Animal Models Take Center Stage at White House Briefing on Coronavirus Vaccine | Dr. Fauci – YouTube Video – Images @ Epidemiology of Covid-19 @ Protein detection by sequencing: Towards a definitive cellular phenotype – Methods that allow researchers to simultaneously sequence RNA and detect extracellular proteins in individual cells reveal new cell types and states associated with disease @ Norway begins first experimental treatment of patients infected with Covid-19 – By Katy Dartford with EVN @ Link identified between COVID-19 and onset of diabetes @ Quantum Programming Breakthrough: First Intuitive Language for Quantum Computers – By ETH ZURICH JUNE 15, 2020 @ @ OTHER VERY IMPORTANT INFORMATION OF THE WORLD, LIKE LINKS AND IMAGES

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Pathol Res Pract. 2012 Jul 15;208(7):377-81. doi: 10.1016/j.prp.2012.04.006. Epub 2012 Jun 8.

The influence of physical activity in the progression of experimental lung cancer in mice

Renato Batista Paceli 1Rodrigo Nunes CalCarlos Henrique Ferreira dos SantosJosé Antonio CordeiroCassiano Merussi NeivaKazuo Kawano NagaminePatrícia Maluf Cury


GRUPO_AF1GROUP AFA1 – Aerobic Physical Activity – Atividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 1GROUP AFAN1 – Anaerobic Physical ActivityAtividade Física Anaeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO_AF2GROUP AFA2 – Aerobic Physical ActivityAtividade Física Aeróbia – ´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

GRUPO AFAN 2GROUP AFAN 2 – Anaerobic Physical ActivityAtividade Física Anaeróbia´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto

Slides – mestrado´´My´´ Dissertation – Faculty of Medicine of Sao Jose do Rio Preto



Avaliação da influência da atividade física aeróbia e anaeróbia na progressão do câncer de pulmão experimental – Summary – Resumo´´My´´ Dissertation Faculty of Medicine of Sao Jose do Rio Preto


Lung cancer is one of the most incident neoplasms in the world, representing the main cause of mortality for cancer. Many epidemiologic studies have suggested that physical activity may reduce the risk of lung cancer, other works evaluate the effectiveness of the use of the physical activity in the suppression, remission and reduction of the recurrence of tumors. The aim of this study was to evaluate the effects of aerobic and anaerobic physical activity in the development and the progression of lung cancer. Lung tumors were induced with a dose of 3mg of urethane/kg, in 67 male Balb – C type mice, divided in three groups: group 1_24 mice treated with urethane and without physical activity; group 2_25 mice with urethane and subjected to aerobic swimming free exercise; group 3_18 mice with urethane, subjected to anaerobic swimming exercise with gradual loading 5-20% of body weight. All the animals were sacrificed after 20 weeks, and lung lesions were analyzed. The median number of lesions (nodules and hyperplasia) was 3.0 for group 1, 2.0 for group 2 and 1.5-3 (p=0.052). When comparing only the presence or absence of lesion, there was a decrease in the number of lesions in group 3 as compared with group 1 (p=0.03) but not in relation to group 2. There were no metastases or other changes in other organs. The anaerobic physical activity, but not aerobic, diminishes the incidence of experimental lung tumors.

@ CROWDED GALAXY – New Research: There Could Be 36 Alien Civilizations in Our Galaxy They say there “should be around 36 active civilizations in our Galaxy.” VICTOR TANGERMANN – JUNE 15TH 2020

– New research indicates COVID-19 could trigger the development of diabetes in healthy people, prompting experts to establish a registry for COVID-19 and diabetes data.

