In a move the companies say will secure communications and data for critical infrastructures and government institutions in the country, Airbus, CNRS, Cryptonext Security, Direction Gnrale de lAviation Civile, Orange, Sorbonne Universit, Telecom Paris, Thales, Thales Alenia Space, Universit Cte dAzur, Veriqloud and Welinq have announced the official launch of FranceQCI, a programme to deploy aQuantum Communication Infrastructure (QCI) networks in France.

Co-funded by the European Commission, the FranceQCI project will span 30 months, aiming to build a secure quantum communication network infrastructure across the entire European Union (EU), including its overseas territories. The FranceQCI project is also part of the European Quantum Communication Infrastructure (EuroQCI) initiative launched by the EU in 2019.

EuroQCI is one of the main pillars of the EUs Cybersecurity Strategy for the coming decades, and aims at securing communications and data for critical infrastructures and government institutions. As part of the programme, 26 out of 27 member countries of the EU will have their own scheme to deploy a national QCI network.

FranceQCI comes under the EUs Digital Europe Programme, and is regarded as being able to make a significant contribution to the objective of deploying a secure quantum communication infrastructure for the EU (EuroQCI) in France, paving the way towards the future European Quantum Information Network (QIN).

As a consortium, FranceQCI includes stakeholders with diverse and complementary expertise and resources. Participating in the effort will be large industries that are world leaders in their respective domains, small and medium-sized enterprises with deep and unique expertise, and academic institutions among the best in the world.

In particular, its objective is to deploy advanced national quantum systems and networks to test quantum communication technologies and integrate them into existing communication networks in France. FranceQCI will aim to capitalise on existing infrastructures in the Paris (ParisRegionQCI) and Nice ([emailprotected]/Nice) areas to progress towards operational Quantum Key Distribution (QKD) services and contribute to the development of the European technological autonomy.

France QCI also represents a first step towards a global European quantum communication infrastructure that could be implemented with cross-border links to connect similar networks in other countries, either through terrestrial fibre links or space connectivity.

Airbus Defense and Space, Orange, Thales, and Thales Alenia Space will be FranceQCIs leading industrial partners. Key players in the field of Quantum Communication and post-quantum cryptography namely Cryptonext Security, VeriQloud and WeLinQ will also offer technological support. The French civil aviation authority that provides air traffic control service, Direction des Services de la Navigation Arienne (DSNA), has joined the project as a public institution to allow the consortium to test realistic use cases.

The notable presence of academic institutions is said to be able to allow the development of the educational purpose of the call and build a training environment for all stakeholders in France, including research staff, engineers, and users from public and private entities.

Leading research organisation and academic institutions, namely the Centre National de la Recherche Scientifique, Sorbonne University, Universit Cte dAzur and Tlcom Paris, will bring the consortium their expertise and research capabilities. A quantum network will also be implemented in Toulouse (in DGAC/DSNA/DTI lab) to test a real user service for the French Civil Aviation Authority. It will consist of exchanging simulated operational air traffic control data secured by QKD.

Orange has been appointed coordinator of the FranceQCI consortium as the sole telco operator, and will contribute its expertise in network deployment and integration.

In October 2022, Orange presented, for the first time in France, encrypted video streaming over itsfibre network using quantum key distribution. This system is designed to use cryptographic techniques to ensure the confidentiality of communications and make any intrusion by a hacker immediately detectable during the key exchange process. Having demonstrated the technological capabilities of the setup, Orange noted that potential customers it was talking to include those in banking and defence, with the former likely to be first for a proof of concept.

The FranceQCI consortiums objective is to drive a significant impulse towards a European quantum communication infrastructure that will be able to safeguard sensitive data and critical communications for governmental institutions, data centres, hospitals, energy grids, and more, said Michal Trabbia, executive vice-president and CEO of Orange Wholesale.

We are delighted to benefit from fundings from the European Union through the Digital Europe Program to contribute to one of the main pillars of the EUs cyber security strategy.

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Orange joins forces with industry, academia to build French ... - ComputerWeekly.com

Imagine a gigantic brick of highly compressed dirt -- as big as a pickup truck but, at 24 tons, about five times heavier. An elevator powered by solar panels or wind turbines hoists it over 300 feet up the side of a huge building, and a trolley stows it inside. But it's not alone. An automated system lifts and stores hundreds more bricks, like giant Pez candies, as the sun shines and the wind blows.

Now imagine the building's control system lowering those hundreds of bricks one by one, spinning electrical power generators in the process. They drop down every evening just as demand for power peaks but solar panel output fades away.

In effect, the brick-filled building is a giant battery that stores energy with gravity instead of chemistry.

Gravity batteries are a potentially important solution to a critical problem with the green energy revolution: making sure electricity is available when we need it, not just during the times when sun and wind supply it.

And it isn't just an idea. With two sites under construction one in Rudong, China, just north of Shanghai, and the other in Snyder, Texas, about 250 miles west of Dallas startup Energy Vault will begin seriously testing the viability of the gravity storage technology. An earlier pilot generated 5 megawatts of power, but these two facilities and expected successors will show whether gravity storage is economical and efficient enough to work at large scale.

You may think putting solar panels on your roof will help fight climate change, but without some form of energy storage in your home or on the grid, you'll likely rely on carbon dioxide-spewing gas and coal power plants to run your home's lights, TV and dishwasher as the sun sets.

Energy Vault tested its technology at a smaller scale in Switzerland, where the 170-person company is headquartered. Its two EVx systems under construction are much bigger. The Chinese system, built for waste management and recycling company China Tianying, is in a 400-foot-tall building and will have an energy storage capacity of 100 megawatt-hours. That's enough to power 3,400 homes for an entire day, and the system should be complete by June. The Texas system, in a 460-foot-tall (but narrower) building, will provide power company Enel with 36MWh of capacity.

Solar panels and wind turbines now generate power more cheaply than coal and natural gas plants, making them a clear choice in the push to replace fossil fuels. Solar power costs dropped 83% from 2009 to 2023 and wind costs dropped 63% over the same period, according to tracking from investment advisory and asset management firm Lazard. But in many parts of the country, new solar panels often just supply a glut of power during the middle of the day without helping in the evenings.

The mismatch between power production and power usage is responsible for the infamous "duck curve," a graph with a birdlike shape showing the disparity increasing with each passing year. That disparity makes it hard for utilities to adjust to rapid changes in demand and means fossil fuel powered sources like natural gas "peaker plants" supply power in the evenings.

"If we're ever going to wean ourselves more and more off of fossil fuels and replace that with renewable generation that's intermittent, the only way to solve that is storage," said Energy Vault Chief Executive Robert Piconi.

Energy Vault isn't alone. Another startup, Gravitricity, has built a 250-kilowatt demonstration system and is planning a larger 4MW to 8MW system in an unused mine shaft.

