Magic-state distillation is one of the leading overheads in fault-tolerant quantum computation. Existing methods for finding distillation factories require a transversal gate to act correctly on the entire codespace, a constraint that limits both generality and search efficiency. We introduce a strictly weaker borrowed-identity condition, requiring only that the distillation circuit act as the identity on a single input state. It applies uniformly across all levels of the Clifford hierarchy and unifies, within a single level, factories that distill different magic states -- for example, the $|T\rangle$, $|CS\rangle$, and $|CCZ\rangle$ factories. A brute-force search over borrowed-identity circuits with two-group symmetry recovers, within the search range, all distance-2 factories known from code-construction approaches, including entangled-output and multi-output factories previously outside the scope of any single numerical search. This unification yields parent circuits that encode multiple factories, so the output magic-state type can be chosen at compile time rather than fixed by a hard-coded design. The framework also extends beyond CSS codes, unifying constructions, including
Technology plays an undeniable role in today's industrial world, especially in manufacturing and smart factories. Unlike previous industrial revolutions, humans are at the core of the fifth generation of the Industrial Revolution. One of the critical aspects of Industry 5.0 (I 5.0) is its emphasis on human-centricity. The integration of modern technologies can be clearly observed in smart factories, which offer enhanced comfort and professionalism. This study highlights the significance of I 5.0 and smart factory production (SFP). A total of 36 articles are reviewed and systematically categorized using the meta-synthesis methodology. The research emphasizes the influence of I 5.0 on SFP through the use of modern technologies and comprehensive policy frameworks. This new paradigm has the potential to streamline people's lives and bring a transformative shift to smart factory production lines. Enhancing the structure of factories appears feasible under this optimistic perspective.
Mossel and Peres (2005) established a comprehensive framework for designing Bernoulli factories. Notably, they demonstrated that a single-variable function admits a finite-automata Bernoulli factory if and only if it is a rational function. Their Theorem 2.9 claims an extension of this result to multivariable functions, but it contains a subtle technical oversight in the application of Pólya's Theorem. We provide a direct counterexample: a rational function in three variables that admits a general Bernoulli factory but cannot be implemented by a finite-automata Bernoulli factory.
Modern cyber-physical systems are operated by complex software that increasingly takes over safety-critical functions. Software enables rapid iterations and continuous delivery of new functionality that meets the ever-changing expectations of users. As high-speed development requires discipline, rigor, and automation, software factories are used. These entail methods and tools used for software development, such as build systems and pipelines. To keep up with the rapid evolution of software, we need to bridge the disconnect in methods and tools between software development and safety engineering today. We need to invest more in formality upfront - capturing safety work products in semantically rich models that are machine-processable, defining automatic consistency checks, and automating the generation of documentation - to benefit later. Transferring best practices from software to safety engineering is worth exploring. We advocate for safety factories, which integrate safety tooling and methods into software development pipelines.
Two industry-grade datasets are presented in this paper that were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023. These datasets are generated by a manufacturing assembly line that utilizes industrial standards with respect to actuators, control mechanisms, and transducers. The two datasets were both generated simultaneously by operating the assembly line for 30 consecutive hours (with minor filtering) and collecting data from sensors equipped throughout the system. During operation, defects were also introduced into the assembly operation by manually removing parts needed for the final assembly. The datasets generated include a time series analog dataset and the other is a time series multi-modal dataset which includes images of the system alongside the analog data. These datasets were generated with the objective of providing tools to further the research towards enhancing intelligence in manufacturing. Real manufacturing datasets can be scarce let alone datasets with anomalies or defects. As such these datasets hope to address this gap and provide researchers with a foundation to build and train Artificial Intelligence
The high precision measurements of the Higgs mass and couplings at the future Higgs factories are sensitive to the parameter space of the Minimal Supersymmetric Standard Model (MSSM). Focused on the dominant stop sector contributions, we study the implication of the Higgs precision measurements on MSSM using multi-variable $χ^2$ fit. The results show nice complementarity between the indirect searches at Higgs factories and the direct searches at the current LHC program.