@ ´´Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.[1]:I-5 Quantum computers are believed to be able to solve certain computational problems, such as integer factorization (which underlies RSA encryption), substantially faster than classical computers. The study of quantum computing is a subfield of quantum information science.´´

Quantum Programming Breakthrough: First Intuitive Language for Quantum Computers

Quantum Programming Breakthrough: First Intuitive Language for Quantum Computers

Link identified between COVID-19 and onset of diabetes




Epidemiology of Covid-19


With regard to the Perspective article by Lipsitch et al. (published Feb. 19 at,1 cases of Covid-19 (the illness caused by SARS-CoV-2 infection) with no epidemiologic link to travel to China or known cases outside China have caused international concern about undetected introduction of the virus from subclinical infection. It is also possible that local zoonotic spillover of this coronavirus from an intermediate animal reservoir or reservoirs into human populations might have occurred, particularly in Southeast Asia.

For example, coronaviruses that are phylogenetically close to SARS-CoV-2 have been detected in pangolins (scaly anteaters),2 especially in Malayan pangolins (also known as Sunda pangolins) that are obtained in antismuggling operations in Guangdong Province and the Guangxi Zhuang Autonomous Region in China.3 This species, which is located throughout Southeast Asia, is thus currently considered to be a potential intermediate host of SARS-CoV-2. In addition, a recent phyloepidemiologic analysis suggested that the Covid-19 outbreak did not arise from a “Big Bang”–like event at Huanan Seafood Wholesale Market in Wuhan, China, but rather it may have originated elsewhere and probably involved more than one zoonotic spillover.4 Finally, a person with a confirmed case of Covid-19 in Shanghai was probably infected by consuming bush meat (i.e., wild animals hunted for food) while traveling in Guangdong Province in China.5

Taken together, the possibility of novel coronavirus spillover to humans in Southeast Asia cannot be ruled out. Surveillance to detect coronaviruses in pangolins is needed to address this concern.

Xiaodong Zhang, Ph.D.
Jilin University, Changchun, China

No potential conflict of interest relevant to this letter was reported.

This letter was published on March 27, 2020, at


The authors reply: Zhang hypothesizes that Covid-19 cases continue to occur from repeated zoonotic spillover events from pangolins to humans. There are several ways to distinguish repeated spillover events from human-to-human transmission. Genomic sequencing of animal and human isolates as well as observational studies,1 case–control studies,2 and modeling can distinguish the number of independent introductions from animals to humans.3 Zhang’s citations neither support nor refute the spillover hypothesis, whereas sequencing data provide support for worldwide human-to-human transmission.4 Nevertheless, we agree that surveillance to detect spillover events is important.

Since our Perspective article about Covid-19 was published, much has been learned about case fatality rates, transmission, asymptomatic cases, and risk factors for severe disease and death. We have seen marked declines in transmission in some areas. Now is the time to conduct community household serosurveys to answer two important outstanding questions: How many infections are out there, going undetected? And what level of population-wide immunity is being attained? The answers to those questions have worldwide implications.

David L. Swerdlow, M.D.
Pfizer Vaccines, Collegeville, PA

Lyn Finelli, Dr.P.H.
Merck, Kenilworth, NJ

Marc Lipsitch, D.Phil.
Harvard T.H. Chan School of Public Health, Boston, MA

Since publication of their article, the authors report no further potential conflict of interest.

This letter was published on March 27, 2020, at

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Protein detection by sequencing: Towards a definitive cellular phenotype

Methods that allow researchers to simultaneously sequence RNA and detect extracellular proteins in individual cells reveal new cell types and states associated with disease.

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Nature Research Custom Media

DNA-tagged antibodies allow for detection of proteins with results read by sequencing.Credit: Ella Maru Studio, Inc.

Proteins are the workhorses of the cell, involved in almost every aspect of structure and function. Protein expression patterns help define a cell’s identity and state. RNA transcripts are often used as a surrogate for protein expression, but it has long been understood that the relationship between abundance of proteins and mRNA is not one-to-one. There are differences caused by regulation of post-transcriptional, translational and protein degradation.

