arXiv subjects

Christoph Simon

Publications and source records attributed to Christoph Simon.

At least 19 recordsLinked to original sources

arXiv2026

Magnetosensitivity of amphibian morphological pigmentation is light- and eye-dependent and consistent with the radical pair mechanism

Weak magnetic fields influence a wide range of biological processes, yet the underlying mechanisms are poorly understood. The radical pair mechanism (RPM), which involves quantum spin dynamics, is a leading hypothesis. Here we show that weak magnetic fields modulate morphological pigmentation -- specifically, the number of perioptic melanophores -- in Xenopus laevis tadpoles in a field-strength-dependent manner. The response is light- and eye-dependent. The observed field-strength dependence is quantitatively consistent with a radical pair model. These properties are reminiscent of the light-dependent magnetoreception that is thought to operate in migratory birds, and establish amphibian pigmentation as a tractable vertebrate system for the study of radical-pair quantum biology.

physics.bio-ph↗

arXiv2026

Characterization of errors in photon-heralded quantum operations between non-interacting quantum emitters

We develop an analytic perturbative framework that enables the analysis of small Markovian errors in probabilistic, photon-heralded quantum operations between non-interacting emitters. Building on and extending the Zero-Photon-Generation (ZPG) framework, we derive closed-form perturbative solutions that capture both ideal (zero-order) and noisy (low-order) gate dynamics conditioned on time-integrated photon counting. Our framework provides analytic solutions to process matrices and Pauli error weights up to leading order, bridging the gap between detailed physical imperfections of a system and its corresponding abstract Pauli noise models. Moreover, our framework captures imperfections across the full physical system stack, from photon generation to optical manipulation. We benchmark the resulting perturbative predictions on a repeat-until-success CZ gate against numerical simulations, demonstrating accurate modeling of source-induced noise, and then apply the same framework to analyze coherent phase-shifter miscalibrations as a representative example of optical-manipulation errors. The methods developed in this work enable physics-informed parameter tuning to optimize gate designs and develop tailored quantum error correction protocols toward fault-tolerant quantum computing using hybrid light--matter quantum systems.

quant-ph↗

arXiv2026

Modeling Optical Polarization Evolution in Myelinated Axon Waveguides with Realistic Imperfections

Biophotonic signaling via axons has been proposed as a potential mode of neural communication, where information might be encoded not only in photon number and wavelength but also in polarization. Although earlier computational studies have examined how structural imperfections influence optical transmission, their effects on polarization fidelity remain unexplored; previous modeling of polarization fidelity in myelinated axons has largely focused on idealized geometries. This study incorporates three structural imperfections characteristic of axons in vivo: variation in myelin thickness, non-circular cross-sectional geometry, and axonal bending, within a model that includes four nodes of Ranvier. We find that variation in myelin thickness alone has minimal impact on fidelity, while non-circular cross-sections show strong mode dependence. Axonal bending has the most significant influence, generating large fluctuations and deep fidelity dips. When all imperfections are combined in a single axon model, the simulations show substantial drops in fidelity, yet certain modes exhibit recovery, with repeated revivals reaching values of around 0.8, which exceeds the revivals observed in the single imperfection cases. Overall, the results indicate that although structural imperfections affect polarization, polarization-based biophotonic signals might remain recoverable even in realistic axons, lending support to the plausibility of polarization-based biophotonic signaling in the brain.

physics.bio-ph↗

arXiv2026

From quantum storage to amplification: the effect of unwanted couplings and an additional level in cavity-based ensemble quantum memories

