arXiv subjects

Keitaro Anai

Publications and source records attributed to Keitaro Anai.

7 recordsLinked to original sources

arXiv2026

Environment-Assisted Decoherence Suppression of Optical Non-Gaussian States

Optical loss is a common bottleneck in photonic quantum information processing, undermining the quantum advantage over classical approaches. Although several countermeasures, such as quantum distillation and error correction, have been proposed, they typically require experimentally demanding non-Gaussian operations. Here, we demonstrate a Gaussian-only scheme that suppresses loss-induced decoherence for general, unknown optical quantum states. By injecting a squeezed vacuum state into an environment of the loss channel and performing feedforward based on environmental monitoring, the scheme effectively suppresses loss-induced noise. Our programmable loop-based optical circuit allows us to implement the scheme for several types of loss-sensitive non-Gaussian states under various loss conditions for up to five steps, and directly compare the results with the unsuppressed case. Our results show that the scheme consistently mitigates state degradation, preserving higher fidelity and Wigner negativity than without suppression. This approach can be applied to mitigating a broad class of errors in optical systems and extending quantum memory lifetimes. Moreover, it is compatible with other loss-suppression techniques and extendable to physical platforms beyond optics, offering a promising route toward reducing the overhead required for fault-tolerant quantum information processing.

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arXiv2026

Systematic construction of digital autonomous quantum error correction for state preparation and error suppression via conditional Gaussian operations

In continuous-variable quantum computing, autonomous quantum error correction (QEC) can dissipatively steer a noisy quantum state into a target state or manifold, enabling robust quantum information processing without explicit syndrome measurements and feedback. Here, we propose a nullifier-based digital autonomous QEC enabled by conditional Gaussian operations. By designing jump operators for target nullifiers and compiling the resulting Lindbladian into a Trotterized sequence of elementary conditional Gaussian operations, we demonstrate two use cases: (i) deterministic preparation of non-Gaussian resource states for universal computation, including finitely squeezed cubic phase states and approximate trisqueezed states, and (ii) autonomous suppression of dephasing error for cat and squeezed cat states. We provide explicit gate decompositions for the required conditional Gaussian operations and numerically evaluate the performance under realistic imperfections, including photon loss in the bosonic mode and ancillary-qubit decoherence. Our results clarify the resource requirements and trade-offs, such as circuit depth, time-step choices, and the required set of conditional Gaussian operations, for scalable, gate-level implementations of autonomous state preparation and error suppression.

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arXiv2024

Unitary-transformed projective squeezing: applications for circuit-knitting and state-preparation of non-Gaussian states

Continuous-variable (CV) quantum computing is a promising candidate for quantum computation because it can, even with one mode, utilize infinite-dimensional Hilbert spaces and can efficiently handle continuous values. Although photonic platforms have been considered as a leading platform for CV computation, hybrid systems that use both qubits and bosonic modes, e.g., superconducting hardware, have shown significant advances because they can prepare non-Gaussian states by utilizing the nonlinear interaction between the qubits and the bosonic modes. However, the size of hybrid hardware is currently restricted. Moreover, the fidelity of the non-Gaussian state is also restricted. This work extends the projective squeezing method to establish a formalism for projecting quantum states onto the states that are unitary-transformed from the squeezed vacuum at the expense of the sampling cost. Based on this formalism, we propose methods for simulating larger quantum devices and projecting states onto the cubic phase state, a typical non-Gaussian state, with a higher squeezing level and higher nonlinearity. To make implementation practical, we can, by leveraging the interactions in hybrid systems of qubits and bosonic modes, apply the smeared projector by using either the linear-combination-of-unitaries or virtual quantum error detection algorithms. We numerically verify the performance of our methods and show that projection can suppress the effect of photon-loss errors.

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arXiv2024

Continuous-variable quantum kernel method on a programmable photonic quantum processor

Among various quantum machine learning (QML) algorithms, the quantum kernel method has especially attracted attention due to its compatibility with noisy intermediate-scale quantum devices and its potential to achieve quantum advantage. This method performs classification and regression by nonlinearly mapping data into quantum states in a higher dimensional Hilbert space. Thus far, the quantum kernel method has been implemented only on qubit-based systems, but continuous-variable (CV) systems can potentially offer superior computational power by utilizing its infinite-dimensional Hilbert space. Here, we demonstrate the implementation of the classification task with the CV quantum kernel method on a programmable photonic quantum processor. We experimentally prove that the CV quantum kernel method successfully classifies several datasets robustly even under the experimental imperfections, with high accuracies comparable to the classical kernel. This demonstration sheds light on the utility of CV quantum systems for QML and should stimulate further study in other CV QML algorithms.

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arXiv2024

Quantum-enhanced optical phase-insensitive heterodyne detection beyond 3-dB noise penalty of image band

Optical phase-insensitive heterodyne (beat-note) detection, which measures the relative phase of two beams at different frequencies through their interference, is a key sensing technology for various spatial/temporal measurements, such as frequency measurements in optical frequency combs. However, its sensitivity is limited not only by shot noise from the signal frequency band but also by the extra shot noise from an image band, known as the 3-dB noise penalty. Here, we propose a method to remove shot noise from all these bands using squeezed light. We also demonstrate beyond-3-dB noise reduction experimentally, confirming that our method actually reduces shot noise from both the signal and extra bands simultaneously. Our work should boost the sensitivity of various spatial/temporal measurements beyond the current limitations.

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arXiv2024

Projective squeezing for translation symmetric bosonic codes

The design of translation symmetric bosonic codes, e.g., Gottesmann-Kitaev-Preskill and squeezed cat codes, is robust against photon loss, but the computation accuracy is limited by the available squeezing level. Here, we introduce the \textit{projective squeezing} (PS) method for computing outcomes for a higher squeezing level by revealing that a linear combination of displacement operators with periodic displacement values constitutes the smeared projector onto the better code space; we also show the analytical relationship between the increased squeezing level and the projection probability. We introduce concrete implementation methods for PS based on linear-combination-of-unitaries and virtual quantum error detection. We also numerically verify our analytical arguments and show that our protocol can mitigate the effect of photon loss.

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arXiv2023

Continuous-variable quantum approximate optimization on a programmable photonic quantum processor

Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly-distributed wavefunction to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.

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