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Quantum noise is inherent in any open quantum system as it affects not only the statistical properties of the initial state but also the time evolution of the system. A transversely pumped atom-cavity setup is a prototypical example of such an open system, wherein limit cycles (LCs) have been observed and identified as continuous time crystals (CTCs). Using truncated Wigner approximation (TWA), we show that the inherent quantum noise pushes the system to exhibit signatures of LCs for interaction strengths lower than the critical value predicted by mean-field theory, suggesting a noise-induced enhancement of temporal ordering. By comparing the oscillation frequencies of the LCs for the one-dimensional (1D) and two-dimensional (2D) regimes, we find that the LC frequencies have larger shot-to-shot fluctuations in 1D than in 2D. We demonstrate that this has an important consequence in the effectiveness of entrainment of LCs for a periodically driven pump intensity or light-matter coupling strength., Comment: 8 pages, 8 figures

We propose a novel approach to mitigate the decoherence effects on a target system S by coupling it to an ancillary system A with tunable parameters. By suitably engineering the S-A interaction Hamiltonian, a dark factorized compound state is found that achieves effective noise cancellation and significantly preserves quantum coherence in the target system S. We illustrate our methodology for a system $S$ consisting of two-mode Bosons trapped in a double-well potential and affected by decoherence through a Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation. By a suitable coupling of the system $S$ with an ancillary two-mode system of the same kind of S via a density-density interaction, we enhance the resilience of the system NOON state, a quantum-mechanical many-body entangled state crucial for quantum computing. We further explore potential experimental implementations of the proposed noise cancellation technique, focusing on configurations involving dipole-dipole interactions in optical lattices. Finally, we propose a numerical optimization protocol that learns the system A and its interaction with S to maximize the survival probability of specific quantum states which can be potentially used in more generic quantum systems.

The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; overheads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the optimal combination is a non-trivial issue, as it not only depends on user requirements, but also on details of the hardware and software stack. Appropriate automation can lift the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive best-possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, we argue, mark the relevant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from high-level problem specifications., Comment: To be published as a workshop paper at QCE 2024 (WIHPQC 2024)

In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms., Comment: 13 pages, 9 figures

Dynamic critical fluctuations in magnetic materials encode important information about magnetic ordering in the associated critical exponents. Using nitrogen-vacancy centers in diamond, we implement $T_2$ (spin-decoherence) noise magnetometry to study critical dynamics in a 2D Van der Waals magnet CrSBr. By analyzing NV decoherence on time scales approaching the characteristic correlation time $\tau_c$ of critical fluctuations, we extract the critical exponent $\nu$ for the correlation length. Our result deviates from the Ising prediction and highlights the role of long-range dipolar interactions in 2D CrSBr. Furthermore, analyzing the divergence of the correlation length suggests the possibility of 2D-XY criticality in CrSBr in a temperature window near $T_C$ where static magnetic domains are absent. Our work provides a first demonstration of $T_2$ noise magnetometry to quantitatively analyze critical scaling behavior in 2D materials., Comment: 19 pages main text, 4 main text figures, 26 pages Supplementary Materials, 13 Supplementary figures

We show nonreciprocal light propagation for single-photon inputs due to quantum noise in coupled optical systems with gain and loss. We consider two parity-time ($\mathcal{PT}$) symmetric linear optical systems consisting of either two directly coupled resonators or two finite-length waveguides evanescently coupled in parallel. One resonator or waveguide is filled with an active gain medium and the other with a passive loss medium. The light propagation is reciprocal in such $\mathcal{PT}$ symmetric linear systems without quantum noise. We show here that light transmission becomes nonreciprocal when we include quantum noises in our modeling, which is essential for a proper physical description. The quantum nonreciprocity is especially pronounced in the $\mathcal{PT}$ broken phase. Transmitted light intensity in the waveguide of incidence is asymmetric for two waveguides even without noise. Quantum noise significantly enhances such asymmetry in the broken phase., Comment: 12 pages, 4 figures

