Category Archives: Quantum Computing
The Schizophrenic World Of Quantum Interpretations – Forbes
Quantum Interpretations
To the average person, most quantum theories sound strange, while others seem downright bizarre.There are many diverse theories that try to explain the intricacies of quantum systems and how our interactions affect them.And, not surprisingly, each approach is supported by its group of well-qualified and well-respected scientists.Here, well take a look at the two most popular quantum interpretations.
Does it seem reasonable that you can alter a quantum system just by looking at it? What about creating multiple universes by merely making a decision?Or what if your mind split because you measured a quantum system?
You might be surprised that all or some of these things might routinely happen millions of times every day without you even realizing it.
But before your brain gets twisted into a knot, lets cover a little history and a few quantum basics.
The birth of quantum mechanics
Classical physics describes how large objects behave and how they interact with the physical world.On the other hand, quantum theory is all about the extraordinary and inexplicable interaction of small particles on the invisible scale of such things as atoms, electrons, and photons.
Max Planck, a German theoretical physicist, first introduced the quantum theory in 1900. It was an innovation that won him the Nobel Prize in physics in 1918.Between 1925 and 1930, several scientists worked to clarify and understand quantum theory.Among the scientists were Werner Heisenberg and Erwin Schrdinger, both of whom mathematically expanded quantum mechanics to accommodate experimental findings that couldnt be explained by standard physics.
Heisenberg, along with Max Born and Pascual Jordan, created a formulation of quantum mechanics called matrix mechanics. This concept interpreted the physical properties of particles as matrices that evolved in time.A few months later, Erwin Schrdinger created his famous wave mechanics.
Although Heisenberg and Schrdinger worked independently from each other, and although their theories were very different in presentation, both theories were essentially mathematically the same. Of the two formulations, Schrdingers was more popular than Heisenbergs because it boiled down to familiar differential equations.
While today's physicists still use these formulations, they still debate their actual meaning.
First weirdness
A good place to start is Schrdingers equation.
Erwin Schrdingers equation provides a mathematical description of all possible locations and characteristics of a quantum system as it changes over time.This description is called the systems wave function.According to the most common quantum theory, everything has a wave function. The quantum system could be a particle, such as an electron or a photon, or even something larger.
Schrdingers equation won't tell you the exact location of a particle.It only reveals the probability of finding the particle at a given location.The probability of a particle being in many places or in many states at the same time is called its superposition. Superposition is one of the elements of quantum computing that makes it so powerful.
Almost everyone has heard about Schrdingers cat in a box.Simplistically, ignoring the radiation gadgets, while the cat is in the closed box, it is in a superposition of being both dead and alive at the same time.Opening the box causes the cat's wave function to collapse into one of two states and you'll find the cat either alive or dead.
There is little dispute among the quantum community that Schrdingers equation accurately reflects how a quantum wave function evolves.However, the wave function itself, as well as the cause and consequences of its collapse, are all subjects of debate.
David Deutsch is a brilliant British quantum physicist at the University of Cambridge. In his book, The Fabric of Reality, he said: Being able to predict things or to describe them, however accurately, is not at all the same thing as understanding them. Facts cannot be understood just by being summarized in a formula, any more than being listed on paper or committed to memory.
The Copenhagen interpretation
Quantum theories use the term "interpretation" for two reasons.One, it is not always obvious what a particular theory means without some form of translation.And, two, we are not sure we understand what goes on between a wave functions starting point and where it ends up.
There are many quantum interpretations.The most popular is the Copenhagen interpretation, a namesake of where Werner Heisenberg andNiels Bohr developed their quantum theory.
Werner Heisenberg (left) with Niels Bohr at a Conference in Copenhagen in 1934.
Bohr believed that the wave function of a quantum system contained all possible quantum states.However, when the system was observed or measured, its wave function collapsed into a single state.
Whats unique about the Copenhagen interpretation is that it makes the outside observer responsible for the wave functions ultimate fate. Almost magically, a quantum system, with all its possible states and probabilities, has no connection to the physical world until an observer interacts or measures the system. The measurement causes the wave function to collapse into one of its many states.
You might wonder what happens to all the other quantum states present in the wave function as described by the Copenhagen Interpretation before it collapsed?There is no explanation of that mystery in the Copenhagen interpretation. However, there is a quantum interpretation that provides an answer to that question.Its called the Many-Worlds Interpretation or MWI.
Billions of you?
Because the many-worlds interpretation is one of the strangest quantum theories, it has become central to the plot of many science fiction novels and movies.At one time, MWI was an outlier with the quantum community, but many leading physicists now believe it is the only theory that is consistent with quantum behavior.
The MWI originated in a Princeton doctoral thesis written by a young physicist named Hugh Everett in the late 1950s. Even though Everett derived his theory using sound quantum fundamentals, it was severely criticized and ridiculed by most of the quantum community. Even Everetts academic adviser at Princeton, John Wheeler, tried to distance himself from his student. Everette became despondent over the harsh criticism. He eventually left quantum research to work for the government as a mathematician.
The theory proposes that the universe has a single, large wave function that follows Schrdingers equation.Unlike the Copenhagen Interpretation, the MWI universal wave function doesnt collapse.
