arXiv subjects

Tomasz Rybotycki

Publications and source records attributed to Tomasz Rybotycki.

At least 19 recordsLinked to original sources

arXiv2026

Identical, independent quantum weak measurements violate objective realism

We demonstrate violation of objective realism in quantum world using unconstrained weak measurements. Instead of limited Leggett-Garg approach with artificial bounds on the observed values, we assume two identical and indepenent weak detectors and final conditioning. The experimental verification has been performed on public quantum computers, IBM and IonQ. Thanks to sufficiently large statistics, the violation is observed at the level of 10 standard deviations. The tests confirmed also high quality of parametric two-qubit gates offered by main quantum hardware providers.

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arXiv2026

Auto Quantum Machine Learning for Multisource Classification

With fault-tolerant quantum computing on the horizon, there is growing interest in applying quantum computational methods to data-intensive scientific fields like remote sensing. Quantum machine learning (QML) has already demonstrated potential for such demanding tasks. One area of particular focus is quantum data fusion -- a complex data analysis problem that has attracted significant recent attention. In this work, we introduce an automated QML (AQML) approach for addressing data fusion challenges. We evaluate how AQML-generated quantum circuits perform compared to classical multilayer perceptrons (MLPs) and manually designed QML models when processing multisource inputs. Furthermore, we apply our method to change detection using the multispectral ONERA dataset, achieving improved accuracy over previously reported QML-based change detection results.

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arXiv2026

On the Importance of Fundamental Properties in Quantum-Classical Machine Learning Models

We present a systematic study of how quantum circuit design, specifically the depth of the variational ansatz and the choice of quantum feature mapping, affects the performance of hybrid quantum-classical neural networks on a causal classification task. The architecture combines a convolutional neural network for classical feature extraction with a parameterized quantum circuit acting as the quantum layer. We evaluate multiple ansatz depths and nine different feature maps. Results show that increasing the number of ansatz repetitions improves generalization and training stability, though benefits tend to plateau beyond a certain depth. The choice of feature mapping is even more critical: only encodings with multi-axis Pauli rotations enable successful learning, while simpler maps lead to underfitting or loss of class separability. Principal Component Analysis and silhouette scores reveal how data distributions evolve across network stages. These findings offer practical guidance for designing quantum circuits in hybrid models. All source codes and evaluation tools are publicly available.

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arXiv2026

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers

We demonstrate the first violation of the Leggett-Garg inequality and time-order noninvariance on public quantum computers using genuine noninvasive measurements. By gathering sufficiently large statistics, we have been able to violate Leggett-Garg inequality and time-order invariance. The detailed analysis of the data on 10 qubit sets from 5 devices available on IBM Quantum and one on IonQ reveals violations beyond 5 standard deviations in almost all cases. We implemented our protocols using fractional gates, newly available on the IBM Heron devices, allowing us to benchmark them in application to weak measurements. The noninvasiveness is supported by a qualitative and quantitative agreement with the model of weak disturbance. Moreover, our data expose statistically significant deviations from theoretical predictions that exceed declared device error rates, establishing weak measurement protocols as a sensitive benchmark for quantum hardware. These advances transform public quantum computers into practical testbeds for probing foundational questions of realism and temporal order with unprecedented accessibility and precision.

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arXiv2025

Explainable Quantum Machine Learning for Multispectral Images Segmentation: Case Study

The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.

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arXiv2025

Device-independent prepare-and-prepare bipartite null witness dimension test with a single joint measurement

We propose a device-independent null witness dimensionality test with bipartite measurements and input from two separate parties. The dimension is determined from the rank of the matrix of measurements for pairs of states prepared by the parties. We have applied the test to various IBM Quantum devices. The results demonstrate extreme precision of the test, which is able to detect disagreements with the qubit (two-level) space of bipartite measurement even in the presence of technical imperfections. The deviations beyond 6 standard deviations have no simple origin and need urgent explanations to unblock progress in quantum computing.