Quantum-memory models often reduce complex level structures to an idealized $\Lambda$ system, potentially missing nearby levels and unwanted couplings that can qualitatively alter the predicted performance. Here, we study an extension of a cavity-based $\Lambda$-type ensemble memory, a four-level model with unwanted couplings from both the control field and signal, using a fully quantum treatment. We derive explicit expressions for the single-photon storage efficiency, retrieval efficiency, and fidelity, and on this basis identify three distinct dynamical regimes: stable, threshold, and unstable. Within the stable regime, we additionally discriminate between two qualitatively different sub-regimes. Applying the theory to warm-vapor-inspired parameters, we determine the conditions under which the system can still operate as a high-quality quantum memory. More generally, our results provide a practical framework for distinguishing genuine memory operation from amplification and for optimizing realistic quantum memories beyond idealized models.

quant-ph↗

arXiv2026

Recurrent Quantum Feature Maps for Reservoir Computing

Reservoir computing promises a fast method for handling large amounts of temporal data. This hinges on constructing a good reservoir--a dynamical system capable of transforming inputs into a high-dimensional representation while remembering properties of earlier data. In this work, we introduce a reservoir based on recurrent quantum feature maps where a fixed quantum circuit is reused to encode both current inputs and a classical feedback signal derived from previous outputs. We evaluate the model on the Mackey-Glass time-series prediction task using our recently introduced CP feature map, and find that it achieves lower mean squared error than standard classical baselines, including echo state networks and multilayer perceptrons, while maintaining compact circuit depth and qubit requirements. We further analyze memory capacity and show that the model effectively retains temporal information, consistent with its forecasting accuracy. Finally, we study the impact of realistic noise and find that performance is robust to several noise channels but remains sensitive to two-qubit gate errors, identifying a key limitation for near-term implementations.

quant-ph↗

arXiv2026

Revisiting claims of extracranial biophoton detection from the human brain

Ultraweak photon emission, also referred to as biological autoluminescence or biophoton emission, is the spontaneous emission of extremely low levels of light from a broad range of biological systems. Recent studies have reported that UPE measured extracranially can serve as a potential non-invasive biomarker of brain activity. Here, we show that this interpretation suffers from serious problems. We show that, when observed under properly dark conditions, the UPE from the head is much weaker than what is reported in certain papers on 'brain UPE' from human heads. We also show that the large signals reported in these studies can be explained by background light contamination. Furthermore, photons with wavelengths < 600 nm are strongly attenuated by scalp and skull tissues, and longer wavelengths fall largely outside the effective spectral sensitivity of the photomultiplier tubes (PMTs) used. As a consequence, even if UPE from the head is detected under properly background-free conditions, it is likely to be dominated by emission from the scalp rather than from the brain, certainly as long as PMTs are used. Our results emphasize the importance of careful experimental design to make genuine progress on this important question.

physics.bio-ph↗

arXiv2025

Spin-Network Quantum Reservoir Computing with Distributed Inputs: The Role of Entanglement

Reservoir computing is a promising neuromorphic paradigm, and its quantum implementation using spin networks has shown some advantage when entanglement is present. Here, we consider a distributed scenario in which two distinct input time series are injected into separate qubits of a spin-network reservoir. We investigate how the overall entanglement, as well as its localization in the system, influence the performance of the reservoir. Focusing on bilinear memory tasks that require computing the product of the two inputs, we evaluate the short-term memory capacity and correlate it with logarithmic negativity as a measure of bipartite entanglement. We find that short-term memory capacity reaches its maximum at relatively small coupling strengths. In contrast, average entanglement peaks at larger couplings. Analyzing entanglement across all bipartitions, we find that the entanglement between the two input qubits is consistently the strongest and most relevant for task performance. In the small coupling strength regime where the short-term memory capacity is maximized, the reservoir exhibits an extended memory tail: performance remains high for a long time. Finally, a pronounced dip in performance at zero time delay, observed across frequencies, indicates that information requires a finite propagation time through the reservoir before it can be effectively recalled. In summary, our results show that moderate entanglement, particularly between the two input qubits, plays a key role in enhancing short-term memory performance.

