Scale Quantum Research Articles

The Invisible Reality of Quantum Mechanics: The ‎‎‎Deterministic Perspective

The fundamental deterministic nature of quantum mechanics (QM) is mathematically demonstrated by the modeling of the ‎‎‎quantum (multiple) slit experiments; hence this paper is written from a deterministic perspective (DP) in quantum mechanics.‎ ‎Due to approximately 90 years of indeterminism and probabilistic statistical results, the theory consists of many ‎‎‎interpretations and is often regarded as counterintuitive. The latter is in the deterministic perspective not the case and is ‎‎‎illustrated with some examples. After a brief historic perspective, ‘invisible’ i.e. invisible entities of reality in mathematical ‎‎‎treatment, are introduced. These entities are handled by mathematics indirectly i.e. are described in a transformed domain ‎‎‎without variables violating the Heisenberg relation. In nature, i.e. on micro and macro levels, causality is fully ‘entangled’ with ‎‎‎energy, information - in the meaning of ordering or coding - as well as time. In contrast with the macro scale with many forms ‎‎‎and types of memory functions, without a memory property of quanta, causality is the bearer of information symmetry on ‎‎the ‎quantum scale. Further on in this DP paper: mathematical and philosophical consequences and influences in several ‎‎‎paragraphs, regarding subjects such as ‘free will’ and expected ‘threats to science’, the Bell inequalities, Alice and Bob & ‎‎‎entanglement, causality, information, retro-causality, spooky action, encryption and computing, teleportation, and in general ‎‎‎interpretations rooted in indeterminacy in QM as well as associated topics on independence, fine-tuning. The last paragraph ‎‎‎outlines the mathematical treatment of QM more extensively and may clarify references to the above-mentioned topics of ‎‎‎discussion in QM further.‎

Entanglement entropy production in Quantum Neural Networks

Quantum Neural Networks (QNN) are considered a candidate for achieving quantum advantage in the Noisy Intermediate Scale Quantum computer (NISQ) era. Several QNN architectures have been proposed and successfully tested on benchmark datasets for machine learning. However, quantitative studies of the QNN-generated entanglement have been investigated only for up to few qubits. Tensor network methods allow to emulate quantum circuits with a large number of qubits in a wide variety of scenarios. Here, we employ matrix product states to characterize recently studied QNN architectures with random parameters up to fifty qubits showing that their entanglement, measured in terms of entanglement entropy between qubits, tends to that of Haar distributed random states as the depth of the QNN is increased. We certify the randomness of the quantum states also by measuring the expressibility of the circuits, as well as using tools from random matrix theory. We show a universal behavior for the rate at which entanglement is created in any given QNN architecture, and consequently introduce a new measure to characterize the entanglement production in QNNs: the entangling speed. Our results characterise the entanglement properties of quantum neural networks, and provides new evidence of the rate at which these approximate random unitaries.

How the informed relations between physical, digital and biological dimensions are changing the design practice, as well as the sustainability paradigm.

In the "century of biotechnology", a new form of "bio-digital industry" is emerging in which, thanks to increasingly sophisticated and digitized technologies that allow engineering and production on a biological quantum scale, it is possible to analyze and reproduce the generative, chemical, physical, and molecular processes underlying natural mechanisms. Inheriting methodologies and technologies from biological fabrication, bio-digital practices foster a new material-based biological paradigm that, bringing biomimicry to a material level, allows designers to observe substances and logic used by nature for assembling and structuring its materials, developing more sustainable and strategic ways for artifice manufacturing, as well as replicating complex, tailored, and emergent biological qualities. The paper aims to describe the new hybrid manufacturing techniques, demonstrating how the transition from form-based to material-based approaches also leads to the change of logic and conceptual frameworks in design practices, allowing greater alignment with the paradigms of biological growth. In particular, the focus is on informed relations between physical, digital, and biological dimensions, allowing interaction, development, and mutual empowerment between entities and disciplines belonging to them. Such a correlative strategy can help design to apply systemic thinking, from the scale of the material to that of the product and the process, paving the way to sustainable scenarios, not simply to reduce the human impact on the ecosystem but to enhance nature through original cooperation and integration forms between humans, biology, and machines.

Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware

The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for quantum algorithms. We present the first quantum implementation of join ordering, one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes conforming to current quantum annealers. Yet, we also confirm that limits of early-stage technology are quickly reached. Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future QPUs, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and problem size. In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.

Gravitational waves and dark matter from classical scale invariance

In this paper we consider a minimal classically conformal U(1) model of fermionic dark matter. We calculate the one loop effective potential which generates the mass scale quantum mechanically via dimensional transmutaion in the spirit of Gildener and Weinberg, and examine the effects of the new dark sector on the Standard Model Higgs as well as how the dark fermions receive a mass and can produce the observed relic abundance. We then consider constraints on the model coming from collider and direct detection experiments before calculating the thermal effects on the potential in the early universe. We examine the nature and conditions for a strongly first order phase transition in our model and calculate the associated gravitational wave signal and compare to the sensitivities of current and proposed experiments.

Quantum gravity constraints on fine structure constant from GUP in braneworlds

The Generalized Uncertainty Principle (GUP) has been discussed in the thick braneworld scenario. By considering Rydberg atoms in this background, we show that the spacetime geometry affects Maxwell equations inducing an effective dielectric constant on the space. In its turn, the corrected Coulomb potential by the gravitational interaction yields a deviation on the 3-dimensional Bohr radius. Then, we compute the corrections on the fine structure constant owing to the GUP in higher-dimensional spacetime. We also found constraints for the deformation parameter beta and D-dimensional Planck length l_{D} by comparing the predicted deviations with the recent empirical data of the fine structure constant. We compute the intermediate length scale, which in principle may be larger than the Planck length scale. It is conjectured that below such scale Quantum Gravity effects should take place.

Critical fermions with spontaneously broken scale symmetry

We study relativistic fermions in three euclidean dimensions with four- and six-fermion interactions of the Gross-Neveu type. In the limit of many fermion flavors, and besides the isolated free fixed point, the theory displays a line of interacting ultraviolet fixed points. At the endpoint of the critical line, we establish that mass is generated through the spontaneous breaking of quantum scale invariance. Curiously, broken parity symmetry is a prerequisite for the spontaneous generation of mass rather than a consequence thereof. We also calculate critical exponents and find that hyperscaling relations are violated. Further similarities with critical scalar theories, and implications for conformal field theories and higher spin theories are discussed.

Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network

The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as Intermediate Scale Quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this paper, we propose a Machine Learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using Graph Neural Network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.

Implementation of Quantum Key Distribution Algorithm in Real Time IBM Quantum Computers

Abstract: We are Currently in the NISQ(noisy intermediate scale quantum) era. Current Quantum computers have a high noise error rate. With the increasing number of qubits every year we can develop fault tolerant quantum systems which have immense power to change our current computing power. Current quantum systems have shown to have the potential to break the classical RSA algorithm using shors algorithm. With more development and increase in number of qubits available it could easily break the current security systems of our computing. In future we could transmit data safely using the same quantum principles in the post quantum cryptography era. Quantum key distribution could be the future protocol used to transfer our secure information without getting hacked even by using quantum computing and break other quantum principles like shors algorithm which can break current encryption techniques like the RSA algorithms. We need to develop Quantum network infrastructure so we can implement the quantum key distribution algorithm in the future to enable secure transmission of data.

Quantum Otto cycle under strong coupling.

Quantum heat engines are often discussed under the weak-coupling assumption that the interaction between the system and the reservoirs is negligible. Although this setup is easier to analyze, this assumption cannot be justified on the quantum scale. In this study, a quantum Otto cycle model that can be generally applied without the weak-coupling assumption is proposed. We replace the thermalization process in the weak-coupling model with a process comprising thermalization and decoupling. The efficiency of the proposed model is analytically calculated and indicates that, when the contribution of the interaction terms is neglected in the weak-interaction limit, it reduces to that of the earlier model. The sufficient condition for the efficiency of the proposed model not to surpass that of the weak-coupling model is that the decoupling processes of our model have a positive cost. Moreover, the relation between the interaction strength and the efficiency of the proposed model is numerically examined by using a simple two-level system. Furthermore, we show that our model's efficiency can surpass that of the weak-coupling model under particular cases. From analyzing the majorization relation, we also find a design method of the optimal interaction Hamiltonians, which are expected to provide the maximum efficiency of the proposed model. Under these interaction Hamiltonians, the numerical experiment shows that the proposed model achieves higher efficiency than that of its weak-coupling counterpart.

