Quantum Noise Research Articles

Taking advantage of noise in quantum reservoir computing

The biggest challenge that quantum computing and quantum machine learning are currently facing is the presence of noise in quantum devices. As a result, big efforts have been put into correcting or mitigating the induced errors. But, can these two fields benefit from noise? Surprisingly, we demonstrate that under some circumstances, quantum noise can be used to improve the performance of quantum reservoir computing, a prominent and recent quantum machine learning algorithm. Our results show that the amplitude damping noise can be beneficial to machine learning, while the depolarizing and phase damping noises should be prioritized for correction. This critical result sheds new light into the physical mechanisms underlying quantum devices, providing solid practical prescriptions for a successful implementation of quantum information processing in nowadays hardware.

Noise analysis of the atomic superheterodyne receiver based on flat-top laser beams.

Since its theoretical sensitivity is limited by quantum noise, radio wave sensing based on Rydberg atoms has the potential to replace its traditional counterparts with higher sensitivity and has developed rapidly in recent years. However, as the most sensitive atomic radio wave sensor, the atomic superheterodyne receiver lacks a detailed noise analysis to pave its way to achieve theoretical sensitivity. In this work, we quantitatively study the noise power spectrum of the atomic receiver versus the number of atoms, where the number of atoms is precisely controlled by changing the diameters of flat-top excitation laser beams. The results show that under the experimental conditions that the diameters of excitation beams are less than or equal to 2 mm and the read-out frequency is larger than 70 kHz, the sensitivity of the atomic receiver is limited only by the quantum noise and, in the other conditions, limited by classical noise. However, the experimental quantum-projection-noise-limited sensitivity this atomic receiver reaches is far from the theoretical sensitivity. This is because all atoms involved in light-atom interaction will contribute to noise, but only a fraction of them participating in the radio wave transition can provide valuable signals. At the same time, the calculation of the theoretical sensitivity considers both the noise and signal are contributed by the same amount of atoms. This work is essential in making the sensitivity of the atomic receiver reach its ultimate limit and is significant in quantum precision measurement.

Review Paper on Quantum Computing and Quantum Cryptography

Quantum computing and quantum cryptography are two areas of research that have the potential to transform modern computing and communication. Quantum computing promises to solve computational problems that are beyond the reach of classical computers, while quantum cryptography provides a way to secure communication channels that is immune to eavesdropping. Quantum cryptography is one of the emerging topics in the field of computer industry. This article focuses on quantum cryptography and how this technology makes a valuable contribution to an intensive defense strategy of completely secure key distribution. The scope of this article covers the weaknesses of modern digital cryptographic systems, the basic concepts of quantum cryptography, the practical implementations of this technology, and its limitations. , and finally the future direction that quantum cryptography is headed. We describe the result of an apparatus and protocol designed to perform quantum key distribution, whereby two users do not share any secret information (no private or public keys are known). previously known) initially exchanges a random quantum transmission consisting of veryweakly polarized light rays. In particular, readers can explore the following topics in depth: current cryptographic schemes (symmetric and asymmetric), the difference between quantum and classical computing, challenges of quantum computing, quantum algorithms (Shor and Grover), public key cryptographic schemes affected, symmetry affected schemes, impact on hash functions and post-quantum cryptography

Suppression of Crosstalk in Quantum Circuit Based on Instruction Exchange Rules and Duration.

Crosstalk is the primary source of noise in quantum computing equipment. The parallel execution of multiple instructions in quantum computation causes crosstalk, which causes coupling between signal lines and mutual inductance and capacitance between signal lines, destroying the quantum state and causing the program to fail to execute correctly. Overcoming crosstalk is a critical prerequisite for quantum error correction and large-scale fault-tolerant quantum computing. This paper provides an approach for suppressing crosstalk in quantum computers based on multiple instruction exchange rules and duration. Firstly, for the majority of the quantum gates that can be executed on quantum computing devices, a multiple instruction exchange rule is proposed. The multiple instruction exchange rule reorders quantum gates in quantum circuits and separates double quantum gates with high crosstalk on quantum circuits. Then, time stakes are inserted based on the duration of different quantum gates, and quantum gates with high crosstalk are carefully separated in the process of quantum circuit execution by quantum computing equipment to reduce the influence of crosstalk on circuit fidelity. Several benchmark experiments verify the proposed method's effectiveness. In comparison to previous techniques, the proposed method improves fidelity by 15.97% on average.

