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Abstract Exploiting the utility of near-term quantum devices is a long-standing challenge whereas hybrid quantum machine learning emerges as a promising candidate. Here we propose a quantum-enhanced deep generative algorithm based on programmable quantum circuit-induced quantum latent codes. To validate the effectiveness of the algorithm, we conduct optical ghost imaging experiments, collecting dataset under varying physical sampling rates. Leveraging a physically enhanced loss function and a pretrained neural network, the quantum algorithm exhibits superior reconstruction performance compared to conventional algorithms. We observe the learnable quantum latent space enhances the out-of-distribution generalization capability of the pretrained model. In the context of computer vision problems, numerical results indicate that quantum latent space can increase the generation diversity of the pretrained model. Furthermore, the algorithm outperforms classical counterparts, particularly in image inpainting and colorization, by a significant margin. Our study demonstrates the utility of hybrid quantum-classical algorithms in enhancing generalization capability, highlighting the potential of near-term quantum devices in large-scale generative artificial intelligence.

Abstract An emerging challenge of integrated communication and sensing is the extraction of weak sensing signals transmitted through an unknown non-stationary channel. In this work, we propose a weak signal extraction method with weak measurement. Taking advantage of time division multiplexing, we preliminarily estimate the channel via adjustable finite impulse response filter, further suppressing the interfering signal caused by background noises via spectrum shift. By subsequently using the time-varying phase estimation method via weak measurement, the real-time detection of weak signals in the non-stationary channel is achieved. We demonstrate via theoretical analysis and confirmatory experiment that our method is able to amplify the phase shift, to suppress technical noise and to improve detection resolution limit, while proving robust against light source fluctuations, initial phase differences and detector saturation. The method hence enables weak sensing signal extraction with a low signal-to-noise ratio non-stationary channel. Furthermore, we interface our measurement method to squeezed light sources, offering the possibility of surpassing standard quantum limit.

Chemical and sensory attributes play a vital role in evaluating the quality of grapes and wines. This study compared basic physicochemical parameters, organic acids, phenolic compounds, and aroma profiles of grapes and wines of six cultivars using chemometrics. The results showed that the reducing sugar contents of Beibinghong, Gongniang, and Granoir grapes were significantly higher than those of others cultivars, whereas their juice yields were significantly lower. The phenolic compound contents in Moldova, Beibinghong, and Gongniang grape skins and wines were higher than those in others cultivars. The organic acid contents in Beibinghong grape and Dunkelfelder wine were highest. Beibinghong and Gongniang grapes and wines showed richer aldehyde and ester concentrations. Beibinghong wine obtained the highest sensory scores. Ethyl decanoate, coumaric acid, and methyl dodecanoate were characteristic variables distinguishing wine cultivars, exhibiting important contributions to their sensory characteristics. These findings were useful for viticulturists and winemakers to select grape varieties.

Abstract Demonstrating the practical advantage of quantum computation remains a long-standing challenge whereas quantum machine learning becomes a promising application that can be resorted to. In this work, we investigate the practical advantage of quantum machine learning in ghost imaging by overcoming the limitations of classical methods in blind object identification and imaging. We propose two hybrid quantum-classical machine learning algorithms and a physical-inspired patch strategy to allow distributed quantum learning with parallel variational circuits. In light of the algorithm, we conduct experiments for imaging-free object identification and blind ghost imaging under different physical sampling rates. We further quantitatively analyze the advantage through the lens of information geometry and generalization capability. The numerical results showcase that quantum machine learning can restore high-quality images but classical machine learning fails. The advantage of identification rate are up to 10% via fair comparison with the classical machine learning methods. Our work explores a physics-related application capable of practical quantum advantage, which highlights the prospect of quantum computation in the machine learning field.

The integration of sensing and communication can achieve ubiquitous sensing while enabling ubiquitous communication. Within the gradually improving global communication, the integrated sensing and communication system based on optical fibers can accomplish various functionalities, such as urban structure imaging, seismic wave detection, and pipeline safety monitoring. With the development of quantum communication, quantum networks based on optical fiber are gradually being established. In this paper, we propose an integrated sensing and quantum network (ISAQN) scheme, which can achieve secure key distribution among multiple nodes and distributed sensing under the standard quantum limit. The continuous variables quantum key distribution protocol and the round-trip multiband structure are adopted to achieve the multinode secure key distribution. Meanwhile, the spectrum phase monitoring protocol is proposed to realize distributed sensing. It determines which node is vibrating by monitoring the frequency spectrum and restores the vibration waveform by monitoring the phase change. The scheme is experimentally demonstrated by simulating the vibration in a star structure network. Experimental results indicate that this multiuser quantum network can achieve a secret key rate of approximately 0.7 Mbits/s for each user under 10-km standard fiber transmission, and its network capacity is 8. In terms of distributed sensing, it can achieve a vibration response bandwidth ranging from 1 Hz to 2 kHz, a strain resolution of 0.50 [Formula: see text], and a spatial resolution of 0.20 m under shot-noise-limited detection. The proposed ISAQN scheme enables simultaneous quantum communication and distributed sensing in a multipoint network, laying a foundation for future large-scale quantum networks and high-precision sensing networks.

Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms.

In quantum multiparameter estimation, achieving the best precision for each parameter is hindered by the Heisenberg principle. Here, the authors demonstrate how to mitigate this problem by using appropriate probe states.

Abstract Quantum simulation is a technology of using controllable quantum systems to study new quantum phases of matter. Certification for quantum simulators is a challenging problem whereas identification and properties estimation are two crucial approaches that can be resorted to. In this work, we propose Ab initio end-to-end machine learning certification protocol briefly named MLCP. The learning protocol is trained with a million-level size of randomized measurement samples without relying on the assistance of quantum tomography. In the light of MLCP, we can identify different types of quantum simulators to observe their distinguishability hardness. We also predict the physical properties of quantum states evolved in quantum simulators such as entanglement entropy and maximum fidelity. The impact of randomized measurement samples on the identification accuracy is analyzed to showcase the potential capability of classical machine learning on quantum simulation results. The entanglement entropy and maximum fidelity with varied subsystem partitions are also estimated with satisfactory precision. This work paves the way for large-scale intelligent certification of quantum simulators and can be extended onto an artificial intelligence center to offer easily accessible services for local quantum simulators in the noisy intermediate-size quantum (NISQ) era.

Quantum key distribution (QKD) is one quantum technology that can provide secure encryption keys for data transmission. The secret key rate (SKR) is a core performance indicator in QKD, which directly determines the transmission rate of enciphered data. Here, for the first time, we demonstrate a high-key-rate Gaussian-modulated continuous-variable QKD (CV-QKD) using telecom optical components. The framework of CV-QKD over these components is constructed. Specifically, the high-rate low-noise Gaussian modulation of coherent states is realized by a classical optical IQ modulator. High-baud low-intensity quantum signals are received by an integrated coherent receiver under the shot-noise limit. A series of digital signal processing algorithms are proposed to achieve accurate signal recovery and key distillation. The system can yield a high asymptotic SKR of 10.37 Mbps within 20 km standard telecom fiber, and the secure distance can exceed 100 km. This result confirms the feasibility of CV-QKD with state-of-the-art performance using telecom optical components. Besides, due to the ease of integrating these discrete components, it provides a high-performance and miniaturized QKD solution for the metropolitan quantum network.

Abstract Parameter estimation is a pivotal task, where quantum technologies can enhance precision greatly. We investigate the time-dependent parameter estimation based on deep reinforcement learning, where the noise-free and noisy bounds of parameter estimation are derived from a geometrical perspective. We propose a physical-inspired linear time-correlated control ansatz and a general well-defined reward function integrated with the derived bounds to accelerate the network training for fast generating quantum control signals. In the light of the proposed scheme, we validate the performance of time-dependent and time-independent parameter estimation under noise-free and noisy dynamics. In particular, we evaluate the transferability of the scheme when the parameter has a shift from the true parameter. The simulation showcases the robustness and sample efficiency of the scheme and achieves the state-of-the-art performance. Our work highlights the universality and global optimality of deep reinforcement learning over conventional methods in practical parameter estimation of quantum sensing.

Continuous-variable quantum key distribution (CV-QKD) is a promising protocol that can be easily integrated with classical optical communication systems. However, in the case of quantum-classical co-transmissions, such as dense wavelength division multiplexing with classical channels and time division multiplexing with large-power classical signal, a quantum signal is more susceptible to crosstalk caused by a classical signal, leading to signal distortion and key distribution performance reduction. To address this issue, we propose a noise-suppression scheme based on carrier frequency switching (CFS) that can effectively mitigate the influence of large-power random noise on the weak coherent state. In this noise-suppression scheme, a minimum-value window of the channel’s noise power spectrum is searched for and the transmission signal frequency spectrum shifts to the corresponding frequency to avoid large-power channel noise. A digital filter is also utilized to filter out most of the channel noise. Simulation results show that compared to the traditional fixed carrier frequency scheme, the proposed noise-suppression scheme can reduce the excess noise to 1.8%, and the secret key rate can be increased by 1.43 to 2.86 times at different distances. This noise-suppression scheme is expected to be applied in scenarios like quantum–classical co-transmission and multi-QKD co-transmission to provide noise-suppression solutions.

