arXiv subjects

Stanislav S. Straupe

Publications and source records attributed to Stanislav S. Straupe.

12 recordsLinked to original sources

arXiv2026

Universal Broadband Linear Optical Transformations by an Interlaced Structured Integrated Photonic Processor

Reconfigurable photonic integrated circuits enable applications in a wide range of fields. They represent a powerful platform for signal processing due to the ability to perform parallel computations. In this work, we demonstrate a photonic processor based on a six-channel universal chip fabricated by femtosecond laser writing with an interlaced architecture comprising multiport beam splitters. We exploit and enhance a previously demonstrated calibration procedure for a single building block, and extend it to a complete six-channel interferometer, reconstructing the corresponding interferometer models over a broad spectral range. We validate them by measuring a set of 100 Haar-random unitary matrices, achieving mean amplitude fidelities of 95.9 %, 98.0 %, and 96.0 % for radiation wavelengths 910, 945, and 980 nm, respectively, without on-device optimization. Furthermore, we experimentally realize spectral demultiplexing and multiplexing at these wavelengths, yielding routing fidelities ranging from 90.1 % to 96.5 % and total crosstalk values ranging from -9.6 to -14.4 dB, confirming the applicability of the fabricated photonic processor to practical wavelength-routing tasks.

physics.optics↗

arXiv2026

Quantum-optical reset with classical memory

Dynamic quantum circuits generate states that depend on the measurement results obtained during circuit execution. To date such a quantum computing model has mainly been implemented with qubit-based superconducting hardware utilizing reset operations and classical logic. Here we develop a model of optical reset by using time-bin self-looped interferometers demonstrated in recent experiments. Synchronizing the optical reset with a simple classical device storing history of measurement results allows one to decrease uncertainty of future measurements, which suggests new possibilities for constructing dynamical circuits on optical platforms. Information flow with significant multi-time correlations and memory depth is identified through distinct information-theoretic measures. We discuss potential applications of the proposed reset model, including the realization of boson sampling and experimental tests of the quantum-mechanical formulation of Landauer's principle.

quant-ph↗

arXiv2026

Resource-efficient linear-optical generation of GHZ-like states

Heralded multi-photon entanglement generation is a central bottleneck for photonic quantum computing, where resource costs typically skyrocket with target size. We explore efficient methods for generating photon states with tunable entanglement, providing a flexible tool for quantum state engineering. We introduce a theoretical framework that has been numerically validated, demonstrating the capacity to generate GHZ-like states incrementally from non-logical intermediate states. We demonstrate that in certain scenarios $-$ such as reducing the resource cost for building large maximally entangled GHZ states $-$ these variable-entanglement states can outperform their fixed-entanglement counterparts. By adjusting intermediate states and optimizing interferometer schemes, we improve photon number cost efficiency of GHZ-like states generation. Our findings indicate that while not a universal solution, non-maximally entangled states offer practical advantages for specific photonic quantum information tasks.

quant-ph↗

arXiv2026

Effective programming of a photonic processor with complex interferometric structure

Reconfigurable photonics have rapidly become an invaluable tool for information processing. Light-based computing accelerators are promising for boosting neural network learning and inference and optical interconnects are foreseen as a solution to the information transfer bottleneck in high-performance computing. In this study, we demonstrate the successful programming of a transformation implemented using a reconfigurable photonic circuit with a non-conventional architecture. The core of most photonic processors is an MZI-based architecture that establishes an analytical connection between the controllable parameters and circuit transformation. However, several architectures that are substantially more difficult to program have improved robustness to fabrication defects. We use two algorithms that rely on different initial datasets to reconstruct the circuit model of a complex interferometer, and then program the required unitary transformation. Both methods performed accurate circuit programming with an average fidelity greater than 99% and 97%, respectively. Our results provide a strong foundation for the introduction of non-conventional interferometric architectures for photonic information processing.

physics.optics↗

arXiv2025

All-to-all connectivity of Rydberg-atom-based quantum processors with messenger qubits