Recent attention has been drawn to temperature gradient generated $\Delta_T$ noise at vanishing charge current. This study delves into examining the properties of spin-polarised $\Delta T$ noise in conjunction with $\Delta_T$-shot noise, $\Delta_T$-thermal noise, and quantum noise (again both shot and thermal noise) in a one-dimensional (1D) structure comprising metal/spin-flipper/metal/insulator/superconductor junction to probe Yu-Shiba-Rusinov (YSR) bound states. YSR bound states, which are localized states within the superconducting gap of a superconductor are induced by a magnetic impurity acting as a spin-flipper. A YSR bound state should be distinguished from a Majorana bound state (MBS), which too can occur due to interaction with magnetic impurities, e.g., magnetic adatoms on superconductors, and this can lead to false positives in detecting MBS. Clarifying this by providing a unique signature for the YSR-bound state is the main aim of this work. In this paper, we show that YSR bound states can be effectively probed using quantum noise and the recently discovered $\Delta_T$ noise, with a focus on especially spin transport. We see that the spin $\Delta_T$ noise is a superior tool compared to the charge $\Delta_T$ noise as a probe for YSR bound states. Additionally, our analysis of quantum noise reveals that similar to $\Delta_T$ noise, spin quantum noise is more effective than charge quantum noise in detecting YSR bound states., Comment: 14 pages, 6 figures, 3 tables

Parameterized Quantum Circuits (PQCs) have been acknowledged as a leading strategy to utilize near-term quantum advantages in multiple problems, including machine learning and combinatorial optimization. When applied to specific tasks, the parameters in the quantum circuits are trained to minimize the target function. Although there have been comprehensive studies to improve the performance of the PQCs on practical tasks, the errors caused by the quantum noise downgrade the performance when running on real quantum computers. In particular, when the quantum state is transformed through multiple quantum circuit layers, the effect of the quantum noise happens cumulatively and becomes closer to the maximally mixed state or complete noise. This paper studies the relationship between the quantum noise and the diffusion model. Then, we propose a novel diffusion-inspired learning approach to mitigate the quantum noise in the PQCs and reduce the error for specific tasks. Through our experiments, we illustrate the efficiency of the learning strategy and achieve state-of-the-art performance on classification tasks in the quantum noise scenarios.

Precision measurements of space and time, like those made by the detectors of the Laser Interferometer Gravitational-wave Observatory (LIGO), are often confronted with fundamental limitations imposed by quantum mechanics. The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot both be precisely measured, giving rise to an apparent limitation called the Standard Quantum Limit (SQL). Reducing quantum noise below the SQL in gravitational-wave detectors, where photons are used to continuously measure the positions of freely falling mirrors, has been an active area of research for decades. Here we show how the LIGO A+ upgrade reduced the detectors' quantum noise below the SQL by up to 3 dB while achieving a broadband sensitivity improvement, more than two decades after this possibility was first presented.

Nonequilibrium quantum noise $S^>(\omega,V)$ measured at finite frequencies $\omega$ and bias voltages $V$ probes Majorana bound states in a host nanostructure via fluctuation fingerprints unavailable in average currents or static shot noise. When Majorana interference is brought into play, it enriches nonequilibrium states and makes their nature even more unique. Here we demonstrate that an interference of two Majorana modes via a nonequilibrium quantum dot gives rise to a remarkable finite frequency response of the differential quantum noise $\partial S^>(\omega,V,\Delta\phi)/\partial V$ driven by the Majorana phase difference $\Delta\phi$. Specifically, at low bias voltages there develops a narrow resonance of width $\hbar\Delta\omega\sim\sin^2\Delta\phi$ at a finite frequency determined by $V$, whereas for high bias voltages there arise two antiresonances at two finite frequencies controlled by both $V$ and $\Delta\phi$. We show that the maximum and minimum of these resonance and antiresonances have universal fractional values, $3e^3/4h$ and $-e^3/4h$. Moreover, detecting the frequencies of the antiresonances provides a potential tool to measure $\Delta\phi$ in nonequilibrium experiments on Majorana finite frequency quantum noise., Comment: 14 pages, 8 figures

This work contributes to the advancement of quantum communication by visualizing hybrid quantum noise in higher dimensions and optimizing the capacity of the quantum channel by using machine learning (ML). Employing the expectation maximization (EM) algorithm, the quantum channel parameters are iteratively adjusted to estimate the channel capacity, facilitating the categorization of quantum noise data in higher dimensions into a finite number of clusters. In contrast to previous investigations that represented the model in lower dimensions, our work describes the quantum noise as a Gaussian Mixture Model (GMM) with mixing weights derived from a Poisson distribution. The objective was to model the quantum noise using a finite mixture of Gaussian components while preserving the mixing coefficients from the Poisson distribution. Approximating the infinite Gaussian mixture with a finite number of components makes it feasible to visualize clusters of quantum noise data without modifying the original probability density function. By implementing the EM algorithm, the research fine-tuned the channel parameters, identified optimal clusters, improved channel capacity estimation, and offered insights into the characteristics of quantum noise within an ML framework.

