A system for realizing many-photon quantum circuits is presented, comprising a programmable nanophotonic chip operating at room temperature, interfaced with a fully automated control system.
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In this, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. This is the first time this important application in bioinformatics is modeled using quantum computation. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with a proof-of-concept example to target both the genomics research community and quantum application developers in a self-contained manner. The implementation and results on executing the algorithm from a set of DNA reads to a reconstructed sequence, on a gate-based quantum simulator, the D-Wave quantum annealing simulator and hardware are detailed. We also highlight the limitations of current classical simulation and available quantum hardware systems. The implementation is open-source and can be found on https://github.com/QE-Lab/QuASeR.
Citation: Sarkar A, Al-Ars Z, Bertels K (2021) QuASeR: Quantum Accelerated de novo DNA sequence reconstruction. PLoS ONE 16: e0249850. https://doi.org/10.1371/journal.pone.
Editor: Archana Kamal, University of Massachusetts Lowell, UNITED STATES.
Researchers propose a way to relieve the material requirements needed to realize topological quantum computers.
Quantum information is a powerful technology for increasing the amount of information that can be processed and communicated securely. Using quantum entanglement to securely distribute a secret quantum state among multiple parties is known as “quantum state sharing.”
An important protocol in quantum networks and cryptography, quantum state sharing works like this (in simple terms): a secret quantum state is divided into n shares and given to n players. The secret state can only be reconstructed if k (where kn/2) players cooperate, while the remaining n-k players cannot access the information. This protocol can also be used for quantum error correction, allowing the reconstruction of the secret state even if some of the information is lost.
In quantum information, there are two types of systems: discrete variable and continuous variable systems. Discrete variable systems are good because they don’t lose information easily, while continuous variable systems are good because the generation and processing of quantum states are deterministic rather than probabilistic, which enables a high degree of precision.
A team of researchers at Kyoto University is exploring the use of higher dimensions in de Sitter space to explain gravity in the early universe. By developing a method to compute correlation functions among fluctuations, they aim to bridge the gap between Einstein’s theory of general relativity and quantum mechanics. This could potentially validate superstring theory and enable practical calculations about the early universe’s subtle changes. Although initially tested in a three-dimensional universe, the analysis may be extended to a four-dimensional universe for real-world applications.
Having more tools helps; having the right tools is better. Utilizing multiple dimensions may simplify difficult problems — not only in science fiction but also in physics — and tie together conflicting theories.
For example, Einstein’s theory of general relativity — which resides in the fabric of space-time warped by planetary or other massive objects — explains how gravity works in most cases. However, the theory breaks down under extreme conditions such as those existing in black holes and cosmic primordial soups.
Open-standard computer chip instructions could help speed up futuristic technology like autonomous cars and quantum computers.
This could lead to chat gpt infinite ♾️ ✨️
Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.
Developing new quantum devices relies on controlling how electrons behave. A material called graphene, a single layer of carbon atoms, has fascinated researchers in recent years because its electrons behave as if they have no mass. For decades, scientists have also been interested in high-temperature superconductors: ceramic materials where electron interactions yield a macroscopic quantum state where electrons pair with each other. They do so at a temperature above the usual superconducting temperature of metals, which approaches absolute zero.
In a recent study published in Physical Review Letters, researchers from the SUNY Polytechnic Institute, Stony Brook University and the Brookhaven National Laboratory in the US, along with Aalto University in Finland, demonstrated a new electronic device that employs the unique ways in which electrons behave in these two materials— graphene and high-temperature superconductors.
The experiment, led by Sharadh Jois and Ji Ung Lee from SUNY with the support of theoretical work done by Jose Lado, assistant professor at Aalto, demonstrated a new quantum device that combines both graphene and an unconventional high-temperature superconductor.
Quantum computing and simulations are creating transformative opportunities by exploiting the principles of quantum mechanics in new ways to generate and process information. It is expected that a variety of areas ranging from day-to-day activities to making advanced scientific discoveries are going to benefit from such computations. Several early stage applications of quantum computing and simulation have already been demonstrated, and these preliminary results show that quantum computing and simulations could significantly accelerate the deployment of new technologies urgently needed to meet the growing demand for energy while safeguarding the environment.