“It has been eye-opening to see how much we might be missing by using conventional sequencing,” concedes Adeeb Rahman, an immunologist at Icahn School of Medicine at Mount Sinai in New York. Rahman is developing innovative assays to monitor disease-relevant molecular profiles and discover new biomarkers and drug targets. “In some cases, we have populations of cells that are all clearly expressing a particular protein, but we may detect the corresponding transcript in only 5-10% of them,” he explains.

Rahman and others are using techniques such as CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and REAP-seq (RNA expression and protein sequencing assay) to investigate the relationship between RNA transcripts and proteins, and identify new cell types and cell states associated with disease. Both methods combine antibody-oligonucleotides with existing single-cell RNA-sequencing (scRNA-seq) approaches to measure the expression levels of genes and cell-surface proteins in individual cells.

A major advantage of CITE-seq and REAP-seq is that, with an appropriate antibody, they can detect a protein of interest even if the RNA that encodes it is in low abundance. “CITE-seq gives us more confidence in the presence of a marker of interest in a given cell type,” Rahman explains.

For decades, researchers have used flow cytometry to detect proteins in single cells and characterize cell types based on protein expression patterns. Flow cytometry relies on antibodies directed against proteins of interest. This means that to obtain useful information researchers need to know what they are looking for.

In recent years, single cell sequencing has become the method of choice for uncovering cell types and states based on gene expression patterns. One of the advantages of single cell RNA (or DNA) sequencing is that it is unbiased, it requires no prior knowledge about the sample — RNA (or DNA) from any cell type can be run through a sequencer to obtain its genomic or transcriptional profile. Advances in single-cell genomics are enabling large scale projects such as the Human Cell Atlas Project, which aims to create comprehensive reference maps of all 37 trillion cells in the human body.

Now, by providing details about both protein and RNA expression, CITE-seq and REAP-seq allow researchers to fill in the gaps left by flow cytometry or single cell RNA-seq.

“Advances in multimodal single cell sequencing methods are providing an unprecedented opportunity to look at biological mechanisms of disease,” says Tom Maniatis, a renowned biochemist and biophysicist leading Columbia University’s Precision Medicine Initiative and the New York Genome Center. Technologies that combine single cell DNA or RNA sequencing with methods that provide details about the regulation of gene expression, protein expression, genetic mutations, cell lineage, and even the spatiotemporal order of molecular events not only allow the creation of a more refined human cell atlas, but offer new insights into cancer evolution, neurodegeneration and the immune system’s response to disease.

Multimodal single cell sequencing in action

In CITE-seq and REAP-seq, cells are incubated with cell-surface antibodies linked to oligonucleotide barcodes which tag the protein’s identity, and a stretch of adenine bases that serve as a starting point for RNA sequencing (FIG). The technologies differ in the way the DNA barcode binds to the antibody. In both cases, protein detection has been shown to be as robust as by flow cytometry.

To validate CITE-seq, the developers at the New York Genome Center and scientists at NYU Lagone Health used it to simultaneously profile the transcriptome and 13 cell-surface proteins of human and mouse immune cells. They showed that CITE-seq not only correctly identified different immune cell populations, it enhanced the characterization of known subtypes. Using CITE-seq they were able to identify subsets of natural killer cells with different roles in regulating the immune response in disease states that could not be detected using single-cell RNA-seq methods alone1.

REAP-seq was first employed to examine the response of a T cell subset to a drug that activates the immune system to attack and kill cancer cells in mice by targeting a cell surface protein. Differences between the proteins and genes expressed in the treated and untreated cells provided new insights into the drug’s mechanism of action2.

Since these studies were published, more than 200 oligo-conjugated antibodies have become available for CITE-seq and REAP-seq. These antibodies have been used to assess immune cell activation in patients with advanced lung cancer after immunotherapy3, and to investigate immune dysregulations at atherosclerotic sites associated with clinical cerebrovascular events. When Rahman and colleagues used CITE-seq to examine the immune cell types in atherosclerotic lesions in stroke patients, they found T cell subsets that were more activated, differentiated and exhausted compared to their blood counterparts. Characterizing immune cell profiles with these technologies could aid the design of tailored therapies and help predict a patient’s treatment response4.