Gravity storage is just one way to smooth out the spurts of wind and solar. Big batteries like Tesla Megapacks industrial-scale versions of the same batteries that power your phone or electric vehicle are increasingly common on the power grid. "Pumped hydro," an older form of gravity battery that pumps water to an uphill reservoir then generates power when it flows back downhill, has been used for decades but is attracting new interest. Other methods include filling underground chambers with compressed air, storing hydrogen that later powers fuel cells, and developing different types of batteries, like zinc batteries and flow batteries.

Energy Vault's EVx system hoists these 24-ton bricks up hundreds of feet to then recapture that potential energy by lowering them when power is needed. The bricks are made of compressed dirt with a polymer matrix and are shuttled within the system using a trolley setup beneath the bricks.

Cost will be a major factor in determining what storage technology prevails, including initial manufacturing and continuing operations.

"At the end of the day it will all come down to price," said Selene Law, an energy analyst at consulting firm Cleantech Group. And for gravity storage, questions about total cost persist, she said.

Indeed, a 2022 US Department of Energy study concluded that gravity energy storage is relatively expensive in smaller installations. Where it's most economical is in high-capacity systems that generate power for relatively long periods of time 10 hours or more.

Energy Vault hasn't disclosed the cost of the two systems under construction, but it agrees the technology offers advantages for long-duration power needs.

Longevity is a cost factor over the lifetime of the plant. Batteries lose capacity with use, the same way your phone doesn't run as long after a couple of years of ownership, but gravity storage components, like pulleys and generators, can be maintained.

"The key of our value proposition is the lack of degradation of the storage medium," said Marco Terruzzin, a mechanical engineer and Energy Vault's chief commercial product officer. "We provide a guarantee on the system for at least 35 years."

Though Energy Vault has taken only its first steps in proving the technology's value, two customers have concluded it's worth paying for today. And the 100MWh system in China is only the first there, the company said. The country expects to fund another 4GWh to 6GWh of capacity later an increase of 40 to 60 times the initial plant.

Energy Vault's gravity EVx storage system is a giant rectangular building that largely runs automatically. Here's how it works.

The bricks at the heart of the system each measure 3.5 by 2.7 by 1.3 meters (about 11 by 9 by 4 feet) and weigh 24 metric tons. They're made of 99% compressed dirt with some water and a polymer mixture to stabilize it using a recipe from Mexican building materials company Cemex. They're 2.4 times denser than water and about the same as concrete.

Energy Vault's first large-scale gravity-based energy storage system in Rudong, China, is hundreds of feet tall.

The bricks are stored side by side within the building, like dominoes jammed together. Before they're raised or lowered, a trolley system hefts each brick and trundles it to the elevator.

Bricks are housed on the top eight levels of the building to store energy and drop down to the corresponding lower eight levels to generate power. Each brick, descending at 1.9 meters per second (6.23 feet), turns out about a megawatt, Terruzzin said. That's about enough to power 2,000 refrigerators.

Once each brick reaches the bottom, it's robotically transferred to the trolley system and moved toward the center of the building.

Making the building longer, with longer trolley tracks, means more bricks can be stored for more hours of power generation. Making the building wider, with more elevators, means the system can generate a higher peak power rate.

As for efficiency, Energy Vault guarantees EVx systems will generate at least 80% of the energy required to lift the bricks and scoot them around, including factors like friction. That overall efficiency is comparable to pumped hydro.

Key to Energy Vault's business is the control system that decides when and where to position the bricks for optimal storage and even energy output.

"While we're decelerating one at the bottom, the next one is loading and beginning to accelerate," said Bill Gross, Idealabs chief executive and Energy Vault co-founder, speaking at the Techonomy Climate conference in March.

The maximum output will be 25MW at the China system and 18MW at the Texas system.

Energy Vault settled on its current design after evaluating several other options gravel in carts, water in tanks, concrete blocks hanging from cranes.

The EVx is designed to overcome problems with those designs. It's weatherproof, which means bricks don't get wet or blown around, for example.

Energy Vault isn't putting all its energy storage eggs in the gravity basket. A contract with California's Pacific Gas and Electric, could result in a system to power the city of Calistoga for up to 48 hours with a combination of hydrogen fuel cells and batteries. Hybrid systems with gravity and battery storage also make sense, since batteries can respond very rapidly to increase or decrease, Terruzzin said.

With Energy Vault's plants headed into production, we should start getting a better idea this year how well gravity storage really works. "This will be a crucial year for Energy Vault," Cleantech Group's Law said. "The proof will be in the pudding."

The company isn't profitable, reporting a net loss of $78 million for 2022 on revenue of $146 million. And its stock has slumped by 89% over the last year, a fate many startups suffered with economic troubles and skeptical investors.

Energy Vault's Piconi is convinced the company is on the right path toward making energy storage more economical, though.

"Wind and solar are so much cheaper than fossil fuels now... The problem is storage is typically 10 to 15 times that," Piconi said. "We have to get there."

Correction, 9:26 a.m. PT: A previous version of this story misstated Energy Vault's 2022 financial results. It reported a net loss of $78 million on revenue of $146 million.

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How Hundreds of 24-Ton Bricks Could Fix a Huge Renewable ... - CNET

For research institutions, a solid IT foundation can prove to be the difference in delivering meaningful results for scientific endeavors and thereby in securing valuable funding for further research.

To that end, University of California, Riverside has launched an ambitious cloud transformation to shift from a small on-premises data center to an advanced research platform powered by Google Cloud Platform and its various service offerings.

As part of a three-year partnership with Google Public Sector, which kicked off in January, UC Riverside aims to empower its researchers in computer science, materials and quantum engineering, genomics, and precision agriculture to fully exploit Googles location-agnostic application modernization platform, as well as its scalable compute and high performance computing (HPC) capabilities, says Matthew Gunkel, CIO of IT solutions at UCR.

Gunkel enlisted Google Public Sector professional services specifically as part of a strategy to quickly evolve UC Riversides small data center into an advanced cloud hub with robust research computing capabilities that would enable researchers to better compete for grants and funding opportunities.

We identified Google as being well aligned with us strategically, says Gunkel. They have an agile infrastructure. They have the ability to facilitate industry-leading service concepts in additional clouds through a service they run called Anthos.

Googles Anthos is a hybrid cloud container platform for managing Kubernetes workloads across on-prem and public cloud environments. Gunkel also cited Googles Looker and Big Query BI data analysis tools and its Chronicle security operations suite as important for enabling the university to operate a wide variety of applications and research on the cloud.

With roughly 180 staff members, UC Riverside IT is relatively small, with largely traditional on-premises IT skills. As such, migrating to the cloud alone was not part of Gunkels plan.

Googles assistance in developing a more efficient cloud architecture and training UCRs IT staff in cloud technologies has been an immeasurably valuable service, he says, adding that Google is in a support role and is not running the show. UCRs cloud architecture, for example, has been designed to be location-agnostic so the university is not locked into any one vendor and can adopt a multicloud platform over the long term.

The services engagement is consulting and training to assist us in moving initial cloud workloads and to assist in our architecture to align to GCP services, Gunkel says. This is a teach us to fish model. Its all our work.