Everything in the universe is quantum, including ourselves. As we interact with parts of the universe, we become entangled with it.As the universal wave function evolves, some of our superposition states decohere. When that happens, our reality becomes separated from the other possible outcomes associated with that event. Just to be clear, the universe doesn't split and create a new universe. The probability of all realities, or universes, already exists in the universal wave function, all occupying the same space-time.
Schrdinger's Cat, many-worlds interpretation, with universe branching. Visualization of the ... [+] separation of the universe due to two superposed and entangled quantum mechanical states.
In the Copenhagen interpretation, by opening the box containing Schrdingers cat, you cause the wave function to collapse into one of its possible states, either alive or dead.
In the Many -Worlds interpretation, the wave function doesn't collapse. Instead, all probabilities are realized.In one universe, you see the cat alive, and in another universe the cat will be dead.
Right or wrong decisions become right and wrong decisions
Decisions are also events that trigger the separation of multiple universes. We make thousands of big and little choices every day. Have you ever wondered what your life would be like had you made different decisions over the years?
According to the Many-Worlds interpretation, you and all those unrealized decisions exist in different universes because all possible outcomes exist in the universal wave function.For every decision you make, at least two of "you" evolve on the other side of that decision. One universe exists for the choice you make, and one universe for the choice you didnt make.
If the Many-Worlds Interpretation is correct, then right now, a near infinite versions of you are living different and independent lives in their own universes.Moreover, each of the universes overlay each other and occupy the same space and time.
It is also likely that you are currently living in a branch universe spun off from a decision made by a previous version of yourself, perhaps millions or billions of previous iterations ago.You have all the old memories of your pre-decision self, but as you move forward in your own universe, you live independently and create your unique and new memories.
A Reality Check
Which interpretation is correct?Copenhagen or Many-Worlds?Maybe neither. But because quantum mechanics is so strange, perhaps both are correct.It is also possible that a valid interpretation is yet to be expressed. In the end, correct or not, quantum interpretations are just plain fun to think about.
Note: Moor Insights & Strategy writers and editors may have contributed to this article.
Disclosure: Moor Insights & Strategy, like all research and analyst firms, provides or has provided paid research, analysis, advising, or consulting to many high-tech companies in the industry, including Amazon.com, Advanced Micro Devices,Apstra,ARM Holdings, Aruba Networks, AWS, A-10 Strategies,Bitfusion,Cisco Systems, Dell, DellEMC, Dell Technologies, Diablo Technologies, Digital Optics,Dreamchain, Echelon, Ericsson, Foxconn, Frame, Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries,Google,HPInc., Hewlett Packard Enterprise, HuaweiTechnologies,IBM, Intel, Interdigital, Jabil Circuit, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation, MACOM (Applied Micro),MapBox,Mavenir, Mesosphere,Microsoft,National Instruments, NetApp, NOKIA, Nortek,NVIDIA, ON Semiconductor, ONUG, OpenStack Foundation, Panasas,Peraso, Pixelworks, Plume Design,Portworx, Pure Storage,Qualcomm, Rackspace, Rambus,RayvoltE-Bikes, Red Hat, Samsung Electronics, Silver Peak, SONY,Springpath, Sprint, Stratus Technologies, Symantec, Synaptics, Syniverse,TensTorrent,TobiiTechnology, Twitter, Unity Technologies, Verizon Communications,Vidyo, Wave Computing,Wellsmith, Xilinx, Zebra, which may be cited in this article.
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The Schizophrenic World Of Quantum Interpretations - Forbes
Disrupt The Datacenter With Orchestration – The Next Platform
Since 1965, the computer industry has relied on Moores Law to accelerate innovation, pushing more transistors into integrated circuits to improve computation performance. Making transistors smaller helped lift all boats for the entire industry and enable new applications. At some point, we will reach a physical limit that is, a limit stemming from physics itself. Even with this setback, improvements kept on pace thanks to increased parallelism of computation and consolidation of specialized functions into single chip packages, such as systems on chip).
In recent years, we are nearing another peak. This article proposes to improve computation performance not only by building better hardware, but by changing how we use existing hardware. More specifically, the focusing on how we use existing processor types. I call this approach Compute Orchestration: automatic optimization of machine code to best use the modern datacenter hardware (again, with special emphasis on different processor types).
So what is compute orchestration? It is the embracing of hardware diversity to support software.
There are many types of processors: Microprocessors in small devices, general purpose CPUs in computers and servers, GPUs for graphics and compute, and programmable hardware like FPGAs. In recent years, specialized processors like TPUs and neuromorphic processors for machine learning are rapidly entering the datacenter.
There is potential in this variety: Instead of statically utilizing each processor for pre-defined functions, we can use existing processors as a swarm, each processor working on the most suitable workloads. Doing that, we can potentially deliver more computation bandwidth with less power, lower latency and lower total cost of ownership).
Non-standard utilization of existing processors is already happening: GPUs, for example, were already adapted from processors dedicated to graphics into a core enterprise component. Today, GPUs are used for machine learning and cryptocurrency mining, for example.