This paper provides a comprehensive overview of the development of flux-driven Josephson Parametric Amplifiers (JPAs) as Quantum Noise Limited Amplifier for axion search experiments conducted at the Center for Axion and Precision Physics Research (CAPP) of the Institute for Basic Science. It focuses on the characterization, and optimization of JPAs, which are crucial for achieving the highest sensitivity in axion particle detection. We discuss various characterization techniques, methods for improving bandwidth, and the attainment of ultra-low noise temperatures. JPAs have emerged as indispensable tools in CAPPs axion search endeavors, playing a significant role in advancing our understanding of fundamental physics and unraveling the mysteries of the universe., Comment: 29 pages, 15 figures

In this paper, for the first time to our knowledge, an explicit expression for the maximum detection range of an entangled quantum two-mode squeezed (QTMS) radar, in which a two-mode squeezed vacuum state of microwave electromagnetic fields is used, has been derived by considering both the quantum properties of the entangled microwave fields and radar parameters. By comparing this equation with that of traditional radars, we show that one can view a QTMS radar as a traditional radar with a reduced threshold signal-to-noise ratio. By discussing the current limitations, it has been shown that the critical parameter to achieve both simultaneous quantum advantage and substantial radar range is increasing the bandwidth of the generated output signal in the quantum entangled source. It has been demonstrated that, by considering the current feasible system parameters, it is possible to implement a QTMS radar with a maximum detection range of up to 2km, which is suitable for recognizing small unmanned aerial vehicles at urban distances. Moreover, based on the false alarm rate, we introduce two classes of early alarm and track QTMS radars. This approach can be generalized to other quantum radars with different types of quantum sources, such as electro-opto-mechanical sources, and may shed new light on investigating quantum radar systems for practical applications, such as far-distance ultrasensitive contactless vital sign detection and counter-Drone technology. Finally, we discuss potential outlooks to improve and develop quantum entangled radar systems to be practical from an engineering point of view.

Characterizing temporally correlated (``non-Markovian'') noise is a key prerequisite for achieving noise-tailored error mitigation and optimal device performance. Quantum noise spectroscopy can afford quantitative estimation of the noise spectral features; however, in its current form it is highly vulnerable to implementation non-idealities, notably, state-preparation and measurement (SPAM) errors. Further to that, existing protocols have been mostly developed for dephasing-dominated noise processes, with competing dephasing and relaxation effects being largely unaccounted for. We introduce quantum noise spectroscopy protocols inspired by spin-locking techniques that enable the characterization of arbitrary temporally correlated multi-axis noise on a qubit with fixed energy splitting, while remaining resilient to realistic static SPAM errors. By validating our protocol's performance in both numerical simulation and cloud-based IBM quantum processors, we demonstrate the successful separation and estimation of native noise spectrum components as well as SPAM error rates. We find that SPAM errors can significantly alter or mask important noise features, with spectra overestimated by up to 26.4% in a classical noise regime. Clear signatures of non-classical noise are manifest in the reconstructed IBM-qubit dephasing spectra, once SPAM-error effects are compensated for. Our work provides a timely tool for benchmarking realistic sources of noise in qubit devices.

This paper delves into the intricate dynamics of quantum noise and its influence on the onset and mitigation of barren plateaus (BPs) - a phenomenon that critically impedes the scalability of QNNs. We find that BPs appear earlier in noisy quantum environments compared to ideal, noise-free conditions.However, strategic selection of qubit measurement observables can effectively tackle this issue. To this end, we examine a variety of observables, such as PauliZ,PauliX, PauliY, and a specially designed arbitrary Hermitian observable, tailored to the requirements of the cost function and the desired outputs of quantum circuits. Our analysis encompasses both global and local cost function definitions, with the former involving measurements across all qubits and the latter focusing on single-qubit measurements within the QNN framework. Our findings indicate that in a global cost function scenario, PauliX and PauliY observables lead to flatter optimization landscapes, signaling BPs with increasing qubits, especially in noisy conditions. Conversely, the PauliZ observable maintains trainability up to 8 qubits but encounters BPs at 10 qubits. Notably, the arbitrary Hermitian observable, when used with a global cost function, shows a unique advantage as it benefits from noise, facilitating effective training up to 10 qubits. Furthermore, with a local cost function, out of the three conventional observables (PauliX, PauliY and PauliZ), PauliZ is more effective, sustaining training efficiency under noisy conditions for up to 10 qubits, while PauliX and PauliY do not show similar benefits and remain susceptible to BPs. Our results highlight the importance of noise consideration in QNN training and propose a strategic approach to observable selection to improve QNN performance in noisy quantum computing environments thus contributing to the advancement of quantum machine learning research.

