Abstract

Quantum mechanics has long been recognized as a counter-intuitive theory, with ideas such as wave-particle duality, quantum superposition and entanglement defying our natural way of thinking. In recent years, these sorts of uniquely quantum properties are being exploited to develop revolutionary technologies, such as quantum cryptography, quantum metrology and perhaps the most well-known example, quantum computation. In the field of quantum computation, the circuit model was used to show that universal quantum computation is possible1, and the circuit model has since been an incredibly successful tool, leading to important quantum algorithms which greatly outperform their classical counterparts2. The circuit model takes advantage of the fact that quantum mechanics allows for the superposition and interference of quantum bits (qubits) in different states to achieve a computational speed-up. However, in addition to the superpositions of states, quantum mechanics also allows for the superposition of quantum circuits3,4—a feature which is not used in the standard quantum circuit model. Nevertheless, such superpositions of quantum circuits are rapidly becoming central to several foundational research programs studying the role of time and causality in quantum theory5,6,7,8,9. These superpositions of quantum circuits (sometimes called a ‘superposition of causal orders’) give rise to new counter-intuitive quantum predictions, and it has recently been predicted that they could provide quantum computers with even further computational advantages8,10. In particular, superimposing quantum circuits, each with a different gate ordering, can allow one to accomplish a specific computational task with fewer quantum gate uses than a quantum computer which has a fixed-gate order10. Quantum computers achieve a speed-up by placing quantum bits (qubits) in superpositions of different states. However, it has recently been appreciated that quantum mechanics also allows one to ‘superimpose different operations’. Furthermore, it has been shown that using a qubit to coherently control the gate order allows one to accomplish a task—determining if two gates commute or anti-commute—with fewer gate uses than any known quantum algorithm. Here we experimentally demonstrate this advantage, in a photonic context, using a second qubit to control the order in which two gates are applied to a first qubit. We create the required superposition of gate orders by using additional degrees of freedom of the photons encoding our qubits. The new resource we exploit can be interpreted as a superposition of causal orders, and could allow quantum algorithms to be implemented with an efficiency unlikely to be achieved on a fixed-gate-order quantum computer.An in silico designed dosimetric autologous living vaccine consisting of with Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted Peptide mimotopic Epitopes RMFPNAPYLP pulsed dendritic cells on a personalized Active Network analysis for asymptomatic or minimally symptomatic metastatic Pancreatic Cancer.

Keywords

Experimental superposition of orders of quantum gatesAn in silico designed dosimetric autologous living vaccine consisting of with Multiple Wilms’ Tumor 1 WT1-ConSynthetic–Restricted Peptide mimotopic Epitopes RMFPNAPYLP pulsed dendritic cells on a personalized Active Network analysis for asymptomatic or minimally symptomatic metastatic Pancreatic Cancer.

Abstract

It is well known that quantum computers are superior to classical computers in efficiently simulating quantum systems. Here we report the first experimental simulation of quantum tunneling through potential barriers, a widespread phenomenon of a unique quantum nature, via NMR techniques. Our experiment is based on a digital particle simulation algorithm and requires very few spin-1/2 nuclei without the need of ancillary qubits. The occurrence of quantum tunneling through a barrier, together with the oscillation of the state in potential wells, are clearly observed through the experimental results. This experiment has clearly demonstrated the possibility to observe and study profound physical phenomena within even the reach of small quantum computers. Quantum simulation is one of the most important aims of quantum computation ever since Feynman studied the likelihood of simulating one quantum system by another1. Recent years have witnessed fruitful results in the development of quantum computation, and it has been demonstrated that quantum computers can solve certain types of problems with a level of efficiency beyond the capability of classical computers2,3,4,5,6, among which the simulation of the dynamics of quantum systems is especially attractive because of the exponential improvement in computational resources and speeds. Quantum simulation has become a subject of intense investigation and has been realized in various situations, such as system evolution with a many-body interaction Hamiltonian7,8,9,10, the dynamics of entanglement11,12, quantum phase transitions13,14, and calculations of molecular properties15,16,17,18,19. Since we live in a dirty environment, we have developed many host defenses to contend with microorganisms. The epithelial lining of our skin, gastrointestinal tract and bronchial tree produces a number of antibacterial peptides, and our phagocytic neutrophils rapidly ingest and enzymatically degrade invading organisms, as well as produce peptides and enzymes with antimicrobial activities. Some of these antimicrobial moieties also appear to alert host cells involved in both innate host defense and adaptive immune responses.RNAs fold into intricate and precise secondary structures. In this study for the first time we have been evaluted experimental simulation of Novel procedure quantum tunneling in small Computational Scaffolding systems on tumorigenic stem cell bacterial infected hybrids for the in silico rescaffolding and side-chain optimization on the neutrophil immune defense CAP37 protein.