Investigating quantum advantage in the NISQ era is a challenging problem whereas quantum machine learning becomes the most promising application that can be resorted to. However, no proposal has been investigated for arguably challenging inverse reinforcement learning to demonstrate the potential advantage. In this work, we propose a hybrid quantum–classical inverse reinforcement learning algorithm based on the variational quantum circuit with the generative adversarial framework. We find an important connection between the quantum gradient anomaly and the performance degradation, which suggest a gradient clipping strategy to stabilize the training process. In light of the algorithm, we study three classic control problems and the Hamiltonian parameter estimation in quantum sensing with shallow quantum circuits. The numerical results showcase that the control-enhanced quantum sensor can saturate quantum Cramér-Rao bound only with a single variational layer, empirically demonstrating a parameter complexity advantage over the classical learning control. The proposed generative adversarial reinforcement learning algorithm achieves state-of-the-art performance in classical and quantum sensor control in terms of required number of parameters.

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Imaging objects hidden behind an opaque shelter provides a crucial advantage when physically going around the obstacle is impossible or dangerous. Previous methods have demonstrated that is possible to reconstruct the image of a target hidden from view. However, these methods enable the reconstruction by using the reflected light from a wall which may not be feasible in the wild. Compared with the wall, the “plug and play” scattering medium is more naturally and artificially accessible, such as smog and fogs. Here, we introduce a scattering-assisted technique that requires only a remarkably small block of single-shot speckle to perform transmission imaging around in-line-of-sight barriers. With the help of extra inserted scattering layers and a deep learning algorithm, the target hidden from view can be stably recovered while the directly uncovered view is reduced to 0.097% of the whole field of view, successfully removing the influence of large foreground occlusions. This scattering-assisted computational imaging has wide potential applications in real-life scenarios, such as covert imaging, resuming missions, and detecting hidden adversaries in real-time.

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) allows remote parties to share information-theoretical secure keys while defending all the side-channel attacks on measurement devices. However, the secure transmission distance and the secret key rate are quite limited due to the high untrusted equivalent excess noise in the Gaussian modulation. More particularly, extremely high-efficiency homodyne detections are required for even non-zero secure transmission distances, which directly restrict its practical realization. Here, we propose a CV-MDI-QKD protocol by encoding the key information into matched discrete phases of two groups of coherent states, which decreases the required detection efficiency for ideally asymmetric cases, and makes it possible to practically achieve secure key distribution with current low-efficiency homodyne detections. Besides, a proof-of-principle experiment with a locally generated oscillator is implemented, which, for the first time, demonstrates the realizability of CV-MDI-QKD using all fiber-based devices. The discrete-modulated phase-matching method provides an alternative direction of an applicable quantum key distribution with practical security.

Privacy amplification is an indispensable procedure to share secure secret key between legitimate parties in quantum key distribution (QKD) because a portion of bits are still leaked to eavesdropper Eve after the secret reconciliation. Here, we propose a novel privacy amplification scheme with an optimal hash function. In particular, by utilizing the linear feedback shift register (LFSR) technique, we optimize and simplify the construction of the Toeplitz matrix. In this way, the LFSR-based Toeplitz matrix is used to execute the privacy amplification algorithm. The hardware storage resources can then be drastically saved, and the time consuming will be only about half of that without using the LFSR-based Toeplitz matrix. This work breaks the bottleneck of privacy amplification in high-speed continuous-variable QKD (CVQKD), and thus contributes to the real-time performance of the whole CVQKD system.

The speed of continuous variable quantum key distribution is limited by the reconciliation efficiency and the reconciliation frame error rate (FER) in the reconciliation phase. In this paper, we propose an approach that may increase the reconciliation efficiency and decrease the FER. In detail, to increase the reconciliation efficiency, rather than using the block low-density parity-check (LDPC) codes, such as quasi-cyclic LDPC codes, multiedge-type LDPC codes, and punctured LDPC codes, this paper introduces spatially coupled (SC) LDPC codes to the reconciliation phase. To decrease the FER, we propose a new reconciliation scheme based on the special structure of SC-LDPC codes. To demonstrate the performance of the proposed approach, we construct the SC-LDPC codes based on quasi-cyclic repeat-accumulate codes. It is shown that the proposed scheme leads to higher reconciliation speed than that of the previous reconciliation schemes.

