Quantum Computing Architecture Could Connect Large-Scale Devices

 Architecture from MIT, superconducting quantum chips 

Quantum Computing Architecture Could Connect Large-Scale Devices

MIT News, Adam Zewe, January 5, 2023

A new quantum computing architecture developed by Massachusetts Institute of Technology (MIT) scientists can facilitate extensible, high-fidelity communication between superconducting quantum chips. The architecture can be used to thread multiple processing modules along one waveguide; MIT's Bharath Kannan said the same module can function as both transmitter and receiver. The researchers have demonstrated the deterministic emission of single photons in a user-specified direction with more than 96% fidelity. Said Kannan, "The ability to communicate between smaller subsystems will enable a modular architecture for quantum processors, and this may be a simpler way of scaling to larger system sizes compared to the brute-force approach of using a single large and complicated chip."  .... 

Large Scale Quantum Computing

 Thinking larger scale, how soon?

ACM TECHNEWS

Approach May Help Clear Hurdle to Large-Scale Quantum Computing  By Harvard Gazette  May 4, 2022

Researchers at Harvard University, the Massachusetts Institute of Technology, QuEra Computing, and Austria's University of Innsbruck have developed an approach for shuffling qubits during the computation process while maintaining their quantum state, resulting in a programmable, error-correcting quantum computer operating at 24 qubits.

The process involves an initial pairing of qubits, the creation of a quantum gate that entangles the pairs and stores the information in hyperfine qubits, moving these qubits into new pairs with other atoms in the system to entangle them as well, and repeating the steps to generate different quantum circuits to perform different algorithms.

The atoms ultimately become connected in a "cluster state" and act as backups for each other in the event of an error, according to the researchers.

From Harvard Gazette .... 

View Full Article 

GigaOM re File Storage to Scale

A project led me to this, saving it for later use.   Scaling should always be in your mind for problems.  Not that some projects are not one-off for tests, but it is also useful to think about scaling as well   A corporate project I did came back to me many years later, and we had to scurry to make it right.   In the final report you can make it clear how you have considered future conditions

Key Criteria for Evaluating Scale-Out File Storage v1.0
An Evaluation Guide for Technology Decision Makers    By Enrico Signoretti

Summary
File storage is one of the most popular ways to store data, both on premises and in the cloud, and scale-out file storage is becoming the default choice for most organizations because of its ability to expand quickly while increasing throughput. There are other reasons for the success of scale-out file storage:

Object storage is rising in popularity but file systems, often accessed via network protocols like NFS and SMB, are still the data storage system of choice for a large number of workloads, including big data analytics, artificial intelligence/machine learning (AI/ML), high-performance computing (HPC), and more.

Modern file systems are much more scalable than in the past, providing a familiar user interface and authentication methods with performance and scalability.

Legacy applications are usually written to work with POSIX-compliant file systems and multiple applications accessing the same data sets are quite common. Rewriting these old applications to take advantage of object stores is not always a viable option, so many end users keep investing in file storage. .... "   (Much more at the link) ... '

Cybersecurity Risk

Advancing for some time now,  but at least we should be able to assemble defenses for the inevitable?

Why Is Cybersecurity Not a Human-Scale Problem Anymore?
By Gaurav Banga
Communications of the ACM, April 2020, Vol. 63 No. 4, Pages 30-3410.1145/3347144

Rarely a day goes by that we don't see news about the poor state of affairs in cybersecurity. From data breaches at Target, the U.S. Office of Personnel Management, Sony, Disney, Yahoo!, Equi-fax and Marriot, the drumroll continues unabated. We are now in a world, where it's a matter of when, not if, an organization is compromised by a cyber-attack.

Most of us think of cybersecurity as a series of controls (tools and knobs) that an organization has to implement, and it seems perplexing why cyber-defenders in the situations mentioned here failed to take the necessary steps to protect themselves. Our focus on addressing cybersecurity challenges has been around inventing new controls (or enhancing existing ones) and implementing them correctly in the enterprise. This is an inadequate view.  ... ." 

Scaling up Digital and Analytics Results in Consumer Goods

Thoughtful formulation here.

Solving the digital and analytics scale-up challenge in consumer goods in McKinsey

" ... Many consumer-goods companies have entered the digital and analytics race, but very few are scaling impact. Here’s what leaders are doing right.... '

sk any consumer-goods executive if his or her company has invested in digital and analytics, and you’ll almost certainly get an affirmative response. But ask whether those investments have yielded the desired results—and more than half of the time the answer will be no. Our research shows that only 40 percent of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. The rest are stuck in “pilot purgatory,” eking out small wins but failing to make an enterprise-wide impact.

