量子科技

Computation is an input-output process, where a program encoding a problem to be solved is inserted into a machine that outputs a solution. Quantum computation conventionally relies on classical, external control outside the quantum computer to execute a program, obscuring computational and thermodynamic resources required. To understand the fundamental limits of computation, however, it is pivotal to work with a fully self-contained description of a quantum computation modeling the resources on the same footing as the computation itself. By developing a framework that we dub the autonomous Quantum Processing Unit (aQPU) we model quantum computation in the framework of autonomous thermal machines. Consisting of an internal quantum timekeeping mechanism, instruction register and memory system the aQPU allows investigating relationships between thermodynamic cost, complexity, speed and fidelity of a desired quantum computation.

Computational protein design (CPD) is a central problem in biotechnology, with applications in enzyme engineering and therapeutic design, but its combinatorial complexity poses a significant challenge for classical optimization methods. In this work, we formulate fixed-backbone CPD as a quadratic Hamiltonian over rotamer variables, enabling solution using Quantum Computing Inc.'s photonic entropy computing platform, Dirac-3. We evaluate solution quality by benchmarking against an exact classical cost function network (CFN) solver, which provides provably optimal baselines. On a set of standard benchmark proteins ranging from 493 to 943 variables, Dirac-3 produces best-observed solutions (over 100 samples per instance) within 0.16-2.47% of the optimal energies. These results show that the proposed formulation can identify low-energy configurations on directly solvable CPD instances, while sample-level energy distributions vary by instance. Runtime behavior is reported over the tested regime, where CFN remains faster in absolute terms, while Dirac-3 exhibits moderate growth in runtime with problem size. This study focuses on optimization performance as measured by energy relative to exact baselines under a pairwise fixed-backbone energy model. Exploratory experiments on larger instances using decomposition-based approaches are provided in the Supplementary Material. Overall, the results establish a benchmark for entropy-based optimization on CPD formulations within the directly solvable regime.

Performing quantum algorithms for critical problems in physics and chemistry requires substantially lower error rates than the physical error rates of present quantum computers. Achieving such low logical error rates requires quantum error correction1,2 and physical error rates below a critical threshold value3-8. We experimentally demonstrate on a trapped-ion quantum charge-coupled device (QCCD)9,10 improvements in logical error rates ranging from 11× to 800× compared with several physical circuit baselines, including quantum computation on multiple qubits. Our results hinge on two quantum error correction code constructions optimized for an ion-trap processor: a 12-qubit code encoding two qubits inspired by Knill11 and a 16-qubit tesseract colour code encoding four qubits12,13. These constructions are combined with a scalable method of error detection and post-selection to achieve reduced logical error rates. Our results show that state-of-the-art quantum devices are already able to make use of fault tolerance and error correction to strongly suppress errors in non-trivial quantum circuit computations.

Scattering experiments with energetic particles, such as free electrons, have been historically used to reveal the quantum structure of matter. However, realizing coherent interactions between free-electron beams and solid-state quantum systems has remained out of reach, owing to their intrinsically weak coupling. Realizing such coherent control would open up opportunities for hybrid quantum platforms combining free electrons and solid-state qubits for coincident quantum information processing and nanoscale sensing. Here, we present a framework that employs negatively charged nitrogen-vacancy centers (NV-) in diamond as quantum sensors of a bunched electron beam. We develop a Lindblad master equation description of the magnetic free-electron-qubit interactions and identify spin relaxometry as a sensitive probe of the interaction. Experimentally, we integrate a confocal fluorescence microscopy setup into a microwave-bunched electron beam line. We monitor charge-state dynamics and assess their impact on key sensing performance metrics (such as spin readout contrast), defining safe operating parameters for quantum sensing experiments. By performing [Formula: see text] relaxometry under controlled electron beam exposure, we do not resolve a measurable reduction in [Formula: see text] within experimental uncertainty, and instead establish an upper bound on the free-electron-spin coupling strength. Our results establish NV- centers as quantitative probes of free electrons, providing a metrological benchmark for free-electron-qubit coupling under realistic conditions, and chart a route toward solid-state quantum control with electron beams.

