惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Forbes - Security
Forbes - Security
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
P
Palo Alto Networks Blog
Martin Fowler
Martin Fowler
T
Threatpost
D
Docker
S
Schneier on Security
M
MIT News - Artificial intelligence
G
Google Developers Blog
L
LINUX DO - 热门话题
J
Java Code Geeks
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
IT之家
IT之家
博客园 - Franky
C
Cyber Attacks, Cyber Crime and Cyber Security
K
Kaspersky official blog
Google DeepMind News
Google DeepMind News
N
News and Events Feed by Topic
V
Vulnerabilities – Threatpost
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
人人都是产品经理
人人都是产品经理
Spread Privacy
Spread Privacy
T
Tailwind CSS Blog
爱范儿
爱范儿
阮一峰的网络日志
阮一峰的网络日志
U
Unit 42
C
CERT Recently Published Vulnerability Notes
The GitHub Blog
The GitHub Blog
Simon Willison's Weblog
Simon Willison's Weblog
NISL@THU
NISL@THU
MongoDB | Blog
MongoDB | Blog
Application and Cybersecurity Blog
Application and Cybersecurity Blog
H
Heimdal Security Blog
Recorded Future
Recorded Future
云风的 BLOG
云风的 BLOG
SecWiki News
SecWiki News
P
Privacy International News Feed
P
Proofpoint News Feed
O
OpenAI News
B
Blog
腾讯CDC
F
Full Disclosure
Apple Machine Learning Research
Apple Machine Learning Research
T
Tor Project blog
H
Hacker News: Front Page
Project Zero
Project Zero
Hugging Face - Blog
Hugging Face - Blog
C
Cisco Blogs
S
Security Affairs

IBM Research

This could be the largest synthetic code dataset yet How to measure the performance of a quantum computer | IBM Quantum Computing Blog Release News: Qiskit v2.5 is here! | IBM Quantum Computing Blog CoFrGeNets replace the ‘bones’ of transformer-based models How training environments can teach AI models to misbehave What’s new at IBM Quantum - Q2 2026 | IBM Quantum Computing Blog Modeling the chemistry of fusion reactor material | IBM Quantum Computing Blog Apply to IBM Quantum Developer Conference 2026 | IBM Quantum Computing Blog Qiskit Paulice: postselected quantum error correction | IBM Quantum Computing Blog What is IBM’s nanostack chip architecture? IBM introduces the smallest computer chip in the world A new playbook for quantum optimization benchmarking Running AI on mixed hardware for speed and affordability Explore next-gen quantum algorithms with IBM Quantum Credits | IBM Quantum Computing Blog Allstate explores quantum computing for insurance portfolios | IBM Quantum Computing Blog Can LLMs discover quantum error correction codes? Prototype and validate fermionic circuits faster with ffsim | IBM Quantum Computing Blog Bringing the power of semantic AI to IBM Db2 The fast Fourier transform, how and why it works Building AI more like software The future of quantum takes center stage at NY Tech Week Qiskit Fall Fest 2026: Applications open | IBM Quantum Computing Blog IBM to invest $10 billion in quantum computing | IBM Quantum Computing Blog Renowned mathematician Subhash Khot joins IBM Research Ponder This Challenge - June 2026 - The Superhero Team Movies New Classroom Accounts expand quantum access for educators | IBM Quantum Computing Blog Qiskit Global Summer School 2026: Registration now open | IBM Quantum Computing Blog How researchers built a record-setting quantum circuit | IBM Quantum Computing Blog IBM charts a new research path with MIT How IBM is using quantum computing to understand the operating system of the universe How to use sample-based quantum diagonalization on IBM hardware Quantum-centric supercomputing simulates 12,635-atom protein | IBM Quantum Computing Blog A decade of quantum on the cloud | IBM Quantum Computing Blog Ponder This Challenge - May 2026 - The Powers of a Binary Matrix Where the frontiers of high-speed racing and computing meet Introducing the IBM Granite 4.1 family of models Building the future of computing, together Next-generation algorithms could move fusion from the lab to the grid Bringing quantum-centric supercomputing to Illinois What’s new at IBM Quantum - Q1 2026 | IBM Quantum Computing Blog Release News: Qiskit v2.4 is here! | IBM Quantum Computing Blog How IBM Quantum is enabling healthcare and biology research | IBM Quantum Computing Blog How an extra training step can unlock AI’s reasoning power IBM demonstrates extreme scale for content-aware storage with a 100-billion vector database Ponder This Challenge - April 2026 - The Unlabeled Clock IBM Research and ETH Zurich open a new era of innovation IBM’s newest time-series models cover a full range of enterprise prediction tasks Toward a transparent supply chain for AI Quantum computers take a step into real materials science Donating llm-d to the Cloud Native Computing Foundation Cleveland Clinic & IBM debut new quantum simulation workflow | IBM Quantum Computing Blog Turning turbulence into transcripts Like the information in a dream: IBM’s Charles H. Bennett receives ACM Turing award Doubling down on open-access quantum computing | IBM Quantum Computing Blog Unveiling the first reference architecture for quantum-centric supercomputing Realizing Feynman’s vision for the future of simulation | IBM Quantum Computing Blog IBM is working today to secure communication from tomorrow’s quantum risks Building PyTorch-native support for the IBM Spyre Accelerator Quantum simulates properties of the first-ever half-Möbius molecule, designed by IBM and researchers A look back at the International Year of Quantum | IBM Quantum Computing Blog TerraStackAI: Bringing Earth and space AI to Red Hat and the world Ponder This Challenge - March 2026 - Path game on a hole-riddled chessboard IBM demonstrates High NA EUV process capability on track for insertion below 2 nm nodes at SPIE 2026 Quantum Advantage Tracker: the race to advantage | IBM Quantum Computing Blog
Ponder This Challenge - July 2026 - Return of the Superheroes
Gadi Aleksandrowicz · 2026-07-01 · via IBM Research

