Lifeboat Foundation

This paper aims to promote a quantum framework that analyzes Industry 4.0 cyber-physical systems more efficiently than traditional simulations used to represent integrated systems. The paper proposes a novel configuration of distributed quantum circuits in multilayered complex networks that enable the evaluation of industrial value creation chains. In particular, two different mechanisms for the integration of information between circuits operating at different layers are proposed, where their behavior is analyzed and compared with the classical conditional probability tables linked to the Bayesian networks. With the proposed method, both linear and nonlinear behaviors become possible while the complexity remains bounded. Applications in the case of Industry 4.0 are discussed when a component’s health is under consideration, where the effect of integration between different quantum cyber-physical digital twin models appears as a relevant implication.

Subject terms: Quantum simulation, Qubits.

Cyber-physical systems (CPS) are integrations of computational and physical components that can interact with humans through new and different modalities. A key to future technological development is precisely this new and different capacity of interaction together with the new possibilies that these systems pose for expanding the capabilities of the physical world through computation, communication and control¹. When CPS are understood within the industrial practice fueled by additional technologies such as Internet of Things (IoT), people refer to the Industry 4.0 paradigm². The design of many industrial engineering systems has been performed by separately considering the control system design from the hardware and/or software implementation details.

Researchers have put a sapphire crystal containing quadrillions of atoms into a superposition of quantum states, bringing quantum effects into the macroscopic world.

By Leah Crane

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Can quantum computing replace classical computing? State, Superposition, Measurement, Entanglement, Qubit Implementation, No-cloning Theorem, Error Correction, Caveats.

Hidden stripes in a crystal could help scientists understand the mysterious behavior of electrons in certain quantum systems, including high-temperature superconductors, an unexpected discovery by RIKEN physicists suggests.

The electrons in most materials interact with each other very weakly. But physicists often observe interesting properties in materials in which electrons strongly interact with each other. In these materials, the electrons often collectively behave as particles, giving rise to ‘quasiparticles’.

“A crystal can be thought of like an alternative universe with different laws of physics that allow different fundamental particles to live there,” says Christopher Butler of the RIKEN Center for Emergent Matter Science.

Ludovico Lami of QuSoft and the University of Amsterdam and Mark M. Wilde of Cornell have achieved a major breakthrough in the field of quantum computing by developing a formula that predicts the impact of environmental noise. This formula is critical in the creation of quantum computers that can wo.

The Oxford Martin Programme on Bio-Inspired Technologies is investigating the possibility of making computers real.

We aim to develop a completely new methodology for overcoming the extreme fragility of memory. By learning how biological molecules shield fragile states from the environment, we hope to create the building blocks of future computers.

The unique power of computers comes from their ability to carry out all possible calculations in parallel.

As quantum advantage has been demonstrated on different quantum computing platforms using Gaussian boson sampling,^1–3 quantum computing is moving to the next stage, namely demonstrating quantum advantage in solving practical problems. Two typical problems of this kind are computational-aided material design and drug discovery, in which quantum chemistry plays a critical role in answering questions such as ∼Which one is the best?∼. Many recent efforts have been devoted to the development of advanced quantum algorithms for solving quantum chemistry problems on noisy intermediate-scale quantum (NISQ) devices,^2,4–14 while implementing these algorithms for complex problems is limited by available qubit counts, coherence time and gate fidelity. Specifically, without error correction, quantum simulations of quantum chemistry are viable only if low-depth quantum algorithms are implemented to suppress the total error rate. Recent advances in error mitigation techniques enable us to model many-electron problems with a dozen qubits and tens of circuit depths on NISQ devices,⁹ while such circuit sizes and depths are still a long way from practical applications.

The difference between the available and actually required quantum resources in practical quantum simulations has renewed the interest in divide and conquer (DC) based methods.^15–19 Realistic material and (bio)chemistry systems often involve complex environments, such as surfaces and interfaces. To model these systems, the Schrödinger equations are much too complicated to be solvable. It therefore becomes desirable that approximate practical methods of applying quantum mechanics be developed.²⁰ One popular scheme is to divide the complex problem under consideration into as many parts as possible until these become simple enough for an adequate solution, namely the philosophy of DC.²¹ The DC method is particularly suitable for NISQ devices since the sub-problem for each part can in principle be solved with fewer computational resources.^{15–18,22–25} One successful application of DC is to estimate the ground-state potential energy surface of a ring containing 10 hydrogen atoms using the density matrix embedding theory (DMET) on a trapped-ion quantum computer, in which a 20-qubit problem is decomposed into ten 2-qubit problems.¹⁸

DC often treats all subsystems at the same computational level and estimates physical observables by summing up the corresponding quantities of subsystems, while in practical simulations of complex systems, the particle–particle interactions may exhibit completely different characteristics in and between subsystems. Long-range Coulomb interactions can be well approximated as quasiclassical electrostatic interactions since empirical methods, such as empirical force filed (EFF) approaches,²⁶ are promising to describe these interactions. As the distance between particles decreases, the repulsive exchange interactions from electrons having the same spin become important so that quantum mean-field approaches, such as Hartree–Fock (HF), are necessary to characterize these electronic interactions.

Quantum networking uses subatomic matter to deliver data in a way that goes beyond today’s fiber-optic systems. Amazon wants to grow diamonds which would be part of a component that lets the data travel farther without breaking down.

Pretty futuristic!

Amazon.com Inc. is teaming up with a unit of De Beers Group to grow artificial diamonds, betting that custom-made gems could could help revolutionize computer networks.

Continue reading “Amazon Looks to Grow Diamonds in Bid to Boost Computer Networks” »

The human brain, with its intricate networks of neurons, has long been a subject of fascination and mystery. Concurrently, the cosmos, with its vastness and complexity, has intrigued scientists and philosophers for centuries.

Recent research has begun to explore the possibility that the brain and the cosmos might be connected on a quantum scale. This article will delve into the research paper titled “Quantum transport in fractal networks” and discuss its implications for our understanding of the relationship between the brain and the cosmos.