Gaussian-modulated coherent-state (GMCS) continuous-variable quantum key distribution (CVQKD) protocol can allow authenticated users to share secret key with unconditional security. So far, all previous experimental implementations of GMCS CVQKD schemes are based on active modulations, i.e. amplitude and phase modulators and quantum random number generator (QRNG) are required. However, high-speed modulation with high extinction ratio and stability is challenging, which is extremely remarkable in chip-scale silicon photonic realization. While the passive-state-preparation (PSP) CVQKD scheme, which explores the intrinsic field fluctuations of a thermal source, avoids the uses of active modulations and QRNG. In this paper, we experimentally realize the intact PSP CVQKD through a realistic optical fiber channel using off-the-shelf amplified spontaneous emission source. In particular, specially designed frame synchronization method is used to build the correlation between the data measured from the two legitimate parties, and excess noise are synthetically controlled to generate secure secret keys at the metro-area distances when considering the practical and non-negligible finite-size effects under collective Gaussian attacks. Due to the avoidance of modulators and QRNG, the passive state encoding scheme provides a promising direction of applicable high-speed, chip-based and even sunlight-based CVQKD with less cost and complexity.

Scattering effects are ubiquitous in practical wireless optical links. Here a transmission model with complete consideration of scattered light and beam wandering effects for underwater link is developed, with the aim to completely characterize the received quantum state of light through dense scattering medium. Based on this model, we show the influence of scattered photons on the improvement of the entanglement after transmission through turbid water may vary for different copropagation scenarios, i.e., the contribution of scattered light on entanglement transmission may be turned from positive to negative, with increase of the strength of underwater beam wandering. And the attenuation coefficient and aperture size are found to be the dominant factors affecting the entanglement through underwater link. While for the counterpropagation scenario, the scattered photons will severely deteriorate the entanglement transmission especially for the high-loss scattering links. These findings may shed light on quantum entanglement transmission and help to develop its applications through dense scattering medium.

The excess noise inducing in the process of the quantum communication procedure is the major obstacle restricting the performance of continuous-variable quantum key distribution (CVQKD). In order to effectively suppress the excess noise through correcting the propagation-induced distortions on the quality of the propagated quantum signal, we propose a general scheme of suppressing excess noise for CVQKD via adaptive optics (AO) approach. The analysis shows that phase-only AO compensation exhibits excellent performance in controlling the excess noise, which is embodied in substantially extending the secure propagation distance and improving the secret key rate of the system. And thereby the development and improvement of AO has the potential advantage to break the distance constraints due to the excess noise results from propagation-dominated factors. Our scheme provides a feasible method for further implementation of practical large-scale CVQKD.

We propose a long-distance continuous-variable quantum key distribution (CVQKD) with a four-state protocol using non-Gaussian state-discrimination detection. A photon subtraction operation, which is deployed at the transmitter, is used for splitting the signal required for generating the non-Gaussian operation to lengthen the maximum transmission distance of the CVQKD. Whereby an improved state-discrimination detector, which can be deemed as an optimized quantum measurement that allows the discrimination of nonorthogonal coherent states beating the standard quantum limit, is applied at the receiver to codetermine the measurement result with the conventional coherent detector. By tactfully exploiting the multiplexing technique, the resulting signals can be simultaneously transmitted through an untrusted quantum channel, and subsequently sent to the state-discrimination detector and coherent detector, respectively. Security analysis shows that the proposed scheme can lengthen the maximum transmission distance up to hundreds of kilometers. Furthermore, by taking the finite-size effect and composable security into account we obtain the tightest bound of the secure distance, which is more practical than that obtained in the asymptotic limit.

Compared to fiber continuous-variable quantum key distribution (CVQKD), atmospheric link offers the possibility of a broader geographical coverage and more flexible transmission. However, there are many negative features of the atmospheric channel that will reduce the achievable secret key rate, such as beam extinction and a variety of turbulence effects. Here we show how these factors affect performance of CVQKD, by considering our newly derived key rate formulas for fading channels, which involves detection imperfections, thus form a transmission model for CVQKD. This model can help evaluate the feasibility of experiment scheme in practical applications. We found that performance deterioration of horizontal link within the boundary layer is primarily caused by transmittance fluctuations (including beam wandering, broadening, deformation, and scintillation), while transmittance change due to pulse broadening under weak turbulence is negligible. Besides, we also found that communication interruptions can also cause a perceptible key rate reduction when the transmission distance is longer, while phase excess noise due to arrival time fluctuations requires new compensation techniques to reduce it to a negligible level. Furthermore, it is found that performing homodyne detection enables longer transmission distances, whereas heterodyne allows higher achievable key rate over short distances.

We show that the successful use of a noiseless linear amplifier (NLA) can help increase the maximum transmission distance and tolerate more excess noise of the plug-and-play dual-phase-modulated continuous-variable quantum key distribution. In particular, an equivalent entanglement-based scheme model is proposed to analyze the security, and the secure bound is derived with the presence of a Gaussian noisy and lossy channel. The analysis shows that the performance of the NLA-based protocol can be further improved by adjusting the effective parameters.