Return of the Superheroes

Continuing the theme of last month, we deal with a movie franchise consisting of nn superheroes. They are joined by nn supervillains. The producers intend to pair the superheroes and the supervillains to form (hero, villain) pairs where each hero has a unique villain serving as their nemesis.

Each pairing is accepted differently by the audiences. After elaborate work, a method of assigning numerical value f(a,b)f(a,b) to each pairing (a,b)(a,b) to estimate the audiences' reaction was developed. The producers wish to find the list of pairings that maximizes the value of the pairing with the minimal value in the list. This minimal value is called the hero-villain value.

The way f(a,b)f(a,b) is computed is as follows: Let pp be some prime and define a function Ta,b(x)=x2+ax+b (mod p)T_{a,b}(x)=x^2+ax+b\ (\text{mod}\ p). By setting x0=0x_0=0 and xn+1=T(xn)x_{n+1}=T(x_n) we obtain a sequence x0,x1,x2,…x_0,x_1,x_2,\ldots which eventually repeats. Let f(a,b)f(a,b) be the number of steps until the first repeat happens. i.e. if xnx_n is the first element in the sequence such that there exists m<nm<n for which xn=xmx_n=x_m, then f(a,b)=nf(a,b)=n.

For example, for n=5n=5 and p=101p=101, one possible list of pairings is (1,3),(2,1),(3,4),(4,2),(5,5)(1,3), (2,1), (3,4), (4,2), (5,5) which yields the values 14,18,19,22,1414, 18, 19, 22, 14 for which the minimum is 14. It turns out that every list of pairings gives a value of at most 14, so 14 is hero-villain value for this case.

Your goal Find the hero-villain value for n=611n=611 and p=14411p=14411

A bonus "*" will be given for finding the optimal nn in the range 1<n<N1<n<N for N=1000N=1000 which gives the maximal hero-villain value for nn and p=17377p=17377.