Expanding the multimodal sequencing toolbox

Using the same concept of DNA barcodes, CITE-seq has been extended by Peter Smibert and Neville Sanjana at the New York Genome Center to reveal even more information from single cells. ECCITE-seq (expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing), can be used for simultaneous detection of the transcriptome, proteins, clonotypes and CRISPR perturbations5.

“I love this technology,” says Ya-Chi Ho, a molecular virologist at Yale School of Medicine, who is trying to understand how HIV persists in cells. HIV infected cells are very rare: less than 1% of T cells in peripheral blood contain the virus. Moreover, HIV infected cells do not display any markers that distinguish them from uninfected cells, making them practically impossible to detect and analyse.

ECCITE-seq can capture the variability of T cell receptor sequences that confer specificity for particular antigens. These sequences are found at the 5’ end of RNA molecules and are difficult to capture in scRNA-seq approaches that profile the 3′ ends of RNAsFurthermore, this method can be expanded to capture HIV RNA in the same cell. “ECCITE-seq allows us to tackle the heterogeneity, rarity and the lack of marker in these cells, which is very powerful,” she explains.

Ho and her team are currently looking at cells in the blood of HIV-infected individuals during high-level viraemia and after antiretroviral therapy to determine how infected cells are able to continue dividing, leading to the clonal expansion of HIV-positive cells. Understanding the features of these resistant clones may offer clues for new therapies that target cells bearing the virus and, effectively, cure HIV.

Abbas Rizvi is a molecular neuroscientist working with Maniatis and colleagues at Columbia University to combine other single cell sequencing technologies to develop a comprehensive cell atlas of the human spinal cord. They are carrying out RNA-seq and ATAC-seq (assay for transposase-accessible chromatin using sequencing), which identifies active transcriptional regulatory elements, in nuclei obtained from postmortem spinal cord cells to develop new treatments for spinal-cord disease and injury.

Rizvi is working closely with bioinformaticians and statisticians to develop mathematical approaches to harmonize the data. “Multimodal strategies enable us to make connections among simultaneously collected measurements in any cell type, and therefore decipher key molecular events associated with the development, establishment and maintenance of neural circuits,” he explains.

When the team compares the transcriptional expression patterns from cells in cervical, thoracic and lumbar regions of the spinal cord, they see remarkable differences. “Ultimately, we want to carry out the same type of analysis on the spinal cord of patients with amyotrophic lateral sclerosis to understand the mechanisms underlying motor neuron degeneration, with the goal of identifying novel therapeutic targets,” says Maniatis.

Computational tools are crucial to make biological sense out of all the data that are being generated by new sequencing technologies. Rizvi observed that these tools are leading to a ‘second wave’ of excitement around single cell technologies. “Now we can begin to really understand the complexity of neurodegenerative diseases, and the contribution of multiple genes and variants to disease processes in a way that was not possible before,” Maniatis adds.

Future outlook

Barcoding cellular content is enabling researchers to obtain multiple readouts from single cells. As such, they can characterize cellular phenotypes in more detail than by using transcriptome measurements alone. These multi-omic readouts hold great potential to advance translational genomic science and precision medicine.

“We are also excited about the development of spatially resolved transcriptomic approaches to place single cell RNA sequencing data in its anatomical context,” says Rizvi. This will allow the identification of cells that are vulnerable to neurodegeneration, as well as to define the features of cells that surround them, and which are likely to contribute to disease progression.

At present, single cell multi-omics technologies are primarily used to identify correlations among data types. But soon, by combining them with experimental perturbations, it will be possible to explore causal relationships.

Protein detection by sequencing requires large, pre-optimized panels of antibodies. At present there seems to be no technical limit to the number of antibodies that can be used to detect extracellular proteins via CITE-seq. Mild permeabilization methods that allow antibodies to get inside cells may enable researchers to sequence intracellular proteins in the future. “Such improvements would dramatically increase the power and applicability of this method to other systems,” says Maniatis.