Stannic oxide quantum dots constructed evenly alloyable layer stabilizing lithium metal batteries

Dendrite growth is still a problem that high energy density lithium metal batteries must face to commercialize. At present, the introduction of lithiophilic materials on the current collector has proved to be an effective strategy. However, most works are not conducive to the practical application of lithium metal batteries due to the neglect of the preparation cost and the excessive and uncontrollable modifier. Herein, stannic oxide (SnO2) quantum dots (QDs) were prepared by a simple dehydration reaction to construct a lithiophilic modification layer on the surface of copper foil. In addition, SnO2 QDs could induce uniform nucleation and growth of lithium ions at the quantum scale. More importantly, when the copper collector was modified by SnO2 QDs, the symmetric cell could cycle stably for 3200 h at a current density of 1 mA cm−2, which is better than other reports. LiFePO4 was employed as cathodic materials to fabricate a full battery, which delivered a capacity of 130.4 mAh g−1 with a capacity retention rate of 87.75% at 0.5 C for 200 cycles. Therefore, this work can be extended to practical applications, and help lithium metal batteries to be industrialized.

Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC) , a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs- –multi-programming (MP) , crosstalk mitigation (CM) —as well as a combination of their optimization goals (i.e., a combined MP-CM).

A Gradient‐Cost Multiobjective Alternate Framework for Variational Quantum Eigensolver with Variable Ansatz

AbstractVariational quantum eigensolver (VQE), aiming at determining the ground state energy of a quantum system described by a Hamiltonian on noisy intermediate scale quantum (NISQ) devices. However, the accuracy and trainability of the current VQE algorithm are significantly influenced due to the barren plateau (BP), the non‐negligible gate error and limited coherence time in NISQ devices. To tackle these issues, a gradient‐cost multiobjective alternate framework with variable ansatz is proposed. A theoretical framework is first proposed for VQE with variable ansatz (VA‐VQE) via alternately solving a multiobjective optimization problem defined by cost function values and gradient magnitudes and the original VQE problem. Then, a novel implementation method based on the double ε‐greedy strategy with the candidate tree and modified multiobjective genetic algorithm is proposed. As a result, the local optima are avoided both in ansatz and parameter perspectives, the BP phenomenon is alleviated, and the stability of output ansatz is enhanced. The experimental results indicate that this framework shows considerably average improvement of the error and stability by 59.6% and 78.8% compared with the state‐of‐the‐art cost‐criterion‐based VA‐VQE implementation, and 54.1% and 73.5% compared with the gradient‐criterion‐based counterpart, respectively, with lower quantum costs.

Quantum scale invariance in gauge theories and applications to muon production

We discuss quantum scale invariance in (scale invariant) gauge theories with both ultraviolet (UV) and infrared (IR) divergences. Firstly, their BRST invariance is checked in two apparently unrelated approaches using a scale invariant regularisation (SIR). These approaches are then shown to be equivalent. Secondly, for the Abelian case we discuss both UV and IR quantum corrections present in such theories. We present the Feynman rules in a form suitable for offshell Green functions calculations, together with their one-loop renormalisation. This information is then used for the muon production cross section at one-loop in a quantum scale invariant theory. Such a theory contains not only new UV poles but also IR poles. While the UV poles bring new quantum corrections (in the form of counterterms), finite or divergent, that we compute, it is shown that the IR poles do not bring new physics. The IR quantum corrections, both finite and divergent, cancel out similarly to the way the IR poles themselves cancel in the traditional approach to IR divergences (in the cross section, after summing over virtual and real corrections). Hence, the evanescent interactions induced by the scale-invariant analytical continuation of the SIR scheme do not affect IR physics, as illustrated at one-loop for the muon production ($e^+ e^- \to \mu^+\mu^-$) cross section.

