paranoid_crypto

um correlations, and uses distributed intertwined hash chains to cryptographically trace and verify the extraction process. This protocol forms the basis for a public traceable and certifiable quantum randomness beacon that we have launched. FWIU also there are already QRNG or QTRNG based upon photonic speckle pattern interference; LEDs and a photodiode and an FPGA or an RP2040 or better.

ion, GPUs, TPUs/NPUs, Hypervisors, OS schedulers, IPC,] Still though, coverage-based fuzzing; From https://news.ycombinator.com/item?id=30786239 : > OSS-Fuzz runs CloudFuzz[Lite?] for many open source repos and feeds OSV OpenSSF Vulnerability Format: https://github.com/google/osv#current-data-sources From "Automated Unit Test Improvement using Large Language Models at Meta" https://news.ycombinator.com/item?id=39416628 : > "Fuzz target generation using LLMs" (2023), OSSF//fuzz-introspector* > gh topic: https://github.com/topics/coverage-guided-fuzzing The Fuzzing computational task is similar to the Genetic Algorithm computational task, in that both explore combinatorial Hilbert spaces of potentially infinite degree and thus there is need for parallelism and thus there is need for partitioning for distributed computation. (But there is no computational oracle to predict that any particular sequence of combinations of inputs under test will deterministically halt on any of the distributed workers, so second-order methods like gradient descent help to skip over apparently desolate territory when the error hasn't changed in awhile) The Fuzzing computational task: partition the set of all combinations of inputs for distributed execution with execute-once or consensus to resolve redundant results. DbC Design-By-Contract patterns include Preconditions and Postconditions (which include tests of Invariance) We test Preconditions to exclude Inputs that do not meet the specified Ranges, and we verify the Ranges of Outputs in Postconditions. We test Invariance to verify that there haven't been side-effects in other scopes; that variables and their attributes haven't changed after the function - the Command - returns. DbC: https://en.wikipedia.org/wiki/Design_by_contract : > Design by contract has its roots in work on formal verification, formal specification and Hoare logic. TLA+ > Language: https://en.wikipedia.org/wiki/TLA%2B#Language Formal verification: https://en.wikipedia.org/wiki/Formal_verification From https://news.ycombinator.com/item?id=38138319 : > Property testing: https://en.wikipedia.org/wiki/Property_testing > awesome-python-testing#property-based-testing: https://github.com/cleder/awesome-python-testing#property-ba... > Fuzzing: https://en.wikipedia.org/wiki/Fuzzing Software testing > Categorization > [..., Property testing, Metamorphic testing] https://en.wikipedia.org/wiki/Software_testing#Categorizatio... -- From https://news.ycombinator.com/item?id=28494885#28513982 : > https://github.com/dafny-lang/dafny #read-more > Dafny Cheat Sheet: https://docs.google.com/document/d/1kz5_yqzhrEyXII96eCF1YoHZ... > Looks like there's a Haskell-to-Dafny converter. haskell2dafny: https://gitlab.doc.ic.ac.uk/dcw/haskell-subset-to-dafny-tran... -- Controlled randomness: tests of randomness, random uniform not random norm, rngd, tests of randomness: From https://news.ycombinator.com/item?id=40630177 : > google/paranoid_crypto.lib.randomness_tests: https://github.com/google/paranoid_crypto/tree/main/paranoid... docs: https://github.com/google/paranoid_crypto/blob/main/docs/ran...

om Number Generator Based on Vacuum Fluctuations" https://link.aps.org/doi/10.1103/PRXQuantum.4.010330 google/paranoid_crypto.lib.randomness_tests: https://github.com/google/paranoid_crypto/tree/main/paranoid...

