Quantum 6, 649 (2022).

The characterization of open quantum systems is a central and recurring problem for the development of quantum technologies. For time-independent systems, an (often unique) steady state describes the average physics once all the transient processes have faded out, but interesting quantum properties can emerge at intermediate timescales. Given a Lindblad master equation, these properties are encoded in the spectrum of the Liouvillian whose diagonalization, however, is a challenge even for small-size quantum systems. Here, we propose a new method to efficiently provide the Liouvillian spectral decomposition. We call this method an Arnoldi-Lindblad time evolution, because it exploits the algebraic properties of the Liouvillian superoperator to efficiently construct a basis for the Arnoldi iteration problem. The advantage of our method is double: (i) It provides a faster-than-the-clock method to efficiently obtain the steady state, meaning that it produces the steady state through time evolution shorter than needed for the system to reach stationarity. (ii) It retrieves the low-lying spectral properties of the Liouvillian with a minimal overhead, allowing to determine both which quantum properties emerge and for how long they can be observed in a system. This method is $textit{general and model-independent}$, and lends itself to the study of large systems where the determination of the Liouvillian spectrum can be numerically demanding but the time evolution of the density matrix is still doable. Our results can be extended to time evolution with a time-dependent Liouvillian. In particular, our method works for Floquet (i.e., periodically driven) systems, where it allows not only to construct the Floquet map for the slow-decaying processes, but also to retrieve the stroboscopic steady state and the eigenspectrum of the Floquet map. Although the method can be applied to any Lindbladian evolution (spin, fermions, bosons, …), for the sake of simplicity we demonstrate the efficiency of our method on several examples of coupled bosonic resonators (as a particular example). Our method outperforms other diagonalization techniques and retrieves the Liouvillian low-lying spectrum even for system sizes for which it would be impossible to perform exact diagonalization.

Managing the customer journey means understanding context and measuring the right metrics. Customer experience (CX) is a critical component of modern business management, but it’s far too easy to get lost in the sheer mass of available data.Customer goals need to be aligned with business goals. It really is that simple. Unfortunately, CX measurement remains […]

The post How to Measure CX Through These 6 Crucial Customer Experience Metrics appeared first on Baremetrics.

Introduction There is a very famous adage that has passed on as a legacy right from the classical Roman era, and that is, ‘Clothes maketh the man’. Though fashion and apparel is an indispensable part of the human lifestyle and one of the basic necessities, we can also say that it has gone beyond the scope of being one and has become a yardstick to judge an individual’s personality. Therefore, the fashion industry has become a force to reckon with in the last century in particular. Multiple global brands have blossomed over the years leaving iconic imprints on the fashion […]

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 In November of 2019 I wrote an article entitled "What Do FOMO and LinkedIn Have To Do With Supply Chain". FOMO is short for "Fear of Missing Out" and the general thesis of my article was if you become consumed with chasing every rabbit down every rabbit hole for "fear of missing out" then you will likely not get much done. 

The same can be true about LinkedIn. I wrote this in 2019 and I submit it has become 100x worse than when I wrote this article. 

And yes, we can somewhat blame the pandemic for this phenomenon in supply chain.  Ever since everyone has been home there has been an explosion of podcasts and home grown "T.V." shows discussing supply chains.  Some of them are hosted by people who have worked for a very short time, if at all, as a practitioner of supply chains.  

The "free money" aspect of the pandemic has also driven an explosion in supply chain / logistics technology. Again, some have driven huge value but by far the vast majority have not.  They have just been the recipients of a lot of money sloshing around in the economy looking for a place to land.  

Practitioners have some culpability in this as well.  Many have scrambled to do something - anything to show their leadership they are trying everything to overcome the effects of disruption.  So, what do they do?  They layer technology on top of technology and it still does not get them very far.  This is FOMO.  This is "I am going to try anything and everything because I am afraid I am going to miss out on the latest greatest thing".  Here are my simple few suggestions for the practitioner to avoid this trap:

1. Do the detailed work BEFORE you talk to a technology company:  This means you have to process map out how your business operates.  You need to identify the key metrics you are using and you have to identify what success looks like.  Use the Amazon methodology which is just what Covey taught us when he said, "Begin With The End in Mind".  Write the press release you will release 4 years from now.  What will you have accomplished. 
   
   
2. Operate Manually First if at All Possible:  This will allow you to be incredibly flexible as you "test and learn" all different ways of getting things accomplished. 
   
   
3. Follow the Tom Brady rule of focusing and ignoring all the noise. (See my posting on this: What Separates "Vital Few Metrics" from "Nice to Know" Metrics - And What Can We Learn from Tom Brady...). 
   
   
4. Write the spec! This is not necessarily hard work but some may find it tedious.  This is the work where you get as specific as possible on what you really need and what will really add value to your organization.  The more detailed this is written the more likely it is you will not get enamored by "shiny spinning plates" but rather will identify and get what you really need.  
   
   
5. Then and only then do you start inviting in technology providers and identify which is best to fill the gaps you identified by going through the 4 step process above. 

This nonprofit organization, the Soil Carbon Coalition, was inspired in part by Allan Yeomans's 2005 book, Priority One: Together we can beat global warming, which Abe Collins and I had been reading. Yeomans suggested that increased soil carbon could make a difference for climate. In 2007 Joel Brown of the NRCS gave a talk in Albuquerque in which he said that according to the published literature, good management by land stewards did not result in soil carbon increase, and that it was too difficult to measure anyhow. With that, I resolved to begin measuring soil carbon change on ranches and farms that were consciously aiming at greater soil health.