The value at stake isn’t trivial: our analysis suggests that a company’s aptitude at scaling up digital and analytics programs is correlated with its financial performance. In this article, we describe the most common pitfalls that companies encounter in their journey toward digital and analytics scale-up. We also explore an emerging recipe for sustained success.    ... " 

Dimensional Analysis of Failure and Fixing

Brought to my attention.  In Facebook Engineering Blog.   We also attempted to use neural methods to determine when agents, machine or otherwise, were likely to fail,  and afterwards what were the necessary means of repairing the problem.      Scale may be the biggest problem.  I realize the below is more about machine-agents, which we also saw, but could it be broadened to human agent behavior?

Fast dimensional analysis for root cause analysis at scale
Nikolay Pavlovich Laptev,  Fred Lin  Keyur Muzumdar,  Mihai-Valentin Curelea

What the research is: 
A fast dimensional analysis (FDA) framework that automates root cause analysis on structured logs with improved scalability. When a failure event happens in a large-scale distributed production environment, performing root cause analysis can be challenging. Various hardware, software, and tooling logs are often maintained separately, making it difficult to detect issues across multiple logs. Additionally, at scale, there could easily be millions of entities, each with hundreds of features, making it difficult to debug issues.

Our proposed FDA framework combines structured logs from a number of sources and provides a meaningful combination of features. That information arms engineers with actionable insights and helps them determine where to begin their investigation. And improved Apriori/FP-Growth algorithms sustain analysis at Facebook scale.

In the figure above, our system finds highly correlated features that explain the exception spike (red line).

How it works:
The FDA framework first fetches structured logs from various sources. Log data is deduplicated at query time (deduping significantly improves algorithm performance). Each duplicated row will have a samples column that counts original row frequency. The resulting data is then one-hot encoded (a Boolean 0/1 value, depending on whether a given feature is present in a given row) to transform it into a schema that fits the frequent pattern mining formulation. Frequent pattern mining is applied to identify frequent item-sets.   .... " 

AI Generating Value at Scale

Agree, have seen first hand.

From McKinsey Insights.

AI adoption advances, but foundational barriers remain
Survey respondents report the rapid adoption of AI and expect only a minimal effect on head count. Yet few companies have in place the foundational building blocks that enable AI to generate value at scale. ... "

Leveraging AI and Machine Learning

Had not heard of this concept.   Useful examples.  Thinking next Practices.

Three 'Next Practices' That Leverage AI And Machine Learning
 Forbes Technology Council,   By Ashok Santhanam

 If you are a CIO, VP of IT operations or some other type of IT leader, you are under constant pressure to ensure that IT systems operate at maximum efficiency. Systems must meet increasing service-level expectations in terms of performance, availability and security. In fact, you're probably already anticipating that this challenge is only going to get bigger. After all, you must deal with skills shortages and are tasked with supporting a growing number of IT initiatives such as cloud migrations, digital transformation, M&A integrations and other strategic projects. To address these challenges, you need to think about leveraging "next practices," not best practices. Let me explain.

The pace of change in today’s increasingly digitalized business environment means that what has worked in the past (as codified by "best practices") increasingly will not work moving forward. This has given rise to the concept of next practices. Next practices do not focus on improving existing processes since existing processes are becoming increasingly obsolete due to transformative technologies. Instead, they deal with the best ways to rethink your processes for the future, leveraging transformative technologies like artificial intelligence (AI) and machine learning (ML) to make your processes smarter.

Let me give you three interconnected examples of how AI-powered next practices can be applied to the system incident detection and resolution process. If properly applied, they will address limitations in managing your current setup while transforming your process in a way that allows you to both meet service-level targets today and create scalability for tomorrow. ... " 

IIOT Platforms

Interesting thoughts and visuals on the topic.

IIoT platforms: The technology stack as value driver in industrial equipment and machinery

Equipment and machinery companies considering a transformation to embrace the Industrial Internet of Things (IIoT) need to develop a clear perspective to drive impact at scale....

" ... Seeing the limits of hardware-driven growth, industrial equipment and machinery companies are looking to the Industrial Internet of Things (IIoT) to develop new customer-oriented, revenue-boosting business models. On the operations side, IIoT could increase production efficiency. Whether the focus is on revenue from new business models, savings from more efficient production, or both, digital-enabled advances in manufacturing require IIoT transformation. ...  "

In McKinsey