Efficient verification of entangled states is crucial to many applications in quantum information processing. However, the effectiveness of standard quantum state verification (QSV) is based on the condition of independent and identical distribution (IID), which impedes its applications in many practical scenarios. Here we demonstrate a defensive QSV protocol, which is effective in all kinds of non-IID scenarios, including the extremely challenging adversarial scenario. To this end, we build a high-speed preparation-and-measurement apparatus controlled by quantum random-number generators. Our experiments clearly show that standard QSV protocols often provide unreliable fidelity certificates in non-IID scenarios. In sharp contrast, the defensive QSV protocol based on a homogeneous strategy can provide reliable and nearly tight fidelity certificates at comparable high efficiency, even under malicious attacks. Moreover, our scheme is tolerant of the imperfections in a realistic experiment, which is very appealing to practical applications.

Quantum network coding provides a solution for information transmission at bottleneck channels. However, the single-path scheduling approach is hampered in the face of massive data transmission demands, which is particularly challenging in large-scale network communication scenarios. Here we propose a continuous-variable quantum network coding (CVQNC) scheme for parallel transmission of multiple quantum states based on optical frequency combs. The quantum communication channel is naturally constructed due to the nonclassical correlations exhibited by the symmetric sideband modes at around half the pump frequency on the squeezed field. Source nodes with preshared multiplex entanglement enable the transmission of multiple quantum states in parallel and simultaneously, which increases the channel capacity of the network and relieves the single-path transmission pressure. In particular, fidelity is calculated as a critical measure of transmission performance, and we provide a method to increase fidelity using photon catalysis, along with squeezing parameters and beam splitter transmittance for non-Gaussian source states at optimal fidelity. Furthermore, the channel capacity and fidelity of the CVQNC system are predicted on the basis of the $k$-nearest neighbor algorithm, and the predicted data match well with the actual derivation.

Qubit noise spectroscopy is an important tool for the experimental investigation of open quantum systems. However, conventional techniques for noise spectroscopy are time-consuming, because they require measurements of the noise spectral density at many different frequencies. Here we describe an alternative approach to noise spectroscopy, which requires fewer resources, and relies on direct measurement of arbitrary linear functionals of the noise spectral density. This method uses random pulse sequences with carefully controlled correlations, which are chosen using algorithms for phase retrieval. These measurements allow us to reconstruct sparse noise spectra via compressed sensing. Our simulations of the performance of the random pulse sequences on a realistic physical system, self-assembled quantum dots, reveal a speedup of an order of magnitude in extracting the noise spectrum, compared to conventional dynamical decoupling approaches.

Hardware-efficient methods for high-fidelity quantum state measurements are crucial for superconducting qubit experiments, as qubit numbers grow and feedback and state reset begin to be employed for quantum error correction. We present a three-dimensional reentrant-cavity filter designed for frequency-multiplexed readout of superconducting qubits. The cavity filter is situated out of the plane of the qubit circuit and capacitively couples to an array of on-chip readout resonators in a manner that can scale to large qubit arrays. The reentrant cavity functions as a large-linewidth bandpass filter with intrinsic Purcell filtering. We demonstrate the concept with a four-qubit multiplexed device in which we achieve an average readout fidelity of 98.6% and measurement-induced crosstalk dephasing rates below 0.15 kHz.

Spin qubits in $\mathrm{Si}\mathrm{C}$ have become important platforms for quantum information and nanoscale quantum sensing. The performance and sensitivity of these applications greatly depend on the coherence properties of the spin qubits in $\mathrm{Si}\mathrm{C}$. This requires a quantitative understanding of the spin bath noise spectrum, which induces decoherence. In this work, we quantitatively investigate the noise spectrum of PL6 divacancy spins in $\mathrm{Si}\mathrm{C}$ generated by two different implanted doses, utilizing the spectral decomposition technique. First, three types of dynamical decoupling pulse sequences are performed to extend the spin coherence times, and both the Carr-Purcell-Meiboom-Gill (CPMG) and XY types of dynamical decoupling pulse sequences have the optimized effect. The coherence time increases by more than 12-fold as the number of CPMG pulses increases from 1 to 100 for both samples. On this basis, we analyze the noise spectrum using CPMG methods, determining the bath coupling strength and correlation time for the two samples. These experiments provide a foundation for the spectral decomposition technique in investigating spin bath environments of the spin qubits in $\mathrm{Si}\mathrm{C}$.

Chiral excitation flows have attracted significant attention due to their unique unidirectionality. Such flows have been studied in three-node networks with synthetic gauge fields, but the general theory of chiral flows in multinode networks requires further research and development. In this work, we propose a scheme to achieve chiral flows in $n$-node networks, where an auxiliary node is introduced to govern the system. This auxiliary node is coupled to all the network nodes, forming subtriangle structures with interference paths in these networks. We find the implicit chiral symmetry behind the perfect chiral flow and propose universal criteria that incorporate previous models, facilitating the implementation of chiral transmission in various networks. By investigating the symmetries within these models, we present different features of chiral flows in bosonic and spin networks. Furthermore, we extend the four-node model into a ladder network, which is promising for remote state transfer in practical systems with reduced complexity. Our scheme can be realized in state-of-the-art experimental systems, such as superconducting circuits, magnetic photonic lattices, and ultracold atoms, thereby opening up possibilities for future quantum networks.

