We present a fault-tolerant universal quantum computing architecture based on a code concatenation of biased-noise qubits and the parity architecture. The parity architecture can be understood as a low-density parity-check code tailored specifically to obtain any desired logical connectivity from nearest-neighbor physical interactions. The code layout can be dynamically adjusted to algorithmic requirements on the fly. This allows for implementations with any desired code distance with a universal set of fault-tolerant gates. In addition to the previously explored toolsets for concatenated cat codes, our approach features parallelizable interactions between arbitrary sets of qubits by directly addressing the parity qubits in the code. The proposed scheme supports codes with lower physical qubit overhead than the repetition code at the same code distances, while requiring only weight-3 and weight-4 stabilizers and nearest-neighbor connectivity on a two-dimensional square lattice.

The accurate first-principles description of strongly correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here, we demonstrate that using quantum algorithms to obtain the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate ${\mathrm{Ca}}_2{\mathrm{Cu}\mathrm{O}}_3$, the excitonic ground state of monolayer ${\mathrm{W}\mathrm{Te}}_2$, and the charge-ordered state of correlated metal ${\mathrm{Sr}\mathrm{VO}}_3$. Numerical simulations using a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to 54 qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical preoptimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms.

Interest in quantum machine learning is growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use of quantum machine-learning techniques for image-classification tasks. We exploit a quantum extreme learning machine by taking advantage of its rich feature map provided by the quantum reservoir substrate. We systematically analyze different phases of the quantum extreme learning machine process, from dataset preparation to final image classification. In particular, we test different encodings, together with principal component analysis and the use of autoencoders, and we examine the dynamics of the model through the use of different Hamiltonians for the quantum reservoir. Our results show that the introduction of a quantum reservoir systematically improves the accuracy of the classifier. In addition, our findings indicate that variations in encoding methods can significantly influence performance and that Hamiltonians with distinct structures exhibit the same discrimination rate, depending on how their eigenstates are related to the encoding and measurement basis.

Materials imperfections in $\mathrm{Nb}$-based superconducting quantum circuits—in particular, two-level-system (TLS) defects—are a major source of decoherence, ultimately limiting the performance of quantum computation and sensing. Thus, identifying and understanding the microscopic origin of possible TLS defects in these devices will help develop strategies that eliminate them, which are key to superconducting qubit performance improvement. In this paper, we demonstrate an order-of-magnitude reduction in two-level system losses in three-dimensional superconducting radio frequency (SRF) niobium resonators by a 10-h high vacuum (HV) heat treatment at 650 °C, even after exposure to air and high-pressure rinsing (HPR). X-ray photoelectron spectroscopy (XPS) and high-resolution scanning transmission electron microscopy (STEM) reveal an alteration of the native oxide composition regrown after air exposure and HPR and the creation of nanoscale crystalline oxide regions, which correlates with the measured tenfold quality factor enhancement at low fields of the 1.3 GHz niobium resonator. Tunneling spectroscopy measurements show a pronounced proximity effect that further confirms the presence of metallic layers on the niobium surface.

A squeezed state is one of the most vital quantum resources in quantum information processing. The extensive attention has increasingly shifted to not only the squeezed degree but also the squeezed bandwidth in quantum precise measurement. Here, we demonstrate the generation of a bandwidth-tunable squeezed state by exploiting the entangled sideband modes and the postprocessing scheme, with the quantum noise reduction of $8.0\pm 0.3$ dB across a wide frequency range from 10 Hz to 10 MHz. Moreover, the bandwidth and rotation frequency can be conveniently manipulated in the whole frequency range according to the application requirement. There is no doubt that the demonstration will open up some potential applications about squeezed-state interaction with atoms.

We study the implementation of holonomic quantum gates preserved from arbitrary single-qubit and two-qubit decoherence or errors using a dynamical decoupling approach. To integrate the holonomic feature with the dynamical decoupling technique, we require only one auxiliary qubit for single-qubit gates and no auxiliary qubit for two-qubit gates. Our scheme uses far fewer physical qubits to achieve this integration compared to existing proposals in the literature. Moreover, the scheme is based on system interactions that can be experimentally realized with superconducting qubits. These advantages suggest that our scheme is a promising step toward developing noise-resistant quantum devices.

We introduce iQSync, a clock-offset recovery method designed for implementation on low-level hardware, such as field-programmable gate arrays or microcontrollers, for quantum key distribution (QKD). iQSync requires minimal memory, only a simple instruction set (e.g., no floating-point operations), and can be evaluated with sublinear time complexity, typically involving no more than a few thousand iterations of a simple loop. Furthermore, iQSync allows for a precise clock-offset recovery within a few seconds, even for large offsets, and is well suited for scenarios with high channel loss and low signal-to-noise ratio, irrespective of the prepare-and-measure QKD protocol used. We implemented the method on our QKD platform, demonstrating its performance and conformity with analytically derived success probabilities for channel attenuations exceeding $70\mathrm{dB}$.