Neutrino oscillation experiments show that neutrinos have mass; however, the absolute mass scale is exceedingly difficult to measure and is currently unknown. A promising approach is to measure the energies of the electrons released during the radioactive decay of tritium. The energies of interest are within a few eV of the 18.6 keV end point, and so are mildly relativistic. By capturing the electrons in a static magnetic field and measuring the frequency of the cyclotron radiation emitted the initial energy can be determined, but end-point events are infrequent, the observing times short, and the signal to noise ratios low. To achieve a resolution of $<$ 10 meV, single-electron emission spectra need to be recorded over large fields of view with highly sensitive receivers. The principles of Cylotron Radiation Emission Spectroscopy (CRES) have already been demonstrated by Project 8, and now there is considerable interest in increasing the FoV to $>$ 0.1 m$^3$. We consider a range of issues relating to the design and optimisation of inward-looking quantum-noise-limited microwave receivers for single-electron CRES, and present a single framework for understanding signal, noise and system-level behaviour. Whilst there is a great deal of literature relating to the design of outward-looking phased arrays for applications such as radar and telecommunications, there is very little coverage of the new issues that come into play when designing ultra-sensitive inward-looking phased arrays for volumetric spectroscopy and imaging., Comment: 60 pages, 33 figures

Characterizing noise is key to the optimal control of the quantum system it affects. Using a single-qubit probe and appropriate sequences of $\pi$ and non-$\pi$ pulses, we show how one can characterize the noise a quantum bath generates across a wide range of frequencies -- including frequencies below the limit set by the probe's $\mathbb{T}_2$ time. To do so we leverage an exact expression for the dynamics of the probe in the presence of non-$\pi$ pulses, and a general inequality between the symmetric (classical) and anti-symmetric (quantum) components of the noise spectrum generated by a Gaussian bath. Simulation demonstrates the effectiveness of our method., Comment: 23 pages. Comments welcome

Complex quantum systems and their various applications are susceptible to noise of coherent and incoherent nature. Characterization of noise and its sources is an open, key challenge in quantum technology applications, especially in terms of distinguishing between inherent incoherent noise and systematic coherent errors. In this paper, we study a scheme of repeated sequential measurements that enables the characterization of incoherent errors by reducing the effects of coherent errors. We demonstrate this approach using a coherently controlled Nitrogen Vacancy in diamond, coupled to both a natural nuclear spin bath (non-Markovian) and to experimentally controlled relaxation through an optical pumping process (nearly Markovian). Our results show mitigation of coherent errors both for Markovian and Non-Markovian incoherent noise profiles. We apply this scheme to the estimation of the dephasing time ($T_2^*$) due to incoherent noise. We observe an improved robustness against coherent errors in the estimation of dephasing time ($T_2^*$) compared to the standard (Ramsey) measurement.

Quantum locking using optical spring and homodyne detection has been devised to reduce quantum noise that limits the sensitivity of DECIGO, a space-based gravitational wave antenna in the frequency band around 0.1 Hz for detection of primordial gravitational waves. The reduction in the upper limit of energy density ${\Omega}_{\mathrm{GW}}$ from $2{\times}10^{-15}$ to $1{\times}10^{-16}$, as inferred from recent observations, necessitates improved sensitivity in DECIGO to meet its primary science goals. To accurately evaluate the effectiveness of this method, this paper considers a detection mechanism that takes into account the influence of vacuum fluctuations on homodyne detection. In addition, an advanced signal processing method is devised to efficiently utilize signals from each photodetector, and design parameters for this configuration are optimized for the quantum noise. Our results show that this method is effective in reducing quantum noise, despite the detrimental impact of vacuum fluctuations on its sensitivity., Comment: 12 pages, 5 figures

