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is a versatile microbial cell factory, but efficient recovery of intracellular macromolecules remains a major challenge. In this study, we engineered environmentally controllable lysis systems to enable programmable product release. A glucose-responsive module, combining the promoter with phage-derived holin-endolysin genes ( ), triggered lysis when extracellular glucose dropped below 0.19-0.36 g/L. A separate temperature-inducible system employing the module activated lysis at 42 °C. These modules were further integrated into a dual-input circuit, enhancing regulatory precision and suppressing premature lysis, with additional operator copies allowing temporal tuning of induction. Functional validation using fluorescence, cell density measurements, and scanning electron microscopy confirmed robust, tunable responses under defined environmental cues. Collectively, these programmable lysis systems demonstrate that stimulus-responsive genetic circuits can be rationally designed to control cell disruption, providing a promising approach to streamline downstream processing and reduce extraction costs in industrial fermentation of .

Using a global optimization algorithm to optimize the initial weights and thresholds of traditional neural network models can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slow convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA–QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA–QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA–QGCNN model. Experimental results show that the proposed intrusion detection model has a lower false alarm rate and significant accuracy compared to conventional attack detection models. And QGCNN optimized by QGA improves the convergence performance of the model.

Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. So far, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian boson sampling, which permits the quantum computational advantage to be realized. Here we report a cloud-accessible versatile quantum computing prototype based on single photons. The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation, we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of 99.6 ± 0.1%, 93.8 ± 0.6% and 86 ± 1.2%, respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with chemical accuracy. For photon native computation, we implement a classifier algorithm using a three-photon-based quantum neural network and report a six-photon boson sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a heralded three-photon entanglement generation, a key milestone toward measurement-based quantum computing. A versatile cloud-accessible single-photon-based quantum computing machine is developed, which shows a six-photon sampling rate of 4 Hz over weeks. Heralded generation of a three-photon Greenberger–Horne–Zeilinger state—a key milestone toward measurement-based quantum computing—is implemented.

It is vital to minimize the impact of errors for near-future quantum devices that will lack the resources for full fault tolerance. Two quantum error mitigation (QEM) techniques have been introduced recently, namely, error extrapolation [Y. Li and S. C. Benjamin, Phys. Rev. X 7, 021050 (2017); K. Temme et al., Phys. Rev. Lett. 119, 180509 (2017)] and quasiprobability decomposition [K. Temme et al., Phys. Rev. Lett. 119, 180509 (2017)]. To enable practical implementation of these ideas, here we account for the inevitable imperfections in the experimentalist’s knowledge of the error model itself. We describe a protocol for systematically measuring the effect of errors so as to design efficient QEM circuits. We find that the effect of localized Markovian errors can be fully eliminated by inserting or replacing some gates with certain single-qubit Clifford gates and measurements. Finally, having introduced an exponential variant of the extrapolation method we contrast the QEM techniques using exact numerical simulation of up to 19 qubits in the context of a “swap” test circuit. Our optimized methods dramatically reduce the circuit’s output error without increasing the qubit count.

Photons are readily generated, are fast and can travel vast distances, and are ideal carriers of quantum information. Practical applications, such as quantum computing, will likely be based on an optical-chip platform and require the manipulation of multiphoton states. The inevitable scattering and loss of photons in such a platform would be detrimental for application. Blanco-Redondo et al. show how a specially designed optical circuit based on topology can offer protection for propagating biphoton states. The results show that topological design consideration could provide the desired robustness required for quantum optical circuitry. Science , this issue p. 568 A specially designed waveguide can offer topological protection to propagating biphotons. The robust generation and propagation of multiphoton quantum states are crucial for applications in quantum information, computing, and communications. Although photons are intrinsically well isolated from the thermal environment, scaling to large quantum optical devices is still limited by scattering loss and other errors arising from random fabrication imperfections. The recent discoveries regarding topological phases have introduced avenues to construct quantum systems that are protected against scattering and imperfections. We experimentally demonstrate topological protection of biphoton states, the building block for quantum information systems. We provide clear evidence of the robustness of the spatial features and the propagation constant of biphoton states generated within a nanophotonics lattice with nontrivial topology and propose a concrete path to build robust entangled states for quantum gates.

