Quantum Blockchain: A blockchain that utilizes quantum computing technology to improve security and performance.
Bio Blockchain: A blockchain that utilizes biological computing technologies, such as DNA storage, to enhance performance and efficiency.
Quantum Internet: A global network that utilizes quantum computing technologies to transmit data more securely and efficiently than classical networks.
DNA Data Storage: A technology that uses DNA as a storage medium for digital data, allowing for high-density, long-term data storage.
Quantum Computing: A field of computing that utilizes quantum mechanical phenomena, such as superposition and entanglement, to perform complex computations more efficiently than classical computers.
Quantum Cryptography: A form of cryptography that utilizes quantum mechanics to enhance security and privacy.
Quantum Artificial Intelligence: The intersection of quantum computing and artificial intelligence, which can potentially enable more efficient and accurate machine learning algorithms.
Bio Artificial Intelligence: The intersection of biological computing and artificial intelligence, which can potentially enable more efficient and accurate machine learning algorithms.
DNA Computing: A field of computing that utilizes DNA as a computational medium, allowing for the solution of certain computational problems more efficiently than classical computers.
Quantum Nanotechnologies: The application of quantum mechanics to the development of nanotechnology, which can potentially enable the creation of more precise and efficient nanoscale devices.
NanoBiotechnologies: The application of nanotechnology to the field of biotechnology, which can potentially enable the creation of more efficient and precise medical treatments and devices.
Hachimoji DNA: A synthetic DNA system that includes eight nucleotide bases instead of the four found in natural DNA, potentially allowing for the creation of new forms of life and new types of DNA-based technologies.
Internet of Things: The interconnection of physical devices, vehicles, buildings, and other objects, which allows for the collection and exchange of data.
Nanomaterials: Materials with dimensions at the nanoscale, which can exhibit unique physical and chemical properties that can potentially be used in a wide range of applications, including electronics, medicine, and energy.
Quantum blockchain based on asymmetric quantum encryption and a stake vote consensus algorithm
Blockchain in the Quantum World
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 1, 2022
Abstract—Blockchain is one of the most discussed and highly accepted technologies, primarily due to its application in almost every field where third parties are needed for trust. Blockchain technology relies on distributed consensus for trust, which is accomplished using hash functions and public-key cryptography. Most of the cryptographic algorithms in use today are vulnerable to quantum attacks. In this work, a systematic literature review is done so that it can be repeated, starting with identifying the research questions. Focusing on these research questions, literature is analysed to find the answers to these questions. The survey is completed by answering the research questions and identification of the research gaps. It is found in the literature that 30% of the research solutions are applicable for the data layer, 24% for the application and presentation layer, 23% for the network layer, 16% for the consensus layer and only 1% for hardware and infrastructure layer. We also found that 6% of the solutions are not blockchain-based but present different distributed ledger technology.
A last-in first-out stack data structure implemented in DNA
DNA-based memory systems are being reported with increasing frequency. However, dynamic DNA data structures able to store and recall information in an ordered way, and able to be interfaced with external nucleic acid computing circuits, have so far received little attention. Here we present an in vitro implementation of a stack data structure using DNA polymers. The stack is able to record combinations of two different DNA signals, release the signals into solution in reverse order, and then re-record. We explore the accuracy limits of the stack data structure through a stochastic rule-based model of the underlying polymerisation chemistry. We derive how the performance of the stack increases with the efficiency of washing steps between successive reaction stages, and report how stack performance depends on the history of stack operations under inefficient washing. Finally, we discuss refinements to improve molecular synchronisation and future open problems in implementing an autonomous chemical data structure.
Experimental photonic quantum memristor
[quantum machine learning - quantum neuromorphic architectures] [Michele Spagnolo, Joshua Morris, Simone Piacentini, Michael Antesberger, Francesco Massa, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame & Philip Walther Nature Photonics volume 16, pages318–323 (2022)]
Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.
Promiscuous molecules for smarter file operations in DNA-based data storage
Quantum Blockchain: A Decentralized, Encrypted and Distributed Database Based on Quantum Mechanics
Chuntang Li1, Yinsong Xu1, Jiahao Tang1, Wenjie Liu
Resilient three-dimensional ordered architectures assembled from nanoparticles by DNA
Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction
Lottery and Auction on Quantum Blockchain
by Xin Sun 1,Piotr Kulicki 1,* andMirek Sopek 2
Department of the Foundations of Computer Science, John Paul II Catholic University of Lublin, 20-950 Lublin, Poland
Entropy 2020, 22(12), 1377; https://doi.org/10.3390/e22121377
A Simple Voting Protocol on Quantum Blockchain
Efficient storage and analysis of quantitative genomics data with the Dense Depth Data Dump (D4) format and d4tools
Hao Hou, Brent Pedersen, Aaron Quinlan
Twin-Field Quantum Key Distribution over 511 km Optical Fiber Linking two Distant Metropolitans
The basic principle of quantum mechanics guarantee the unconditional security of quantum key distribution (QKD) at the cost of inability of amplification of quantum state. As a result, despite remarkable progress in worldwide metropolitan QKD networks over the past decades, long haul fiber QKD network without trustful relay has not been achieved yet. Here, through sending-or-not-sending (SNS) protocol, we complete a twin field QKD (TF-QKD) and distribute secure keys without any trusted repeater over a 511 km long haul fiber trunk linking two distant metropolitans. Our secure key rate is around 3 orders of magnitudes greater than what is expected if the previous QKD field test system over the same length were applied. The efficient quantum-state transmission and stable single-photon interference over such a long distance deployed fiber paves the way to large-scale fiber quantum networks.