Keywords

Novel procedure Computational Scaffolding, tumorigenic stem cell, bacterial infected hybrids, in silico rescaffolding, side-chain optimization, neutrophil immune defense, CAP37 protein, Experimental simulation, quantum tunneling, small systems.

Abstract

As quantum counterpart of classical computer, quantum computer reveals incredible efficiency to execute arithmetic tasks and threatens the security of classical communication. Quantum algorithm is the sole of quantum computation, which shows the amazing power of quantum parallelism and quantum interference. It attracts particular concern to develop new quantum algorithms in recent years. The concept of simulating physics progresses with quantum computers was originated in Richard Feynman’s observation that computers built from quantum mechanical components would be ideally suited to simulating quantum mechanics1. Since then, the first efficient quantum algorithm was proposed by Deutsch in 19852 and generalized by Deutsch and Jozsa in 19873. Lately, an increasing number of practical programs were presented, such as factoring large integer4, Grover’s searching algorithm for database5 and Simon’s exponential acceleration algorithm for the black box problem6. What’s more, Harrow et al. came up with a quantum scheme to decrease the computational complexity of solving linear system of equations from O(n) to log(n) , and this was the first quantum algorithm to work out the most fundamental problems in engineering science7. Some quantum algorithms have been demonstrated in different physical systems, such as ion traps8,9,10,11, superconducting devices12,13,14, optical lattices15,16, quantum dots17,18, and linear optics19,20,21,22,23,24,25. Due to its good scalability, easy-handling and high stability, linear optical system is a good candidate for implementing quantum algorithms.We report an experiment to demonstrate a quantum permutation determining algorithm with linear optical system. By employing photon’s polarization and spatial mode, we realize the quantum ququart states and all the essential permutation transformations. The quantum permutation determining algorithm displays the speedup of quantum algorithm by determining the parity of the permutation in only one step of evaluation compared with two for classical algorithm. This experiment is accomplished in single photon level and the method exhibits universality in high-dimensional quantum computation.Combinatorial learning procedures and graph transformations for the discovery of tumor-like cardiomyocyte derived eletroporated combined hybrids on a Meta-Dynamic Meta-node stemness reconstructing approach for the in silico generation of a anti-(JAM-A) drug-construct.

Keywords

Demonstration, quantum permutation algorithm, single photon, ququart, Combinatorial learning procedures, graph transformations, discovery tumor-like cardiomyocyte, eletroporated, combined hybrids, Meta-Dynamic, Meta-node, stemness, reconstructing, approach, in silico, generation, anti-(JAM-A), drug-construct.

Abstract

We report an experiment to demonstrate a quantum permutation determining algorithm with linear optical system. By employing photon’s polarization and spatial mode, we realize the quantum ququart states and all the essential permutation transformations. The quantum permutation determining algorithm displays the speedup of quantum algorithm by determining the parity of the permutation in only one step of evaluation compared with two for classical algorithm. This experiment is accomplished in single photon level and the method exhibits universality in high-dimensional quantum computation. As quantum counterpart of classical computer, quantum computer reveals incredible efficiency to execute arithmetic tasks and threatens the security of classical communication. Quantum algorithm is the sole of quantum computation, which shows the amazing power of quantum parallelism and quantum interference. It attracts particular concern to develop new quantum algorithms in recent years. The concept of simulating physics progresses with quantum computers was originated in Richard Feynman’s observation that computers built from quantum mechanical components would be ideally suited to simulating quantum mechanics1. Since then, the first efficient quantum algorithm was proposed by Deutsch in 19852 and generalized by Deutsch and Jozsa in 19873. Lately, an increasing number of practical programs were presented, such as factoring large integer4, Grover’s searching algorithm for database5 and Simon’s exponential acceleration algorithm for the black box problem6. What’s more, Harrow et al. came up with a quantum scheme to decrease the computational complexity of solving linear system of equations from O(n) to log(n) , and this was the first quantum algorithm to work out the most fundamental problems in engineering science7. Some quantum algorithms have been demonstrated in different physical systems, such as ion traps8,9,10,11, superconducting devices12,13,14, optical lattices15,16, quantum dots17,18, and linear optics19,20,21,22,23,24,25. Due to its good scalability, easy-handling and high stability, linear optical system is a good candidate for implementing quantum algorithms. Here, for the first time we have performed a fast stochastic optimization of a quantum permutation algorithm with a single photon ququart algorithm on DC-tumor like high yield minimal magnetic signatures of electrotransfectioned ex vivo mediated hybrids for the generation of a computer-aided designed candidate drugable Toll-like receptor (Pam2IDG) peptide-domain agonistic agent.