I call the technology to utilize the processors as a swarm Compute Orchestration. Its tenets can be described in four simple bullets:
Compute orchestration is, in short, automatic adaptation of binary code and automatic allocation to the most suitable processor types available. I split the evolution of compute orchestration into four generations:
Compute Orchestration Gen 1: Static Allocation To Specialized Co-Processors
This type of compute orchestration is everywhere. Most devices today include co-processors to offload some specialized work from the CPU. Usually, the toolchain or runtime environment takes care of assigning workloads to the co-processor. This is seamless to the developer, but also limited in functionality.
Best known example is the use of cryptographic co-processors for relevant functions. Being liberal in our definitions of co-processor, Memory Management Units (MMUs) to manage virtual memory address translation can also be considered an example.
Compute Orchestration Gen 2: Static Allocation, Heterogeneous Hardware
This is where we are at now. In the second generation, the software relies on libraries, dedicated run time environments and VMs to best use the available hardware. Lets call the collection of components that help better use the hardware frameworks. Current frameworks implement specific code to better use specific processors. Most prevalent are frameworks that know how to utilize GPUs in the cloud. Usually, better allocation to bare metal hosts remains the responsibility of the developer. For example, the developer/DevOps engineer needs to make sure a machine with GPU is available for the relevant microservice. This phenomenon is what brought me to think of Compute Orchestration in the first place, as it proves there is more slack in our current hardware.
Common frameworks like OpenCL allow programming compute kernels to run on different processors. TensorFlow allows assigning nodes in a computation graph to different processors (devices).
This better use of hardware by using existing frameworks is great. However, I believe there is a bigger edge. Existing frameworks still require effort from the developer to be optimal they rely on the developer. Also, no legacy code from 2016 (for example) is ever going to utilize a modern datacenter GPU cluster. My view is that by developing automated and dynamic frameworks, that adapt to the hardware and workload, we can achieve another leap.
Compute Orchestration Gen 3: Dynamic Allocation To Heterogeneous Hardware
Computation can take an example from the storage industry: Products for better utilization and reliability of storage hardware have innovated for years. Storage startups develop abstraction layers and special filesystems that improve efficiency and reliability of existing storage hardware. Computation, on the other hand, remains a stupid allocation of hardware resources. Smart allocation of computation workloads to hardware could result in better performance and efficiency for big data centers (for example hyperscalers like cloud providers). The infrastructure for such allocation is here, with current data center designs pushing to more resource disaggregation, introduction of diverse accelerators, and increased work on automatic acceleration (for example: Workload-aware Automatic Parallelization for Multi-GPU DNN Training).
For high level resource management, we already have automatic allocation. For example, project Mesos (paper) focusing on fine-grained resource sharing, Slurm for cluster management, and several extensions using Kubernetes operators.
To further advance from here would require two steps: automatic mapping of available processors (which we call the compute environment) and workload adaptation. Imagine a situation where the developer doesnt have to optimize her code to the hardware. Rather, the runtime environment identifies the available processing hardware and automatically optimizes the code. Cloud environments are heterogeneous and changing, and the code should change accordingly (in fact its not the code, but the execution model in the run time environment of the machine code).
Compute Orchestration Gen 4: Automatic Allocation To Dynamic Hardware
A thought, even a possibility, can shatter and transform us. Friedrich Wilhelm Nietzsche
The quote above is to say that there we are far from practical implementation of the concept described here (as far as I know). We can, however, imagine a technology that dynamically re-designs a data center to serve needs of running applications. This change in the way whole data centers meet computation needs as already started. FGPAs are used more often and appear in new places (FPGAs in hosts, FPGA machines in AWS, SmartNICs), providing the framework for constant reconfiguration of hardware.
To illustrate the idea, I will use an example: Microsoft initiated project Catapult, augmenting CPUs with an interconnected and configurable compute layer composed of programmable silicon. The timeline in the projects website is fascinating. The project started off in 2010, aiming to improve search queries by using FPGAs. Quickly, it proposed the use of FPGAs as bumps in the wire, adding computation in new areas of the data path. Project Catapult also designed an architecture for using FPGAs as a distributed resource pool serving all the data center. Then, the project spun off Project BrainWave, utilizing FPGAs for accelerating AI/ML workloads.
This was just an example of innovation in how we compute. Quick online search will bring up several academic works on the topic. All we need to reach the 4th generation is some idea synthesis, combining a few concepts together:
Low effort HDL generation (for example Merlin compiler, BORPH)
In essence, what I am proposing is to optimize computation by adding an abstraction layer that:
Automatic allocation on agile hardware is the recipe for best utilizing existing resources: faster, greener, cheaper.
The trends and ideas mentioned in this article can lead to many places. It is very likely, that we are already working with existing hardware in the optimal way. It is my belief that we are in the midst of the improvement curve. In recent years, we had increased innovation in basic hardware building blocks, new processors for example, but we still have room to improve in overall allocation and utilization. The more we deploy new processors in the field, the more slack we have in our hardware stack. New concepts, like edge computing and resource disaggregation, bring new opportunities for optimizing legacy code by smarter execution. To achieve that, legacy code cant be expected to be refactored. Developers and DevOps engineers cant be expected to optimize for the cloud configuration. We just need to execute code in a smarter way and that is the essence of compute orchestration.
The conceptual framework described in this article should be further explored. We first need to find the killer app (what type of software we optimize to which type of hardware). From there, we can generalize. I was recently asked in a round table what is the next generation of computation? Quantum computing? Tensor Processor Units? I responded that all of the above, but what we really need is better usage of the existing generation.