Leading approaches to fault-tolerant quantum computation dedicate a significant portion of the hardware to computational factories that churn out high-fidelity ancillas called magic states. Consequently, efficient and realistic factory design is of paramount importance. Here we present the most detailed resource assessment to date of magic state factories within a surface code quantum computer, along the way introducing a number of new techniques. We show that the block codes of Bravyi and Haah [Phys. Rev. A 86, 052329 (2012)] have been systematically undervalued; we track correlated errors both numerically and analytically, providing fidelity estimates without appeal to the union bound. We also introduce a subsystem code realisation of these protocols with constant time and low ancilla cost. Additionally, we confirm that magic state factories have space-time costs that scale as a constant factor of surface code costs. We find that the magic state factory required for post-classical factoring can be as small as 6.3 million data qubits, ignoring ancilla qubits, assuming $10^{-4}$ error gates, and the availability of long range interactions.
Software development is still based on manufactory production, and most of the programming code is still hand-crafted. Software development is very far away from the ultimate goal of industrialization in software production, something which has been achieved long time ago in the other industries. The lack of software industrialization creates an inability to cope with fast and frequent changes in user requirements, and causes cost and time inefficiencies during their implementation. Analogous to what other industries had done long time ago, industrialization of software development has been proposed using the concept of software factories. We have accepted this vision about software factories, and developed our own software factory which produces three-layered ASP.NET web applications. In this paper we report about our experience with using this approach in the process of software development, and present comparative results on performances and deliverables in both traditional development and development using software factories.
Neither Dark Matter nor scalar particles in the Higgs sector are ruled out at energies accessible to bottom and charm factories. In Dark Matter searches, the error on the mass of Dark Matter is $\sim 4$ GeV in the best LHC studies. For light Dark Matter this could represent a 100% (or more) error. In Higgs searches, the presence of a light singlet Higgs can make the LHC Higgs search difficult, if not impossible. If Dark Matter or a Higgs scalar is light, it will {\it require} a low-energy machine to precisely determine the couplings. We review the models, modes of discovery and rate expectations for these new particle searches at bottom and charm factories. We also discuss the options for new runs at bottom and charm factories relevant for these searches.
In 2010 we celebrated 50 years since commissioning of the first particle storage ring ADA in Frascati (Italy) that also became the first electron-positron collider in 1964. After that date the particle colliders have increased their intensity, luminosity and energy by several orders of magnitude. Namely, because of the high stored beam currents and high rate of useful physics events (luminosity) the modern electron-positron colliders are called "factories". However, the fundamental physics has required luminosities by 1-2 orders of magnitudes higher with respect to those presently achieved. This task can be accomplished by designing a new generation of factories exploiting the potential of a new collision scheme based on the Crab Waist (CW) collision concept recently proposed and successfully tested at Frascati. In this paper we discuss the performance and limitations of the present generation electron-positron factories and give a brief overview of new ideas and collision schemes proposed for further collider luminosity increase. In more detail we describe the CW collision concept and the results of the crab waist collision tests in DAFNE, the Italian PHi-factory. Finally, we brie
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how machine learning that is specifically tailored towards industrial applications can help in knowledge graph completion. In particular, we show how knowledge completion can benefit from event logs that are common in smart factories. We evaluate this on the knowledge graph from a real world-inspired smart factory with encouraging results.
We construct explicit combinatorial Bernoulli factories for the class of \emph{flow-based polytopes}; integral 0/1-polytopes defined by a set of network flow constraints. This generalizes the results of Niazadeh et al. (who constructed an explicit factory for the specific case of bipartite perfect matchings) and provides novel exact sampling procedures for sampling paths, circulations, and $k$-flows. In the process, we uncover new connections to algebraic combinatorics.
