Meta-genetic programming for static quantum circuits

Quantum programs are difficult for humans to develop due to their complex semantics that are rooted in quantum physics. It is there- fore preferable to write specifications and then use techniques such as genetic programming (GP) to generate quantum programs in- stead. We present a new genetic programming system for quantum circuits which can evolve solutions to the full-adder and quantum Fourier transform problems in fewer generations than previous work, despite using a general set of gates. This means that it is no longer required to have any previous knowledge of the solution and choose a specialised gate set based on it

Parallel window decoding enables scalable fault tolerant quantum computation

Quantum Error Correction (QEC) continuously generates a stream of syndrome data that contains information about the errors in the system. Useful fault-tolerant quantum computation requires online decoders that are capable of processing this syndrome data at the rate it is received. Otherwise, a data backlog is created that grows exponentially with the $T$-gate depth of the computation. Superconducting quantum devices can perform QEC rounds in sub-1 $\mu$s time, setting a stringent requirement on the speed of the decoders. All current decoder proposals have a maximum code size beyond which the processing of syndromes becomes too slow to keep up with the data acquisition, thereby making the fault-tolerant computation not scalable. Here, we will present a methodology that parallelizes the decoding problem and achieves almost arbitrary syndrome processing speed. Our parallelization requires some classical feedback decisions to be delayed, leading to a slow-down of the logical clock speed. However, the slow-down is now polynomial in code size and so an exponential backlog is averted. Furthermore, using known auto-teleportation gadgets the slow-down can be eliminated altogether in exchange for increased qubit overhead, all polynomially scaling. We demonstrate our parallelization speed-up using a Python implementation, combining it with both union-find and minimum weight perfect matching. Furthermore, we show that the algorithm imposes no noticeable reduction in logical fidelity compared to the original global decoder. Finally, we discuss how the same methodology can be implemented in online hardware decoders.Comment: 12 pages, 7 figure

State public assistance spending and survival among adults with cancer

IMPORTANCE: Social determinants of health contribute to disparities in cancer outcomes. State public assistance spending, including Medicaid and cash assistance programs for socioeconomically disadvantaged individuals, may improve access to care; address barriers, such as food and housing insecurity; and lead to improved cancer outcomes for marginalized populations. OBJECTIVE: To determine whether state-level public assistance spending is associated with overall survival (OS) among individuals with cancer, overall and by race and ethnicity. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included US adults aged at least 18 years with a new cancer diagnosis from 2007 to 2013, with follow-up through 2019. Data were obtained from the Surveillance, Epidemiology, and End Results program. Data were analyzed from November 18, 2021, to July 6, 2023. EXPOSURE: Differential state-level public assistance spending. MAIN OUTCOME AND MEASURE: The main outcome was 6-year OS. Analyses were adjusted for age, race, ethnicity, sex, metropolitan residence, county-level income, state fixed effects, state-level percentages of residents living in poverty and aged 65 years or older, cancer type, and cancer stage. RESULTS: A total 2 035 977 individuals with cancer were identified and included in analysis, with 1 005 702 individuals (49.4%) aged 65 years or older and 1 026 309 (50.4%) male. By tertile of public assistance spending, 6-year OS was 55.9% for the lowest tertile, 55.9% for the middle tertile, and 56.6% for the highest tertile. In adjusted analyses, public assistance spending at the state-level was significantly associated with higher 6-year OS (0.09% [95% CI, 0.04%-0.13%] per $100 per capita; P \u3c .001), particularly for non-Hispanic Black individuals (0.29$100 per capita; P = .01) and non-Hispanic White individuals (0.12% [95% CI, 0.08%-0.16%] per $100 per capita; P \u3c .001). In sensitivity analyses examining the roles of Medicaid spending and Medicaid expansion including additional years of data, non-Medicaid spending was associated with higher 3-year OS among non-Hispanic Black individuals (0.49$100 per capita when accounting for Medicaid spending; 0.17% [95% CI, 0.02%-0.31%] per $100 per capita Medicaid expansion effects). CONCLUSIONS AND RELEVANCE: This cohort study found that state public assistance expenditures, including cash assistance programs and Medicaid, were associated with improved survival for individuals with cancer. State investment in public assistance programs may represent an important avenue to improve cancer outcomes through addressing social determinants of health and should be a topic of further investigation

A real-time, scalable, fast and highly resource efficient decoder for a quantum computer

Quantum computers promise to solve computing problems that are currently intractable using traditional approaches. This can only be achieved if the noise inevitably present in quantum computers can be efficiently managed at scale. A key component in this process is a classical decoder, which diagnoses the errors occurring in the system. If the decoder does not operate fast enough, an exponential slowdown in the logical clock rate of the quantum computer occurs. Additionally, the decoder must be resource efficient to enable scaling to larger systems and potentially operate in cryogenic environments. Here we introduce the Collision Clustering decoder, which overcomes both challenges. We implement our decoder on both an FPGA and ASIC, the latter ultimately being necessary for any cost-effective scalable solution. We simulate a logical memory experiment on large instances of the leading quantum error correction scheme, the surface code, assuming a circuit-level noise model. The FPGA decoding frequency is above a megahertz, a stringent requirement on decoders needed for e.g. superconducting quantum computers. To decode an 881 qubit surface code it uses only $4.5\%$ of the available logical computation elements. The ASIC decoding frequency is also above a megahertz on a 1057 qubit surface code, and occupies 0.06 mm$^2$ area and consumes 8 mW of power. Our decoder is optimised to be both highly performant and resource efficient, while its implementation on hardware constitutes a viable path to practically realising fault-tolerant quantum computers.Comment: 11 pages, 4 figure

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

You & Me: Test and Treat study protocol for promoting COVID-19 test and treatment access to underserved populations

Abstract Background Infections and deaths from the COVID-19 pandemic have disproportionately affected underserved populations. A community-engaged approach that supports decision making around safe COVID-19 practices is needed to promote equitable access to testing and treatment. You & Me: Test and Treat (YMTT) will evaluate a systematic and scalable community-engaged protocol that provides rapid access to COVID-19 at-home tests, education, guidance on next steps, and information on local resources to facilitate treatment in underserved populations. Methods This direct-to-participant observational study will distribute at-home, self-administered, COVID-19 testing kits to people in designated communities. YMTT features a Public Health 3.0 framework and Toolkit prescribing a tiered approach to community engagement. We will partner with two large community organizations, Merced County United Way (Merced County, CA) and Pitt County Health Department (Pitt County, NC), who will coordinate up to 20 local partners to distribute 40,000 COVID tests and support enrollment, consenting, and data collection over a 15-month period. Participants will complete baseline questions about their demographics, experience with COVID-19 infection, and satisfaction with the distribution event. Community partners will also complete engagement surveys. In addition, participants will receive guidance on COVID-19 mitigation and health-promoting resources, and accessible and affordable therapeutics if they test positive for COVID-19. Data collection will be completed using a web-based platform that enables creation and management of electronic data capture forms. Implementation measures include evaluating 1) the Toolkit as a method to form community-academic partnerships for COVID-19 test access, 2) testing results, and 3) the efficacy of a YMTT protocol coupled with local resourcing to provide information on testing, guidance, treatment, and links to resources. Findings will be used to inform innovative methods to address community needs in public health research that foster cultural relevance, improve research quality, and promote health equity. Discussion This work will promote access to COVID-19 testing and treatment for underserved populations by leveraging a community-engaged research toolkit. Future dissemination of the toolkit can support effective community-academic partnerships for health interventions in underserved settings. Trial registration ClinicalTrials.gov Identifier: NCT05455190 . Registered 13 July 2022