Hachimoji DNA and RNA: A genetic system with eight building blocks
Shuichi Hoshika, Nicole A. Leal, Myong-Jung Kim, Myong-Sang Kim1, Nilesh B. Karalkar
Quantum compiling by deep reinforcement learning
The general problem of quantum compiling is to approximate any unitary transformation that describes the quantum computation as a sequence of elements selected from a finite base of universal quantum gates. The Solovay-Kitaev theorem guarantees the existence of such an approximating sequence. Though, the solutions to the quantum compiling problem suffer from a tradeoff between the length of the sequences, the precompilation time, and the execution time. Traditional approaches are time-consuming, unsuitable to be employed during computation. Here, we propose a deep reinforcement learning method as an alternative strategy, which requires a single precompilation procedure to learn a general strategy to approximate single-qubit unitaries. We show that this approach reduces the overall execution time, improving the tradeoff between the length of the sequence and execution time, potentially allowing real-time operations. https://www.nature.com/articles/s42005-021-00684-3
Quantifying Molecular Bias in DNA Data Storage
DNA has recently emerged as an attractive medium for future digital data storage because of its extremely high information density and potential longevity. Recent work has shown promising results in developing proof-of-principle prototype systems. However, very uneven (biased) sequencing coverage distributions have been reported, which indicates inefficiencies in the storage process and points to optimization opportunities. These deviations from the average coverage in oligonucleotide copy distribution result in sequence drop-out and make error-free data retrieval from DNA more challenging. The uneven copy distribution was believed to stem from the underlying molecular processes, but the interplay between these molecular processes and the copy number distribution has been poorly understood until now. In this paper, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that two important sources of bias are the synthesis process and the Polymerase Chain Reaction (PCR) process. By mapping the sequencing coverage of a large complex oligonucleotide pool back to its spatial distribution on the synthesis chip, we find that significant bias comes from array-based oligonucleotide synthesis. We also find that PCR stochasticity is another main driver of oligonucleotide copy variation. Based on these findings, we develop a statistical model for each molecular process as well as the overall process and compare the predicted bias with our experimental data. We further use our model to explore the trade-offs between synthesis bias, storage physical density and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems.
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
Arthur Pesah ,1,2 M. Cerezo,1,3 Samson Wang,1,4 Tyler Volkoff,1 Andrew T. Sornborger,5 and Patrick J. Coles1
Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA 2
Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom
Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87544 4
Imperial College London, London, United Kingdom 5
Information Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA
(Received 12 March 2021; revised 13 July 2021; accepted 2 August 2021; published 15 October 2021)
Organic electrochemical neurons and synapses with ion mediated spiking
Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on all-printed complementary organic electrochemical transistors. We demonstrate facile bio-integration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and long-term plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.
QUTAC - Quantum Technology & Application Consortium
We bring quantum computing to the level of large-scale industrial application and position our companies for a new digital future. Together, we want to strengthen Germany’s digital sovereignty, develop applications to market maturity and highlight opportunities for funding.
DNA DATA STORAGE ALLIANCE
The first and most extensive alliance of industry and academic organizations in DNA data storage that came together to help address the world's exponentially growing demand for archival storage.
Blockchain Techniques for Internet of Things Security
A Blockchain System Based on Quantum-Resistant Digital Signature
Blockchain, which has a distributed structure, has been widely used in many areas. Especially in the area of smart cities, blockchain technology shows great potential. The security issues of blockchain affect the construction of smart cities to varying degrees. With the rapid development of quantum computation, elliptic curves cryptosystems used in blockchain are not secure enough. This paper presents a blockchain system based on lattice cipher, which can resist the attack of quantum computation. The most challenge is that the size of public keys and signatures used by lattice cryptosystems is typically very large. As a result, each block in a blockchain can only accommodate a small number of transactions. It will affect the running speed and performance of the blockchain. For overcoming this problem, we proposed a way that we only put the hash values of public keys and signatures on the blockchain and store the complete content of them on an IPFS (interplanetary file system). In this way, the number of bytes occupied by each transaction is greatly reduced. We design a bitcoin exchange scheme to evaluate the performance of the proposed quantum-resistant blockchain system. The simulation platform is verified to be available and effective.
Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh,
*E-mail: firstname.lastname@example.org; email@example.com
ABSTRACT: Based on combined density functional theory and non-equilibrium Green’s function quantum transport studies, we have demonstrated quantum interference (QI) effects on the transverse conductance of Hachimoji (synthetic) nucleic acids placed between the oxygenterminated zigzag graphene nanoribbon (O-ZGNR) nanoelectrodes. We theorize that the QI effect could be well preserved in π-π coupling between a nucleobase molecule and the carbon-based nanoelectrode.
Our study indicates that QI effects such as anti-resonance or Fano-resonance that affect the variation of transverse conductance depending on the nucleobase conformation. Further, a variation of up to 2-5 orders of magnitude is observed in the conductance upon rotation for all the nucleobases. The current-voltage (I-V) characteristics results suggest a distinct variation in the electronic tunnelling current across the proposed nanogap device for all the five nucleobases with the applied bias voltage. The different rotation angles keep the distinct feature of the nucleobases in both transverse conductance and tunnelling current features. Both features could be utilized in an accurate synthetic DNA sequencing device.
KEYWORDS: Quantum interference effect, conductance, current-voltage characteristics, tunnelling effect, DNA sequencing.