UC Riverside IT is well on its way to migrating its core data to the cloud, developing its research platform, and shifting a range of applications to support the needs of its user base, which ranges from quantum engineering researchers to administrators, faculty, and students.

To date, UCR has moved the vast majority of our data stores to Google, Gunkel says, noting that his staff is currently refining the architecture and ETL processes for management and organization of the data long term.

In addition, UC Riverside IT is aligning its data to be accessed from Looker, Googles enterprise BI and analytics platform, though which UCR will be deploying its Oracle Finance application for scaled reporting. UC Riverside is also rewriting a number of legacy applications to be cloud-native while revamping others for the cloud there will be no lift and shift of any applications, Gunkel says.

To that end, Google helped UC Riverside re-architect and migrate certain legacy services, including an LDAP configuration on a Solaris Unix server, as part of a process of identifying increased efficiencies for the deployment and operation of those services, which has been an educational experience for a lot of my staff, Gunkel says, noting that the overall transformation has required cultural change management.

But the universitys evolving research hub is the crown jewel of the cloud migration.

We have been working with a number of researchers on a platform that we are calling Ursa Major where we committed to a number of compute instances and storage and RAM and GPUs that would be available to our researchers over a three-year time period, Gunkel says.

Jim Kennedy, CTO of UC Riverside, says Google is helping architect the research hub and is also helping the IT chiefs make connections with researchers beyond UCR to help train UCRs research faculty on Ursa Major, which will expand and grow beyond the three-year agreement with Google.

Google connects us to experts in various research fields, and have conversations with our faculty directly, such as our genomics researcher on campus. There are experts on Googles side, too, Kennedy says.

Google also helped the Gunkel and Kennedy extend the universitys subscription-based compute and storage services to researchers in a multitude of disciplines. In the past, if a materials engineering researcher wanted to run workloads on several thousand processors, they would often have to write proposals to gain access to external supercomputer clusters.

With HPC requiring vast computing power, Gunkel also notes the benefit for efficiency and sustainability of shifting those workloads to the cloud. Were in a fairly constrained region against mountains and our ability to bring power into the university is something were constantly battling, Gunkel says. One of the things our researchers were very concerned about was [building] a sustainable, more eco-friendly solution. Its something UCR values heavily but its also a challenge for us locally.

Still, the migration, still in its early days, is being designed to accommodate a wide range of computing constituencies. For instance, UCR is also using Salesforce and MuleSoft as well as Googles API layer to provide the connective tissue that is required across the universitys many enterprise platforms.

The best way to think of the university is really as a collection or community of small businesses, Gunkel says. A lot of what we try to provide on the service stack side are tools that empower all of them in their different endeavors.

See more here:

UC Riverside turns to cloud to supercharge scientific research - CIO

Let's look at example that shows how quantum computers can succeed where classical computers fail:

A supercomputer might be great at difficult tasks like sorting through a big database of protein sequences. But it will struggle to see the subtle patterns in that data that determine how those proteins behave.

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. In the case of a protein folding problem, that pattern might be the combination of folds requiring the least energy to produce. That combination of folds is the solution to the problem.

Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

How complexity stumps supercomputers

Proteins are long strings of amino acids that become useful biological machines when they fold into complex shapes. Figuring out how proteins will fold is a problem with important implications for biology and medicine.

A classical supercomputer might try to fold a protein with brute force, leveraging its many processors to check every possible way of bending the chemical chain before arriving at an answer. But as the protein sequences get longer and more complex, the supercomputer stalls. A chain of 100 amino acids could theoretically fold in any one of many trillions of ways. No computer has the working memory to handle all the possible combinations of individual folds.

Quantum computers are built for complexityQuantum algorithms take a new approach to these sorts of complex problems -- creating multidimensional spaces where the patterns linking individual data points emerge. Classical computers can not create these computational spaces, so they can not find these patterns. In the case of proteins, there are already early quantum algorithms that can find folding patterns in entirely new, more efficient ways, without the laborious checking procedures of classical computers. As quantum hardware scales and these algorithms advance, they could tackle protein folding problems too complex for any supercomputer.

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What is Quantum Computing? | IBM

by YK Sugi

Hi everyone!

The other day, I visited D-Wave Systems in Vancouver, Canada. Its a company that makes cutting-edge quantum computers.

I got to learn a lot about quantum computers there, so Id like to share some of what I learned there with you in this article.

The goal of this article is to give you an accurate intuition of what a quantum computer is using a simple example.

This article will not require you to have prior knowledge of either quantum physics or computer science to be able to understand it.

Okay, lets get started.

Edit (Feb 26, 2019): I recently published a video about the same topic on my YouTube channel. I would recommend watching it (click here) before or after reading this article because I have added some additional, more nuanced arguments in the video.

Here is a one-sentence summary of what a quantum computer is:

There is a lot to unpack in this sentence, so let me walk you through what it is exactly using a simple example.

To explain what a quantum computer is, Ill need to first explain a little bit about regular (non-quantum) computers.

Now, a regular computer stores information in a series of 0s and 1s.

Different kinds of information, such as numbers, text, and images can be represented this way.

Each unit in this series of 0s and 1s is called a bit. So, a bit can be set to either 0 or 1.

A quantum computer does not use bits to store information. Instead, it uses something called qubits.

Each qubit can not only be set to 1 or 0, but it can also be set to 1 and 0. But what does that mean exactly?

Let me explain this with a simple example. This is going to be a somewhat artificial example. But its still going to be helpful in understanding how quantum computers work.

Now, suppose youre running a travel agency, and you need to move a group of people from one location to another.

To keep this simple, lets say that you need to move only 3 people for now Alice, Becky, and Chris.

And suppose that you have booked 2 taxis for this purpose, and you want to figure out who gets into which taxi.

Also, suppose here that youre given information about whos friends with who, and whos enemies with who.

Here, lets say that:

And suppose that your goal here is to divide this group of 3 people into the two taxis to achieve the following two objectives:

Okay, so this is the basic premise of this problem. Lets first think about how we would solve this problem using a regular computer.

To solve this problem with a regular, non-quantum computer, youll need first to figure out how to store the relevant information with bits.

Lets label the two taxis Taxi #1 and Taxi #0.

Then, you can represent who gets into which car with 3 bits.

For example, we can set the three bits to 0, 0, and 1 to represent:

Since there are two choices for each person, there are 2*2*2 = 8 ways to divide this group of people into two cars.

Heres a list of all possible configurations:

A | B | C0 | 0 | 00 | 0 | 10 | 1 | 00 | 1 | 11 | 0 | 01 | 0 | 11 | 1 | 01 | 1 | 1

Using 3 bits, you can represent any one of these combinations.

Now, using a regular computer, how would we determine which configuration is the best solution?

To do this, lets define how we can compute the score for each configuration. This score will represent the extent to which each solution achieves the two objectives I mentioned earlier:

Lets simply define our score as follows:

(the score of a given configuration) = (# friend pairs sharing the same car) - (# enemy pairs sharing the same car)

For example, suppose that Alice, Becky, and Chris all get into Taxi #1. With three bits, this can be expressed as 111.