Broadband multi-channel quantum noise suppression and phase-sensitive modulation based on entangled beam

We present a theoretical scheme for broadband multi-channel quantum noise suppression and phase-sensitive modulation of continuous variables in a coupled resonant system with quantum entanglement properties. The effects of different coupling strengths, pumping power in suppressing quantum noise and controlling the width of quantum interference channels are analyzed carefully. Furthermore, quantum noise suppression at quadrature amplitude is obtained with phase-sensitive modulation. It shows that the entanglement strength of the output field and the quantum noise suppression effect can be enhanced significantly by a strong pumping filed due to interaction of pumping light with the nonlinear crystal. The full width at half maxima (FWHM) of the noise curve at the resonant peak (Δ = 0 MHz) is broadened up to 2.17 times compared to the single cavity. In the strong coupling resonant system, the FWHM at Δ = 0 MHz (Δ = ±3.1 MHz) is also broadened up to 1.27 (3.53) times compared to the weak coupling resonant system case. The multi-channel quantum interference creates an electromagnetically induced transparent-like line shape, which can be used to improve the transmission efficiency and stability of wave packets in quantum information processing and quantum memory.

Indefinite Causal Order for Quantum Phase Estimation with Pauli Noise

This paper further explores the recent scheme of switched quantum channels with indefinite causal order applied to the reference metrological task of quantum phase estimation in the presence of noise. We especially extend the explorations, previously reported with depolarizing noise and thermal noise, to the class of Pauli noises, important to the qubit and not previously addressed. Nonstandard capabilities, not accessible with standard quantum phase estimation, are exhibited and analyzed, with significant properties that are specific to the Pauli noises, while other properties are found in common with the depolarizing noise or the thermal noise. The results show that the presence and the type of quantum noise are both crucial to the determination of the nonstandard capabilities from the switched channel with indefinite causal order, with a constructive action of noise reminiscent of stochastic resonance phenomena. The study contributes to a more comprehensive and systematic characterization of the roles and specificities of quantum noise in the operation of the novel devices of switched quantum channels with indefinite causal order.

Cyclic nonlinear interferometry with entangled non-Gaussian spin states

We propose an efficient nonlinear readout scheme for entangled non-Gaussian spin states based on the intrinsic quasicyclic dynamics of interacting spin-$\frac{1}{2}$ systems. We focus on two well-known spin models of twist-and-turn (TNT) and two-axis-countertwisting (TACT), where the entangled non-Gaussian spin state can be generated by spin dynamics starting from unstable fixed points. In the TNT model, the non-Gaussian probe state evolves directly back to the vicinity of the initial state during the subsequent time-forward evolution for path recombining, accompanied by quantum magnification of an encoded signal and refocusing of the associated quantum noise. Based on low-order moment measurement, we find that the optimal metrological gain nearly saturates the quantum Cram\'er-Rao bound (QCRB) and follows the Heisenberg scaling. For the TACT case, the QCRB can also be nearly approached when the state converges to either of the two unstable fixed points corresponding to the initial state or its orthogonal coherent state, respectively. The latter case goes beyond previous studies where tracing back to or crossing the initial states was mostly considered. The present protocol does not require time reversal as in typical nonlinear interferometries and it also avoids complicated measurement of nonlinear observables or full probability distributions. The operational approach we discuss presents a practical way to realize high-precision and detection-noise-robust quantum metrology with entangled non-Gaussian spin states.

Analysis of the encryption penalty in a QAM-based quantum noise stream cipher.

Quantum noise stream cipher based on quadrature-amplitude-modulation (QAM/QNSC) is a kind of physical layer encryption technology. However, the additional encryption penalty will significantly affect the practical deployment of QNSC, especially in the high capacity and long-haul transmission system. With our research, the encryption process of QAM/QNSC degrades the transmission performance of plaintext information. In this paper, we quantitatively analyze the encryption penalty of QAM/QNSC based on the proposed concept of effective minimum Euclidean distance. We calculate the theoretical signal-to-noise ratio sensitivity and encryption penalty of QAM/QNSC signals. A modified feedforward pilot-aided two-stage carrier phase recovery scheme is used to reduce the effect of laser phase noise and the encryption penalty. Experimental results achieve single-channel 205.9 Gbit/s 640km transmission with single carrier polarization-diversity-multiplexing 16-QAM/QNSC signal.