We present an exact $n$-qubit computational-basis amplitude encoder of real- or complex-valued data vectors of $d=(\genfrac{}{}{0ex}{}{n}{k})$ components into a subspace of fixed Hamming weight $k$. This represents a polynomial space compression of degree $k$. The circuit is optimal in that it expresses an arbitrary data vector using only $d-1$ (controlled) reconfigurable beam splitter (RBS) gates. It is constructed by an efficient classical algorithm that sequentially generates all bitstrings of weight $k$ and identifies the gates that superpose the corresponding states with the correct amplitudes. An explicit compilation into $\mathrm{CNOT}\mathrm{s}$ and single-qubit gates is presented, with the total $\mathrm{CNOT}$-gate count of $\mathcal{O}(kd)$ provided in analytical form. In addition, we show how to load data in the binary basis by sequentially stacking encoders of different Hamming weights using $\mathcal{O}(d\log (d))\mathrm{CNOT}$ gates. Moreover, using generalized RBS gates that mix states of different Hamming weights, we extend the construction to efficiently encode arbitrary sparse vectors. We perform a proof-of-principle demonstration of our scheme on a commercial trapped-ion quantum computer. We successfully upload a $q$-Gaussian probability distribution in the non-log-concave regime with $n=6$ and $k=2$. We also illustrate how the effect of hardware noise can be alleviated by quantum error mitigation. Numerically, we show how our encoder can improve the performance of variational quantum algorithms for problems that include particle-preserving symmetries. Our results constitute a versatile framework for quantum data compression with various potential applications in fields such as quantum chemistry, quantum machine learning, and constrained combinatorial optimizations.

Geometric quantum machine learning uses the symmetries inherent in data to design tailored machine learning tasks with reduced search space dimension. The field has been well studied recently in an effort to avoid barren plateau issues while improving the accuracy of quantum machine learning models. This work explores the related problem of learning an equivariant map given two unitary representations of a finite group, which in turn allows the symmetric embedding of the data to be learned rather than simply required. Moreover, this procedure allows the learning of covariant quantum channels, which are an essential tool in quantum information theory. We demonstrate the feasibility of this task and give examples to illustrate the procedure.

In this paper, we adopt $\chi$-type states to design an alternative circular semiquantum private comparison (SQPC) protocol, which can determine the equality of private inputs from two semiquantum users within one round implementation under the help of a semihonest third party (TP) who possesses complete quantum capabilities. Here, it is assumed that the semihonest TP has the abilities to launch all possible attacks to steal useful information about two semiquantum users’ private inputs but cannot conspire with anyone else. The traveling particles go from TP to Alice, then from Alice to Bob, and back from Bob to TP. The security analysis turns out the proposed SQPC protocol can resist both the outside attacks and the inside attacks. Furthermore, it has been proven to be information-theoretically secure in the asymptotic scenario. Compared with some existing SQPC protocols of equality with quantum entangled states, the proposed SQPC protocol has some advantages: $◯1$ it requires no preshared key among different participants; $◯2$ it does not need quantum entanglement swapping; $◯3$ it employs no delay lines; $◯4$ it adopts no unitary operations; and $◯5$ it has fewer qubits transmitted and fewer classical modulo 2 addition computations.

Quantum sources with strong correlations are essential but delicate resources in quantum information science and engineering. Decoherence and loss are the primary factors that degrade nonclassical quantum correlations, with scattering playing a role in both processes. In this work, we present a method that leverages long short-term memory, a machine-learning technique known for its effectiveness in time-series prediction, to mitigate the detrimental impact of scattering in quantum systems. Our setup involves generating two-mode squeezed light via four-wave mixing in warm rubidium vapor, with one mode subjected to a scatterer to disrupt quantum correlations. Mutual information and intensity-difference squeezing between the two modes are used as metrics for quantum correlations. We demonstrate a 74.7% recovery of mutual information and an 87.7% recovery of two-mode squeezing, despite significant photon loss that would otherwise eliminate quantum correlations. This approach marks a significant step toward recovering quantum correlations from random disruptions without the need for hardware modifications, paving the way for practical applications of quantum protocols.

We report on a laser-frequency-stabilization and phase-noise-filtering scheme at 871 nm for high-quality quantum operations with an optical ${}^2{\mathrm{S}}_{1/2}$–${}^2{\mathrm{D}}_{3/2}$ qubit in the ${}^{171}\mathrm{Yb}{}^{+}$ ion. The phase noise of the laser field in a frequency range close to the trapped ion’s secular frequencies (0.5–2 MHz) causes undesirable excitations and thus reduces the fidelities of both single- and two-qubit gates. The most common way to overcome this problem is to filter this noise by passing the laser light through a cavity that suppresses high-frequency phase noise and provides a pure laser spectrum. Usually, the same cavity also serves as a reference for long-term frequency stabilization. We propose and characterize an improved laser-stabilization scheme consisting of (i) a reference laser locked to a high-finesse optical cavity providing a narrow linewidth and long-term frequency stability and (ii) a main laser locked to a tunable medium-finesse cavity for phase-noise filtering. The phase locking of the medium-finesse cavity resonance to the reference laser transfers its long-term stability to the main laser. This approach allows independent optimization of both the phase-noise filtering and long-term frequency-stabilization processes, as well as ensuring the robustness of the setup. The resulting laser spectrum is characterized by direct spectroscopy of an ${}^{171}\mathrm{Yb}{}^{+}$ ion in a linear Paul trap. We estimate the expected two-qubit gate infidelity with the filtered laser field to be well below 1%.