In this case, there is only one friend pair sharing the same car Alice and Becky.

However, there are two enemy pairs sharing the same car Alice and Chris, and Becky and Chris.

So, the total score of this configuration is 1-2 = -1.

With all of this setup, we can finally go about solving this problem.

With a regular computer, to find the best configuration, youll need to essentially go through all configurations to see which one achieves the highest score.

So, you can think about constructing a table like this:

A | B | C | Score0 | 0 | 0 | -10 | 0 | 1 | 1 <- one of the best solutions0 | 1 | 0 | -10 | 1 | 1 | -11 | 0 | 0 | -11 | 0 | 1 | -11 | 1 | 0 | 1 <- the other best solution1 | 1 | 1 | -1

As you can see, there are two correct solutions here 001 and 110, both achieving the score of 1.

This problem is fairly simple. It quickly becomes too difficult to solve with a regular computer as we increase the number of people in this problem.

We saw that with 3 people, we need to go through 8 possible configurations.

What if there are 4 people? In that case, well need to go through 2*2*2*2 = 16 configurations.

With n people, well need to go through (2 to the power of n) configurations to find the best solution.

So, if there are 100 people, well need to go through:

This is simply impossible to solve with a regular computer.

How would we go about solving this problem with a quantum computer?

To think about that, lets go back to the case of dividing 3 people into two taxis.

As we saw earlier, there were 8 possible solutions to this problem:

A | B | C0 | 0 | 00 | 0 | 10 | 1 | 00 | 1 | 11 | 0 | 01 | 0 | 11 | 1 | 01 | 1 | 1

With a regular computer, using 3 bits, we were able to represent only one of these solutions at a time for example, 001.

However, with a quantum computer, using 3 qubits, we can represent all 8 of these solutions at the same time.

There are debates as to what it means exactly, but heres the way I think about it.

First, examine the first qubit out of these 3 qubits. When you set it to both 0 and 1, its sort of like creating two parallel worlds. (Yes, its strange, but just follow along here.)

In one of those parallel worlds, the qubit is set to 0. In the other one, its set to 1.

Now, what if you set the second qubit to 0 and 1, too? Then, its sort of like creating 4 parallel worlds.

In the first world, the two qubits are set to 00. In the second one, they are 01. In the third one, they are 10. In the fourth one, they are 11.

Similarly, if you set all three qubits to both 0 and 1, youd be creating 8 parallel worlds 000, 001, 010, 011, 100, 101, 110, and 111.

This is a strange way to think, but it is one of the correct ways to interpret how the qubits behave in the real world.

Now, when you apply some sort of computation on these three qubits, you are actually applying the same computation in all of those 8 parallel worlds at the same time.

So, instead of going through each of those potential solutions sequentially, we can compute the scores of all solutions at the same time.

With this particular example, in theory, your quantum computer would be able to find one of the best solutions in a few milliseconds. Again, thats 001 or 110 as we saw earlier:

A | B | C | Score0 | 0 | 0 | -10 | 0 | 1 | 1 <- one of the best solutions0 | 1 | 0 | -10 | 1 | 1 | -11 | 0 | 0 | -11 | 0 | 1 | -11 | 1 | 0 | 1 <- the other best solution1 | 1 | 1 | -1

In reality, to solve this problem, you would need to give your quantum computer two things:

Given these two things, your quantum computer will spit out one of the best solutions in a few milliseconds. In this case, thats 001 or 110 with a score of 1.

Now, in theory, a quantum computer is able to find one of the best solutions every time it runs.

However, in reality, there are errors when running a quantum computer. So, instead of finding the best solution, it might find the second-best solution, the third best solution, and so on.

These errors become more prominent as the problem becomes more and more complex.

So, in practice, you will probably want to run the same operation on a quantum computer dozens of times or hundreds of times. Then pick the best result out of the many results you get.

Even with the errors I mentioned, the quantum computer does not have the same scaling issue a regular computer suffers from.

When there are 3 people we need to divide into two cars, the number of operations we need to perform on a quantum computer is 1. This is because a quantum computer computes the score of all configurations at the same time.

When there are 4 people, the number of operations is still 1.

When there are 100 people, the number of operations is still 1. With a single operation, a quantum computer computes the scores of all 2 ~= 10 = one million million million million million configurations at the same time.

As I mentioned earlier, in practice, its probably best to run your quantum computer dozens of times or hundreds of times and pick the best result out of the many results you get.

However, its still much better than running the same problem on a regular computer and having to repeat the same type of computation one million million million million million times.

Special thanks to everyone at D-Wave Systems for patiently explaining all of this to me.

D-Wave recently launched a cloud environment for interacting with a quantum computer.

If youre a developer and would like actually to try using a quantum computer, its probably the easiest way to do so.

Its called Leap, and its at https://cloud.dwavesys.com/leap. You can use it for free to solve thousands of problems, and they also have easy-to-follow tutorials on getting started with quantum computers once you sign up.

Footnote:

See original here:

What is a quantum computer? Explained with a simple example.

- Delivers 127 qubits on a single IBM quantum processor for the first time with breakthrough packaging technology

- New processor furthers IBM's industry-leading roadmaps for advancing the performance of its quantum systems

- Previews design for IBM Quantum System Two, a next generation quantum system to house future quantum processors

Nov 16, 2021

ARMONK, N.Y., Nov. 16, 2021 /PRNewswire/ --IBM (NYSE: IBM) today announced its new 127-quantum bit (qubit) 'Eagle' processor at the IBM Quantum Summit 2021, its annual event to showcase milestones in quantum hardware, software, and the growth of the quantum ecosystem. The 'Eagle' processor is a breakthrough in tapping into the massive computing potential of devices based on quantum physics. It heralds the point in hardware development where quantum circuits cannot be reliably simulated exactly on a classical computer. IBM also previewed plans for IBM Quantum System Two, the next generation of quantum systems.

Quantum computing taps into the fundamental quantum nature of matter at subatomic levels to offer the possibility of vastly increased computing power. The fundamental computational unit of quantum computing is the quantum circuit, an arrangement of qubits into quantum gates and measurements. The more qubits a quantum processor possesses, the more complex and valuable the quantum circuits that it can run.

IBM recently debuted detailed roadmaps for quantum computing, including a path for scaling quantum hardwareto enable complex quantum circuits to reach Quantum Advantage, the point at which quantum systems can meaningfully outperform their classical counterpoints. Eagle is the latest step along this scaling path.

IBM measures progress in quantum computing hardware through three performance attributes: Scale, Quality and Speed. Scale is measured in the number of qubits on a quantum processor and determines how large of a quantum circuit can be run. Quality is measured by Quantum Volume and describes how accurately quantum circuits run on a real quantum device. Speed is measured by CLOPS(Circuit Layer Operations Per Second), a metric IBM introduced in November 2021, and captures the feasibility of running real calculations composed of a large number of quantum circuits.