Keywords

Fast stochastic optimization algorithm, DC-tumor like high yield, minimal magnetic signatures, electrotransfectioned ex vivo mediated hybrids, generation, computer-aided, designed candidate, drugable, Toll-like receptor, (Pam2IDG) peptide-domain, agonistic agent, demonstration, quantum permutation, algorithm, single, photon ququart.

Abstract

The structure of peptide p6.7, a mimotope of the nicotinic receptor ligand site that binds alpha-bungarotoxin and neutralizes its toxicity, was compared to that of the acetylcholine binding protein. The central loop of p6.7, when complexed with alpha-bungarotoxin, fits the structure of the acetylcholine binding protein (AChBP) ligand site, whereas peptide terminal residues seem to be less involved in toxin binding. The minimal binding sequence of p6.7 was confirmed experimentally by synthesis of progressively deleted peptides. Affinity maturation was then achieved by random addition of residues flanking the minimal binding sequence and by selection of new alpha-bungarotoxin binding peptides on the basis of their dissociation kinetic rate. The MAP peptide binds alpha-bungarotoxin in solution and inhibits its binding to the receptor with a K(A) and an IC(50) similar to the monomeric peptide. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. In this regard, we discuss its potential to become a routinely used drug design tool of QM/MM in rational drug discovery and molecular diversity for the construction of anti-alpha-bungarotoxin binding peptide mimetic antidotes consisting of essential elements with high affinity and promised vivo efficiency.

Keywords

QM/MM, rational drug discovery, molecular diversity, construction, anti-alpha-bungarotoxin, binding peptide, mimetic antidotes, elements, high affinity, MAP-p6.7 peptide mimetic ligand, nicotinic receptor, binding site, potent snake neurotoxin, synthetic antidote, merging, scoring, molecular interactions,

Abstract

The Schrödinger perturbation energy for an arbitrary order N of the perturbation has been presented with the aid of a circular scale of time. The method is of a recurrent character and developed for a non-degenerate quantum state. It allows one to reduce the inflation of terms necessary to calculate known from the Feynman’s diagrammatical approach to a number below that applied in the original Schrödinger perturbation theory. he structure of peptide p6.7, a mimotope of the nicotinic receptor ligand site that binds alpha-bungarotoxin and neutralizes its toxicity, was compared to that of the acetylcholine binding protein. The central loop of p6.7, when complexed with alpha-bungarotoxin, fits the structure of the acetylcholine binding protein (AChBP) ligand site, whereas peptide terminal residues seem to be less involved in toxin binding. The minimal binding sequence of p6.7 was confirmed experimentally by synthesis of progressively deleted peptides. Affinity maturation was then achieved by random addition of residues flanking the minimal binding sequence and by selection of new alpha-bungarotoxin binding peptides on the basis of their dissociation kinetic rate. The MAP peptide binds alpha-bungarotoxin in solution and inhibits its binding to the receptor with a K(A) and an IC(50) similar to the monomeric peptide. Peptidomimetics are designed to circumvent some of the problems associated with a natural peptide: e.g. stability against proteolysis (duration of activity) and poor bioavailability. In this regard, we discuss its potential to become a routinely used drug design tool of QM/MM in rational drug discovery and molecular diversity for the construction of anti-alpha-bungarotoxin binding peptide mimetic antidotes consisting of essential elements with high affinity and promised vivo efficiency. Finally, we applied to the Circular Scale of Time computations as a Way of Calculating the Quantum-Mechanical Perturbation Energy Given by the Schrödinger Method in QM/MM rational drug discovery and molecular diversity for the construction of an anti-alpha-bungarotoxin binding MAP-p6.7 peptide mimetic ligand against nicotinic receptor binding site as a potent snake neurotoxin synthetic antidote.

