Hierarchical simulations of hybrid polymer-solid materials

Complex polymer-solid materials have gained a lot of attention during the last 2-3 decades due to the fundamental physical problems and the broad spectrum of technological applications in which they are involved. Therefore, significant progress concerning the simulations of such hybrid soft-hard nanostructured systems has been made in the last few years. Simulation techniques vary from quantum to microscopic (atomistic) up to mesoscopic (coarse-grained) level. Here we give a short overview of simulation approaches on model polymer-solid interfacial systems for all different levels of description. In addition, we also present a brief outlook concerning the open questions in this field, from the point of view of both physical problems and computational methodologies

Students’ view of Quantum Information Technologies, part 3

The article is part of a course on Quantum InformationTechnologies QIT conducted at the Faculty of Electronicsand Information Technology of the Warsaw University of Technology.The subject includes a publishing workshop exercised byengineering students. How do ICT engineers see QIT from theirpoint of view? How can they implement quantum technologies intheir future work? M.Sc. students usually have strictly declaredtopics for their master’s theses. The implementation of someworks is at an advanced stage. The potential areas of applicationof QIT are defined and narrow if they are to intellectuallyexpand the area of the completed theses. This is the idea ofincorporating QIT components or interfaces into classic ICTsolutions at the software and hardware level. It is possible topropose a solution in the form of a functional hybrid system. QITsystems should be functionally incorporated into the existing ICTenvironment, generating measurable added value. Such a task isquite demanding, but practice shows that it interests students.Solutions don’t have to be mature or even feasible. They canbe dreams of young engineers. The exercise is a publicationworkshop related to the fast development of QIT. The article isa continuation of publication exercises conducted with previousgroups of students participating in QIT lectures

Simulating Quantum Computations on Classical Machines: A Survey

We present a comprehensive study of quantum simulation methods and quantum simulators for classical computers. We first study an exhaustive set of 150+ simulators and quantum libraries. Then, we short-list the simulators that are actively maintained and enable simulation of quantum algorithms for more than 10 qubits. As a result, we realize that most efficient and actively maintained simulators have been developed after 2010. We also provide a taxonomy of the most important simulation methods, namely Schrodinger-based, Feynman path integrals, Heisenberg-based, and hybrid methods. We observe that most simulators fall in the category of Schrodinger-based approaches. However, there are a few efficient simulators belonging to other categories. We also make note that quantum frameworks form their own class of software tools that provide more flexibility for algorithm designers with a choice of simulators/simulation method. Another contribution of this study includes the use and classification of optimization methods used in a variety of simulators. We observe that some state-of-the-art simulators utilize a combination of software and hardware optimization techniques to scale up the simulation of quantum circuits. We summarize this study by providing a roadmap for future research that can further enhance the use of quantum simulators in education and research.Comment: 20 pages, 8 figures, under revie

The Impact of a Nuclear Disturbance on a Space-Based Quantum Network

Quantum communications tap into the potential of quantum mechanics to go beyond the limitations of classical communications. Currently, the greatest challenge facing quantum networks is the limited transmission range of encoded quantum information. Space-based quantum networks offer a means to overcome this limitation, however the performance of such a network operating in harsh conditions is unknown. This dissertation analyzes the capabilities of a space-based quantum network operating in a nuclear disturbed environment. First, performance during normal operating conditions is presented using Gaussian beam modeling and atmospheric modeling to establish a baseline to compare against a perturbed environment. Then, the DEfense Land Fallout Interpretive Code software and computational fluid dynamics study the effect of a nuclear explosion on the surrounding environment. Finally, these sources of noise are combined to estimate the degradation of quantum states being transmitted through a nuclear disturbed environment. It is found that the effects of a nuclear environment on a quantum network is a function of the height of blast, the explosive yield, and the network design. Debris lofted into the atmosphere during a surface blast dissipate after a couple of hours, yet the concentration is initially high and results in heavy signal loss. The nuclear fireball produced additional background light interference that scatters into the receiver\u27s detector from tens of seconds to a couple of minutes, causing excessive noise in the detector. All these effects are likely to impede a quantum network’s ability to distribute quantum information between a ground station and low Earth orbit satellite for approximately one transmission period. Afterwards, by the next satellite pass, normal operation is expected to resume. These results provide the operational capabilities of space-based optical quantum networks following a nuclear explosion. The model can be expanded to model satellite-based quantum networks in other harsh atmospheric environments

Quantum-Inspired Machine Learning: a Survey

Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within classical computational frameworks. However, current review literature often presents a superficial exploration of QiML, focusing instead on the broader Quantum Machine Learning (QML) field. In response to this gap, this survey provides an integrated and comprehensive examination of QiML, exploring QiML's diverse research domains including tensor network simulations, dequantized algorithms, and others, showcasing recent advancements, practical applications, and illuminating potential future research avenues. Further, a concrete definition of QiML is established by analyzing various prior interpretations of the term and their inherent ambiguities. As QiML continues to evolve, we anticipate a wealth of future developments drawing from quantum mechanics, quantum computing, and classical machine learning, enriching the field further. This survey serves as a guide for researchers and practitioners alike, providing a holistic understanding of QiML's current landscape and future directions.Comment: 56 pages, 13 figures, 8 table

Efficient Representation of Minimally Entangled Typical Thermal States in two dimensions via Projected Entangled Pair States

The Minimally Entangled Typical Thermal States (METTS) are an ensemble of pure states, equivalent to the Gibbs thermal state, that can be efficiently represented by tensor networks. In this article, we use the Projected Entangled Pair States (PEPS) ansatz as to represent METTS on a two-dimensional (2D) lattice. While Matrix Product States (MPS) are less efficient for 2D systems due to their complexity growing exponentially with the lattice size, PEPS provide a more tractable approach. To substantiate the prowess of PEPS in modeling METTS (dubbed as PEPS-METTS), we benchmark it against the purification method for the 2D quantum Ising model at its critical temperature. Our analysis reveals that PEPS-METTS achieves accurate long-range correlations with significantly lower bond dimensions. We further corroborate this finding in the 2D Fermi Hubbard model at half-filling. At a technical level, we introduce an efficient \textit{zipper} method to obtain PEPS boundary matrix product states needed to compute expectation values. The imaginary time evolution is performed with the neighbourhood tensor update.Comment: 14 pages, 14 figure