As CITE-seq and ECCITE-seq antibodies become increasingly available, Rahman expects these approaches will become rapidly and widely adopted: “If you are already doing single cell RNA-seq, adding in CITE-seq is a pretty minimal investment for the amount of extra data you get.”

Click here for more information about protein detection by sequencing from Illumina.


  1. 1.

    Stoeckius, M. et al. Nat Methods 14, 865–868 (2017)

  2. 2.

    Peterson, V. et al. Nat Biotechnol 35, 936–939(2017)

  3. 3.

    Chin, V. T. et al. J Clin Onc 37, 15_suppl e20563 (2019)

  4. 4.

    Fernandez, D.M. et al. Preprint at bioRxiv (2019)

  5. 5.

    Mimitou, E. P. et al. Nat Methods 16, 409–412 (2019)

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New Research: There Could Be 36 Alien Civilizations in Our Galaxy

They say there “should be around 36 active civilizations in our Galaxy.”


According to a new study in The Astrophysical Journal, there could be dozens of intelligent alien civilizations hiding in our galaxy, all capable of communicating.

“There should be at least a few dozen active civilizations in our Galaxy under the assumption that it takes 5 billion years for intelligent life to form on other planets, as on Earth,” Christopher Conselice, Professor of Astrophysics at the University of Nottingham and lead author of the study, said in a statement.

“The idea is looking at evolution, but on a cosmic scale,” he added.

The researchers say the numbers suggest there are a number of technological civilizations in the Milky Way galaxy.

“In the strong criteria, whereby a metal content equal to that of the Sun is needed (the Sun is relatively speaking quite metal rich), we calculate that there should be around 36 active civilizations in our Galaxy,” Tom Westby, first author and assistant professor at the University of Nottingham, said in the statement.

Previous estimations have been based on educated guesses and opinions — something that can cause numbers to “vary quite substantially,” according to Westby. But the astronomers say their estimation takes the latest research into account.

“Our new study simplifies these assumptions using new data, giving us a solid estimate of the number of civilizations in our Galaxy,” Westby added.

Hidden in the cool research is a major bummer: the average alien civilization would be 17,000 light-years away. It’s also possible that all other intelligent civilizations already kicked the bucket a long time ago.

But finding them could give us important clues about our own humanity as well.

“If we find that intelligent life is common then this would reveal that our civilization could exist for much longer than a few hundred years, alternatively if we find that there are no active civilizations in our Galaxy it is a bad sign for our own long-term existence,” Conselice said.

“By searching for extraterrestrial intelligent life — even if we find nothing — we are discovering our own future and fate,” he added.

READ MORE: Research sheds new light on intelligent life existing across the galaxy [University of Nottingham]

More on alien life: Harvard Prof: Deep Space Signal May Be From Alien Civilization

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Astronomers: Something Is Warping Our Entire Galaxy


Watch a Galaxy Form Over More Than 13 Billion Years


Astronomers Find Evidence of A “Monster Galaxy” Like No Other


“Cold Quasar” Discovery Sheds Light on Galactic Death


Scientists Found a Galaxy With Almost No Dark Matter. Here’s What That Means.


Link identified between COVID-19 and onset of diabetes

New research indicates COVID-19 could trigger the development of diabetes in healthy people, prompting experts to establish a registry for COVID-19 and diabetes data.


According to new research and emerging evidence, COVID-19 could trigger the onset of diabetes in healthy people and also cause severe complications for patients with pre-existing diabetes.

With fresh news every day and space for you to discuss and publish your coronavirus research, our new COVID-19 research hub has everything you need to keep up to date with R&D during the pandemic. Why not see for yourself?

Visit our COVID-19 research hub

A published letter signed by an international group of 17 leading diabetes experts involved in the CoviDiab Registry project, a collaborative international research initiative, has announced the establishment of a global registry for new cases of diabetes in patients with COVID-19.