Guy Harpak is the head of technology at Mercedes-Benz Research & Devcelopment in its Tel Aviv, Israel facility. Please feel free to contact him on any thoughts on the topics above at harpakguy@gmail.com. Harpak notes that this contributed article reflects his personal opinion and is in no way related to people or companies that he works with or for.
Related Reading: If you find this article interesting, I would recommend researching the following topics:
Some interesting articles on similar topics:
Return Of The Runtimes: Rethinking The Language Runtime System For The Cloud 3.0 Era
The Deep Learning Revolution And Its Implications For Computer Architecture And Chip Design (by Jeffrey Dean from Google Research)
Beyond SmartNICs: Towards A Fully Programmable Cloud
Hyperscale Cloud: Reimagining Datacenters From Hardware To Applications
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Disrupt The Datacenter With Orchestration - The Next Platform
Quantum Computing strikes technology partnership with Splunk – Proactive Investors USA & Canada
Initial efforts with San Franciscos Splunk will focus on three key challenges: network security, dynamic logistics and scheduling
Quantum Computing Inc (OTC:QUBT), an advanced technology company developing quantum-ready applications and tools, said Tuesday that it has struck a technology alliance partnership with ().
San Francisco, California-based Splunk creates software for searching, monitoring, and analyzing machine-generated big data via a web-style interface.
Meanwhile, staffed by experts in mathematics, quantum physics, supercomputing, financing and cryptography, Leesburg, Virginia-based Quantum Computing is developing an array of applications to allow companies to exploit the power of quantum computing to their advantage. It is a leader in the development of quantum ready software with deep experience developing applications and tools for early quantum computers.
Splunk brings a leading big-data-analytics platform to the partnership, notably existing capabilities in its Machine/Deep Learning Toolkit in current use by Splunk customers, said the company.
Implementation of quantum computing applications will be significantly accelerated by tools that allow the development and execution of applications independent of any particular quantum computing architecture.
We are excited about this partnership opportunity, said Quantum Computing CEO Robert Liscouski. Splunk is a proven technology leader with over 17,500 customers world-wide, that has the potential to provide great opportunities for QCIs quantum ready software technologies.
Both the companies will partner to do fundamental and applied research and develop analytics that exploit conventional large-data cybersecurity stores and data-analytics workflows, combined with quantum-ready graph and constrained-optimization algorithms.
The company explained that these algorithms will initially be developed using Quantums Mukai software platform, which enables quantum-ready algorithms to execute on classical hardware and also to run without modification on quantum computing hardware when ready.
Once proofs of concept are completed, QCI and Splunk will develop new analytics with these algorithms in the Splunk data-analytics platform, to evaluate quantum analytics readiness on real-world data, noted the company.
The Splunk platform/toolkits help customers address challenging analytical problems via neural nets or custom algorithms, extensible to Deep Learning frameworks through an open source approach that incorporates existing and custom libraries.
The initial efforts of our partnership with Splunk will focus on three key challenges: network security, dynamic logistics and scheduling, said Quantum Computing.
Contact the author Uttara Choudhury at[emailprotected]
Follow her onTwitter:@UttaraProactive
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Quantum Computing strikes technology partnership with Splunk - Proactive Investors USA & Canada
Devs: Alex Garland on Tech Company Cults, Quantum Computing, and Determinism – Den of Geek UK
Yet that difference between the common things a company can sell and the uncommon things they quietly develop is profoundly important. In Devs, the friendly exterior of Amaya with its enormous statue of a childa literal monument to Forests lost daughteris a public face to the actual profound work his Devs team is doing in a separate, highly secretive facility. Seemingly based in part on mysterious research and development wings of tech giantsthink Googles moonshot organizations at X Development and DeepMindDevs is using quantum computing to change the world, all while keeping Forests Zen ambition as its shield.
I think it helps, actually, Garland says about Forest not being a genius. Because I think what happens is that these [CEO] guys present as a kind of front between what the company is doing and the rest of the world, including the kind of inspection that the rest of the world might want on the company if they knew what the company was doing. So our belief and enthusiasm in the leader stops us from looking too hard at what the people behind-the-scenes are doing. And from my point of view thats quite common.
A lifelong man of words, Garland describes himself as a writer with a laymans interest in science. Yet its fair to say he studies almost obsessively whatever field of science hes writing about, which now pertains to quantum computing. A still largely unexplored frontier in the tech world, quantum computing is the use of technology to apply quantum-mechanical phenomena to data a traditional computer could never process. Its still so unknown that Google AI and NASA published a paper only six months ago in which they claimed to have achieved quantum supremacy (the creation of a quantum device that can actually solve problems a classical computer cannot).
Whereas binary computers work with gates that are either a one or a zero, a quantum qubit [a basic unit of measurement] can deal with a one and a zero concurrently, and all points in between, says Garland. So you get a staggering amount of exponential power as you start to run those qubits in tandem with each other. What the filmmaker is especially fascinated by is using a quantum system to model another quantum system. That is to say using a quantum computer with true supremacy to solve other theoretical problems in quantum physics. If we use a binary way of doing that, youre essentially using a filing system to model something that is emphatically not binary.