Rydberg atom arrays are a front-running platform for quantum processors. A major challenge threatening the scalability of this platform is the limited qubit connectivity due to the finite range of interatomic interactions. We explore an approach to realize dynamical all-to-all connectivity with the use of moving "messenger" atomic qubits that couple distant "computational" qubits held in a static tweezer array. We detail and compare four specific architectures based on this concept, each presenting distinct advantages and challenges tied to the efficacy of techniques used to couple, move and measure atomic qubits. We demonstrate that, though technologically demanding, the messenger-qubit paradigm opens a promising avenue to a truly scalable quantum processor based on Rydberg atoms.

quant-ph↗

arXiv2025

Complexity-energy trade-off in programmable unitary interferometers

Coherent multiport interferometers are a promising approach to realize matrix multiplication in integrated photonics. However, most known architectures - such as MZI and beamsplitter meshes, as well as more general interferometers - suffer from complicated procedures for mapping the matrix elements of the desired transformation to specific phaseshifts in the device. We point out that the high programming complexity is intrinsic, rather than accidental. At the same time, we argue that interferometers admitting efficient programming algorithms in general yield a much lower useful output energy, which ultimately limits their accuracy and energy efficiency.

physics.optics↗

arXiv2025

Leveraging machine learning features for linear optical interferometer control

We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a training set derived from the device being studied. A straightforward optimization procedure leverages this trained model to determine the phase shifts of the interferometer with a specific architecture, obtaining the required unitary transformation. This approach enables the effective tuning of interferometers without requiring a precise analytical solution, paving the way for the exploration of new interferometric circuit architectures.

quant-ph↗

arXiv2023

Learning the tensor network model of a quantum state using a few single-qubit measurements

The constantly increasing dimensionality of artificial quantum systems demands for highly efficient methods for their characterization and benchmarking. Conventional quantum tomography fails for larger systems due to the exponential growth of the required number of measurements. The conceptual solution for this dimensionality curse relies on a simple idea - a complete description of a quantum state is excessive and can be discarded in favor of experimentally accessible information about the system. The probably approximately correct (PAC) learning theory has been recently successfully applied to a problem of building accurate predictors for the measurement outcomes using a dataset which scales only linearly with the number of qubits. Here we present a constructive and numerically efficient protocol which learns a tensor network model of an unknown quantum system. We discuss the limitations and the scalability of the proposed method.

quant-ph↗

arXiv2023

Large-scale error-tolerant programmable interferometer fabricated by femtosecond laser writing

We introduce a programmable 8-port interferometer with the recently proposed error-tolerant architecture capable of performing a broad class of transformations. The interferometer has been fabricated with femtosecond laser writing and it is the largest programmable interferometer of this kind to date. We have demonstrated its advantageous error tolerance by showing an operation in a broad wavelength range from $920$ to $980$ nm, which is particularly relevant for quantum photonics due to efficient photon sources. Our work highlights the importance of developing novel architectures of programmable photonics for information processing.

quant-ph↗

arXiv2023

Benchmarking a boson sampler with Hamming nets

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to required amount of measurements. A special attention to this problem appears within the context of characterizing outcomes of noisy intermediate-scale quantum devices, which produce quantum states with specific properties so that it is expected to be hard to simulate such states using classical resources. In this work, we address the problem of characterization of a boson sampling device, which uses interference of input photons to produce samples of non-trivial probability distributions that at certain condition are hard to obtain classically. For realistic experimental conditions the problem is to probe multi-photon interference with a limited number of the measurement outcomes without collisions and repetitions. By constructing networks on the measurements outcomes, we demonstrate a possibility to discriminate between regimes of indistinguishable and distinguishable bosons by quantifying the structures of the corresponding networks. Based on this we propose a machine-learning-based protocol to benchmark a boson sampler with unknown scattering matrix. Notably, the protocol works in the most challenging regimes of having a very limited number of bitstrings without collisions and repetitions. As we expect, our framework can be directly applied for characterizing boson sampling devices that are currently available in experiments.

quant-ph↗

arXiv2021

Benchmarking quantum tomography completeness and fidelity with machine learning

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly.

quant-ph↗

arXiv2008

Preparation and characterization of arbitrary states of four-dimensional qudits based on biphotons

We report interferometric schemes to prepare arbitrary states of four-dimensional qudits (ququarts) based on biphoton states of ultrafast-pumped frequency-nondegenerate spontaneous parametric down-conversion. Preparation and tomographic characterization of a few examples of general single-ququart states, a pure state, a completely mixed state, and a partially-mixed state, are experimentally demonstrated.

quant-ph↗