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arXiv2025

Cross-platform certification of the qubit space with a minimal number of parameters

We demonstrate a determinant dimension witness of a qubit space. Our test has a minimal number of independent parameters. We achieve it by mapping the Bloch sphere $π/2$-rotation axis angle on the non-planar so-called Viviani curve. We ran our test on different platforms: IBM Quantum, IQM Resonance, and IonQ. Our investigations show that numerous qubits, especially from the newest IBM Heron family devices, fail the test by more than ten standard deviations. The nature of those deviations has no simple explanation as the test is robust against common imperfections.

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arXiv2025

QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions

As hybrid quantum-classical models gain traction in machine learning, there is a growing need for tools that assess their effectiveness beyond raw accuracy. We present QMetric, a Python package offering a suite of interpretable metrics to evaluate quantum circuit expressibility, feature representations, and training dynamics. QMetric quantifies key aspects such as circuit fidelity, entanglement entropy, barren plateau risk, and training stability. The package integrates with Qiskit and PyTorch, and is demonstrated via a case study on binary MNIST classification comparing classical and quantum-enhanced models. Code, plots, and a reproducible environment are available on GitLab.

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arXiv2025

Violation of no-signaling on a public quantum computer

No-signaling is a consequence of the no-communication theorem that states that bipartite systems cannot transfer information unless a communication channel exists. It is also a by-product of the assumptions of Bell theorem about quantum nonlocality. We have tested no-signaling in bipartite systems of qubits from IBM Quantum devices in extremely large statistics, resulting in significant violations. Although the time and space scales of IBM Quantum cannot in principle rule out subluminal communications, there is no obvious physical mechanism leading to signaling. The violation is also at similar level as observed in Bell tests. It is therefore mandatory to check possible technical imperfections that may cause the violation and to repeat the loophole-free Bell test at much larger statistics, in order to be ruled out definitively at strict spacelike conditions.

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arXiv2025

Piquasso: A Photonic Quantum Computer Simulation Software Platform

We introduce the Piquasso quantum programming framework, a full-stack open-source software platform for the simulation and programming of photonic quantum computers. Piquasso can be programmed via a high-level Python programming interface enabling users to perform efficient quantum computing with discrete and continuous variables. Via optional high-performance C++ backends, Piquasso provides state-of-the-art performance in the simulation of photonic quantum computers. The Piquasso framework is supported by an intuitive web-based graphical user interface where the users can design quantum circuits, run computations, and visualize the results.

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arXiv2025

Hyperspectral image segmentation with a machine learning model trained using quantum annealer

Training of machine learning models consumes large amounts of energy. Since the energy consumption becomes a major problem in the development and implementation of artificial intelligence systems there exists a need to investigate the ways to reduce use of the resources by these systems. In this work we study how application of quantum annealers could lead to reduction of energy cost in training models aiming at pixel-level segmentation of hyperspectral images. Following the results of QBM4EO team, we propose a classical machine learning model, partially trained using quantum annealer, for hyperspectral image segmentation. We show that the model trained using quantum annealer is better or at least comparable with models trained using alternative algorithms, according to the preselected, common metrics. While direct energy use comparison does not make sense at the current stage of quantum computing technology development, we believe that our work proves that quantum annealing should be considered as a tool for training at least some machine learning models.

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arXiv2025

Efficient discrimination between real and complex quantum theories

We improve the test to show the impossibility of a quantum theory based on real numbers by a larger ratio of complex-to-real bound on a Bell-type parameter. In contrast to previous theoretical and experimental proposals the test requires three settings for the parties $A$ and $C$, but also six settings for the middle party $B$, assuming separability of the sources. The bound we found for this symmetric configuration imposed on a real theory is $14.69$ while the complex maximum is $18$. This large theoretical difference enables us to demonstrate the concomitant experimental violation on IBM quantum computer via a designed quantum network, without resorting to error mitigation, obtaining as a result $15.44$ at more than $100$ standard deviations above the found real bound.