127-qubit Eagle processor

'Eagle' is IBM's first quantum processor developed and deployed to contain more than 100 operational and connected qubits. It follows IBM's 65-qubit 'Hummingbird' processor unveiled in 2020 and the 27-qubit 'Falcon' processor unveiled in 2019. To achieve this breakthrough, IBM researchers built on innovations pioneered within its existing quantum processors, such as a qubit arrangement design to reduce errors and an architecture to reduce the number of necessary components. The new techniques leveraged within Eagle place control wiring on multiple physical levels within the processor while keeping the qubits on a single layer, which enables a significant increase in qubits.

The increased qubit count will allow users to explore problems at a new level of complexity when undertaking experiments and running applications, such as optimizing machine learning or modeling new molecules and materials for use in areas spanning from the energy industry to the drug discovery process. 'Eagle' is the first IBM quantum processor whose scale makes it impossible for a classical computer to reliably simulate. In fact, the number of classical bits necessary to represent a state on the 127-qubit processor exceeds the total number of atoms in the more than 7.5 billion people alive today.

"The arrival of the 'Eagle' processor is a major step towards the day when quantum computers can outperform classical computers for useful applications," said Dr. Daro Gil, Senior Vice President, IBM and Director of Research. "Quantum computing has the power to transform nearly every sector and help us tackle the biggest problems of our time. This is why IBM continues to rapidly innovate quantum hardware and software design, building ways for quantum and classical workloads to empower each other, and create a global ecosystem that is imperative to the growth of a quantum industry."

The first 'Eagle' processor is available as an exploratory device on the IBM Cloud to select members of the IBM Quantum Network.

For a more technical description of the 'Eagle' processor, read this blog.

IBM Quantum System Two

In 2019, IBM unveiled IBM Quantum System One, the world's first integrated quantum computing system. Since then, IBM has deployed these systems as the foundation of its cloud-based IBM Quantum services in the United States, as well as in Germany for Fraunhofer-Gesellschaft, Germany's leading scientific research institution, in Japan for the University of Tokyo, and a forthcoming system in the U.S. at Cleveland Clinic. In addition, we announced today a new partnership with Yonsei University in Seoul, South Korea, to deploy the first IBM quantum system in the country. For more details, click here.

As IBM continues scaling its processors, they are expected to mature beyond the infrastructure of IBM Quantum System One. Therefore, we're excited to unveil a concept for the future of quantum computing systems: IBM Quantum System Two. IBM Quantum System Two is designed to work with IBM's future 433-qubit and 1,121 qubit processors.

"IBM Quantum System Two offers a glimpse into the future quantum computing datacenter, where modularity and flexibility of system infrastructure will be key towards continued scaling," said Dr. Jay Gambetta, IBM Fellow and VP of Quantum Computing. "System Two draws on IBM's long heritage in both quantum and classical computing, bringing in new innovations at every level of the technology stack."

Central to IBM Quantum System Two is the concept of modularity. As IBM progresses along its hardware roadmap and builds processors with larger qubit counts, it is vital that the control hardware has the flexibility and resources necessary to scale. These resources include control electronics, which allow users to manipulate the qubits, and cryogenic cooling, which keeps the qubits at a temperature low enough for their quantum properties to manifest.

IBM Quantum System Two's design will incorporate a new generation of scalable qubit control electronics together with higher-density cryogenic components and cabling. Furthermore, IBM Quantum System Two introduces a new cryogenic platform, designed in conjunction with Bluefors, featuring a novel, innovative structural design to maximize space for the support hardware required by larger processors while ensuring that engineers can easily access and service the hardware.

In addition, the new design brings the possibility to provide a larger shared cryogenic work-space ultimately leading to the potential linking of multiple quantum processors. The prototype IBM Quantum System Two is expected to be up and running in 2023.

Statements regarding IBM's future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.

About IBMFor more information, visit: https://research.ibm.com/quantum-computing.

ContactHugh CollinsIBM Research CommunicationsHughdcollins@ibm.com

Kortney EasterlyIBM Research CommunicationsKortney.Easterly@ibm.com

SOURCE IBM

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IBM Unveils Breakthrough 127-Qubit Quantum Processor

Another record has been broken on the way to fully operational and capable quantum computers: the complete control of a 6-qubit quantum processor in silicon.

Researchers are calling it "a major stepping stone" for the technology.

Qubits (or quantum bits) are the quantum equivalents of classical computing bits, only they can potentially process much more information. Thanks to quantum physics, they can be in two states at once, rather than just a single 1 or 0.

The difficulty is in getting a lot of qubits to behave as we need them to, which is why this jump to six is important. Being able to operate them in silicon the same material used in today's electronic devices makes the technology potentially more viable.

"The quantum computing challenge today consists of two parts," says quantum computing researcher Stephan Philips from the Delft University of Technology in the Netherlands. "Developing qubits that are of good enough quality, and developing an architecture that allows one to build large systems of qubits."

"Our work fits into both categories. And since the overall goal of building a quantum computer is an enormous effort, I think it is fair to say we have made a contribution in the right direction."

The qubits are made from individual electrons fixed in a row, 90 nanometers apart (a human hair is around 75,000 nanometers in diameter). This line of 'quantum dots' is placed in silicon, using a structure similar to the transistors used in standard processors.

By making careful improvements to the way the electrons were prepared, managed, and monitored, the team was able to successfully control their spin the quantum mechanical property that enables the qubit state.

The researchers were also able to create logic gates and entangle systems of two or three electrons, on demand, with low error rates.

Researchers used microwave radiation, magnetic fields, and electric potentials to control and read electron spin, operating them as qubits, and getting them to interact with each other as required.

"In this research, we push the envelope of the number of qubits in silicon, and achieve high initialization fidelities, high readout fidelities, high single-qubit gate fidelities, and high two-qubit state fidelities," says electrical engineer Lieven Vandersypen, also from the Delft University of Technology.

"What really stands out though is that we demonstrate all these characteristics together in one single experiment on a record number of qubits."

Up until this point, only 3-qubit processors have been successfully built in silicon and controlled up to the necessary level of quality so we're talking about a major step forward in terms of what's possible in this type of qubit.

There are different ways of building qubits including on superconductors, where many more qubits have been operated together and scientists are still figuring out the method that might be the best way forward.

The advantage of silicon is that the manufacturing and supply chains are all already in place, meaning the transition from a scientific laboratory to an actual machine should be more straightforward. Work continues to keep pushing the qubit record even higher.

"With careful engineering, it is possible to increase the silicon spin qubit count while keeping the same precision as for single qubits," says electrical engineer Mateusz Madzik from the Delft University of Technology.

"The key building block developed in this research could be used to add even more qubits in the next iterations of study."

The research has been published in Nature.

View original post here:

There's a New Quantum Computing Record: Control of a 6-Qubit Processor in Silicon - ScienceAlert

Nearly all schools have computer-based classes, but many dont offer even foundational classes on programming, let alone advanced computing.