sequence of random values that satisfy many of the tests in e.g. Google/paranoid_crypto.lib.randomness_tests, where would Gambler's Fallacy have caused loss? https://en.wikipedia.org/wiki/Gambler%27s_fallacy https://github.com/google/paranoid_crypto/tree/main/paranoid... Combinatorics and physics: https://en.wikipedia.org/wiki/Combinatorics_and_physics /?hnlog Ctrl-f Hilbert : "Matrices and Graph" https://news.ycombinator.com/item?id=36739579 : > [ Multigraph, networkx.MultiDiGraph , RDF Linked Data ] > Tensor product of graphs: https://en.wikipedia.org/wiki/Tensor_product_of_graphs > Hilbert space: https://en.wikipedia.org/wiki/Hilbert_space : >> In mathematics, Hilbert spaces (named after David Hilbert) allow the methods of linear algebra and calculus to be generalized from (finite-dimensional) Euclidean vector spaces to spaces that may be infinite-dimensional. Hilbert spaces arise naturally and frequently in mathematics and physics, typically as function spaces. Formally, a Hilbert space is a vector space equipped with an inner product that induces a distance function for which the space is a complete metric space. >> [...] The inner product between two state vectors is a complex number known as a probability amplitude. Wave interference > Quantum interference: https://en.wikipedia.org/wiki/Wave_interference#Quantum_inte... EM waves have amplitude in the interval [-1,1] or [-inf, +inf]. With EM waves we typically model constructive interference and destructive interference. There is also a particle-like phononic quantum wave interpretation of EM waves. Quantum waves are in the interval [0,1]. Quantum embedding is the process and study of encoding data as wave functions with e.g. phase. https://news.ycombinator.com/item?id=38255569 : > How many ways are there to roll a {2, 8, or 6}-sided die with qubits and quantum embedding? Quantum superposition and combinatorics; why can't the quantum simulator run this code like an actual QC? "Where does energy go during destructive interference?" (2018) https://news.ycombinator.com/item?id=32421509 : > From Conservation_of_energy#Quantum_theory https://en.wikipedia.org/wiki/Conservation_of_energy#Quantum... : >> In quantum mechanics, energy of a quantum system is described by a self-adjoint (or Hermitian) operator called the Hamiltonian, which acts on the Hilbert space (or a space of wave functions) of the system. If the Hamiltonian is a time-independent operator, emergence probability of the measurement result does not change in time over the evolution of the system. Thus the expectation value of energy is also time independent [If the Hamiltonian is a time-independent operator] {Employee, Conference, Computational resource} Scheduling with priority: https://news.ycombinator.com/item?id=22589911 : > https://en.wikipedia.org/wiki/Hilbert_curve_scheduling : >> [...] the Hilbert curve scheduling method turns a multidimensional task allocation problem into a one-dimensional space filling problem using Hilbert curves, assigning related tasks to locations with higher levels of proximity.[1] Other space filling curves may also be used in various computing applications for similar purposes. [2] And then fluids. Computational Fluid Dynamics is still one of the harder problems in classical high performance computing and quantum computing because it is a combinatorically hard problem to model every possible fluid outcome in order to predict the most likely outcome(s). /?hnlog Navier [e Euler, Stokes,] : - "Deep Learning Poised to ‘Blow Up’ Famed Fluid Equations" https://news.ycombinator.com/item?id=31049608 : > [awesome-fluid dynamics, jax-cfd,] And then Evolutionary Algorithms, - Infinite Monkey Theorem: https://en.wikipedia.org/wiki/Infinite_monkey_theorem - https://news.ycombinator.com/item?id=39110110#39139198 : > Holman's "elegant normal form", Evolutionary Algorithms > Convergence: https://en.wikipedia.org/wiki/Evolutionary_algorithm#Converg... : > For EAs in which, in addition to the offspring, at least the best individual of the parent generation is used to form the subsequent generation (so-called elitist EAs), there is a general proof of convergence under the condition that an optimum exists. Without loss of generality, a maximum search is assumed for the proof: [...]

in/rec... pysimdjson-feedstock: https://github.com/conda-forge/pysimdjson-feedstock/blob/mai... simdjson-feedstock: https://github.com/conda-forge/simdjson-feedstock/blob/main/... mkl_random-feedstock: https://github.com/conda-forge/mkl_random-feedstock https://github.com/google/paranoid_crypto/tree/main/paranoid... : > NumPy-based implementation of random number generation sampling using Intel (R) Math Kernel Library, mirroring numpy.random, but exposing all choices of sampling algorithms available in MKL blas: https://github.com/conda-forge/blas-feedstock/blob/main/reci... xtensor-blas-feedstock: https://github.com/conda-forge/xtensor-blas-feedstock xtensor-fftw (FFT with xtensor (c++)) could probably be AVX-512 and SVE -optimized as well? https://github.com/xtensor-stack/xtensor-fftw ggml_cpu_has_avx512() https://github.com/search?q=repo%3Aggerganov%2Fggml%20AVX&ty... https://github.com/search?q=repo%3Aggerganov%2Fllama.cpp%20a... CuPy would also be an impactful place to merge and defend these optimizations; though no GPUs have AVX-512 or SVE? cupyx.scipy.spatial.distance: https://docs.cupy.dev/en/stable/reference/scipy_spatial_dist... https://docs.cupy.dev/en/stable/reference/comparison.html