So in Devs, quantum computing is a gateway into a hell of a trippy concept: a quantum computer so powerful that it can analyze the theoretical data of everything that has or will occur. In essence, Forest and his team are creating a time machine that can project through a probabilistic system how events happened in the past, will happen in the future, and are happening right now. It thus acts as an omnipotent surveillance system far beyond any neocons dreams.
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Devs: Alex Garland on Tech Company Cults, Quantum Computing, and Determinism - Den of Geek UK
Is Machine Learning The Quantum Physics Of Computer Science ? – Forbes
Preamble: Intermittently, I will be introducing some columns which introduce some seemingly outlandish concepts. The purpose is a bit of humor, but also to provoke some thought. Enjoy.
atom orbit abstract
God does not play dice with the universe, Albert Einstein is reported to have said about the field of Quantum Physics. He was referring to the great divide at the time in the physics community between general relativity and quantum physics. General relativity was a theory which beautifully explained a great deal of physical phenomena in a deterministic fashion. Meanwhile, quantum physics grew out of a model which fundamentally had a probabilistic view of the world. Since Einstein made that statement in the mid 1950s, quantum physics has proven to be quite a durable theory, and in fact, it is used in a variety of applications such as semiconductors.
One might imagine a past leader in computer science such as Donald Knuth exclaiming, Algorithms should be deterministic. That is, given any input, the output should be exact and known. Indeed, since its formation, the field of computer science has focused on building elegant deterministic algorithms which have a clear view of the transformation between inputs and outputs. Even in the regime of non-determinism such as parallel processing, the objective of the overall algorithm is to be deterministic. That is, despite the fact that operations can run out-of-order, the outputs are still exact and known. Computer scientists work very hard to make that a reality.
As computer scientists have engaged with the real world, they frequently face very noisy inputs such as sensors or even worse, human beings. Computer algorithms continue to focus on faithfully and precisely translating input noise to output noise. This has given rise to the Junk In Junk Out (JIJO) paradigm. One of the key motivations for pursuing such a structure has been the notion of causality and diagnosability. After all, if the algorithms are noisy, how is one to know the issue is not a bug in the algorithm? Good point.
With machine learning, computer science has transitioned to a model where one trains a machine to build an algorithm, and this machine can then be used to transform inputs to outputs. Since the process of training is dynamic and often ongoing, the data and the algorithm are intertwined in a manner which is not easily unwound. Similar to quantum physics, there is a class of applications where this model seems to work. Recognizing patterns seems to be a good application. This is a key building block for autonomous vehicles, but the results are probabilistic in nature.
In quantum physics, there is an implicit understanding that the answers are often probabilistic Perhaps this is the key insight which can allow us to leverage the power of machine learning techniques and avoid the pitfalls. That is, if the requirements of the algorithm must be exact, perhaps machine learning methods are not appropriate. As an example, if your bank statement was correct with somewhat high probability, this may not be comforting. However, if machine learning algorithms can provide with high probability the instances of potential fraud, the job of a forensic CPA is made quite a bit more productive. Similar analogies exist in the area of autonomous vehicles.
Overall, machine learning seems to define the notion of probabilistic algorithms in computer science in a similar manner as quantum physics. The critical challenge for computing is to find the correct mechanisms to design and validate probabilistic results.
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Is Machine Learning The Quantum Physics Of Computer Science ? - Forbes
Recent PDF Report : Quantum Computing Market 2020: In-depth Industry Analysis By Size, Share, Competition, Opportunities And Growth By 2029 – Sound On…
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A basic outline of the competitive landscape:
The Quantum Computing market report includes a thorough analysis of the competitive landscape of this industry.
The report also encompasses a thorough analysis of the markets competitors scope based on the segmentation of the same into companies such as International Business Machines (IBM) Corporation, Google Inc, Microsoft Corporation, Qxbranch LLC, Cambridge Quantum Computing Ltd, 1QB Information Technologies Inc, QC Ware Corp., Magiq Technologies Inc, D-Wave Systems Inc, Rigetti Computing.
The study covers details on the individual market share of each industry contributor, the region served and more.
Key players Profiles covered in the report alongside facts concerning its futuristic strategies, financials, technological developments, supply chain study, collaboration & mergers, gross margins and price models.
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A complete outline of the regional spectrum:
A crisp outline of the market segmentation:
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Segmentation by Component:
GeneratorConversion DevicesDistribution DevicesBattery Management SystemsOthers (Alternators, etc.)Segmentation by System:
Power GenerationPower DistributionPower ConversionEnergy StorageSegmentation by Platform:
Military AviationCommercial AviationBusiness & General AviationSegmentation by Application:
Cabin SystemFlight Control & OperationConfiguration ManagementPower Generation ManagementAir Pressurization & Conditioning
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Different questions addressed through this research report:
What are the affecting factors for the growth of the market?
What are the major restraints and drivers of market?
What will be the market size in 2029?
Which are the most demanding regions in terms of consumption and production?
key outcomes of industry analysis techniques?
What are the successful key players in market?