Detection of single-electron charges in solid-state nanodevices is a key technique in semiconductor quantum bit readout for quantum information processing and probing electronic properties of nanostructures. This detection has been achieved using gate-based readouts and sensitive charge sensors including quantum dot charge sensors, with their detection speed enhanced by high-speed rf reflectometry. Recently, real-time processing of data from rf reflectometry has attracted much attention to quantum information processing. In this paper, we propose a sequential method based on Bayes’ theorem for estimating the charge state and compare its performance with the averaging approach and threshold judgment. When the noise variance differs between the empty and occupied states, the Bayesian approach demonstrates a lower error score, facilitating the extraction of more data points in real-time charge state estimation. In addition, the Bayesian approach outperforms the averaging method and threshold judgment in terms of error rates for charge state estimation, even during charge transitions. This technique is broadly applicable to single-electron detection and holds substantial utility for quantum bit readout and the operation of nanoprobes that utilize single-electron detection.

Quantum signal processing (QSP), which enables systematic polynomial transformations on quantum data through sequences of qubit rotations, has emerged as a fundamental building block for quantum algorithms and data reuploading quantum neural networks. While recent experiments have demonstrated the feasibility of shallow QSP circuits, the inherent limitations in scaling QSP to achieve complex transformations on quantum hardware remain an open and critical question. Here we report the first experimental realization of deep QSP circuits in a trapped-ion quantum simulator. By manipulating the qubit encoded in a trapped ${}^{43}{\mathrm{Ca}}^{+}$ ion, we demonstrate high-precision simulation of some prominent functions used in quantum algorithms and machine learning, with circuit depths ranging from 15 to 360 layers and implementation times significantly longer than the coherence time of the qubit. Our results reveal a crucial trade-off between the precision of function simulation and the concomitant accumulation of hardware noise, highlighting the importance of striking a balance between circuit depth and accuracy in practical QSP implementation. This work addresses a key gap in understanding the scalability and limitations of QSP-based algorithms on quantum hardware, providing valuable insights for developing quantum algorithms as well as practically realizing quantum singular value transformation and data reuploading quantum machine learning models.

Disorder effects in hybrid semiconductor-superconductor (SM-SC) nanowires, widely recognized as the main obstacle to realizing stable Majorana zero modes (MZMs) in these structures, have been systematically investigated theoretically in recent years. However, there are no corresponding detailed studies of disorder effects in planar Josephson junction (JJ) structures, which represent a promising alternative to the Majorana nanowire platform. In this paper, we perform a numerical analysis of the low-energy physics of JJ structures based on an effective microscopic model that includes two types of disorder, charge impurities inside the semiconductor and roughness on the surface of the superconducting film. We consider different parameter regimes, including low and high chemical potential values, weak and strong effective SM-SC coupling strengths, and weak and strong disorder strengths. The results are benchmarked using disordered hybrid nanowires realized in planar SM-SC structures similar to those involved in the fabrication of Josephson junctions and having similar model parameters and disorder strengths. We find that the topological superconducting phase hosted by a JJ structure is, generally, more robust against disorder than the topological superconductivity realized in a hybrid nanowire with similar parameters. On the other hand, we find that operating the JJ in a regime characterized by large values of chemical potential results in huge finite-size effects that can destroy the stability of MZMs.

In this study, we provide a full experimental characterization of the parametric longitudinal coupling between a complementary metal oxide semiconductor (CMOS) charge qubit and an off-chip rf resonator. Following Corrigan et al., Phys. Rev. Appl. 20, 064005 (2023), we activate parametric longitudinal coupling by driving the charge qubit at the resonator frequency. Managing the crosstalk between the drive applied to the qubit and the resonator allows for the systematic study of the dependence of the longitudinal and dispersive charge-photon couplings on the qubit-resonator detuning and the applied drive. Our experimental estimations of the charge-photon couplings are perfectly reproduced by theoretical simple formulas, without relying on any fitting parameter. We go further by showing a parametric displacement of the resonator’s steady state, conditional on the qubit state, and the insensitivity of the longitudinal coupling constant to the photon population of the resonator. Our results pave the way for the exploration of the photon-mediated longitudinal readout and coupling of multiple and distant spins, with long coherent times, in hybrid CMOS circuit quantum electrodynamics architectures.