A 2022 study by the Code.org Advocacy Coalition found that 53.4% of Ohio high school students attend a school that offers foundational computer science classes such as basic programming. However, only 22% of urban school districts offered foundational computer science courses compared to 57% of suburban schools.

In 2019, Ohio was ranked 37th among all 50 states in the number of college computer science graduates, as a percentage of total college graduates at all levels (Kentucky was ranked 1st), and 44th in growth in number of computer science graduates over five years, according to data from the U.S. Census Bureau.

Ohio updates curriculum

Ohio recently invested heavily in changing this. Last month, the Ohio State Board of Education approved an updated Model Curriculum for Computer Science. The 400 pages of guidance for local districts recommends students as early as kindergarten learning to protect passwords and understand the basics of artificial intelligence, and high schoolers using cybersecurity concepts like cluster computing and quantum key distribution.

The change represents a dramatic update from previous educational standards, initiated by the state last year. Ohio currently has over 20,000 open computer science positions, said Bryan Stewart, workforce director at the Montgomery County Educational Service Center. As Ohio prepares to welcome tech manufacturing giants like Intel, that gap may get worse.

Thats a question that we play with when we look at the future of Ohios workforce, Stewart said. We have to ask ourselves, Will Dayton, will the Miami Valley be a haven for startups? Will we see tech companies born out of the minds of our kids? If we want that to be a reality, if we want venture capital to speed into Ohio, you cant do that unless you teach kids about computer science.

Stebbins High School in the Mad River School District takes a different approach. Many classes through the schools Career Technology Program incorporate computer science in a tangential way, such as engineering and robotics, or graphic design and digital media. Students learn to work with several systems, such as SolidWorks, AutoCAD, and Adobe Photoshop, said Career Tech Director and Assistant Principal Jeff Berk.

We also have career tech courses at our middle school, Berk said, adding that the state of Ohio supports career tech education. We are able to stay up to industry standards within all of our programs, and making sure our students are prepared, and what theyre going to see (in the workplace), they had the chance to see it here.

In recent years, Mad River discontinued a cybersecurity career path based on lack of enrollment and student interest, Berk said, in favor of a Teacher Academy. However, juniors and seniors can also participate in the Tech Prep program, where students do hands-on IT work throughout the building, troubleshooting everything from printers to student laptops.

Obstacles to improvement

Improving computer science education faces several hurdles. One issue governments have grappled with is that the field evolves so quickly that its difficult for educators to keep up, even at the local level.

I think we do the best we can. But computer science changes so quickly. Its not like math where algebra is the same now as it was 100 years ago, Schultz said. Now weve got standard things like quantum computing and artificial intelligence and machine learning, things that werent even spoken of five years ago. So its tough for schools, tough for anybody with a limited budget, to try and stay on top of that.

The State Committee on Computer Science, formed by this years state budget, outlined 10 recommendations in August that, if implemented, would help make Ohio a national leader in computer science education and workforce pipeline, state officials said. Among these include a commitment by the state to fund computer science courses at 1% of the K-12 funding formula, about $94 million today, in future years, as well as making a single credit computer science course a high school graduation requirement.

Funding is important because hardware that educators have access to sometimes lags behind what is used in the industry, Berk said.

A lot of times in education, the access to technology that students have sometimes is outdated, he said. Thats one of the major challenges. Especially in high school, when they go out into to the workforce, that theyre having that opportunity to work with machines and computers that are going to be at the same level

Finding teachers is also huge problem, as often individuals who are qualified to teach the next generation about computer science have no financial incentive to do so.

The majority of them realize that they can go out and find a job in the industry and make double what they would make as a teacher, said Schultz.

Minorities, girls lag

To address teacher shortages, the state committee recommended Teach CS grants that fund training for teachers to obtain computer science licensure, and establishing an Office of Computer Science to support the over 600 Ohio school districts in implementing their own computer science programs.

Stebbins Teacher Academy was created both to address the teacher shortage in the general K-12 sphere and supply a program that matched students interests, Berk said.

Were doing what we can do to help supply the region with the workers that we need for all the different professions, he said.

The states Model Curriculum also includes provisions for equitable access to computer science education. Schools in lower-income neighborhoods and schools with large numbers of minority students often offer only rudimentary user skills rather than problem-solving and computational thinking, according to the curriculum.

Among students who took the Advanced Placement Computer Science exam in 2020, only 6% of students were Black or African American, 16% were Hispanic or Latino and 0.5% were Native American, according to data from the College Board, which administers AP tests.

Female students are also underrepresented in high school computer science classes, accounting for just 34% of AP Computer Science Principles participants and 25% of AP Computer Science A participants, per College Board data. During the 2020-21 school year, female students accounted for only 27% of over 3,700 AP Computer Science exams taken in Ohio.

In order to reach female and minority students, the state board recommends using examples that are equally relevant to both males and females, and tying problems to students everyday lives.

Particularly for young learners and beginners, visual, block-based programming languages help address language and syntax barriers, according to state documents.

Getting more girls and minority students into coding is useful, not just for creating a diverse workforce, but for addressing the huge need for computer-savvy people in todays industry. After-school programs like Girls Who Code also are working to bridge this gap, but the model curriculum aims to tackle these problems inside the classroom.

Private sector companies, the industry side of things, they really want to see a more diverse workforce. But theyre never going to have them unless we start earlier and try to start breaking down some of these barriers or perceptions, Stewart said.

Original post:

Schools get creative with computer science teaching as Ohios state standards try to keep with the times - Dayton Daily News

The idea of using the laws of quantum mechanics for computation was proposed in 1982 by Richard Feynman. But Deutschwho is at the University of Oxford, UKis often credited with establishing the conceptual foundations of the discipline. Computer bits that obey quantum principles, such as superposition and entanglement, can carry out some calculations much faster and more efficiently than ones that obey classical rules. In 1985 Deutsch postulated that a device made from such quantum bits (qubits) could be made universal, meaning it could simulate any quantum system. Deutsch framed his proposal in the context of the many worlds interpretation of quantum mechanics (of which he is an advocate), likening the process of one quantum computation to that of many parallel computations occurring simultaneously in entangled worlds.

To motivate further work in quantum computing, researchers at the time needed problems that a quantum computer could uniquely solve. I remember conversations in the early 1990s in which people would argue about whether quantum computers would ever be able to do anything really useful, says quantum physicist William Wootters of Williams College, Massachusetts, who has worked with Bennett and Brassard on quantum cryptography problems. Then suddenly Peter Shor devised a quantum algorithm that could indeed do something eminently useful.

In 1995 Shor, who is now at the Massachusetts Institute of Technology, developed an algorithm that could factorize large integersdecompose them into products of primesmuch more efficiently than any known classical algorithm. In classical computation, the time that it takes to factorize a large number increases exponentially as the number gets larger, which is why factorizing large numbers provides the basis for todays methods for online data encryption. Shors algorithm showed that for a quantum computer, the time needed increases less rapidly, making factorizing large numbers potentially more feasible. This theoretical demonstration immediately injected energy into the field, Wootters says. Shor has also made important contributions to the theory of quantum error correction, which is more challenging in quantum than in classical computation (see Focus: LandmarksCorrecting Quantum Computer Errors).