Suppressing errors is the central challenge for useful quantum computing 1 , requiring quantum error correction (QEC) 2 – 6 for large-scale processing. However, the overhead in the realization of error-corrected ‘logical’ qubits, in which information is encoded across many physical qubits for redundancy 2 – 4 , poses substantial challenges to large-scale logical quantum computing. Here we report the realization of a programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits. Using logical-level control and a zoned architecture in reconfigurable neutral-atom arrays 7 , our system combines high two-qubit gate fidelities 8 , arbitrary connectivity 7 , 9 , as well as fully programmable single-qubit rotations and mid-circuit readout 10 – 15 . Operating this logical processor with various types of encoding, we demonstrate improvement of a two-qubit logic gate by scaling surface-code 6 distance from d  = 3 to d  = 7, preparation of colour-code qubits with break-even fidelities 5 , fault-tolerant creation of logical Greenberger–Horne–Zeilinger (GHZ) states and feedforward entanglement teleportation, as well as operation of 40 colour-code qubits. Finally, using 3D [[8,3,2]] code blocks 16 , 17 , we realize computationally complex sampling circuits 18 with up to 48 logical qubits entangled with hypercube connectivity 19 with 228 logical two-qubit gates and 48 logical CCZ gates 20 . We find that this logical encoding substantially improves algorithmic performance with error detection, outperforming physical-qubit fidelities at both cross-entropy benchmarking and quantum simulations of fast scrambling 21 , 22 . These results herald the advent of early error-corrected quantum computation and chart a path towards large-scale logical processors. A programmable quantum processor based on encoded logical qubits operating with up to 280 physical qubits is described, in which improvement of algorithmic performance using a variety of error-correction codes is enabled.

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as T / N . When only K  ≪  T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to K / N . Our results imply that the compiling of unitaries into a polynomial number of native gates, a crucial application for the quantum computing industry that typically uses exponential-size training data, can be sped up significantly. We also show that classification of quantum states across a phase transition with a quantum convolutional neural network requires only a very small training data set. Other potential applications include learning quantum error correcting codes or quantum dynamical simulation. Our work injects new hope into the field of QML, as good generalization is guaranteed from few training data. The power of quantum machine learning algorithms based on parametrised quantum circuits are still not fully understood. Here, the authors report rigorous bounds on the generalisation error in variational QML, confirming how known implementable models generalize well from an efficient amount of training data.

The complexity of quantum states has become a key quantity of interest across various subfields of physics, from quantum computing to the theory of black holes. The evolution of generic quantum systems can be modelled by considering a collection of qubits subjected to sequences of random unitary gates. Here we investigate how the complexity of these random quantum circuits increases by considering how to construct a unitary operation from Haar-random two-qubit quantum gates. Implementing the unitary operation exactly requires a minimal number of gates—this is the operation’s exact circuit complexity. We prove a conjecture that this complexity grows linearly, before saturating when the number of applied gates reaches a threshold that grows exponentially with the number of qubits. Our proof overcomes difficulties in establishing lower bounds for the exact circuit complexity by combining differential topology and elementary algebraic geometry with an inductive construction of Clifford circuits. The dynamics of quantum states underlies the emergence of thermodynamics and even recent theories of quantum gravity. Now it has been proven that the quantum complexity of states evolving under random operations grows linearly in time.

We propose VQE circuit fabrics with advantageous properties for the simulation of strongly correlated ground and excited states of molecules and materials under the Jordan–Wigner mapping that can be implemented linearly locally and preserve all relevant quantum numbers: the number of spin up ( α ) and down ( β ) electrons and the total spin squared. We demonstrate that our entangler circuits are expressive already at low depth and parameter count, appear to become universal, and may be trainable without having to cross regions of vanishing gradient, when the number of parameters becomes sufficiently large and when these parameters are suitably initialized. One particularly appealing construction achieves this with just orbital rotations and pair exchange gates. We derive optimal four-term parameter shift rules for and provide explicit decompositions of our quantum number preserving gates and perform numerical demonstrations on highly correlated molecules on up to 20 qubits.

To run large-scale algorithms on a quantum computer, error-correcting codes must be able to perform a fundamental set of operations, called logic gates, while isolating the encoded information from noise 1 – 8 . We can complete a universal set of logic gates by producing special resources called magic states 9 – 11 . It is therefore important to produce high-fidelity magic states to conduct algorithms while introducing a minimal amount of noise to the computation. Here we propose and implement a scheme to prepare a magic state on a superconducting qubit array using error correction. We find that our scheme produces better magic states than those that can be prepared using the individual qubits of the device. This demonstrates a fundamental principle of fault-tolerant quantum computing 12 , namely, that we can use error correction to improve the quality of logic gates with noisy qubits. Moreover, we show that the yield of magic states can be increased using adaptive circuits, in which the circuit elements are changed depending on the outcome of mid-circuit measurements. This demonstrates an essential capability needed for many error-correction subroutines. We believe that our prototype will be invaluable in the future as it can reduce the number of physical qubits needed to produce high-fidelity magic states in large-scale quantum-computing architectures. A scheme to prepare a magic state, an important ingredient for quantum computers, on a superconducting qubit array using error correction is proposed that produces better magic states than those that can be prepared using the individual qubits of the device.