Table of Content
1 Introduction of Quantum Computing Market
1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions
2 Executive Summary
3 Research Methodology of MarketResearch.biz
3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources
4 Quantum Computing Market Outlook
4.1 Overview
4.2 Market Dynamics
4.2.1 Drivers
4.2.2 Restraints
4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis
5 Quantum Computing Market , Segmentation
5.1 Overview
6 Quantum Computing Market , By Geography
6.1 Overview
6.2 North America
6.2.1 U.S.
6.2.2 Canada
6.2.3 Mexico
6.3 Europe
6.3.1 Germany
6.3.2 U.K.
6.3.3 France
6.3.4 Rest of Europe
6.4 Asia Pacific
6.4.1 China
6.4.2 Japan
6.4.3 India
6.4.4 Rest of Asia Pacific
6.5 Rest of the World
6.5.1 Latin America
6.5.2 Middle East
7 Quantum Computing Market Competitive Landscape
7.1 Overview
7.2 Company Market Ranking
7.3 Key Development Strategies
8 Company Profiles
8.1.1 Overview
8.1.2 Financial Performance
8.1.3 Product Outlook
8.1.4 Key Developments
9 Appendix
9.1 Related Research
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Organisms grow in wave pattern, similar to ocean circulation – Big Think
When an egg cell of almost any sexually reproducing species is fertilized, it sets off a series of waves that ripple across the egg's surface.
These waves are produced by billions of activated proteins that surge through the egg's membrane like streams of tiny burrowing sentinels, signaling the egg to start dividing, folding, and dividing again, to form the first cellular seeds of an organism.
Now MIT scientists have taken a detailed look at the pattern of these waves, produced on the surface of starfish eggs. These eggs are large and therefore easy to observe, and scientists consider starfish eggs to be representative of the eggs of many other animal species.
In each egg, the team introduced a protein to mimic the onset of fertilization, and recorded the pattern of waves that rippled across their surfaces in response. They observed that each wave emerged in a spiral pattern, and that multiple spirals whirled across an egg's surface at a time. Some spirals spontaneously appeared and swirled away in opposite directions, while others collided head-on and immediately disappeared.
The behavior of these swirling waves, the researchers realized, is similar to the waves generated in other, seemingly unrelated systems, such as the vortices in quantum fluids, the circulations in the atmosphere and oceans, and the electrical signals that propagate through the heart and brain.
"Not much was known about the dynamics of these surface waves in eggs, and after we started analyzing and modeling these waves, we found these same patterns show up in all these other systems," says physicist Nikta Fakhri, the Thomas D. and Virginia W. Cabot Assistant Professor at MIT. "It's a manifestation of this very universal wave pattern."
"It opens a completely new perspective," adds Jrn Dunkel, associate professor of mathematics at MIT. "You can borrow a lot of techniques people have developed to study similar patterns in other systems, to learn something about biology."
Fakhri and Dunkel have published their results today in the journal Nature Physics. Their co-authors are Tzer Han Tan, Jinghui Liu, Pearson Miller, and Melis Tekant of MIT.
Previous studies have shown that the fertilization of an egg immediately activates Rho-GTP, a protein within the egg which normally floats around in the cell's cytoplasm in an inactive state. Once activated, billions of the protein rise up out of the cytoplasm's morass to attach to the egg's membrane, snaking along the wall in waves.
"Imagine if you have a very dirty aquarium, and once a fish swims close to the glass, you can see it," Dunkel explains. "In a similar way, the proteins are somewhere inside the cell, and when they become activated, they attach to the membrane, and you start to see them move."
Fakhri says the waves of proteins moving across the egg's membrane serve, in part, to organize cell division around the cell's core.
"The egg is a huge cell, and these proteins have to work together to find its center, so that the cell knows where to divide and fold, many times over, to form an organism," Fakhri says. "Without these proteins making waves, there would be no cell division."
MIT researchers observe ripples across a newly fertilized egg that are similar to other systems, from ocean and atmospheric circulations to quantum fluids. Courtesy of the researchers.
In their study, the team focused on the active form of Rho-GTP and the pattern of waves produced on an egg's surface when they altered the protein's concentration.
For their experiments, they obtained about 10 eggs from the ovaries of starfish through a minimally invasive surgical procedure. They introduced a hormone to stimulate maturation, and also injected fluorescent markers to attach to any active forms of Rho-GTP that rose up in response. They then observed each egg through a confocal microscope and watched as billions of the proteins activated and rippled across the egg's surface in response to varying concentrations of the artificial hormonal protein.
"In this way, we created a kaleidoscope of different patterns and looked at their resulting dynamics," Fakhri says.
The researchers first assembled black-and-white videos of each egg, showing the bright waves that traveled over its surface. The brighter a region in a wave, the higher the concentration of Rho-GTP in that particular region. For each video, they compared the brightness, or concentration of protein from pixel to pixel, and used these comparisons to generate an animation of the same wave patterns.
From their videos, the team observed that waves seemed to oscillate outward as tiny, hurricane-like spirals. The researchers traced the origin of each wave to the core of each spiral, which they refer to as a "topological defect." Out of curiosity, they tracked the movement of these defects themselves. They did some statistical analysis to determine how fast certain defects moved across an egg's surface, and how often, and in what configurations the spirals popped up, collided, and disappeared.
In a surprising twist, they found that their statistical results, and the behavior of waves in an egg's surface, were the same as the behavior of waves in other larger and seemingly unrelated systems.