I had done plenty of reporting on land stewardship and plenty of rangeland monitoring. I studied research-grade, repeatable soil sampling and analysis methods and combined them with some rangeland transect methods I had learned from Charley Orchard of Land EKG. In 2011 I bought an old schoolbus, made it into living quarters, and for most of the next decade I traveled North America slowly, putting in hundreds of baseline transects and carbon measuring sites mainly on ranching operations that had some association with holistic planned grazing. I resampled over a hundred at intervals of 3-8 years. The question I was asking was: Where, when, and with whose management, was soil carbon changing over intervals of several years? I called this project the Soil Carbon Challenge.

A lot of data accumulated. What did it show, what did it mean?

In order for there to be meaning or learning, there needs to be a context, a purpose. My purpose in embarking on this project, the question behind the question, was 1) to see if measuring soil carbon change over time could provide relevant feedback or guidance to land stewards who were interested in soil health, and 2) to see what soil carbon change, if it were significant and widespread, might imply for climate policy that was narrowly focused on more technical rather than biological solutions. Everywhere I traveled, water was the main issue for people, whether it was floods or drought. I measured soil carbon because it was central to the flow of sunlight energy through soils, critically influential for soil function, and easier to measure change than measuring soil water. At no point did I advocate for the commodification of soil carbon into credit or offset schemes.

The soil carbon change data that I got on resampling baseline plots was noisy and variable, especially in the top layers (0-10 cm depth). There were some pockets of consistent change, such as a group of graziers in southeast Saskatchewan showing substantial increases, even down to the 40 cm depth that I often sampled to. But the majority of change data that I collected did not offer solid support to the hypothesis that holistic planned grazing or no-till, for example, in a few years would increase soil carbon in every circumstance or locale, or that soil carbon would faithfully reflect changes in forage production, soil cover, or diversity.

Many of the people on whose ranches I sampled did not know what to do with the data or results, or simply interpreted the data as a judgment: a high or increasing level of soil carbon indicated good management, and low or decreasing was bad. Measured soil carbon change, especially at one or two points, was not meaningful, useful, or in some cases timely feedback, and may not have contributed much to their learning and decision making as I had hoped it might. For the most part the ranches I sampled on were widely scattered, and there was little interaction between them or mutual support, little opportunity for discussion or the development of a shared intelligence or a community of practice. The "competition" framing or context that I suggested in 2010 did not help. The effort tended toward an information pipeline rather than a platform that enabled people to take responsibility for their own learning. For a while I posted the data on this website, but that did little to foster discussion or interpretation, or encourage people to add learning to judgment.

Nor did the noisiness and variability of the data I collected offer solid support for soil carbon increase as a strategy for reducing atmospheric carbon dioxide and easing climate change--a strategy that was growing increasingly popular, with many people and organizations advocating for it, and which has resulted in new programs, policies, and markets to try and reward ranchers and farmers for increases (usually modeled rather than measured) in soil carbon.

So the Soil Carbon Challenge was at least a partial failure, in that it took aim at the problems and technical issues at the tip of the iceberg, and fostered judgment more than learning and new questions. I did take some lessons from this decade of travel, conversations, workshops, transects and soil sampling, sample processing and analysis, data entry, and associated reading and research into the history of the discovery of the carbon cycle, water cycle, and climate issues. Some of these lessons resonated with what I had learned, and then forgotten, in the trainings I took in holistic management and consensus building in the 1990s.

Like many attempts at "solutioneering" the problems of soil health and climate, the Soil Carbon Challenge focused on the tip or immediately visible portion of the "iceberg," and was not designed around the center of gravity: human or people issues, paradigms and power, relationships and trust.

What I learned (or saw from a new perspective, or rediscovered):

1. Energy is a context for all life

and energy flow, from sunlight, is a pattern that connects all knowledge and activity. However, energy is an abstraction: we can only know it, sense it, or measure it by its results, the work it does, the changes it creates. Our planet is an open system largely run by sunlight energy. As I wrote here, "We are riding an enormous, incredibly complex, fractal eddying flow of sunlight energy used in many ways by interrelated communities of self-motivated living organisms whose metabolisms, behaviors, and relationships are increasingly influenced by our own." And, as Selman Waksman, Aldo Leopold, and others realized, soil is a major hub for sunlight energy flow.

2. Learning networks

are a context for the emergence of a community of practice, of a shared intelligence. These are social groupings where people share what they are learning, and are able to witness or share in the learning of others, and so gain an enriched perspective, with dialogue. It helps if these are participatory, ongoing, local, and include evidence as well as new questions. Some degree if trust is needed in order for judgments to ripen into learning, and listening is a key ingredient. Over the past year or so I have developed soilhealth.app as a way of supporting learning networks around soil health and sunlight energy flow, and am seeking partnerships on that project.

It's not that measuring soil carbon is a bad or useless thing, but a good context or purpose is needed. We learn from differences. Here are 4 suggestions for learning, about different kinds of differences, all of which may surprise and spark your curiosity:

1. To learn more about flows of sunlight energy, get an infrared heat gun ($15 and up) that measures or estimates radiant heat, and begin playing with it, pointing it at various stages of sky, soil, plants, and other surfaces and objects.
2. Use infiltration rings to gauge how well water infiltrates into various soil surfaces. Remember that soil moisture held in soil pores represents a huge capture of free sunlight energy.
3. Record change over time in some kind of indicator, quantity, or measurement you are interested in or curious about. Precipitation or infiltration for example. For ranchers, animal days of grazing on a particular pasture for example, or pounds of gain. Repeatable observations need some kind of recording system.
4. Share your observations and learning with others in a learning network. As two eyes helps you see depth, so do multiple perspectives enrich and deepen your learning.