Exact quantum conformal symmetry, its spontaneous breakdown, and gravitational Weyl anomaly

The classical Lagrangian of the Standard Model enjoys the symmetry of the full conformal group if the mass of the Higgs boson is put to zero. This is a hint that conformal symmetry may play a fundamental role in the ultimate theory describing Nature. The origin of scales, such as the Higgs vacuum expectation value (vev), may result from the spontaneous breakdown of the conformal symmetry by the dilaton field. In this work, we study whether this classical setup can be implemented in quantum theory and be phenomenologically viable by presenting an explicit construction where the exact conformal symmetry can be preserved and is anomaly free while being spontaneously broken. Not only the Higgs mass but also the genuine quantum scales like the QCD confinement radius are generated by the dilaton vev. We also discuss the extension of these ideas to the theories with dynamical gravity and show that the only finite subgroup of the local Weyl transformations which is anomaly free corresponds to the global scale symmetry. This means that the conformal invariance of the flat space theory is explicitly broken down to the scale symmetry by gravitational effects related to the Weyl anomaly.

Realization of quantum mean filters with different sized on NEQR quantum images by QFT based operations

For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.

Continuous variable quantum conference network with a Greenberger–Horne–Zeilinger entangled state

Quantum conference (QC) is a cryptographic task in secure communications that involves more than two users wishing to establish identical secret keys among N users. The Greenberger–Horne–Zeilinger (GHZ) entangled state is the basic resource for quantum cryptographic communication due to the existence of multipartite quantum correlations. An unconditional and efficient quantum network can be established with a continuous variable (CV) GHZ entangled state because of its deterministic entanglement. Here, we report an implementation of QC scheme using a CV multipartite GHZ entangled state. The submodes of a quadripartite GHZ entangled state are distributed to four spatially separated users. The proposed QC scheme is proved to be secure even when the entanglement is distributed through lossy quantum channels and the collective Gaussian attacks are in the all lossy channels. The presented QC scheme has the capability to be directly extended to a larger scale quantum network by using entangled states with more submodes.

There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines.

The applicability of computational models to the biological world is an active topic of debate. We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Such a view dissolves the contingent dichotomies driven by human cognitive biases (e.g., a tendency to oversimplify) and prior technological limitations in favor of a more continuous view, necessitated by the study of evolution, developmental biology, and intelligent machines. Form and function are tightly entwined in nature, and in some cases, in robotics as well. Thus, efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. This is challenging for many reasons, one of which is that living systems perform multiple functions in the same place at the same time. We refer to this as "polycomputing"-the ability of the same substrate to simultaneously compute different things, and make those computational results available to different observers. This ability is an important way in which living things are a kind of computer, but not the familiar, linear, deterministic kind; rather, living things are computers in the broad sense of their computational materials, as reported in the rapidly growing physical computing literature. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of mesoscale events, as it has already done at quantum and relativistic scales. To develop our understanding of how life performs polycomputing, and how it can be convinced to alter one or more of those functions, we can first create technologies that polycompute and learn how to alter their functions. Here, we review examples of biological and technological polycomputing, and develop the idea that the overloading of different functions on the same hardware is an important design principle that helps to understand and build both evolved and designed systems. Learning to hack existing polycomputing substrates, as well as to evolve and design new ones, will have massive impacts on regenerative medicine, robotics, and computer engineering.

Variational quantum solutions to the Shortest Vector Problem

A fundamental computational problem is to find a shortest non-zero vector in Euclidean lattices, a problem known as the Shortest Vector Problem (SVP). This problem is believed to be hard even on quantum computers and thus plays a pivotal role in post-quantum cryptography. In this work we explore how (efficiently) Noisy Intermediate Scale Quantum (NISQ) devices may be used to solve SVP. Specifically, we map the problem to that of finding the ground state of a suitable Hamiltonian. In particular, (i) we establish new bounds for lattice enumeration, this allows us to obtain new bounds (resp. estimates) for the number of qubits required per dimension for any lattices (resp. random q-ary lattices) to solve SVP; (ii) we exclude the zero vector from the optimization space by proposing (a) a different classical optimisation loop or alternatively (b) a new mapping to the Hamiltonian. These improvements allow us to solve SVP in dimension up to 28 in a quantum emulation, significantly more than what was previously achieved, even for special cases. Finally, we extrapolate the size of NISQ devices that is required to be able to solve instances of lattices that are hard even for the best classical algorithms and find that with approximately 103 noisy qubits such instances can be tackled.