Keywords

Quantum-Mechanical, Perturbation Energy, Circular Scale of Time, QM/MM, rational drug discovery, molecular diversity, construction, anti-alpha-bungarotoxin, binding MAP-p6.7 peptide, mimetic ligand, against nicotinic receptor, binding site, potent snake neurotoxin, synthetic antidote, Circular Scale of Time, Way of Calculating, Quantum-Mechanical, Perturbation Energy, Schrödinger Method

Abstract

There is evidence that the α-synucleinopathies Parkinson’s disease (PD) and the Parkinson variant of multiple system atrophy (MSA-P) overlap at multiple levels. Both disorders are characterized by deposition of abnormally phosphorylated fibrillar α-synuclein within the central nervous system suggesting shared pathophysiological mechanisms. Currently, there is no disease-modifying treatment for MSA. In other senses, it has been previously shown that next-generation active vaccination technology with short peptides, AFFITOPEs®, was effective in two transgenic models of synucleinopathies at reducing behavioral deficits, α-syn accumulation and inflammation. Recently, it is shown that there is a crucial contradiction within von Neumann’s theory [K. Nagata and T. Nakamura, Int. J. Theor. Phys. 49, 162 (2010)]. We derive a proposition concerning a quantum expected value under the assumption of the existence of the directions in a spin-1/2 system. The quantum predictions within the formalism of von Neumann’s projective measurement cannot coexist with the proposition concerning the existence of the directions. Therefore, we have to give up either the existence of the directions or the formalism of von Neumann’s projective measurement. Hence, there is a crucial contradiction within von Neumann’s theory. We discuss that this crucial contradiction makes the theoretical formulation of Deutsch’s algorithm questionable. Especially, we systematically describe our assertion based on more mathematical analysis using raw data. We demonstrate here for the first time a drug discovery platform for the generation of analogues of the heptapeptide H-Arg-Lys-Val-MePhe-Tyr-Thr-Trp- OH2, an novel multitargeted inhibitors of Aβ-peptide aggregation, to cross-react with α-synuclein interfering with its fibril formation through novel Aggregation simulated studies on Amyloid β-sheet helix-rich Val-Gly-Gly-Ala-Thr-Thr-Thr-Gly-Val-Thr peptide mimic modulators of α-Synuclein aggregation as a emerging template for drug discovery in α-synucleinopathy interfering amyloidogenesis pathways.

Keywords

Aggregation simulated studies, Amyloid β-sheet helix-richpeptide, mimic modulators, α-Synuclein, aggregation, emerging template, drug discovery, α-synucleinopathies, interfering amyloidogenesis pathways, Can Von Neumann’s Theory, Meet Quantum, Aggregation Computation, simulated studies, on Amyloid β-sheet helix-rich Val-Gly-Gly-Ala-Thr-Thr-Thr-Gly-Val-Thr, peptide mimic, modulators, α-Synuclein aggregation, emerging template.

Abstract

A statistical-mechanical framework for estimation of solvation entropies and enthalpies is proposed, which is based on the analysis of water as a mixture of correlated water oxygens and water hydrogens. Entropic contributions of increasing order are cast in terms of a Mutual Information Expansion that is evaluated to pairwise interactions. In turn, the enthalpy is computed directly from a distance-based hydrogen bonding energy algorithm. The resulting expressions are employed for grid-based analyses of Molecular Dynamics simulations. In this first assessment of the methodology, we obtained global estimates of the excess entropy and enthalpy of water that are in good agreement with experiment and examined the method’s ability to enable detailed elucidation of solvation thermodynamic structures, which can provide valuable knowledge toward molecular design.An algorithm for high-resolution refinement and binding affinity estimation of inhibitors of CGQMCTVWCSSGC targeted conserved peptide substitution mimetic pharmacostructures antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

Estimation, olvation Entropy, Enthalpy, Analysis, Water, Oxygen–Hydrogen Correlations, algorithm, high-resolution refinement, binding affinity, inhibitors, CGQMCTVWCSSGC targeted, conserved peptide, substitution, mimetic, pharmacostructures, antagonizing, VEGFR-3-mediated oncogenic effects,