We introduce an efficient algorithm based on the quantum noise framework for simulating open quantum systems on quantum devices. We prove that the open system dynamics can be simulated by repeatedly applying random unitary gates acting on the system qubits plus a single ancillary bath qubit representing the environment. This algorithm represents a notable step forward compared to current approaches, not only beacause the ancilla overhead remains always constant regardless of the system size, but also because it provides a perturbative approximation of the full Lindblad equation to first order in the environment coupling constants, allowing to reach a better target accuracy with respect to first order approximation in the time step, thus reducing the total number of steps. When the perturbative approximation does not hold one can take smaller time steps and the approach reduces to the solution to first order in the time step. As a future perspective, this framework easily accomodates non-Markovian effects by relaxing the reset of the bath qubit prescription.

The influence of a Gaussian environment on a quantum system can be described by effectively replacing the continuum with a discrete set of ancillary quantum and classical degrees of freedom. This defines a pseudomode model which can be used to classically simulate the reduced system dynamics. Here, we consider an alternative point of view and analyze the potential benefits of an analog or digital quantum simulation of the pseudomode model itself. Superficially, such a direct experimental implementation is, in general, impossible due to the unphysical properties of the effective degrees of freedom involved. However, we show that the effects of the unphysical pseudomode model can still be reproduced using measurement results over an ensemble of physical systems involving ancillary harmonic modes and an optional stochastic driving field. This is done by introducing an extrapolation technique whose efficiency is limited by stability against imprecision in the measurement data. We examine how such a simulation would allow us to (i) perform accurate quantum simulation of the effects of complex non-perturbative and non-Markovian environments in regimes that are challenging for classical simulation, (ii) conversely, mitigate potential unwanted non-Markovian noise present in quantum devices, and (iii) restructure some of some of the properties of a given physical bath, such as its temperature., Comment: 30 pages, 10 figures

Quantum stabilizer codes often face the challenge of syndrome errors due to error-prone measurements. To address this issue, multiple rounds of syndrome extraction are typically employed to obtain reliable error syndromes. In this paper, we consider phenomenological decoding problems, where data qubit errors may occur between two syndrome extractions, and each syndrome measurement can be faulty. To handle these diverse error sources, we define a generalized check matrix over mixed quaternary and binary alphabets to characterize their error syndromes. This generalized check matrix leads to the creation of a Tanner graph comprising quaternary and binary variable nodes, which facilitates the development of belief propagation (BP) decoding algorithms to tackle phenomenological errors. Importantly, our BP decoders are applicable to general sparse quantum codes. Through simulations of quantum memory protected by rotated toric codes, we demonstrates an error threshold of 3.3% in the phenomenological noise model. Additionally, we propose a method to construct effective redundant stabilizer checks for single-shot error correction. Simulations show that BP decoding performs exceptionally well, even when the syndrome error rate greatly exceeds the data error rate., Comment: 14 pages, 9 figures, 1 table

Generative models realized with machine learning techniques are powerful tools to infer complex and unknown data distributions from a finite number of training samples in order to produce new synthetic data. Diffusion models are an emerging framework that have recently overcome the performance of the generative adversarial networks in creating synthetic text and high-quality images. Here, we propose and discuss the quantum generalization of diffusion models, i.e., three quantum-noise-driven generative diffusion models that could be experimentally tested on real quantum systems. The idea is to harness unique quantum features, in particular the non-trivial interplay among coherence, entanglement and noise that the currently available noisy quantum processors do unavoidably suffer from, in order to overcome the main computational burdens of classical diffusion models during inference. Hence, we suggest to exploit quantum noise not as an issue to be detected and solved but instead as a very remarkably beneficial key ingredient to generate much more complex probability distributions that would be difficult or even impossible to express classically, and from which a quantum processor might sample more efficiently than a classical one. An example of numerical simulations for an hybrid classical-quantum generative diffusion model is also included. Therefore, our results are expected to pave the way for new quantum-inspired or quantum-based generative diffusion algorithms addressing more powerfully classical tasks as data generation/prediction with widespread real-world applications ranging from climate forecasting to neuroscience, from traffic flow analysis to financial forecasting., Comment: 27 pages, 4 figures

Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference-based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results show that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution., Comment: To appear in IEEE QCE 2023