Strongly correlated topological phases of matter are central to modern condensed matter physics and quantum information technology but often challenging to probe and control in material systems. The experimental difficulty of accessing these phases has motivated the use of engineered quantum platforms for simulation and manipulation of exotic topological states. Among these, the Laughlin state stands as a cornerstone for topological matter, embodying fractionalization, anyonic excitations, and incompressibility. Although its bosonic analogs have been realized on programmable quantum simulators, a genuine fermionic Laughlin state has yet to be demonstrated on a quantum processor. Here, we realize the ν = 1/3 fermionic Laughlin state on IonQ's trapped-ion quantum computer using an efficient and scalable Hamiltonian variational ansatz with 369 two-qubit gates on a 16-qubit circuit. Employing symmetry-verification error mitigation, we extract key observables that characterize the Laughlin state, including correlation hole, bulk-edge correspondence, and topological entanglement entropy, with strong agreement to exact diagonalization benchmarks. This work demonstrates an end-to-end workflow to simulate material-intrinsic topological orders and provides a starting point to explore its dynamics and excitations on digital quantum processors.

Random number generation is a fundamental task for modern cryptography, secure communications, and stochastic computing. Quantum random number generators (QRNGs) provide inherently unpredictable data derived from quantum physical processes and therefore offer stronger security guarantees than classical approaches. However, some optical QRNGs rely on blue light-emitting diodes (LEDs), which suffer from reduced spectral matching with commonly used silicon avalanche photodiodes (APDs). This mismatch restricts the achievable signal-to-noise ratio (SNR) and limits the extractable quantum entropy. Here, we demonstrate a spontaneous emission-based QRNG employing a green InGaN LED coupled to a silicon APD. The improved spectral overlap between the emitted light and the APD responsivity results in a significantly higher SNR than with previously reported blue LEDs operating at similar electrical power levels. The measured signal is first filtered with a high-pass filter to suppress low-frequency noise, then randomness is extracted using the SHAKE256 hash function. A detailed statistical and spectral analysis is performed to evaluate the extractable entropy of the generated data. A physically reasonable estimation yields a quantum entropy generation rate of 2.78 Gbit/s. These results establish longer-wavelength nitride-based LEDs as a more suitable entropy source for QRNG systems, using inexpensive and widely available silicon photodiodes.

We investigate the capabilities of quantum generative adversarial networks (QGANs) in image generations tasks. Our analysis centers on fully quantum implementations of both the generator and discriminator. Through extensive numerical testing of current main architectures, we find that QGANs struggle to generalize across datasets, converging on merely the average representation of the training data. When the output of the generator is a pure-state, we analytically derive a lower bound for the discriminator quality given by the fidelity between the pure-state output of the generator and the target data distribution, thereby providing a theoretical explanation for the limitations observed in current models. Our findings reveal fundamental challenges in the generalization capabilities of existing quantum generative models. While our analysis focuses on QGANs, the results carry broader implications for the performance of related quantum generative models.

Modulation and control of lasers and optical signals are necessary for trapped-ion and cold neutral atom quantum systems. Given the diversity of atomic species, experimental modalities, and architectures, integrated optical modulators that are designed to operate across the visible to near-infrared (NIR) spectrum are a key step towards portable, robust, and compact quantum computers, clocks, and sensors. Integrated optical modulators that are wavelength-independent, CMOS-compatible, and capable of maintaining low waveguide losses and a high resonator quality factor (Q), DC-coupled broadband frequency response, and low power consumption are essential for scalable photonic integration. Yet progress towards these goals has remained limited. To show the versatility of this platform, we demonstrate four types of integrated stress-optic lead zirconate titanate (PZT) silicon nitride (Si3N4) modulators - a coil Mach-Zehnder modulator (coil MZM), a coil pure phase modulator, and bus-coupled and add-drop ring resonator modulators, with operation from 493 nm to 780 nm. The PZT-actuated coil MZM operates at 532 nm with a Vπ of 2.8 V, a DC - 0.4 MHz 3-dB bandwidth, and an extinction ratio of 21.5 dB. The PZT-actuated nitride coil phase modulator operates at 493 nm with a Vπ of 2.8 V and low residual amplitude modulation (RAM) of -34 dB at a 10 kHz offset. The bus-coupled ring resonator modulator operates at 493 nm, and the add-drop ring resonator modulator operates at 780 nm. The ring-based modulators have an intrinsic quality factor (Qi) of 3.4 million and 1.9 million, a linear tuning strength of 0.92 GHz/V and 1.01 GHz/V, and a 3-dB bandwidth of DC - 2.6 MHz and DC - 10 MHz, respectively. All four modulator designs maintain the native low optical waveguide loss of SiN, are DC coupled with broadband frequency response, operate independently of wavelength, and consume only tens of nW per actuator. Such solutions unlock the potential for further integration with other precision silicon nitride components to realize chip-scale atomic and quantum systems.