quant-ph↗

arXiv2025

Quantum Optical Techniques for Biomedical Imaging

Quantum imaging is emerging as a transformative approach for biomedical applications, applying nonclassical properties of light, such as entanglement, squeezing, and quantum correlations, to overcome fundamental limits of conventional techniques. These methods promise superior spatial resolution, enhanced signal-to-noise ratios, improved phase sensitivity, and reduced radiation dose, for potentially safer and more precise imaging for delicate biological samples. Here, we present an overview of quantum optical biomedical imaging technologies as well as quantum-inspired imaging methods, including quantum optical coherence tomography, quantum optical microscopy, ghost imaging, multi-parameter quantum imaging, and imaging with quantum-grade cameras. We describe the operating principles, biomedical applications, and unique advantages of each approach, along with the specific challenges for their translation into real-life practice. This review aims to guide future research toward advancing quantum imaging from experimental demonstrations to impactful biomedical tools.

quant-ph↗

arXiv2025

Ultra-narrow homogeneous linewidths of erbium-doped silica glass fibers at millikelvin temperatures: magnetic field and temperature dependence

Erbium-doped solids are promising candidates for fiber-based quantum networks due to their emission wavelength, which aligns with the telecom band over which optical fibers exhibit minimal loss. Among these, erbium-doped silica fiber (EDF) stands out for its availability, ease of use, and seamless integration with existing fiber-optic infrastructure. In this work, using the two-pulse photon-echo (2PPE) technique, we measured the homogeneous linewidth of the 4I13/2 <-> 4I15/2 optical transition under varying magnetic fields and temperatures. We observed an effective homogeneous linewidth of approximately 8 kHz at approximately 7mK at an optimal magnetic field of 0.09 T, representing over two orders of magnitude improvement compared to earlier reports measured at T = 700 mK. We also present a comprehensive model for the combined magnetic field and temperature dependency of the effective homogeneous linewidth. Additionally, we employed three-pulse photon-echo (3PPE) measurements to investigate spectral diffusion and decoherence processes, and conclude that Two-Level System (TLS) effects are significantly suppressed at sufficiently low temperatures, below approximately 100 mK.

quant-ph↗

arXiv2025

The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators

Quantum Reservoir Computing (QRC) uses quantum dynamics to efficiently process temporal data. In this work, we investigate a QRC framework based on two coupled Kerr nonlinear oscillators, a system well-suited for time-series prediction tasks due to its complex nonlinear interactions and potentially high-dimensional state space. We explore how its performance in forecasting both linear and nonlinear time-series depends on key physical parameters: input drive strength, Kerr nonlinearity, and oscillator coupling, and analyze the role of entanglement in improving the reservoir's computational performance, focusing on its effect on predicting non-trivial time series. Using logarithmic negativity to quantify entanglement and normalized root mean square error (NRMSE) to evaluate predictive accuracy, individual parameter sweeps show that optimal performance occurs at moderate but non-zero entanglement. Furthermore, an aggregated binned analysis reveals that this moderate entanglement is consistently associated with the optimal average predictive performance across the parameter space, an observation that persists up to a threshold in the input frequency. This relationship persists under some levels of dissipation and dephasing. In particular, we find that higher dissipation rates can enhance performance. These findings contribute to the broader understanding of quantum reservoirs for high performance, efficient quantum machine learning and time-series forecasting.

quant-ph↗

arXiv2025

Exploring the feasibility of probabilistic and deterministic quantum gates between T centers in silicon

T center defects in silicon provide an attractive platform for quantum technologies due to their unique spin properties and compatibility with mature silicon technologies. We investigate several gate protocols between single T centers, including two probabilistic photon interference-based schemes, a near-deterministic photon scattering gate, and a deterministic magnetic dipole-based scheme. In particular, we study a photon interference-based scheme with feedback which can achieve success probabilities above 50%, and use the photon-count decomposition method to perform the first analytical calculations of its entanglement fidelity and efficiency while accounting for imperfections. We also calculate the fidelity and efficiency of the other schemes. Finally, we compare the performance of all the schemes, considering current and near-future experimental capabilities. In particular, we find that the photon interference-based scheme with feedback has the potential to achieve competitive efficiency and fidelity, making it interesting to explore experimentally.