Correlated noise across multiple qubits poses a significant challenge for achieving scalable and fault-tolerant quantum processors. Despite recent experimental efforts to quantify this noise in various qubit architectures, a comprehensive understanding of its role in qubit dynamics remains elusive. Here, we present an analytical study of the dynamics of driven qubits under spatially correlated noise, including both Markovian and non-Markovian noise. Surprisingly, we find that, while correlated classical noise only leads to correlated decoherence without increasing the quantum coherence in the system, the correlated quantum noise can be exploited to generate entanglement. In particular, we reveal that, in the quantum limit, pure dephasing noise induces a coherent long-range two-qubit Ising interaction that correlates distant qubits. In contrast, for purely transverse noise when qubits are subjected to coherent drives, the correlated quantum noise induces both coherent symmetric exchange and Dzyaloshinskii-Moriya interaction between the qubits, as well as correlated relaxation, both of which give rise to significant entanglement. Remarkably, in this case, we uncover that the system exhibits distinct dynamical phases in different parameter regimes. Finally, we reveal the impact of spatio-temporally correlated 1/f noise on the decoherence rate, and how its temporal correlations restore lost entanglement. Our analysis not only offers critical insights into designing effective error mitigation strategies to reduce harmful effects of correlated noise, but also enables tailored protocols to leverage and harness noise-induced correlations for quantum information processing., Comment: 26 pages, 11 figures

Analog physical neural networks, which hold promise for improved energy efficiency and speed compared to digital electronic neural networks, are nevertheless typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10). What happens if an analog system is instead operated in an ultra-low-power regime, in which the behavior of the system becomes highly stochastic and the noise is no longer a small perturbation on the signal? In this paper, we study this question in the setting of optical neural networks operated in the limit where some layers use only a single photon to cause a neuron activation. Neuron activations in this limit are dominated by quantum noise from the fundamentally probabilistic nature of single-photon detection of weak optical signals. We show that it is possible to train stochastic optical neural networks to perform deterministic image-classification tasks with high accuracy in spite of the extremely high noise (SNR ~ 1) by using a training procedure that directly models the stochastic behavior of photodetection. We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to 0.008 photons per multiply-accumulate (MAC) operation, which is equivalent to 0.003 attojoules of optical energy per MAC. Our experiment used >40x fewer photons per inference than previous state-of-the-art low-optical-energy demonstrations, to achieve the same accuracy of >90%. Our work shows that some extremely stochastic analog systems, including those operating in the limit where quantum noise dominates, can nevertheless be used as layers in neural networks that deterministically perform classification tasks with high accuracy if they are appropriately trained., Comment: 55 pages, 27 figures

The propagation of ultrafast pulses in dispersion-engineered waveguides, exhibiting strong field confinement in both space and time, is a promising avenue towards single-photon nonlinearities in an all-optical platform. However, quantum engineering in such systems requires new numerical tools and physical insights to harness their complicated multimode and nonlinear quantum dynamics. In this work, we use a self-consistent, multimode Gaussian-state model to capture the nonlinear dynamics of broadband quantum fluctuations and correlations, including entanglement. Notably, despite its parametrization by Gaussian states, our model exhibits nonlinear dynamics in both the mean field and the quantum correlations, giving it a marked advantage over conventional linearized treatments of quantum noise, especially for systems exhibiting gain saturation and strong nonlinearities. Numerically, our approach takes the form of a Gaussian split-step Fourier (GSSF) method, naturally generalizing highly efficient SSF methods used in classical ultrafast nonlinear optics; the equations for GSSF evaluate in $O(M^2\log M)$ time for an $M$-mode system with $O(M^2)$ quantum correlations. To demonstrate the broad applicability of GSSF, we numerically study quantum noise dynamics and multimode entanglement in several ultrafast systems, from canonical soliton propagation in third-order ($\chi^{(3)}$) waveguides to saturated $\chi^{(2)}$ broadband parametric generation and supercontinuum generation, e.g., as recently demonstrated in thin-film lithium niobate nanophotonics., Comment: The first two authors contributed equally to this work. 19 pages, 4 figures

Precision experimental determination of photon correlation requires the massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation $g^{(2)}(0)$ of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser and the $g^{(2)}(0)$ of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of the $g^{(2)}(0)$ for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the $g^{(2)}(0)$ experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 seconds and achieving a three orders of magnitude acceleration in data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging., Comment: 6 pages, 6 figures