"When you look at the statistics of these defects, it's essentially the same as vortices in a fluid, or waves in the brain, or systems on a larger scale," Dunkel says. "It's the same universal phenomenon, just scaled down to the level of a cell."
The researchers are particularly interested in the waves' similarity to ideas in quantum computing. Just as the pattern of waves in an egg convey specific signals, in this case of cell division, quantum computing is a field that aims to manipulate atoms in a fluid, in precise patterns, in order to translate information and perform calculations.
"Perhaps now we can borrow ideas from quantum fluids, to build minicomputers from biological cells," Fakhri says. "We expect some differences, but we will try to explore [biological signaling waves] further as a tool for computation."
This research was supported, in part, by the James S. McDonnell Foundation, the Alfred P. Sloan Foundation, and the National Science Foundation.
Reprinted with permission of MIT News. Read the original article.
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Tech incubator Fountech.Ventures launches in US and UK – UKTN
Fountech.Ventures, a next generation incubator for deep tech startups, has launched in the US and UK.
The subsidiary company ofFountech.ai, a four-year-old international AI think tank and parent company to a number of specialist AI and deep tech firms, is based in Austin, Texas, US and originated in London, UK.
Fountech.Ventures goes above and beyond a standard incubator it provides broader services over a longer timeframe so founders of deep tech startups can fast-track their businesses from ideation to commercial success.
Fountech.Ventures develops tailored programmes for members, sharing technical and commercial knowledge, along with the provision of interim CEOs, funding, business advice, office space and international networking opportunities.
Headed by Salvatore Minetti, a team of experienced tech experts will work with deep tech startups spanning artificial intelligence (AI), robotics, quantum computing and blockchain.
Based on progress and continuous assessments, Fountech.Ventures will invest its own funds into its portfolio companies, from pre-seed level right through to Series B.
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Salvatore Minetti, CEO of Fountech.Ventures, said: The US and UK are home to a vast number of deep tech startups that have immense growth potential. However, reaching that potential is difficult tech experts and PhD graduates have incredible ideas for how to use new and advanced technologies but often lack the skills and experience to transform them into successful businesses.
Fountech.Ventures will change all this by delivering the commercial expertise and infrastructure that is sorely needed. Whats more, the fact that our members can also access vital funding and our international hubs means we have a unique ability to bring products and services grounded in leading edge technologies to huge markets.
It is this end-to-end offering that makes us more than a typical incubator Fountech.Ventures is a next generation incubator.
Fountech.Ventures already has six portfolio companies. These include Soffos, an AI TutorBot; Prospex, an AI-powered lead generation tool; and Dinabite, a restaurant app built on an AI platform.
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Rebecca Taylor and Joseph McCall have joined the Fountech.Ventures board as directors. The board is to be bolstered further with additional appointments in the coming weeks.
Nikolas Kairinos, CEO and founder of the parent company Fountech.ai, commented: We are delighted to unveil Fountech.Ventures today.
This next gen incubator is going to propel the growth of deep tech startups across both sides of the Atlantic. In doing so, we will enable innovative leading edge tech solutions to thrive and consequently improve the lives of consumers, businesses and societies.
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Tech incubator Fountech.Ventures launches in US and UK - UKTN
Why resilience is the key to future security – Raconteur
Resilience is at the heart of information security. As threats adapt and evolve and we accept that systems will be compromised, it is no longer enough just to have strong defences in place. The sophisticated tools and techniques of threat actors will find a way around them. Organisations, their security architecture, systems, policies and strategies need to be resilient, able to cope, recover and, most of all, to learn from incidents.
Our sector as a whole needs to be resilient; human skills and expertise are at the heart of this. We must attract, recruit and retain the talent and skills to tackle new and emerging risks and challenges. We must also embrace diversity in all its forms to find, nurture and train professionals.
It is the responsibility of every organisation to drive inclusivity and diversity in the industry. We should look beyond the traditional routes into information security and think about other transferable skills and attitudes that can offer so much. These include broader business skills, such as the ability to negotiate, financial acumen and leadership skill, that are increasingly needed as part of a modern-day security team.
It also includes skills from outside the industry, so it is encouraging to see organisations starting to recruit more people from sectors like healthcare, the emergency services, design and gaming.
But resilience goes much further than this. We, as infosecurity professionals, need to be resilient ourselves, developing new skills and, on a personal level, being resilient to the pressures and stress currently facing our industry.
Employee mental health and wellbeing should be an essential consideration for all employers and be part of company culture and organisational values. But perhaps we could do more in an industry that is faced with growing cyberthreats, longer working hours and individuals often having to make up gaps left by under-resourced teams. Its clear from what we are hearing from our community of chief information security officers that infosecurity professionals are under more pressure than ever before.
But with challenges come opportunities. The industry is undergoing a huge transformation as it embraces new and emerging technologies, such as quantum computing, data analytics and artificial intelligence tools, which can play a key role in enhancing the capabilities of security systems to identify and mitigate risks, and ease the pressure on security teams.
As an information and cybersecurity community, we can help to keep our world safe and unlock more of the good things that technology promises and delivers. There is no time like the future and, ultimately, it is in our hands. But this goes beyond just the information security industry and out to a wider group of individuals and organisations.