Universal Kardar-Parisi-Zhang scaling in noisy hybrid quantum circuits

Measurement-induced phase transitions (MIPT) have attracted increasing attention due to the rich phenomenology of entanglement structures and their relation with quantum information processing. Since physical systems are unavoidably coupled to environment, quantum noise needs be considered in analyzing a system with MIPT, which may qualitatively modify or even destroy certain entanglement structure of the system. In this Letter, we investigate the effect of quantum noise modeled by reset quantum channel acting on each site with probability $q$ on MIPT. Based on the numerical results from the Clifford circuits, we show that the quantum noise can qualitatively change the entanglement properties - the entanglement obeys ``area law'' instead of ``volume law'' with projective measurement rate $p<p_{c}$. In the quantum noise induced ``area law'' phase, the entanglement exhibits a novel $q^{-1/3}$ power-law scaling. Using an analytic mapping of the quantum model to a classical statistical model, we further show that the ``area law'' entanglement is the consequence of the noise-driven symmetry-breaking field and the $q^{-1/3}$ scaling can be understood as the result of Kardar-Parisi-Zhang (KPZ) fluctuations of the directed polymer with an effective length scale $L_{\rm{eff}} \sim q^{-1}$ in a random environment.

Quantum process tomography with unsupervised learning and tensor networks

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, remains the quintessential primitive to completely characterize quantum devices. However, due to the exponential scaling of the required data and classical post-processing, its range of applicability is typically restricted to one- and two-qubit gates. Here, we present a technique for performing quantum process tomography that addresses these issues by combining a tensor network representation of the channel with a data-driven optimization inspired by unsupervised machine learning. We demonstrate our technique through synthetically generated data for ideal one- and two-dimensional random quantum circuits of up to 10 qubits, and a noisy 5-qubit circuit, reaching process fidelities above 0.99 using several orders of magnitude fewer (single-qubit) measurement shots than traditional tomographic techniques. Our results go far beyond state-of-the-art, providing a practical and timely tool for benchmarking quantum circuits in current and near-term quantum computers.

Online quantum time series processing with random oscillator networks

Reservoir computing is a powerful machine learning paradigm for online time series processing. It has reached state-of-the-art performance in tasks such as chaotic time series prediction and continuous speech recognition thanks to its unique combination of high computational power and low training cost which sets it aside from alternatives such as traditionally trained recurrent neural networks, and furthermore is amenable to implementations in dedicated hardware, potentially leading to extremely compact and efficient reservoir computers. Recently the use of random quantum systems has been proposed, leveraging the complexity of quantum dynamics for classical time series processing. Extracting the output from a quantum system without disturbing its state too much is problematic however, and can be expected to become a bottleneck in such approaches. Here we propose a reservoir computing inspired approach to online processing of time series consisting of quantum information, sidestepping the measurement problem. We illustrate its power by generalizing two paradigmatic benchmark tasks from classical reservoir computing to quantum information and introducing a task without a classical analogue where a random system is trained to both create and distribute entanglement between systems that never directly interact. Finally, we discuss partial generalizations where only the input or only the output time series is quantum.

Noisy Random Quantum Circuit Sampling and its Classical Simulation

AbstractDemonstrating quantum advantage on noisy quantum devices is one of the most important tasks of quantum computing research in the near future. Random quantum circuit sampling is proposed as the most promising approach to the task and has been implemented in experiments for achieving quantum advantage. A series of encouraging computational complexity results, based on some plausible assumptions, show that this task is impossible to complete efficiently by classical computers. However, in practical experiments on noisy quantum devices, the approximate average‐case hardness and the effects of the noise need to be further checked. The competition between the classical simulation algorithm (mainly based on tensor network algorithms) and noisy quantum devices is the explicit way to understand quantum advantage in practice. This review briefly overviews the computational complexity arguments for the hardness of classical simulation of random quantum circuits sampling, then focus on various methods of classical simulation of quantum circuits based on tensor network method.

Characterizing the superposition of arbitrary random quantum states and a known quantum state

The superposition of states is one of the most fundamental issues in the quantum world. Generally there do not exist physical operations to superpose two unknown random states with nonzero probability. We investigate the superposition problem of unknown qubit states with respect to a known qubit state. It is shown that under trace-nonincreasing completely positive operations the superposable state sets are located in some circles on the Bloch sphere. Meanwhile, we prove that the quantum states in a circle on the Bloch sphere are superposable with respect to a known state. Finally, for the high-dimensional case, we illustrate that any superposition transformation protocols will violate the no-cloning principle for almost all the states. Our results also promote the understanding and applications of the superposition principle in view of quantum no-go theorems.