Electromagnetic remote sensing technologies such as radar can be mislead by targets that generate spoof pulses. Typically, a would-be spoofer must make measurements to characterize a received pulse in order to design a convincing spoof pulse. The precision of such measurements are ultimately limited by quantum noise. Here we introduce a model of electromagnetic spoofing that includes effects of practical importance that were neglected in prior theoretical studies. In particular, the model includes thermal background noise and digital quantization noise, as well as loss in transmission, propagation, and reception. We derive the optimal probability of detecting a spoofer allowed by quantum physics. We show that heterodyne reception and thresholding closely approaches this optimal performance. Finally, we show that a high degree of certainty in spoof detection can be reached by Bayesian inference from a sequence of received pulses. Together these results suggest that a practically realizable receiver could plausibly detect a radar spoofer by observing errors in the spoof pulses due to quantum noise.

We examine the effect of a trans-Planckian phase on the dynamics of inflationary tensor perturbations. To remedy the fact that this regime is not fully captured by standard perturbation theory, we introduce an effective quantum noise source, whose role is regulated by the energy scale $\Lambda$. The presence of the source modifies the initial conditions for the tensor modes, leaving a distinct imprint. We study the amplitude and shape of the gravitational wave bispectrum of the model and compare these with their counterparts obtained under the assumptions of Bunch-Davies initial conditions and $\alpha$-vacua states. Depending on the value of the scale $\Lambda$, we find distinctive signatures associated with both the bispectrum shape and the non-linear parameter $f_{\rm NL}$., Comment: 16 pages, 7 figures

We introduce a notion of expected utility for quantum tasks and discuss some general conditions under which this is increased by the presence of quantum noise in the underlying resource states. We apply the resulting formalism to the specific problem of playing the parity game with ground states of the random transverse-field Ising model. This demonstrates a separation in the ground-state phase diagram between regions where rational players will be ``risk-seeking'' or ``risk-averse'', depending on whether they win the game more or less often in the presence of disorder. The boundary between these regions depends non-universally on the correlation length of the disorder. Strikingly, we find that adding zero-mean, uncorrelated disorder to the transverse fields can generate a weak quantum advantage that would not exist in the absence of noise., Comment: 18 pages, 6 figures

The quantum phase estimation (QPE) is one of the fundamental algorithms based on the quantum Fourier transform. It has applications in order-finding, factoring, and finding the eigenvalues of unitary operators. The major challenge in running QPE and other quantum algorithms is the noise in quantum computers. In the present work, we study the impact of incoherent noise on QPE, modeled as trace-preserving and completely positive quantum channels. Different noise models such as depolarizing, phase flip, bit flip, and bit-phase flip are taken to understand the performance of the QPE in the presence of noise. The simulation results indicate that the standard deviation of the eigenvalue of the unitary operator has strong exponential dependence upon the error probability of individual qubits. However, the standard deviation increases only linearly with the number of qubits for fixed error probability when that error probability is small., Comment: 6 pages, 4 figures, SPIE Conference Proceedings, 2024

We experimentally present a random phase feedback based on quantum noise to generate a chaotic laser with Gaussian invariant distribution. The quantum noise from vacuum fluctuations is acquired by balanced homodyne detection and injected into a phase modulator to form a random phase feedback. An optical switch using high-speed intensity modulator is employed to reset the chaotic states repeatedly and the time evolutions of intensity statistical distributions of the chaotic states stemming from the initial noise are measured. By the quantum-noise random phase feedback, the transient intensity distributions of the chaotic outputs are improved from asymmetric invariant distributions to Gaussian invariant distributions, and the Gaussian invariant distribution indicates a randomly perturbed dynamical transition from microscopic initial noise to macroscopic stochastic fluctuation. The effects of phase feedback bandwidth and modulation depth on the invariant distributions are investigated experimentally. The chaotic time-delay signature and mean permutation entropy are suppressed to 0.036 and enhanced to 0.999 using the random phase feedback, respectively. The high-quality chaotic laser with Gaussian invariant distribution can be a desired random source for ultrafast random number generation and secure communication., Comment: 11 pages, 10 figures

We study an abstract model of an oscillator realized by an amplifier embedded in a positive feedback loop. The power and frequency stability of the output of such an oscillator are limited by quantum noise added by two elements in the loop: the amplifier, and the out-coupler. The resulting frequency instability gives the Schawlow-Townes formula. Thus the applicability of the Schawlow-Townes formula is extended to a large class of oscillators, and is shown to be related to the Haus-Caves quantum noise limit for a linear amplifier, while identifying the role of quantum noise added at the out-coupler. By illuminating the precise origin of amplitude and frequency quantum noise in the output of an oscillator, we reveal several techniques to systematically evade them.