The discovery of the Higgs boson (and still nothing else) have triggered appearance of many proposals of Higgs factories for precision measurement of the Higgs properties. Among them there are several projects of photon colliders (PC) without e+e- in addition to PLC based on e+e- linear colliders ILC and CLIC. In this paper, following a brief discussion of Higgs factories physics program I give an overview of photon colliders based on linear colliders ILC and CLIC, and of the recently proposed photon-collider Higgs factories with no e+e- collision option based on recirculation linacs in ring tunnels.
Isosinglet fermions naturally arise in a variety of extensions of the Standard Model, in particular in models with extra dimensions. In this paper, we study the effect of the addition of a new isosinglet charged lepton to the standard spectrum, with special emphasis on implications for neutrino asymmetries to be measured at future neutrino factories. Lepton flavour violation in neutral current and lepton universality constraints are extensively discussed. We show that new physics effects in $ν_e-ν_μ$ CP asymmetries are significantly enhanced due to leptonic maximal mixings but still too small to give a signature at future neutrino factories. A signal for CP asymmetries in $ν_μ-ν_τ$ channel due to new physics could be observed at $1-3 σ$ if lepton flavour violating $τ$ decays are seen in a very close future in B-factories like BELLE experiment.
Current experiments at the B factories, designed to perform precision measurements of matter-antimatter asymmetry in the B meson system, have a much broader physics reach especially in the sector of quarkonium spectroscopy. Here we present a minireview on the new charmonium-like states observed at the B factories including the X(3872) and Y(4260).
The B factories were constructed with a very specific purpose in mind: to test the Cabibbo-Kobayashi-Maskawa description of quark mixing and CP violation in the Standard Model of particle physics. The goals of testing this part of the Standard Model were achieved, and have been surpassed beyond all expectation. As a result the B factories have revolutionised our understanding of many areas of the Standard Model of particle physics, and also provide a number of stringent limits on possible scenarios of physics beyond the Standard Model. In some cases these limits on new physics effects equal or surpass those achievable at the CERN based Large Hadron Collider.
In this article, I review the most recent results in radiative penguin decays from the B factories Belle and Babar. Most notably, I will talk about the recent new observations in the decays B -> (rho/omega) gamma, a new analysis technique in b -> s gamma, and first measurements of radiative penguin decays in the B_s meson system. Finally, I will summarize the current status and future prospects of radiative penguin B physics at the B factories.
We propose a design framework for reconstructing gate-based magic state distillation protocols as compact joint-measurement architectures implementable with the surface code. The goal is to reduce the surface-code resource cost of a magic state factory while preserving the logical function and error-detection structure of the distillation protocol. We construct a reduced architecture for implementing an eight-to-three CCZ distillation protocol using smaller surface-code patches. The proposed factory preserves the single-fault-detection property and the leading-order error suppression of the protocol, while producing CCZ magic states with lower spatial cost than the design of Gidney and Fowler. The proposed design perspective can also be applied to T-state factories and other multiqubit non-Clifford resource-state factories. Our approach provides a framework for extending the design space of surface-code magic state factories beyond a single CCZ layout optimization.
Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.
Returned products in circular factories re-enter production with heterogeneous degradation states, usage histories, and remaining capability. Reuse cannot be decided from the current inspection alone, because future function fulfillment and component integrity may evolve differently under the next service scenario. Existing PHM approaches support degradation prediction, but often target fixed operating conditions or isolated component benchmarks, while material-fatigue assessment is rarely linked to system-level functional prognosis. This paper addresses this gap for an angle grinder by combining uncertainty-aware functional prediction with component-level fatigue assessment in an instance-specific reliability workflow. The proposed framework combines the current tool state with recent force--torque usage windows. A convolutional encoder extracts loading patterns from spindle forces and shaft torque, and an LSTM backbone predicts nine functional variables as Gaussian mean and variance estimates. In parallel, the same loading history is translated into output-shaft fatigue information through finite-element-supported stress reconstruction, S--N/Miner damage evaluation with Haibach e