The registry aims to understand the extent and the characteristics of the manifestations of diabetes in patients with COVID-19 and the best strategies for the treatment and monitoring of affected patients, during and after the pandemic.

[LIVE WEBINAR] Cell-based binding affinity and kinetics with SPR microscopy: overview and applications with Biosensing and AstraZeneca

In this webinar taking place on 30 June at 15:00 BST, we will provide an overview of the technology with some application examples and also see a case study how AstraZeneca is exploring SPRM for studies on G-protein-coupled receptors (GPCR) in close collaboration with Biosensing Instrument. Reserve your free place today and pose questions to our keynote speakers during the one-hour session!


According to the researchers, clinical observations so far show a bi-directional relationship between COVID-19 and diabetes. On the one hand, diabetes is associated with increased risk of COVID-19 severity and mortality. Between 20 and 30 percent of patients who died with COVID-19 have been reported to have diabetes. On the other hand, new-onset diabetes and atypical metabolic complications of pre-existing diabetes, including life-threatening ones, have been observed in people with COVID-19.

The authors write that it is still unclear how SARS-Cov-2, the virus that causes COVID-19, impacts diabetes. Previous research has shown that angiotensin-converting enzyme 2 (ACE2), the protein that binds to SARS-Cov-2 allowing the virus to enter human cells, is not only located in the lungs but also in organs and tissues involved in glucose metabolism such as the pancreas, the small intestine, the fat tissue, the liver and the kidney. Researchers hypothesise that by entering these tissues, the virus may cause multiple and complex dysfunctions of glucose metabolism. It has also been known for many years that virus infections can precipitate type 1 diabetes.

The researchers say that the registry focuses on routinely collected clinical data that will help scientists to examine insulin secretory capacity, insulin resistance and autoimmune antibody status to understand how COVID-19 related diabetes develops, its natural history and best management. As such, studying COVID-19-related diabetes may help to uncover novel mechanisms of the disease.

Francesco Rubino, Professor of Metabolic Surgery at King’s College London, UK, and co-lead investigator of the CoviDiab Registry project, said: “Diabetes is one of the most prevalent chronic diseases and we are now realising the consequences of the inevitable clash between two pandemics. Given the short period of human contact with this new coronavirus, the exact mechanism by which the virus influences glucose metabolism is still unclear and we do not know whether the acute manifestation of diabetes in these patients represent classic type 1, type 2 or possibly a new form of diabetes.”

Paul Zimmet, Professor of Diabetes at Monash University, Australia, Honorary President of the International Diabetes Federation and co-lead investigator in the CoviDiab Registry project said: “We do not yet know the magnitude of the new onset diabetes in COVID-19 and if it will persist or resolve after the infection; and if so, whether or not or COVID-19 increases risk of future diabetes. By establishing this global registry, we are calling on the international medical community to rapidly share relevant clinical observations that can help answer these questions.”

The letter can be found in New England Journal of Medicine



Quantum Programming Breakthrough: First Intuitive Language for Quantum Computers

Quantum Programming Language Concept

Several technical advances have been achieved recently in the pursuit of powerful quantum computers. Now, Computer scientists from ETH Zurich have made an important breakthrough in the field of programming languages: their quantum language is the first of its kind that is as elegant, simple, and safe as classical computer languages.

Programming quantum computers is becoming easier: computer scientists at ETH Zurich have designed the first programming language that can be used to program quantum computers as simply, reliably and safely as classical computers. “Programming quantum computers is still a challenge for researchers,” says Martin Vechev, computer science professor in ETH’s Secure, Reliable and Intelligent Systems Lab (SRI), “which is why I’m so excited that we can now continue ETH Zurich’s tradition in the development of quantum computers and programming languages.”