An electron confined by a semiconductor quantum dot (QD) can be displaced by changes in electron occupations of surrounding QDs owing to the Coulomb interaction. For a single-spin qubit in an inhomogeneous magnetic field, such a positional displacement of the host electron results in a qubit energy shift, which must be handled carefully for high-fidelity operations. Here, we spectroscopically investigate the qubit energy shift induced by changes in charge occupations of nearby QDs for a silicon single-spin qubit in a magnetic field gradient. Between two different charge configurations of an adjacent double QD, a spin qubit shows an energy shift of about 4 MHz, which necessitates strict management of electron positions over a QD array. We confirm a correlation between the qubit frequency and the charge configuration by using a postselection analysis.

Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Here, we report a storage efficiency greater than 28% in a $\mathrm{Tm}$${}^{3+}$:YAG crystal (where YAG is yttrium aluminum garnet) at elevated temperatures, achieving this milestone without relying on optical cavities and without compromising memory bandwidth. We introduce different pumping techniques for $\mathrm{Tm}$-based memories that enable multifrequency-window storage and high memory bandwidth reaching 630 MHz, significantly surpassing previous $\mathrm{Tm}$-based memory demonstrations, considering the operating temperature of 3.5 K. Furthermore, we propose a general method for large-bandwidth atomic frequency memory using non-Kramers rare-earth ions (REIs) in solids, paving the way for storage efficiencies and bandwidths approaching fundamental limits. The compatibility of $\mathrm{Tm}$ memories with neutral-atom quantum processors and their seamless integration with scalable photonic platforms, such as lithium niobate on insulator, underscore their potential for distributed quantum computing, hybrid quantum networks, and space-based quantum communication. Our study represents a significant advancement toward practical high-performance quantum memories based on REI-doped crystals, where we predict that gigahertz bandwidth and storage efficiencies exceeding 30% are achievable at lower temperatures.

Quantum entanglement plays an irreplaceable role in various remote quantum information-processing tasks. Here, we present protocols for generating deterministic and heralded $N$-qubit entangled states across multiple network nodes. By utilizing a preshared maximally entangled state and single-qubit operations within an indefinite-causal-order framework, the multiqubit entangled state between distant parties can be generated deterministically. The complex entangled-state measurements and multiple preshared entangled states, are essential in conventional entanglement-swapping techniques but are not required in our approach. This greatly reduces the complexity of the quantum circuit and makes it more experimentally feasible. Furthermore, we develop optical architectures to implement these protocols by encoding qubits in polarization degrees of freedom. The results indicate that our protocols significantly improve the efficiency of long-distance entanglement generation and provide a practical framework for establishing large-scale quantum networks.

A clever choice and design of gate sets can reduce the depth of a quantum circuit, and can improve the quality of the solution one obtains from a quantum algorithm. This is especially important for near-term quantum computers that suffer from various sources of error that propagate with the circuit depth. Parametrized gates in particular have found use in both near-term algorithms and circuit compilation. The one- and two-qubit versions of these gates have been demonstrated on various computing architectures. The neutral-atom platform has the capability to implement native $N$-qubit gates (for $N\ge 2$). However, one needs to first find the control functions that implement these gates on the hardware. We study the numerical optimization of neural networks towards obtaining families of controls—laser pulses to excite an atom to Rydberg states—that implement phase gates with one and two controls, the ${\mathrm{C}}_1\mathrm{P}$ and ${\mathrm{C}}_2\mathrm{P}$ gates, respectively, on neutral-atom hardware. The pulses we obtain have a duration significantly shorter than the loss time scale, set by decay from the Rydberg state. In addition, they do not require single-site addressability and are smooth. Hence, we expect our gates to have immediate benefits for quantum algorithms implemented on current neutral-atom hardware.

In light of the growing widely utilized single-photon avalanche detectors, dead time is inevitable due to the presence of afterpulse. Dead time is a critical factor that influences the count rate and the secret key rate (SKR) of quantum key distribution (QKD). It is particularly significant in time-bin-encoding systems, while prior models fail to accurately estimate the gains. Of note, it introduces correlations between early bins and late bins, hence bits with strong correlation and security risks. Here, we put forward a realistic model that precisely characterizes the response of detectors for time-bin-encoding systems. Our model not only improves the accuracy of gain predictions, showing an excellent match with the Monte Carlo simulation results, but also provides theoretical support for data processing, helping to mitigate security risks. Finally, leveraging our model, we can get a much higher SKR than prior models and further enhance the performance of practical QKD systems by adjusting the detector parameters. Our simulation results demonstrate that our model is essential for the application and deployment of QKD systems in practice.