Abstract

A quantum computer (QC) can operate in parallel on all its possible inputs at once, but the amount of information that can be extracted from the result is limited by the phenomenon of wave function collapse. We present a new computational model, which differs from a QC only in that the result of a measurement is the expectation value of the observable, rather than a random eigenvalue thereof. Such an expectation value QC can solve nondeterministic polynomial-time complete problems in polynomial time. This observation is significant precisely because the computational model can be realized, to a certain extent, by NMR spectroscopy on macroscopic ensembles of quantum spins, namely molecules in a test tube. This is made possible by identifying a manifold of statistical spin states, called pseudo-pure states, the mathematical description of which is isomorphic to that of an isolated spin system. The result is a novel NMR computer that can be programmed much like a QC, but in other respects more closely resembles a DNA computer. Most notably, when applied to intractable combinatorial problems, an NMR computer can use an amount of sample, rather than time, which grows exponentially with the size of the problem. Although NMR computers will be limited by current technology to exhaustive searches over only 15 to 20 bits, searches over as much as 50 bits are in principle possible, and more advanced algorithms could greatly extend the range of applicability of such machines. Cancer is still a major cause of death in the world at the beginning of the- 21st century and remains a major focus for ongoing research and development. In recent years a promising approach to the therapeutic intervention of cancer has focused on antiangiogenesis therapies. VEGFR-3 was detected in advanced human malignancies and correlated with poor prognosis. Previous studies show that activation of the VEGF-C/VEGFR-3 axis promotes cancer metastasis and is associated with clinical progression in patients with lung cancer, indicating that VEGFR-3 is a potential target for cancer therapy. Initial screening has identified other promising VEGFR-3 binding peptides as well. For example, a peptide comprising any of the following amino acid sequences: SGYWWDTWF, SCYWRDTWF, KVGWSSPDW, FVGWTKVLG, YSSSMRWRH, RWRGNAYPG, SAVFRGRWL, WFSASLRFR, and conservative substitution-analogs thereof, binds human VEGFR-3. On the other hand a newly introduced binding energy funnel ‘steepness score’ was applied for the evaluation of the protein–peptide-multi-ligand complexes binding affinity. KNIME-based BiogenetoligandorolTM – Pepcrawler simulations predicted high binding affinity for native protein–peptide-hyper-ligand complexes benchmark and low affinity for low-energy decoy complexes. As a result we managed finally to introduce an algorithm for high-resolution refinement and binding affinity estimation of novel designed inhibitors consisting of CGQMCTVWCSSGC conserved peptide substitution mimetic linked pharmacostructures with potential antagonizing VEGFR-3-mediated oncogenic effects.

Keywords

fast RRT-based algorithm, high-resolution, refinement, binding affinity, estimation, peptide inhibitors, in silico, discovery, performing high resolution, docking refinement, estimation affinity, conserved peptide, substitution, mimetic pharmacostructure, suppressor VEGFR-3 activity, antagonize VEGFR-3-mediated, oncogenic effects, ensemble quantum computing, NMR spectroscopy, high-resolution, refinement, binding affinity, estimation inhibitors, quantum computing, DNA computing, nondeterministic polynomial-time complete,

Abstract

Given a perfect superposition of states on a quantum system of qubits. We propose a fast quantum algorithm for collapsing the perfect superposition to a chosen quantum state without applying any measurements. The basic idea is to use a phase destruction mechanism. Two operators are used, the first operator applies a phase shift and a temporary entanglement to mark in the superposition, and the second operator applies selective phase shifts on the states in the superposition according to their Hamming distance with . The generated state can be used as an excellent input state for testing quantum memories and linear optics quantum computers. We make no assumptions about the used operators and applied quantum gates, but our result implies that for this purpose the number of qubits in the quantum register offers no advantage, in principle, over the obvious measurement-based feedback protocol for the in silico designed of an Anticancer Peptide SVS-1 multipharmacophore as a potential drug-like efficator in Preceding Membrane Neutralization using a web server multi-mimotopic algorithmic approach for biclustering analysis of expression data. Anticancer peptides (ACPs) are polycationic amphiphiles capable of preferentially killing a widespectrum of cancer cells relative to non-cancerous cells. Their primary mode of action is aninteraction with the cell membrane and subsequent activation of lytic effects, however it remainscontroversial the exact mechanism responsible for this mode of action. It has in previous studies been shown that utilizing zeta potential analyses it was possible to demonstrate the interaction of a small anticancer peptide with membrane modelsystems and cancer cells. Electrostatic interactions have a pivotal role in the cell killing processand in contrast to the AMPs action cell death occurs without achieving full neutralization of themembrane charge. The advent of microarray technology has revolutionized the search for genes that are differentially expressed across a range of cell types or experimental conditions. Traditional clustering methods, such as hierarchical clustering, are often difficult to deploy effectively since genes rarely exhibit similar expression pattern across a wide range of conditions. Web-enabled service called GEMS (Gene Expression Mining Server) for biclustering microarray data where Users may upload expression data and specify a set of criteria.GEMS performs bicluster mining based on a Gibbs sampling paradigm. Here, in Biogenea we have for the first time discovered an Anticancer Peptide SVS-1 multipharmacophore with an ini silico evaluated Efficacy in Preceding Membrane Neutralization using a web server for biclustering analysis of expression data.

Keywords

In silico designed, Anticancer Peptide, SVS-1, multipharmacophore, potential drug-like efficator, Preceding Membrane Neutralization, web server, multi-mimotopic, algorithmic, biclustering analysis, expression data, Superposition, Chosen Quantum State, Measurement, In silico, designed,