Closed-loop control of a noisy qubit with reinforcement learning

The exotic nature of quantum mechanics differentiates machine learning applications in the quantum realm from classical ones. Stream learning is a powerful approach that can be applied to extract knowledge continuously from quantum systems in a wide range of tasks. In this paper, we propose a deep reinforcement learning method that uses streaming data from a continuously measured qubit in the presence of detuning, dephasing, and relaxation. The model receives streaming quantum information for learning and decision-making, providing instant feedback on the quantum system. We also explore the agent’s adaptability to other quantum noise patterns through transfer learning. Our protocol offers insights into closed-loop quantum control, potentially advancing the development of quantum technologies.

Deep learning denoising of digital breast tomosynthesis: Observer performance study of the effect on detection of microcalcifications in breast phantom images.

The noise in digital breast tomosynthesis (DBT) includes x-ray quantum noise and detector readout noise. The total radiation dose of a DBT scan is kept at about the level of a digital mammogram but the detector noise is increased due to acquisition of multiple projections. The high noise can degrade the detectability of subtle lesions, specifically microcalcifications (MCs). We previously developed a deep-learning-based denoiser to improve the image quality of DBT. In the current study, we conducted an observer performance study with breast radiologists to investigate the feasibility of using deep-learning-based denoising to improve the detection of MCs in DBT. We have a modular breast phantom set containing seven 1-cm-thick heterogeneous 50% adipose/50% fibroglandular slabs custom-made by CIRS, Inc. (Norfolk, VA). We made six 5-cm-thick breast phantoms embedded with 144 simulated MC clusters of four nominal speck sizes (0.125-0.150, 0.150-0.180, 0.180-0.212, 0.212-0.250mm) at random locations. The phantoms were imaged with a GE Pristina DBT system using the automatic standard (STD) mode. The phantoms were also imaged with the STD+ mode that increased the average glandular dose by 54% to be used as a reference condition for comparison of radiologists' reading. Our previously trained and validated denoiser was deployed to the STD images to obtain a denoised DBT set (dnSTD). Seven breast radiologists participated as readers to detect the MCs in the DBT volumes of the six phantoms under the three conditions (STD, STD+, dnSTD), totaling 18 DBT volumes. Each radiologist read all the 18 DBT volumes sequentially, which were arranged in a different order for each reader in a counter-balanced manner to minimize any potential reading order effects. They marked the location of each detected MC cluster and provided a conspicuity rating and their confidence level for the perceived cluster. The visual grading characteristics (VGC) analysis was used to compare the conspicuity ratings and the confidence levels of the radiologists for the detection of MCs. The average sensitivities over all MC speck sizes were 65.3%, 73.2%, and 72.3%, respectively, for the radiologists reading the STD, dnSTD, and STD+ volumes. The sensitivity for dnSTD was significantly higher than that for STD (p<0.005, two-tailed Wilcoxon signed rank test) and comparable to that for STD+. The average false positive rates were 3.9±4.6, 2.8±3.7, and 2.7±3.9 marks per DBT volume, respectively, for reading the STD, dnSTD, and STD+ images but the difference between dnSTD and STD or STD+ did not reach statistical significance. The overall conspicuity ratings and confidence levels by VGC analysis for dnSTD were significantly higher than those for both STD and STD+ (p≤0.001). The critical alpha value for significance was adjusted to be 0.025 with Bonferroni correction. This observer study using breast phantom images showed that deep-learning-based denoising has the potential to improve the detection of MCs in noisy DBT images and increase radiologists' confidence in differentiating noise from MCs without increasing radiation dose. Further studies are needed to evaluate the generalizability of these results to the wide range of DBTs from human subjects and patient populations in clinical settings.

Neural network analysis of unstable temporal intensity peaks in continuous wave modulation instability

We report the application of a neural network to correlate spectral and temporal properties for modulation instability excited by a continuous wave field with random quantum noise. Using numerical simulations to generate large ensembles of chaotic modulation instability data, a trained neural network is shown to be able to correlate unstable intensity spectra and associated temporal intensity peaks with correlation coefficients exceeding ρ=0.90 for dynamic ranges exceeding 40 dB. Such excellent correlation is obtained both in the initial evolving phase of modulation instability where spectra typically possess significant sideband structure, as well as in the stationary phase of the instability when distinct sideband structure is not apparent. For both cases, we also test the degree to which a neural network may be able to “forecast” temporal instability peaks based on analyzing spectral intensity at an earlier spatial reference position, but conclude that such forecasting is successful over only within a very localized distance range much less than the characteristic nonlinear length.