quant-ph↗

arXiv2025

A global quantum network with ground-based single-atom memories in optical cavities and satellite links

The realization of a global quantum network holds the potential to enable groundbreaking applications such as secure quantum communication and blind quantum computing. However, building such a network remains a formidable challenge, primarily due to photon loss in optical fibers. In this work, we propose a quantum repeater architecture for distributing entanglement over intercontinental distances by leveraging low-Earth-orbit satellites equipped with spontaneous parametric down-conversion (SPDC) photon-pair sources and ground stations utilizing single-atom memories in optical cavities and single-photon detectors to implement the cavity-assisted photon scattering (CAPS) gates for high-fidelity entanglement mapping. The efficient entanglement swapping is achieved by performing high-fidelity Rydberg gates and readouts. We evaluate the entanglement distribution rates and fidelities by analyzing several key imperfections, including time-dependent two-photon transmission and time-dependent pair fidelity, for various satellite heights and ground station distances. We also investigate the impact of pair source fidelity and spin decoherence rate on the repeater performance. Furthermore, we introduce a spatial-frequency multiplexing strategy within this architecture to enhance the design's performance. Finally, we discuss in detail the practical implementation of this architecture. Our results show that this architecture enables entanglement distribution over intercontinental distances. For example, it can distribute over 10000 pairs per flyby over 10000 km with a fidelity above 90%, surpassing the capabilities of terrestrial quantum repeaters

quant-ph↗

arXiv2025

Satellites promise global-scale quantum networks

Academia, governments, and industry around the world are on a quest to build long-distance quantum communication networks for a future quantum internet. Using air and fiber channels, quantum communication quickly faced the daunting challenge of exponential photon loss with distance. Quantum repeaters were invented to solve the loss problem by probabilistically establishing entanglement over short distances and using quantum memories to synchronize the teleportation of such entanglement to long distances. However, due to imperfections and complexities of quantum memories, ground-based proof-of-concept repeater demonstrations have been restricted to metropolitan-scale distances. In contrast, direct photon transmission from satellites through empty space faces almost no exponential absorption loss and only quadratic beam divergence loss. A single satellite successfully distributed entanglement over more than 1,200 km. It is becoming increasingly clear that quantum communication over large intercontinental distances (e.g. 4,000-20,000 km) will likely employ a satellite-based architecture. This could involve quantum memories and repeater protocols in satellites, or memory-less satellite-chains through which photons are simply reflected, or some combination thereof. Rapid advancements in the space launch and classical satellite communications industry provide a strong tailwind for satellite quantum communication, promising economical and easier deployment of quantum communication satellites.

quant-ph↗

arXiv2025

Quantumness can enhance resilience to statistical noise in spin-network quantum reservoir computing

Quantum reservoir computing offers a promising approach to the utilization of complex quantum dynamics in machine learning. Statistical noise inevitably arises in real settings of quantum reservoir computing (QRC) due to the practical necessity of taking a small to moderate number of measurements. We investigate the effect of statistical noise in spin-network QRC on the possible performance benefits conferred by quantumness. As our measures of quantumness, we employ both quantum entanglement, which we quantify by the partial transpose of the density matrix, and coherence, which we quantify as the sum of the absolute values of the off-diagonal elements of the density matrix. We find that reservoirs which enjoy a finite degree of quantum entanglement and coherence are more stable against the adverse effects of statistical noise on performance compared to their unentangled, incoherent counterparts. Our results thus indicate that the potential benefit reservoir computers may derive from quantumness depends on the number of measurements used for training and testing, and that statistical noise, albeit detrimental on the whole, may leave quantum reservoirs in a stronger position relative to less quantum ones. These findings not only emphasize the importance of incorporating realistic noise models, but also suggest that the search for computational regimes that benefit from quantumness may be aided rather than impeded by the practical constraints of implementation within existing machines.