First Quantum Programming Language

He adds: “Our quantum programming language Silq allows programmers to utilize the potential of quantum computers better than with existing languages, because the code is more compact, faster, more intuitive and easier to understand for programmers.” This week, Vechev will introduce Silq to other experts in the field at PLDI 2020, a conference for programming languages. To facilitate discussion, adoption and further development, he and his team have also released Silq on its own website (

Quantum computing has been seeing increased attention over the last decade, since these computers, which function according to the principles of quantum physics, have enormous potential. Today, most researchers believe that these computers will one day be able to solve certain problems faster than classical computers, since to perform their calculations they use entangled quantum states in which various bits of information overlap at a certain point in time. This means that in the future, quantum computers will be able to efficiently solve problems which classical computers cannot solve within a reasonable timeframe.

This quantum supremacy has still to be proven conclusively. However, some significant technical advances have been achieved recently. In late summer 2019, a quantum computer succeeded in solving a problem – albeit a very specific one – more quickly than the fastest classical computer.

For certain “quantum algorithms”, i.e. computational strategies, it is also known that they are faster than classical algorithms, which do not exploit the potential of quantum computers. To date, however, these algorithms still cannot be calculated on existing quantum hardware because quantum computers are currently still too error-​prone.

Expressing the programmer’s intent

Utilizing the potential of quantum computation not only requires the latest technology, but also a quantum programming language to describe quantum algorithms. In principle, an algorithm is a “recipe” for solving a problem; a programming language describes the algorithm so that a computer can perform the necessary calculations.

Today, quantum programming languages are tied closely to specific hardware; in other words, they describe precisely the behaviour of the underlying circuits. For programmers, these “hardware description languages” are cumbersome and error-​prone, since the individual programming instructions must be extremely detailed and thus explicitly describe the minutiae needed to implement quantum algorithms.

This is where Vechev and his group come in with their development of Silq. “Silq is the first quantum programming language that is not designed primarily around the construction and functionality of the hardware, but on the mindset of the programmers when they want to solve a problem – without requiring them to understand every detail of the computer architecture and implementation,” says Benjamin Bichsel, a doctoral student in Vechev’s group who is supervising the development of Silq.

Computer scientists refer to computer languages that abstract from the technical details of the specific type of computer as high-​level programming languages. Silq is the very first high-​level programming language for quantum computers. High-​level programming languages are more expressive, meaning that they can describe even complex tasks and algorithms with less code. This makes them more comprehensible and easier to use for programmers. They can also be used with different computer architectures.

Eliminating errors through automatic uncomputation

The greatest innovation and simplification that Silq brings to quantum programming languages concerns a source of errors that has plagued quantum programming until now. A computer calculates a task in several intermediate steps, which creates intermediate results or temporary values.

In order to relieve the memory, classical computers automatically erase these values. Computer scientists refer to this as “garbage collection”, since the superfluous temporary values are disposed of.

In the case of quantum computers, this disposal is trickier due to quantum entanglement: the previously calculated values can interact with the current ones, interfering with the correct calculation. Accordingly, cleaning up such temporary values on quantum computers requires a more advanced technique of so-​called uncomputation.

“Silq is the first programming language that automatically identifies and erases values that are no longer needed,” explains Bichsel. The computer scientists achieved this by applying their knowledge of classical programming languages: their automatic uncomputation method uses only programming commands that are free of any special quantum operations – they are “qfree”, as Vechev and Bichsel say.

“Silq is a major breakthrough in terms of optimising the programming of quantum computers; it is not the final phase of development,” says Vechev. There are still many open questions, but because Silq is easier to understand, Vechev and Bichsel hope to stimulate both the further development of quantum programming languages and the theory and development of new quantum algorithms.

“Our team of four has made the breakthrough after two years of work thanks to the combination of different expertise in language design, quantum physics and implementation. If other research and development teams embrace our innovations, it will be a great success,” says Bichsel.

Reference: “Silq: a high-level quantum language with safe uncomputation and intuitive semantics” by Benjamin Bichsel, Maximilian Baader, Timon Gehr and Martin Vechev, Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation, June 2020, 286–300.
DOI: 10.1145/3385412.3386007


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