Without Deutsch and Shor we would not have the field of quantum computation as we know it today, says quantum theorist Artur Ekert of the University of Oxford, who considers Deutsch his mentor. David defined the field, and Peter took it to an entirely different level by discovering the real power of quantum computation and by showing that it actually can be done.

Data encryption is the topic cited for the award of Bennett (IBMs Thomas J. Watson Research Center in Yorktown Heights, New York) and Brassard (University of Montreal, Canada). In 1984 the pair described a protocol in which information could be encoded in qubits and sent between two parties in such a way that the information could not be read by an eavesdropper without that intervention being detected. Like quantum computing, this quantum cryptographic scheme relies on entangling qubits, meaning that their properties are interdependent, no matter how far apart they are separated. This BB84 protocol and similar quantum encryption schemes have now been used for secure transmission of data along optical networks and even via satellite over thousands of kilometers (see Focus: Intercontinental, Quantum-Encrypted Messaging and Video).

In 1993 Bennett and Brassard also showed how entanglement may be harnessed for quantum teleportation, whereby the state of one qubit is broadcast to another distant one while the original state is destroyed (see Focus: LandmarksTeleportation is not Science Fiction). This process too has applications in quantum information processing.

I am really gratified by this award because it recognizes the field of quantum information and computation, Shor says. Deutsch echoes the sentiment: Im glad that [quantum information] is now officially regarded as fundamental physics rather than as philosophy, mathematics, computer science, or engineering.

Deutsch, Shor, Bennett, and Brassard deserve recognition for their work, and Im delighted that theyre getting it, Wootters says. He notes that their research not only inspired the development of quantum technologies, but also influenced new research in quantum foundations. Quantum information theory views quantum theory through a novel lens and opens up a new perspective from which to address foundational questions.

Read more from the original source:

Physics - Breakthrough Prize for the Physics of Quantum Informationand of Cells - Physics

Quantum Computing Market Insights 2022 By Types (Simulation, Optimization, Sampling), Applications (Defense, Banking & Finance, Energy & Power, Chemicals, Healthcare & Pharmaceuticals, Others), By Segmentation, Regions and Forecast to 2027. This report provides exclusive information on vital statistics, trends, and competitive landscape.

Global Quantum Computing Market Research Report 2022-2027 provides a comprehensive analysis of future growth trends with current and historic demand status, and SWOT analysis. The report aims to provide insightful data on market size, share, key players financial details with CAGR status, industry revenue, and import-export scenario. Quantum Computing market (112 Pages) report gives intellect analysis on overall market growth, key drivers, challenges, trends, and opportunities. Furthermore, the report focuses on regional developments, industry segments, competitive landscape analysis that includes a company overview, financial statements, gross margin, price trends, and manufacturing cost structure over the forecast period.

The global Quantum Computing market size was valued at USD 494.02 million in 2021 and is expected to expand at a CAGR of 25.06% during the forecast period, reaching USD 1890.42 million by 2027.

Quantum computing is computing using quantum-mechanical phenomena, such as superposition and entanglement. A quantum computer is a device that performs quantum computing. Such a computer is different from binary digital electronic computers based on transistors. Whereas common digital computing requires that the data be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits or qubits, which can be in superpositions of states. A quantum Turing machine is a theoretical model of such a computer, and is also known as the universal quantum computer.

Get a sample PDF of the report at https://www.industryresearch.biz/enquiry/request-sample/21532749

Competitive Analysis:

The report analyses the competitive landscape in terms of market size, trends, types, applications, and geographies to help the vendor outline their capabilities and opportunities for future growth prospects. Also, it describes the optimal analysis of vendors to adopt successive merger and acquisition strategies, innovations and technology, research and development, geography expansion, and new product launch strategies to execute further business growth plans.

The report evaluates and categorizes global vendors in the Quantum Computing Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that helps businesses in better decision making and understanding the competitive landscape.

Which are the prominent Quantum Computing Market players across the globe?

Top Key Players covered in the report are:

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Short Summary About Quantum Computing Market:

The report combines extensive quantitative analysis and exhaustive qualitative analysis, ranging from a macro overview of the total market size, industry chain, and market dynamics to micro details of segment markets by type, application, and region, and, as a result, provides a holistic view of, as well as a deep insight into the Cobalt Tetroxide market covering all its essential aspects.

For the competitive landscape, the report also introduces players in the industry from the perspective of the market share, concentration ratio, etc., and describes the leading companies in detail, with which the readers can get a better idea of their competitors and acquire an in-depth understanding of the competitive situation. Further, mergers and acquisitions, emerging market trends, the impact of COVID-19, and regional conflicts will all be considered.

In a nutshell, this report is a must-read for industry players, investors, researchers, consultants, business strategists, and all those who have any kind of stake or are planning to foray into the market in any manner.

Market Segmentation:

Quantum Computing Market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.

On the basis of product type, this report displays the production, revenue, price, market share, and growth rate of each type, primarily split into:

On the basis of the end users/applications, this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:

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The Research Report focuses on the competitive landscape of the industry including company profiles, business overview, sales area, market performance, and manufacturing cost structure. The report analyzes the global primary production, consumption, and fastest-growing countries with prominent players in the global industry.

Which region is expected to hold the highest market share in the Quantum Computing Market?

Geographically, the report includes several key regions, with sales, revenue, research on production, consumption, market share, and growth rate, and forecast (2017 -2027) of the following regions:

Highlighted Key Points Covered in this Updated Research Reports Include:

Client Focus

Does this report consider the impact of COVID-19 and the Russia-Ukraine war on the Quantum Computing market?Yes. As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and raw material price system, we have definitely taken them into consideration throughout the research, and in Chapters 1.7, 2.7, 4.X.1, 7.5, 8.7, we elaborate at full length on the impact of the pandemic and the war on the Quantum Computing Industry.

How do you determine the list of the key players included in the report?With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale but also the regional small and medium-sized companies that play key roles and have plenty of potential growth.Please find the key player list in the Summary.

What are your main data sources?Both Primary and Secondary data sources are being used while compiling the report.Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users.Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.Please find a more complete list of data sources in Chapters 11.2.1 and 11.2.2.

TO KNOW HOW COVID-19 PANDEMIC AND RUSSIA UKRAINE WAR WILL IMPACT THIS MARKET REQUEST SAMPLE

Some of the key questions answered in this report:

Following Chapter Covered in the Quantum Computing Market Research:

Chapter 1 mainly defines the market scope and introduces the macro overview of the industry, with an executive summary of different market segments ((by type, application, region, etc.), including the definition, market size, and trend of each market segment.