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What's worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, IBM-Jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection.

The distinction between chiral, trivial helical, and topological helical edge modes can be effectively made using quantum noise measurements at finite temperatures. Quantum noise measurements consist of mainly two components. The first is thermal noise, whose provenance is thermal fluctuations, and the second is shot noise, whose origin is the quantum nature of charge particles. Studying these edge modes at finite temperatures is important as it more accurately reflects the conditions in real-world experiments. Additionally, we have verified that our results for finite temperature quantum noise correlations are valid at finite frequencies too., Comment: 33 pages, 17 figures, accepted for publication in Phys. Rev. B

It is shown that the intensity quantum noise of a single-emitter nanolaser can be accurately computed by adopting a stochastic interpretation of the standard rate equation model under the only assumption that the emitter excitation and photon number are stochastic variables with integer values. This extends the validity of rate equations beyond the mean-field limit and avoids using the standard Langevin approach, which is shown to fail for few emitters. The model is validated by comparison to full quantum simulations of the relative intensity noise and second-order intensity correlation function, g(2)({\tau} ). Surprisingly, even when the full quantum model displays vacuum Rabi oscillations, which are not accounted for by rate equations, the intensity quantum noise is correctly predicted by the stochastic approach. Adopting a simple discretization of the emitter and photon populations, thus, goes a long way in describing quantum noise in lasers. Besides providing a versatile and easy-to-use tool for modeling a new generation of nanolasers with many possible applications, these results provide insight into the fundamental nature of quantum noise in lasers., Comment: Revised and resubmitted for review

Recent progress in quantum computing and the development of novel detector technologies for astrophysics is driving the need for high-gain, broadband, and quantum-limited amplifiers. We present a purely traveling-wave parametric amplifier (TWPA) using an inverted NbTiN microstrip and amorphous Silicon dielectric. Through dispersion engineering, we are able to obtain $50~\Omega$ impedance matching and suppress undesired parametric processes while phase matching the three-wave-mixing amplification across a large range of frequencies. The result is a broadband amplifier operating with 20 dB gain and quantum-limited noise performance at 20 mK. At the single frequency where the amplifier is phase sensitive, we further demonstrate 8 dB of vacuum noise squeezing.

We propose a QNSC pre-coding scheme based on probabilistic shaping of the basis, to reduce the probability of ciphertext bits that are easier to be intercepted. Experiment results show this scheme can improve the security performance by 100% in terms of Eve's cipher text BER.

The axion is expected to solve the strong CP problem of quantum chromodynamics and is one of the leading candidates for dark matter. CAPP in South Korea has several axion search experiments based on cavity haloscopes in the frequency range of 1-6 GHz. The main effort focuses on operation of the experiments with the highest possible sensitivity. It requires maintenance of the haloscopes at the lowest physical temperature in the range of mK and usage of low noise components to amplify the weak axion signal. We report development and operation of low noise amplifiers for 5 haloscope experiments targeting at different frequency ranges. The amplifiers show noise temperatures approaching the quantum limit., Comment: 6 pages, 7 figures, 29th International Conference on Low Temperature Physics, August 18-24, 2022, Sapporo, Japan

Quantum reservoir computing is strongly emerging for sequential and time series data prediction in quantum machine learning. We make advancements to the quantum noise-induced reservoir, in which reservoir noise is used as a resource to generate expressive, nonlinear signals that are efficiently learned with a single linear output layer. We address the need for quantum reservoir tuning with a novel and generally applicable approach to quantum circuit parameterization, in which tunable noise models are programmed to the quantum reservoir circuit to be fully controlled for effective optimization. Our systematic approach also involves reductions in quantum reservoir circuits in the number of qubits and entanglement scheme complexity. We show that with only a single noise model and small memory capacities, excellent simulation results were obtained on nonlinear benchmarks that include the Mackey-Glass system for 100 steps ahead in the challenging chaotic regime.