By working together, companies, governments, industry bodies, academia, suppliers and other stakeholders can share their knowledge and intelligence, learn from each other and get ahead of cybercriminals. This need to collaborate and share knowledge has never been more important as new kinds of threats emerge from new breeds of attackers, and we need to stay one step ahead.
Resilience is our conference theme this year, addressing the most relevant and decisive factors in information and cybersecurity in the next five years.
By building resilience across the industry, we can move towards a more secure world and a more secure future.
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Why resilience is the key to future security - Raconteur
Quantum Computing: Will It Actually Produce Jobs? – Dice Insights
If youre interested in tech, youve likely heard about therace to develop quantum computers. These systems compute via qubits, whichexist not only as ones and zeros (as you find intraditional processors) but also in an in-between state known assuperposition.
For tasks such as cryptography, qubits and superpositionwould allow a quantum computer to analyze every potential solutionsimultaneously, making such systems much faster than conventional computers.Microsoft, Google,IBM, and other firms are all throwing tons of resources into quantum-computingresearch, hoping for a breakthrough that will make them a leader in thisnascent industry.
Questions abound about quantum computing, including whetherthese systems will actually produce the answers that companies really need. Forthose in the tech industry, theres a related interest in whether quantumcomputing will actually produce jobs at scale.
Thelarge tech companies and research laboratories who are leading the charge onR&D in the pure quantum computing hardware space are looking for peoplewith advanced degrees in key STEM fields like physics, math and engineering,said John Prisco, President & CEOof Quantum Xchange, which markets a quantum-safe key distribution thatsupposedly will bridge the gap between traditional encryption solutions andquantum computing-driven security. This is in large part because thereare few programs today that actually offer degrees or specializations inquantum technology.
WhenPrisco was in graduate school, he added, There were four of us in theelectrical engineering program with the kind of physics training this fieldcalls for. More recently, Ive recently seen universities like MIT andColumbia investing in offering this training to current students, but itsgoing to take awhile to produce experts.
Theresevery chance that increased demand for quantum-skilled technologists coulddrive even more universities to spin up the right kind of training andeducation programs. The National Institute of Standards and Technology (NIST)is evaluatingpost-quantum cryptography that would replace existing methods, includingpublic-key RSA encryption methods. Time is of the essence when it comes togovernments and companies coming up with these post-quantum algorithms; thenext evolutions in cryptography will render the current generation pretty muchobsolete.
Combinethat quest with the currentshortage of trained cybersecurity professionals, and you start to see wherethe talent and education crunch will hit over the next several years. Whilehackers weaponizing quantum computers themselves is still a far off proposal,the threat of harvesting attacks, where nefarious actors steal encrypted datanow to decrypt later once quantum computers are available, is already here,Prisco said, pointing at Chinas 2015 hack of the U.S. Office of PersonnelManagement, which saw the theft of 21 million government employee records.
Thoughthat stolen data was encrypted and there is no evidence it has been misused todate, the Chinese government is likely sitting on that trove, waiting for theday they have a quantum computer powerful enough to crack public keyencryption, he said. Organizations that store sensitive data with a longshelf-life need to start preparing now. There is no time to waste.
But what will make a good quantum technologist?
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HermanCollins, CEO of StrategicQC, a recruiting agency for the quantum-computingecosystem, believes that sourcing quantum-related talent at this stage comesdown to credentials. Because advanced quantum expertise is rare, the biggest sign thata candidate is qualified is whether they have a degree in one of the fields ofstudy that relates to quantum computing, he said. I would say that degrees,particularly advanced degrees, such as quantum physics obviously, physicstheory, math or computer science are a good start. A focus on machine learningor artificial intelligence would be excellent as part of an augmented dynamicquantum skill set.
Although Google, IBM, and theU.S. government have infinite amounts of money to throw at talent, smallercompanies are occasionally posting jobs for quantum-computing talent. Collinsthinks that, despite the relative lack of resources, these small companies haveat least a few advantages when it comes to attracting the right kind of veryhighly specialized talent.
Smaller firms and startups canoften speak about the ability to do interesting work that will impactgenerations to come and perhaps some equity participation, he said. Likewise,some applicants may be interested in working with smaller firms to buildquantum-related technology from the ground up. Others might prefer a moreclose-knit team environment that smaller firms may offer.
Some 20 percent of thequantum-related positions, Collins continued, are in marketing, sales,management, tech support, and operations. Even if you havent spent yearsstudying quantum computing, in other words, you can still potentially land ajob at a quantum-computing firm, doing all the things necessary to ensure thatthe overall tech stack keeps operating.
It is equally important forcompanies in industries where quantum can have impactful results in the nearerterm begin to recruit and staff quantum expertise now, Collins said.Companies competing in financial services, aerospace, defense, healthcare,telecommunications, energy, transportation, agriculture and others shouldrecognize the vital importance of looking very closely at quantum and addingsome skilled in-house capability.
Given the amount of money andresearch-hours already invested in quantum computing, aswell as some recent (and somewhat controversial) breakthroughs, theresevery chance the tech industry could see an uptick in demand for jobs relatedto quantum computing. Even for those who dont plan on specializing in thisesoteric field, there may be opportunities to contribute.
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Quantum Computing: Will It Actually Produce Jobs? - Dice Insights