Chapter 2 provides a qualitative analysis of the current status and future trends of the market. Industry Entry Barriers, market drivers, market challenges, emerging markets, consumer preference analysis, together with the impact of the COVID-19 outbreak will all be thoroughly explained.

Chapter 3 analyzes the current competitive situation of the market by providing data regarding the players, including their sales volume and revenue with corresponding market shares, price, and gross margin. In addition, information about market concentration ratio, mergers, acquisitions, and expansion plans will also be covered.

Chapter 4 focuses on the regional market, presenting detailed data (i.e., sales volume, revenue, price, gross margin) of the most representative regions and countries in the world.

Chapter 5 provides the analysis of various market segments according to product types, covering sales volume, revenue market share, and growth rate, plus the price analysis of each type.

Chapter 6 shows the breakdown data of different applications, including the consumption and revenue with market share and growth rate, with the aim of helping the readers to take a close-up look at the downstream market.

Chapter 7 provides a combination of quantitative and qualitative analyses of the market size and development trends in the next five years. The forecast information of the whole, as well as the breakdown market, offers the readers a chance to look into the future of the industry.

Chapter 8 is the analysis of the whole market industrial chain, covering key raw materials suppliers and price analysis, manufacturing cost structure analysis, alternative product analysis, also providing information on major distributors, downstream buyers, and the impact of the COVID-19 pandemic.

Chapter 9 shares a list of the key players in the market, together with their basic information, product profiles, market performance (i.e., sales volume, price, revenue, gross margin), recent development, SWOT analysis, etc.

Chapter 10 is the conclusion of the report which helps the readers, sum up, the main findings and points.

Chapter 11 introduces the market research methods and data sources.

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Years considered for this report:

Detailed TOC of Quantum Computing Market Forecast Report 2022-2027:

1 Quantum Computing Market Overview1.1 Product Overview and Scope of Quantum Computing Market1.2 Quantum Computing Market Segment by Type1.2.1 Global Quantum Computing Market Sales Volume and CAGR (%) Comparison by Type (2017-2027)1.3 Global Quantum Computing Market Segment by Application1.3.1 Quantum Computing Market Consumption (Sales Volume) Comparison by Application (2017-2027)1.4 Global Quantum Computing Market, Region Wise (2017-2027)1.4.1 Global Quantum Computing Market Size (Revenue) and CAGR (%) Comparison by Region (2017-2027)1.4.2 United States Quantum Computing Market Status and Prospect (2017-2027)1.4.3 Europe Quantum Computing Market Status and Prospect (2017-2027)1.4.4 China Quantum Computing Market Status and Prospect (2017-2027)1.4.5 Japan Quantum Computing Market Status and Prospect (2017-2027)1.4.6 India Quantum Computing Market Status and Prospect (2017-2027)1.4.7 Southeast Asia Quantum Computing Market Status and Prospect (2017-2027)1.4.8 Latin America Quantum Computing Market Status and Prospect (2017-2027)1.4.9 Middle East and Africa Quantum Computing Market Status and Prospect (2017-2027)1.5 Global Market Size of Quantum Computing (2017-2027)1.5.1 Global Quantum Computing Market Revenue Status and Outlook (2017-2027)1.5.2 Global Quantum Computing Market Sales Volume Status and Outlook (2017-2027)1.6 Global Macroeconomic Analysis1.7 The impact of the Russia-Ukraine war on the Quantum Computing Market

2 Industry Outlook2.1 Quantum Computing Industry Technology Status and Trends2.2 Industry Entry Barriers2.2.1 Analysis of Financial Barriers2.2.2 Analysis of Technical Barriers2.2.3 Analysis of Talent Barriers2.2.4 Analysis of Brand Barrier2.3 Quantum Computing Market Drivers Analysis2.4 Quantum Computing Market Challenges Analysis2.5 Emerging Market Trends2.6 Consumer Preference Analysis2.7 Quantum Computing Industry Development Trends under COVID-19 Outbreak2.7.1 Global COVID-19 Status Overview2.7.2 Influence of COVID-19 Outbreak on Quantum Computing Industry Development

3 Global Quantum Computing Market Landscape by Player3.1 Global Quantum Computing Sales Volume and Share by Player (2017-2022)3.2 Global Quantum Computing Revenue and Market Share by Player (2017-2022)3.3 Global Quantum Computing Average Price by Player (2017-2022)3.4 Global Quantum Computing Gross Margin by Player (2017-2022)3.5 Quantum Computing Market Competitive Situation and Trends

4 Global Quantum Computing Sales Volume and Revenue Region Wise (2017-2022)4.1 Global Quantum Computing Sales Volume and Market Share, Region Wise (2017-2022)4.2 Global Quantum Computing Revenue and Market Share, Region Wise (2017-2022)4.3 Global Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.4 United States Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.5 Europe Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.6 China Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.7 Japan Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.8 India Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.9 Southeast Asia Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.10 Latin America Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)4.11 Middle East and Africa Quantum Computing Sales Volume, Revenue, Price and Gross Margin (2017-2022)

5 Global Quantum Computing Sales Volume, Revenue, Price Trend by Type5.1 Global Quantum Computing Sales Volume and Market Share by Type (2017-2022)5.2 Global Quantum Computing Revenue and Market Share by Type (2017-2022)5.3 Global Quantum Computing Price by Type (2017-2022)5.4 Global Quantum Computing Sales Volume, Revenue and Growth Rate by Type (2017-2022)

6 Global Quantum Computing Market Analysis by Application6.1 Global Quantum Computing Consumption and Market Share by Application (2017-2022)6.2 Global Quantum Computing Consumption Revenue and Market Share by Application (2017-2022)6.3 Global Quantum Computing Consumption and Growth Rate by Application (2017-2022)

7 Global Quantum Computing Market Forecast (2022-2027)7.1 Global Quantum Computing Sales Volume, Revenue Forecast (2022-2027)7.1.1 Global Quantum Computing Sales Volume and Growth Rate Forecast (2022-2027)7.1.2 Global Quantum Computing Revenue and Growth Rate Forecast (2022-2027)7.1.3 Global Quantum Computing Price and Trend Forecast (2022-2027)7.2 Global Quantum Computing Sales Volume and Revenue Forecast, Region Wise (2022-2027)7.3 Global Quantum Computing Sales Volume, Revenue and Price Forecast by Type (2022-2027)7.4 Global Quantum Computing Consumption Forecast by Application (2022-2027)

8 Quantum Computing Market Upstream and Downstream Analysis8.1 Quantum Computing Industrial Chain Analysis8.2 Key Raw Materials Suppliers and Price Analysis8.3 Manufacturing Cost Structure Analysis8.3.1 Labor Cost Analysis8.3.2 Energy Costs Analysis8.3.3 RandD Costs Analysis8.4 Alternative Product Analysis8.5 Major Distributors of Quantum Computing Analysis8.6 Major Downstream Buyers of Quantum Computing Analysis8.7 Impact of COVID-19 and the Russia-Ukraine war on the Upstream and Downstream in the Quantum Computing Industry

Continued

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