We present a framework for efficient extraction of the viscosity solutions of nonlinear Hamilton-Jacobi equations with convex Hamiltonians. These viscosity solutions play a central role in areas such as front propagation, mean-field games, optimal control, machine learning, and a direct application to the forced Burgers' equation. Our method is based on an entropy penalization method which generalizes the Cole-Hopf transform from quadratic to general convex Hamiltonians, allowing an approximation of viscous Hamilton-Jacobi dynamics by a discrete-time linear dynamics which approximates a linear heat-like parabolic equation, and thus can also extend to continuous-time dynamics. This makes the method suitable for quantum simulation. The validity of these results hold for arbitrary nonlinearity that correspond to convex Hamiltonians, and for arbitrarily long times, thus obviating a chief obstacle in most quantum algorithms for nonlinear partial differential equations. We provide quantum algorithms-both analog and digital-for extracting pointwise values, gradients, minima, and function evaluations at the minimizer of the viscosity solution, without requiring nonlinear updates or full state reconstruction.

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively as the modulation can be directly implemented with real devices with finite resolution. Until now, experiments only calculated key rates in the asymptotic regime. Here, we present a CV QKD system using discrete modulation that is especially designed for atmospheric channels. We use polarization encoding to exploit the nonbirefringent nature of the turbulent atmosphere. This allows to expand CV QKD networks beyond the existing fiber backbone. In a laboratory demonstration with a static 3-decibel loss channel, we implemented a recently developed security proof allowing to calculate composable finite-size key rates against independently and identically distributed collective attacks. We applied the full QKD protocol including a quantum random number generator, error correction, and privacy amplification to extract secret keys.

Photocatalytic CO2 reduction reaction (CO2RR) is a promising route for solar-to-chemical conversion, yet its performance is still limited by weak CO2 activation, rapid charge recombination, and the use of sacrificial reagents. Coupling CO2RR with value-added organic oxidation can improve overall redox efficiency, but it requires efficient interfacial charge transport and balanced dual-half-reaction kinetics. Herein, we report an in situ-derived CdSe quantum dots/layered double hydroxide (CdSe/LDH) composite catalyst for visible light driven CO2 to CO reduction coupled with anisyl alcohol oxidation to pinacol, without external photosensitizers or sacrificial donors. In situ X-ray absorption spectroscopy reveals reactant-dependent electronic shifts under illumination, indicating dynamic interfacial charge redistribution during catalysis. Compared with the individual components, the coupled catalyst delivers approximately threefold higher CO evolution, high pinacol selectivity (∼98%), and markedly improved cycling stability under visible-light irradiation (λ ≥ 400 nm). This work demonstrates an in situ interface-engineering strategy to stabilize quantum-dot photocatalysts while promoting synergistic activity, selectivity, and durability in dual-end photocatalytic redox coupling.

Interest in Josephson junctions (JJs) has increased rapidly in recent years not only because of their use in qubits and other quantum devices but also due to the unique physics supported by the JJs. The advent of various novel quantum materials for both the barrier region and the superconducting leads has led to the possibility of adding new functionalities to the JJs. Thus, there is a growing need for accurate modeling of the JJs and related systems to enable their predictive control and atomistic level understanding. This review presents an in-depth discussion of a Green's function-based formalism for computing supercurrents in JJs. The formulation is tailored for large-scale atomistic simulations and encompasses both dc and ac supercurrents. We hope that this review will provide a timely and comprehensive reference for researchers as well as beginning practitioners interested in Green's-function-based methods to model supercurrents in JJs.