quant-ph↗

arXiv2025

Laser-induced spectral diffusion and excited-state mixing of silicon T centres

To find practical application as photon sources for entangled optical resource states or as spin-photon interfaces in entangled networks, semiconductor emitters must produce indistinguishable photons with high efficiency and spectral stability. Nanophotonic cavity integration increases efficiency and bandwidth, but it also introduces environmental charge instability and spectral diffusion. Among various candidates, silicon colour centres have emerged as compelling platforms for integrated-emitter quantum technologies. Here we investigate the dynamics of spectral wandering in nanophotonics-coupled, individual silicon T centres using spectral correlation measurements. We observe that spectral fluctuations are driven predominantly by the near-infrared excitation laser, consistent with a power-dependent Ornstein-Uhlenbeck process, and show that the spectrum is stable for up to 1.5 ms in the dark. We demonstrate a 35x narrowing of the emitter linewidth to 110 MHz using a resonance-check scheme and discuss the advantage for pairwise entanglement rates and optical resource state generators. Finally, we report laser-induced spin-mixing in the excited state and discuss potential mechanisms common to both phenomena. These effects must be considered in calibrating T centre devices for high-performance entanglement generation.

quant-ph↗

arXiv2025

Magnetic Field-dependent Isotope Effect Supports Radical Pair Mechanism in Tubulin Polymerization

Weak magnetic fields and isotopes have been shown to influence biological processes; however, the underlying mechanisms remain poorly understood, particularly because the corresponding interaction energies are far below thermal energies, making classical explanations challenging or impossible. Microtubules, dynamic cytoskeletal fibers, offer an ideal system to test weak magnetic field effects due to their self-assembling capabilities, sensitivity to magnetic fields, and their central role in cellular processes. In this study, we use a combination of experiments and simulations to explore how nuclear spin dynamics affect microtubule polymerization by examining interactions between magnesium isotope substitution and weak magnetic fields. Our experiments reveal an isotope-dependent effect, which can be explained via a radical pair mechanism, explicitly arising from nuclear spin properties rather than isotopic mass differences. This nuclear spin-driven isotope effect is notably enhanced under an applied weak magnetic field of approximately 3 mT. Our theoretical model based on radical pairs achieves quantitative agreement with our experimental observations. These results establish a direct connection between quantum spin dynamics and microtubule assembly, providing new insights into how weak magnetic fields influence cellular and biomolecular functions.

physics.bio-ph↗

arXiv2024

Hole burning experiments and modeling in erbium-doped silica glass fibers down to millikelvin temperatures: evidence for ultra-long population storage

We use spectral hole burning to investigate spin dynamics within the electronic Zeeman sublevels of the ground state of the erbium ions in erbium-doped fibers (EDF). Conducted at ultra-low temperatures and under varying magnetic fields, our study reveals distinct changes in spin relaxation dynamics across different conditions. We identified three decay components at approximately 7 mK, with one achieving spin lifetimes of over 9 hours under optimal conditions, while two components were observed at higher temperatures. The fairly stable relative weights of the decay components across conditions suggest distinct ion populations contributing to the observed relaxation dynamics. While earlier studies struggled to account for all decay components at higher temperatures, our approach successfully models spin dynamics across all observed decay components, using a consistent set of underlying mechanisms, including spin flip-flop interactions, direct coupling to two-level systems, and Raman-type processes, and distinguishes the decay components by the strengths with which these mechanisms contribute. These results suggest EDFs' potential as a promising candidate for quantum memory applications, with further room for optimization.

quant-ph↗

arXiv2024

Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$

Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with a classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si$_{11}$ with 4 Al (4Al@Si$_{11}$) and the results indicate the QAL method is able to find the optimum 4Al@Si$_{11}$ structure. The aim of this work is to present the QAL method -- formulated in a noise-free quantum computing framework -- for automatic structural determination of doped nanoparticles and materials defects.

quant-ph↗