The fundamental 1/f noise in monolayer graphene

A lower bound on the voltage noise power spectrum set by quantum indeterminacy exists in any conducting material. This bound is calculated explicitly for monolayer graphene taking into account graphene pseudospin/valley band structure. It is found to be of the form characteristic of the observed [Formula: see text] noise. It is shown that the deviation of [Formula: see text] from unity is a result of the piezoelectric interaction of charge carriers with acoustic phonons. A comparison with the measured [Formula: see text] noise in graphene and graphene-based heterostructures shows that the observed noise intensity is near the quantum bound. It is demonstrated that the recently observed sharp peak in [Formula: see text] at small gate voltages in the field-effect transistors based on graphene/boron nitride heterostructure is well reproduced by the theory, and is to be attributed to the out-of-plane piezoelectric activity in this heterostructure. A simple kinetic model is proposed to describe the effect of the charge carrier trapping on [Formula: see text].

Approximate Unitary t-Designs by Short Random Quantum Circuits Using Nearest-Neighbor and Long-Range Gates

We prove that {{,textrm{poly},}}(t) cdot n^{1/D}-depth local random quantum circuits with two qudit nearest-neighbor gates on a D-dimensional lattice with n qudits are approximate t-designs in various measures. These include the “monomial” measure, meaning that the monomials of a random circuit from this family have expectation close to the value that would result from the Haar measure. Previously, the best bound was {{,textrm{poly},}}(t)cdot n due to Brandão–Harrow–Horodecki (Commun Math Phys 346(2):397–434, 2016) for D=1. We also improve the “scrambling” and “decoupling” bounds for spatially local random circuits due to Brown and Fawzi (Scrambling speed of random quantum circuits, 2012). One consequence of our result is that assuming the polynomial hierarchy ({{,mathrm{textsf{PH}},}}) is infinite and that certain counting problems are #{textsf{P}}-hard “on average”, sampling within total variation distance from these circuits is hard for classical computers. Previously, exact sampling from the outputs of even constant-depth quantum circuits was known to be hard for classical computers under these assumptions. However the standard strategy for extending this hardness result to approximate sampling requires the quantum circuits to have a property called “anti-concentration”, meaning roughly that the output has near-maximal entropy. Unitary 2-designs have the desired anti-concentration property. Our result improves the required depth for this level of anti-concentration from linear depth to a sub-linear value, depending on the geometry of the interactions. This is relevant to a recent experiment by the Google Quantum AI group to perform such a sampling task with 53 qubits on a two-dimensional lattice (Arute in Nature 574(7779):505–510, 2019; Boixo et al. in Nate Phys 14(6):595–600, 2018) (and related experiments by USTC), and confirms their conjecture that O(sqrt{n}) depth suffices for anti-concentration. The proof is based on a previous construction of t-designs by Brandão et al. (2016), an analysis of how approximate designs behave under composition, and an extension of the quasi-orthogonality of permutation operators developed by Brandão et al. (2016). Different versions of the approximate design condition correspond to different norms, and part of our contribution is to introduce the norm corresponding to anti-concentration and to establish equivalence between these various norms for low-depth circuits. For random circuits with long-range gates, we use different methods to show that anti-concentration happens at circuit size O(nln ^2 n) corresponding to depth O(ln ^3 n). We also show a lower bound of Omega (n ln n) for the size of such circuit in this case. We also prove that anti-concentration is possible in depth O(ln n ln ln n) (size O(n ln n ln ln n)) using a different model.

Speeding axion haloscope experiments using heterodyne-variance-based detection with a power meter

We describe a new axion search method based on measuring the variance in the interference of the axion signal using injected photons with a power detector. The need for a linear amplifier is eliminated by putting a strong signal into the microwave cavity, to acquire not only the power excess but also measure the variance of the output power. The interference of the external photons with the axion to photon converted signal greatly enhances the variance at the particular axion frequency, providing evidence of its existence. This method has an advantage in that it can always obtain sensitivity near the quantum noise limit even for a power detector with high dark count rate. We describe the basic concept of this method both analytically and numerically, and we show experimental results using a simple demonstration circuit.

Precise Tomography of Qudits

Multilevel quantum states (qudits) represent a promising platform for scalable quantum comput-ing. In this paper, we present a method for precisely controlling such systems using fuzzy quantum measure-ments. The developed method is used for a precise reconstruction of quantum states under conditions of a significant effect of decoherence and quantum noise. Protocols for quantum measurements based on mutu-ally unbiased bases (MUBs) of various dimensions are considered. The accuracy characteristics of sets of ran-dom states uniformly distributed with respect to the Haar measure are studied.