Network Community Detection On Small Quantum Computers

In recent years a number of quantum computing devices with small numbers of qubits became available. We present a hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size. The proposed approach is applied to the network community detection problem. QLS is hardware-agnostic and easily extendable to new quantum computing devices as they become available. We demonstrate it to solve the 2-community detection problem on graphs of size up to 410 vertices using the 16-qubit IBM quantum computer and D-Wave 2000Q, and compare their performance with the optimal solutions. Our results demonstrate that QLS perform similarly in terms of quality of the solution and the number of iterations to convergence on both types of quantum computers and it is capable of achieving results comparable to state-of-the-art solvers in terms of quality of the solution including reaching the optimal solutions

Identification of nucleoid associated proteins (NAPs) under oxidative stress in Staphylococcus aureus

BackgroundBacterial nucleoid consists of genome DNA, RNA, and hundreds of nucleoid-associated proteins (NAPs). Escherichia coli nucleoid is compacted towards the stationary phase, replacing most log-phase NAPs with the major stationary-phase nucleoid protein, Dps. In contrast, Staphylococcus aureus nucleoid sustains the fiber structures throughout the growth. Instead, the Dps homologue, MrgA, expresses under oxidative stress conditions to clump the nucleoid, but the composition of the clumped nucleoid was elusive.ResultsThe staphylococcal nucleoid under oxidative stress was isolated by sucrose gradient centrifugation, and the proteins were analyzed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). We identified 299 proteins in the nucleoid under oxidative stress, including 113 csNAPs (contaminant-subtracted NAPs). Comparison with the previously identified csNAPs in log- and stationary phase indicated that one fifth of the csNAPs under oxidative stress were the constitutive nucleoid components; importantly, several factors including HU, SarA, FabZ, and ribosomes were sustained under oxidative stress. Some factors (e.g. SA1663 and SA0092/SA0093) with unknown functions were included in the csNAPs list specifically under oxidative stress condition.ConclusionNucleoid constitutively holds Hu, SarA, FabG, and ribosomal proteins even under the oxidative stress, reflecting the active functions of the clumped nucleoid, unlikely to the dormant E. coli nucleoid compacted in the stationary phase or starvation

SCCmec transformation requires living donor cells in mixed biofilms

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What Happens in the Staphylococcal Nucleoid under Oxidative Stress?

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Multilevel Combinatorial Optimization Across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore\u27s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave\u27s quantum annealer and IBM\u27s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size and observe results comparable to state-of-the-art solvers

Multilevel Combinatorial Optimization Across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore\u27s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave\u27s quantum annealer and IBM\u27s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size and observe results comparable to state-of-the-art solvers

Multilevel Combinatorial Optimization Across Quantum Architectures

Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore's law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave's quantum annealer and IBM's gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size, and we observe results comparable to state-of-the-art solvers in terms of quality of the solution

Fitness of Spontaneous Rifampicin-Resistant Staphylococcus aureus Isolates in a Biofilm Environment

Biofilms of S. aureus accumulate cells resistant to the antibiotic rifampicin. We show here that the accumulation of rifampicin resistant mutants (RifR) in biofilms is not equable but rather is a local event, suggesting that the growth of a few locally emerged mutants is responsible for this. Competition assays demonstrated that, compared to wild-type bacteria, the isolated RifR mutants have a growth advantage in biofilms, but not in planktonic culture. To gain insight into the mechanism of the growth advantage, we tested the involvement of the two-component systems (TCS) that sense and respond to environmental changes. We found that a deletion of SrrAB or NreBC has a drastic effect on the growth advantage of RifR mutants, suggesting the importance of oxygen/respiration responses. All six of the RifR isolates tested showed increased resistance to at least one of the common stresses found in the biofilm environment (i.e., oxidative, nitric acid, and organic acid stress). The RifR mutants also had a growth advantage in a biofilm flow model, which highlights the physiological relevance of our findings

Fitness of Spontaneous Rifampicin-Resistant Staphylococcus aureus Isolates in a Biofilm Environment

Biofilms of S. aureus accumulate cells resistant to the antibiotic rifampicin. We showhere that the accumulation of rifampicin resistant mutants (RifR) in biofilms is notequable but rather is a local event, suggesting that the growth of a few locally emergedmutants is responsible for this. Competition assays demonstrated that, compared towild-type bacteria, the isolated RifR mutants have a growth advantage in biofilms, butnot in planktonic culture. To gain insight into the mechanism of the growth advantage,we tested the involvement of the two-component systems (TCS) that sense andrespond to environmental changes. We found that a deletion of SrrAB or NreBC hasa drastic effect on the growth advantage of RifR mutants, suggesting the importanceof oxygen/respiration responses. All six of the RifR isolates tested showed increasedresistance to at least one of the common stresses found in the biofilm environment (i.e.,oxidative, nitric acid, and organic acid stress). The RifR mutants also had a growthadvantage in a biofilm flow model, which highlights the physiological relevance ofour findings