up to $\approx25\%$ for distance-three. These results open a way for scalable planar trivalent qubit architectures to host a surface-code-based logical quantum processor.

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arXiv2026

Lattice surgery for near-term experimental logical qubit entanglement creation in planar architectures

In the era of early fault-tolerant quantum computing, basic demonstrations of entanglement operations between a few logical qubits are at the frontier of recent developments in quantum computing. In this work, we describe in detail, at both the logical and physical qubit levels, a logical teleportation protocol between two surface code logical qubits based on lattice surgery. We address several aspects of the teleportation protocol pertinent to superconducting qubit architectures. We explore the modularity constraints in the number and location of stabilizer readouts and compare variants of the teleportation protocol in this regard. Additionally, we investigate potential performance improvements related to in-sequence decision logic and the optimal size of the interface region between two surface code patches on a superconducting chip. Based on our simulations, we show possible near-term improvements in lattice surgery protocols that facilitate fault-tolerant quantum computing in superconducting circuit architectures.

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arXiv2025

Lattice surgery-based logical state teleportation via noisy links

For planar architectures surface code-based quantum error correction is one of the most promising approaches to fault-tolerant quantum computation. This is partially due to the variety of fault-tolerant logical protocols that can be implemented in two dimensions using local operations. One such protocol is the lattice surgery-based logical state teleportation, which transfers a logical quantum state from an initial location on a quantum chip to a target location through a linking region of qubits. This protocol serves as a basis for higher-level routines, such as the entangling CNOT gate or magic state injection. In this work we investigate the correctability phase diagram of this protocol for distinct error rates inside the surface code patches and within the linking region. We adopt techniques from statistical physics to describe the numerically observed crossover regime between correctable and uncorrectable quantum error correction phases, where the correctability depends on the separation between the initial and target locations. We find that inside the crossover regime the correctability-threshold lines decay as a power law with increasing separation, which we explain accurately using a finite-size scaling analysis. Our results indicate that the logical state teleportation protocol can tolerate much higher noise rates in the linking region compared to the bulk of the surface code patches, provided the separation between the positions is relatively small.

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arXiv2025

On the interpretability of neural network decoders

Neural-network (NN) based decoders are becoming increasingly popular in the field of quantum error correction (QEC), including for decoding of state-of-the-art quantum computation experiments. In this work, we make use of established interpretability methods from the field of machine learning, to introduce a toolbox to achieve an understanding of the underlying decoding logic of NN decoders, which have been trained but otherwise typically operate as black-box models. To illustrate the capabilities of the employed interpretability method, based on the Shapley value approximation, we provide an examplary case study of a NN decoder that is trained for flag-qubit based fault-tolerant (FT) QEC with the Steane code. We show how particular decoding decisions of the NN can be interpreted, and reveal how the NN learns to capture fundamental structures in the information gained from syndrome and flag qubit measurements, in order to come to a FT correction decision. Further, we show that the understanding of how the NN obtains a decoding decision can be used on the one hand to identify flawed processing of error syndrome information by the NN, resulting in decreased decoding performance, as well as for well-informed improvements of the NN architecture. The diagnostic capabilities of the interpretability method we present can help ensure successful application of machine learning for decoding of QEC protocols.

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arXiv2025

Experimentally Informed Decoding of Stabilizer Codes Based on Syndrome Correlations

High-fidelity decoding of quantum error correction codes relies on an accurate experimental model of the physical errors occurring in the device. Because error probabilities can depend on the context of the applied operations, the error model is ideally calibrated using the same circuit as is used for the error correction experiment. Here, we present an experimental approach guided by a novel analytical formula to characterize the probability of independent errors using correlations in the syndrome data generated by executing the error correction circuit. Using the method on a distance-three surface code, we analyze error channels that flip an arbitrary number of syndrome elements, including Pauli Y errors, hook errors, multi-qubit errors, and leakage, in addition to standard Pauli X and Z errors. We use the method to find the optimal weights for a minimum-weight perfect matching decoder without relying on a theoretical error model. Additionally, we investigate whether improved knowledge of the Pauli Y error channel, based on correlating the X- and Z-type error syndromes, can be exploited to enhance matching decoding. Furthermore, we find correlated errors that flip many syndrome elements over up-to-eight cycles, potentially caused by leakage of the data qubits out of the computational subspace. The presented method provides the tools for accurately calibrating a broad family of decoders, beyond the minimum-weight perfect matching decoder, without relying on prior knowledge of the error model.

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arXiv2025

Realizing Lattice Surgery on Two Distance-Three Repetition Codes with Superconducting Qubits

Quantum error correction is needed for quantum computers to be capable of fault-tolerantly executing algorithms using hundreds of logical qubits. Recent experiments have demonstrated subthreshold error rates for state preservation of a single logical qubit. In addition, the realization of universal quantum computation requires the implementation of logical entangling gates. Lattice surgery offers a practical approach for implementing such gates, particularly in planar quantum processor layouts. In this work, we demonstrate lattice surgery between two distance-three repetition-code qubits by splitting a single distance-three surface-code qubit. Using a quantum circuit fault-tolerant to bit-flip errors, we achieve an improvement in the value of the decoded $ZZ$ logical two-qubit observable compared to a similar non-encoded circuit. By preparing the surface-code qubit in initial states parametrized by a varying polar angle, we evaluate the performance of the lattice surgery operation for non-cardinal states on the logical Bloch sphere and employ logical two-qubit tomography to reconstruct the Pauli transfer matrix of the operation. In this way, we demonstrate the functional building blocks needed for lattice surgery operations on larger-distance codes based on superconducting circuits.

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arXiv2022

Optimal storage capacity of quantum Hopfield neural networks

Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here, quantum generalisations of classical networks realizing associative memories - capable of retrieving patterns, or memories, from corrupted initial states - have been proposed. It is a challenging open problem to analyze quantum associative memories with an extensive number of patterns, and to determine the maximal number of patterns the quantum networks can reliably store, i.e. their storage capacity. In this work, we propose and explore a general method for evaluating the maximal storage capacity of quantum neural network models. By generalizing what is known as Gardner's approach in the classical realm, we exploit the theory of classical spin glasses for deriving the optimal storage capacity of quantum networks with quenched pattern variables. As an example, we apply our method to an open-system quantum associative memory formed of interacting spin-1/2 particles realizing coupled artificial neurons. The system undergoes a Markovian time evolution resulting from a dissipative retrieval dynamics that competes with a coherent quantum dynamics. We map out the non-equilibrium phase diagram and study the effect of temperature and Hamiltonian dynamics on the storage capacity. Our method opens an avenue for a systematic characterization of the storage capacity of quantum associative memories.

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