We have recently developed the constraint coordinate-momentum phase space (CPS) formulation for finite-state quantum systems. It has been implemented for the electronic subsystem in nonadiabatic transition dynamics to develop practical trajectory-based approaches. In the generalized CPS formulation for the mapping Hamiltonian of the classical mapping model with commutator variables (CMMcv) method [J. Phys. Chem. A 2021, 125, 6845-6863, which followed J. Chem. Phys. 2016, 145, 204105 and J. Chem. Phys. 2019, 151, 024105], each connected component of the generalized CPS is the complex Stiefel manifold labeled by the eigenvalue set of the mapping kernel. Such a phase space structure allows for exact trajectory-based dynamics for pure discrete (electronic) degrees of freedom (DOFs), where the equations of motion of each trajectory are isomorphic to the time-dependent Schrödinger equation. We employ covariant kernels within the generalized CPS formulation to develop two approaches that naturally yield exact evaluation of time correlation functions (TCFs) for pure discrete (electronic) DOFs. In addition, we briefly discuss the phase space representations where the contribution of each trajectory to the integral expression of the TCF of population dynamics is strictly positive semi-definite. The generalized CPS formulation also indicates that the mapping Hamiltonian in phase space mapping model I of our previous work [J. Chem. Phys. 2016, 145, 204105] leads to a complex Stiefel manifold U ( F ) / U ( F - 2 ) . The phase space expressions (of TCFs) proposed in this paper are extensively tested in our subsequent work on nonadiabatic dynamics [J. Chem. Theory Comput. 2025, 21, 3775-3813]. It is expected that the generalized CPS formulation will have more implications for studying nonadiabatic transition dynamics and many-body quantum dynamics.

Simulating long-time nonadiabatic dynamics in condensed-phase systems is computationally demanding due to the inherent non-Markovianity of the electronic reduced density matrix evolution. While the generalized quantum master equation (GQME) and transfer tensor method (TTM) allow for the reconstruction of long-time dynamics from short-time projection-free inputs, their accuracy hinges on the rigorous estimation of the memory time, a parameter often determined by heuristic trial-and-error. In this work, we establish a comprehensive framework for estimating memory time and benchmarking propagation accuracy using semiclassical and numerical exact inputs on both standard spin-boson models and general multistate harmonic models. We develop an error estimation scheme that reveals a characteristic three-stage decay pattern in the non-Markovian propagation error: an initial transient drop, an exponential decay, and a saturation plateau. This estimator serves as a critical diagnostic tool for GQME and TTM, successfully distinguishing between converged predictions and reliability failures in complex systems, such as the carotenoid-porphyrin-fullerene triad. These findings provide a robust, quantitative protocol for validating memory-kernel-based simulations of nonadiabatic dynamics.

Converting CO2 into solid carbon under mild conditions remains one of the foremost challenges. Here, a field-induced electron localization strategy that enables room-temperature transformation of CO2 captured in amine solution into graphitic carbon quantum dots (g-CQDs) is reported. Rapid electron injection (at relatively low bias -1.3 to -1.7 V vs. Ag/AgCl) to a nanoscale AgO/Ag interface stabilized by the [BMIM]+[BF4]- ionic liquid generates a dynamic interfacial electron layer that sustains continuous electron transfer under a strong localized electric field. Operando Ag K-Edge XANES combined with theoretical simulations reveal pronounced electron localization at the Ag nanoparticle/electric double layer (Ag-EDL) interface, which activates CO2 reduction via CO intermediates and C2-radical coupling, yielding g-CQDs with negligible gaseous and liquid byproducts. The process requires only 0.9 kWh kg- 1 C, representing a record low energy demand for solid-carbon formation from CO2 under ambient conditions. Scaled operation in a 2-L reactor demonstrates steady g-CQD production and incorporation of only 0.05 wt.% of the CO2-derived g-CQDs into Portland cement enhances compressive strength of the cement mortar by ≈40%, attributed to the high dispersibility and nucleation activity of g-CQDs. This work establishes field-induced electron localization as a versatile platform for ambient CO2 conversion into value-added carbon nanomaterials.

Quantum-chemical calculations often make use of point-group theory to exploit molecular symmetry, resulting in a reduction of the computational cost and in insights into the electronic structure. This exploitation is often limited to subgroups of D2h that are Abelian with real characters. Here, we extend the symmetry exploitation to Abelian point groups with complex characters. Such point groups are often encountered in calculations that involve finite magnetic fields, though their occurrence is not limited to these cases alone. We present the evaluation of integrals over symmetry-adapted orbitals using the double-coset decomposition, as well as the use of these symmetries in the contractions needed within post-Hartree-Fock calculations in the context of block tensors. Efficiency gains are discussed for four simple hydrocarbons that exhibit a complex Abelian point group in the presence of a magnetic field.