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arXiv2024

Device-independent dimension leakage null test on qubits at low operational cost

We construct a null test of the two-level space of a qubit, which is both device independent and needs a small number of different experiments. We demonstrate its feasibility on IBM Quantum, with most qubits failing the test by more than 10 standard deviations. The robustness of the test against common technical imperfections, like decoherence and phase shifts, and supposedly negligible leakage, indicates that the origin of deviations is beyond known effects.

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arXiv2024

Quantum null-hypothesis device-independent Schmidt rank witness

We investigate the dimensionality of bipartite quantum systems by construction of a device-independent null witness test. This test assesses whether a given bipartite state conforms with the expected quantum dimension, Schmidt number, and distinguishes between real and complex spaces. By employing local measurements on each party, the proposed method aims to determine the minimal rank. By performing an experimental demonstration on IBM Quantum devices, we prove the exceptional accuracy of the test and its usefulness in diagnostics beyond routine calibrations. One of the tests shows agreement with theoretical expectations within statistical errors. However, the second test failed by more than 6 standard deviations, indicating unspecified parasitic entanglements, with no known simple origin.

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arXiv2024

AQMLator -- An Auto Quantum Machine Learning E-Platform

A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on automatic architecture search -- a meta method that aims at moving human from ML system design process. The success of ML and the development of quantum computing (QC) in recent years led to a birth of new fascinating field called Quantum Machine Learning (QML) that, amongst others, incorporates quantum computers into ML models. In this paper we present AQMLator, an Auto Quantum Machine Learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. This way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.

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arXiv2024

Quantum dimension witness with a single repeated operation

We present a simple null test of a dimension of a quantum system, using a single repeated operation in the method of delays, assuming that each instance is identical and independent. The test is well-suited to current feasible quantum technologies, with programed gates. We also analyze weaker versions of the test, assuming unitary or almost unitary operations and derive expressions for the statistical error. The feasibility of the test is demonstrated on IBM Quantum. The failure in one of the tested devices can indicate a lack of identity between subsequent gates or an extra dimension in the many worlds/copies model.

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arXiv2023

High performance Boson Sampling simulation via data-flow engines

In this work, we generalize the Balasubramanian-Bax-Franklin-Glynn (BB/FG) permanent formula to account for row multiplicities during the permanent evaluation and reduce the complexity of permanent evaluation in scenarios where such multiplicities occur. This is achieved by incorporating n-ary Gray code ordering of the addends during the evaluation. We implemented the designed algorithm on FPGA-based data-flow engines and utilized the developed accessory to speed up boson sampling simulations up to $40$ photons, by drawing samples from a $60$ mode interferometer at an averaged rate of $\sim80$ seconds per sample utilizing $4$ FPGA chips. We also show that the performance of our BS simulator is in line with the theoretical estimation of Clifford \& Clifford \cite{clifford2020faster} providing a way to define a single parameter to characterize the performance of the BS simulator in a portable way. The developed design can be used to simulate both ideal and lossy boson sampling experiments.

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arXiv2023

Testing accuracy of qubit rotations on a public quantum computer

We analyze the results of the test of $π/2$ qubit rotations on the public quantum computer provided by IBM. We measure a single qubit rotated by $π/2$ about a random axis, and we accumulate vast statistics of the results. The test performed on different devices shows systematic deviations from the theoretical predictions, which appear at the level $10^{-3}$. Some of the differences, beyond 5 standard deviations, cannot be explained by simple corrections due to nonlinearities of pulse generations. The magnitude of the deviation is comparable with the randomized benchmarking of the gate, but we additionally observe a pronounced parametric dependence. We discuss other possible reasons of the deviations, including states beyond the single-qubit space. The deviations have a similar structure for various devices used at different times, and so they can also serve as a diagnostic tool to eliminate imperfect gate implementations, and faithful description of the involved physical systems.

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