We present superconducting quantum circuits that exhibit atomic energy spectra and selection rules in ladder and lambda three-level configurations designed using genetic algorithms. These heuristic optimization techniques are employed to adapt the topology and parameters of a set of electrical circuits to find the suitable architecture matching the required energy levels and relevant transition matrix elements. We analyze the performance of the optimizer on one-dimensional single- and multiloop circuits to design ladder ($\mathrm{\Xi }$) and lambda ($\mathrm{\Lambda }$) three-level systems with specific transition matrix elements. As expected, attaining both the required energy spectrum and the needed selection rules is challenging for single-loop circuits, but they can be accurately obtained even with just two loops. Additionally, we show that our multiloop circuits are robust against random fluctuations in their circuit parameters, i.e., under eventual fabrication flaws. Developing an optimization algorithm for automated circuit quantization opens an avenue to engineering superconducting circuits with specific symmetry to be used as modules within large-scale setups. This may allow us to mitigate the well-known current errors observed in the first generation of quantum processors.

Reducing circuit depth and identifying an optimal trade-off between circuit depth and width is crucial for successful quantum computation. In this context, midcircuit measurement and feedforward have been shown to significantly reduce the depth of quantum circuits, particularly in implementing logical gates. By leveraging these techniques, we propose several parallelization strategies that reduce quantum circuit depth at the expense of increasing width in preparing various quantum states relevant to quantum simulation. With measurements and feedforward, we demonstrate that utilizing unary encoding as a bridge between two quantum states substantially reduces the circuit depth required for preparing quantum states, such as sparse quantum states and sums of Slater determinants within the first quantization framework, while maintaining an efficient circuit width. Additionally, we show that a Bethe wave function, characterized by its high degree of freedom in its phase, can be probabilistically prepared in a constant-depth quantum circuit using measurements and feedforward. We anticipate that our study will contribute to the reduction of circuit depth in initial state preparation, particularly for quantum simulation, which is a critical step toward achieving quantum advantage.

We study the single-photon purity of a resonantly driven single-photon source in the realistic scenario where the excitation laser pulse can leak into the detection path. We find that the duration of the excitation pulse strongly influences the quality of the single-photon source. We calculate the influence of this on the effective parameters describing the most relevant properties, including the two-photon component and Hong-Ou-Mandel visibility. Furthermore, we analyze how these properties can be strongly affected by frequency filtering of the outgoing field. Our results highlight that the relation between the two-photon component of the emission and the Hong-Ou-Mandel visibility is more complicated than typically assumed in the literature, and depends on the specific details of the source.

Measurement is an essential component of robust and practical quantum computation. For superconducting qubits, the measurement process involves the effective manipulation of the joint qubit-resonator dynamics, and it should ideally provide the highest quality for qubit state discrimination with the shortest readout pulse and resonator reset time. Here, we harness model-free reinforcement learning (RL), together with a tailored training environment, to achieve this multifaceted optimization task. Using the IBM quantum device, we demonstrate that the pulse obtained by the RL agent not only successfully achieves state-of-the-art performance, with an assignment error of $(4.6\pm 0.4)\times {10}^{-3}$, but also executes the readout and the subsequent resonator reset almost three times faster than the system’s default process. Furthermore, the learned waveforms are robust against realistic parameter drifts and follow an intuitive form, making them readily implementable on existing hardware with little computational overhead. Our results provide an effective readout strategy to boost the performance of superconducting quantum processors and demonstrate the value of RL in providing optimal and practical solutions for complex quantum information processing tasks.

Predicting and analyzing global behavior of complex systems is challenging due to the intricate nature of their component interactions. Recent work has started modeling complex systems using networks endowed with multiway interactions among nodes, known as higher-order networks. Simplicial complexes are a class of higher-order networks that have received significant attention due to their topological structure and connections to Hodge theory. Topological signal processing (TSP) utilizes these connections to analyze and manipulate signals defined on non-Euclidean domains such as simplicial complexes. Such analysis of higher-order network data is important for many real-world problems, such as detecting failure or error in communication networks, sensor coverage analysis, statistical ranking problems, finding arbitrage currency markets, etc. However, as the dimension of higher-order networks increases, the complexity of TSP scales exponentially. In this work, we present a general quantum algorithm for implementing filtering processes in TSP and describe its application to extracting network data based on the Hodge decomposition. We leverage preexisting tools introduced in recent quantum algorithms for topological data analysis and combine them with spectral filtering techniques using the quantum singular value transformation framework. While this paper serves as a proof of concept, we obtain a superpolynomial improvement over the best-known classical algorithms for TSP filtering processes, modulo some important caveats about encoding and retrieving the data from a quantum state. The proposed algorithm generalizes the applicability of tools from quantum topological data analysis to novel applications in analyzing high-dimensional complex systems.