Indium antimonide (InSb) colloidal quantum dots (CQDs) are attractive heavy-metal-free absorbers for infrared photodetection, yet their synthesis remains challenging because precursor reduction overlaps with nucleation and growth, hindering kinetic control and yielding broad size distributions. Here we employ an automated workflow to achieve precise control over InSb CQD synthesis, leading to improved batch-to-batch reproducibility and narrow size distributions without laborious post-synthetic size-selective precipitation. We find that InSb CQD formation proceeds through a kinetically matched precursor co-reduction pathway, which requires an In-rich environment to compensate for the faster reduction of Sb3+ precursor. Within this framework, we tune CQD size by modulating precursor conversion kinetics through In/Sb precursor molar ratio and reducing agent availability. This kinetically guided size control tunes the first excitonic absorption peak of CQDs across 1120-1650 nm in the short-wave infrared. Optimized CQDs with 0.825 eV bandgap exhibit a small Stokes shift of 32 meV, which is among the smallest reported for InSb CQDs.

Feature selection and hyperparameter optimization are widely used in heart disease prediction, but comparative findings are often affected by differences in preprocessing, search space design, and evaluation budget. This study compared quantum-inspired particle swarm optimization and evolutionary algorithms with their binary variants, namely QI-PSO, QI-EA, QI-BPSO, and QI-BEA, under the same evaluation protocol for joint feature selection and discrete hyperparameter tuning on the UCI Cleveland Heart Disease dataset. The feature mask and classifier hyperparameters were optimized simultaneously in a unified search space using the same preprocessing scheme, inner-validation protocol, and evaluation budget. Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) classifiers were evaluated using a fixed train-test split. Held-out test performance was reported with 95% confidence intervals. In addition, a no-feature selection baseline was evaluated for each classifier using the final hyperparameter configuration of the best selected feature QI model for the corresponding classifier while retaining all 13 input variables. Precision-recall curves, threshold sensitivity profiles, outer-trial CV Accuracy, runtime, selected feature counts, convergence behavior, and cost-performance distributions were also analyzed. Statistical comparisons of outer-trial distributions were performed using Kruskal-Wallis tests followed by Holm-adjusted Mann-Whitney U post-hoc comparisons. The held-out test results showed classifier-dependent differences among the QI variants. For DT and K-NN, the highest selected-feature test Accuracy was obtained with QI-PSO, whereas for SVM it was obtained with QI-BPSO. In the K-NN setting, QI-BEA, defined in this study as the modified binary implementation of the QI-EA update strategy, produced higher selected-feature Accuracy, F1-score, and MCC than QI-EA. For DT, QI-BPSO yielded the highest ROC-AUC, while for LR all four QI methods produced identical held-out test metrics. Compared with the corresponding no-feature selection baselines, the selected feature QI models showed a clear held-out advantage for K-NN. In DT, the no-feature selection baselines matched the best selected feature accuracy, whereas QI-BPSO yielded the highest DT ROC-AUC. In LR and SVM, the no-feature selection baselines produced stronger held-out results than the selected feature configurations. Precision-recall and threshold sensitivity analyses further showed classifier-dependent overlap and separation patterns beyond single point estimates. At the outer-trial level, QI-BPSO occupied the lowest runtime range, while QI-BEA yielded the highest median CV accuracy for DT, K-NN, and LR. These findings show that the relative behavior of QI variants depends on the classifier family and on whether emphasis is placed on held-out test performance, operating-threshold behavior, inner-loop validation level, or computational cost.

We present WMS-Rot and its fitting companion WMS-FitRot as an integrated framework for the early stages of rotational spectral analysis, starting from spectroscopic parameters obtained from electronic-structure computations and progressing to assignment-aware local refinement driven by the same theoretical catalog used for prediction. The framework provides a practical and internally consistent route connecting modern composite quantum-chemical predictions to first-pass assignments and controlled refinement. More fundamentally, it reformulates the incorporation of theoretical information into the spectroscopic inverse problem: calculated parameters act not only as initial guesses but also as active constraints that stabilize assignments and guide early-stage refinement within a unified simulation-fit cycle. Applications to nicotinic acid and thiopronine show that accurate composite inputs markedly improve starting points compared to low-level models, enabling robust assignment, reliable conformer discrimination, and consistent refinement. The approach reproduces matched reduced-Hamiltonian fits while remaining fully compatible with standard SPCAT/SPFIT practice and provides diagnostic insight into parameter correlations, identifiability, and model conditioning.

The oscillator strengths of absorptive transitions from the ground to the resonant excited impurity states for the impurity positioned in and near the GaAs/AlGaAs rectangular quantum well are studied. Due to the resonant nature of the final states, the absorption peaks are broadened. The shape of the peaks is reproduced numerically as a function of impurity position with respect to the well and the well width. Peak parameters, such as maximum, broadening, and integral absorption, are analyzed numerically; the Fano parameter is considered qualitatively.