Category Archives: Quantum Computing

Fujitsu Laboratories Ltd., and Quantum Benchmark Inc. of Canada will conduct joint research on quantum algorithms using Quantum Benchmarks error suppression technology as they aim to advance the capabilities of current generation quantum computing platforms.

Quantum Benchmark, a startup founded by leading researchers from the University of Waterloos Institute for Quantum Computing, provides software solutions for error characterization, error suppression, and performance validation for quantum computing hardware.

In this collaborative research project, the companies will develop practical quantum algorithms utilizing Fujitsus AI algorithm development technology as well as its knowledge gained through Digital Annealer applications in finance, medicine and material development. The Digital Annealer is Fujitsus new quantum-inspired architecture that can rapidly resolve combinatorial optimization problems.

Overview of the joint research.

Quantum Benchmarks patented True-Q software system, which enables optimal performance of current hardware, is a key to this development. Accordingly, Fujitsu Laboratories and Quantum Benchmark will endeavor to solve problems in the fields of materials science, drug development and finance that are intractable to solve with conventional computers.

Quantum computers are expected to be able to perform a new form of computation by harnessing fundamental properties of the quantum world, such as entanglement and superposition. This is often explained by invoking the idea that they can process both 0 and 1 at the same time, and the continuum of states in between 0 and 1. This advantage comes by performing calculations using quantum bits, called "qubits", which is unlike conventional computers which process conventional bits, that can be only 0 or 1. However, quantum bits are fragile and highly vulnerable to errors and noise, and as time goes on, the effects of noise add up, making the quantum calculation results inaccurate. Since calculations for pharmaceuticals and materials are time-consuming, there is a need to develop error-suppression methods enabling algorithms to overcome the effects of noise.

Under the partnership, which is slated to run to March 2021, and planned for extension after April 2021, Fujitsu will develop quantum algorithms for applications such as quantum chemistry and machine learning, and develop performance analysis technology for quantum algorithms in simulations.

Quantum Benchmark will support the implementation of True-Q error diagnosis technology on current quantum computing platforms; support implementation of quantum algorithms on current quantum computing platforms; and support custom specific error suppression strategies and performance evaluation for quantum algorithms on current quantum computing platforms.

Fujitsu Laboratories and Quantum Benchmark will expand the scope of their joint research beyond finance, drug discovery, and materials, as they plan to develop quantum algorithms to be implemented in quantum computers for various applications which could not be solved with conventional computers. The companies aim to demonstrate new applications on a 100+ qubit quantum computer by 2023.

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Fujitsu Laboratories and Quantum Benchmark begin joint research on algorithms with error suppression for quantum computing - Green Car Congress

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

The interdisciplinary team of researchers from UChicago, University of California, Berkeley, Princeton University and Argonne National Laboratory won the $2,500 first-place award for Best Paper. Their research examined how the VQE quantum algorithm could improve the ability of current and near-term quantum computers to solve highly complex problems, such as finding the ground state energy of a molecule, an important and computationally difficult chemical calculation the authors refer to as a killer app for quantum computing.

Quantum computers are expected to perform complex calculations in chemistry, cryptography and other fields that are prohibitively slow or even impossible for classical computers. A significant gap remains, however, between the capabilities of todays quantum computers and the algorithms proposed by computational theorists.

VQE can perform some pretty complicated chemical simulations in just 1,000 or even 10,000 operations, which is good, Gokhale says. The downside is that VQE requires millions, even tens of millions, of measurements, which is what our research seeks to correct by exploring the possibility of doing multiple measurements simultaneously.

Gokhale explains the research in this video.

With their approach, the authors reduced the computational cost of running the VQE algorithm by 7-12 times. When they validated the approach on one of IBMs cloud-service 20-qubit quantum computers, they also found lower error as compared to traditional methods of solving the problem. The authors have shared their Python and Qiskit code for generating circuits for simultaneous measurement, and have already received numerous citations in the months since the paper was published.

For more on the research and the IBM Q Best Paper Award, see the IBM Research Blog. Additional authors on the paper include Professor Fred Chong and PhD student Yongshan Ding of UChicago CS, Kaiwen Gui and Martin Suchara of the Pritzker School of Molecular Engineering at UChicago, Olivia Angiuli of University of California, Berkeley, and Teague Tomesh and Margaret Martonosi of Princeton University.

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Research by University of Chicago PhD Student and EPiQC Wins IBM Q Best Paper - Quantaneo, the Quantum Computing Source

The Global Quantum Computing for Enterprise Market study report presents an in-depth study about the market on the basis of key segments such as product type, application, key companies and key regions, end users and others. The research report presents assessment of the growth and other characteristics of the Global Quantum Computing for Enterprise Market on the basis of key geographical regions and countries. The major regions which have good market in this industry are North America, Latin America, Europe, Asia-Pacific and Middle East Africa.

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Global Market By Type:

HardwareSoftware

Global Market By Application:

BFSITelecommunications and ITRetail and E-CommerceGovernment and DefenseHealthcareManufacturingEnergy and UtilitiesConstruction and EngineeringOthers

The research report of Global Quantum Computing for Enterprise Market offers the comprehensive data about the top most manufacturers and vendors which are presently functioning in this industry and which have good market region and country wise. Furthermore, study report presents a comprehensive study about the market on the basis of various segments such as product type, application, key companies and key regions, top end users and others. Furthermore, the study report provides the analysis about the major reasons or drivers that are responsible for the growth the Global Quantum Computing for Enterprise Market.

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Global Quantum Computing for Enterprise Market 2020 Report With Segmentation, Analysis On Trends, Growth, Opportunities and Forecast Till 2024 - News...

IT stakeholders throughout markets are delighted by the potential customers of quantum computing, but it will take a whole lot a lot more source to make sure both the technologys all set for a large swimming pool of customers, and also those very same customers prepare to release it.

Thats according to a brand-new study by the International Data Corporation (IDC) qualified Quantum Computing Adoption Trends: 2020 Survey Findings, which has actually assembled information and also end-user metrics from over 2,700 European entities associated with the quantum ball, and also the people managing quantum financial investments.

Despite the slower price of quantum fostering total( financial investments consist of in between 0 2 percent of yearly budget plans), end-users are confident that quantum computing will placed them at an affordable benefit, supplied that very early seed financial investment gets on hand.

The favorable overview adheres to the growth of brand-new models and also very early progression in markets such as FinTech, cybersecurity and also production.

Made up of those that would certainly look after financial investment in quantum in their organisations, participants pointed out far better company knowledge information event, enhanced expert system (AI) capacities, in addition to increased effectiveness and also efficiency of their cloud-based systems and also solutions, as one of the most amazing applications.

While the innovation itself still has a lengthy means to precede its practical for organisations, also when it is, IT directors stress over high prices refuting them accessibility, restricted expertise of the area, scarcity of essential sources in addition to the high degree of details entailed within the innovation itself.

However, with such large applications and also possibility of the technology, quantum area makers and also vendors are established on making the innovation readily available for as wide a swathe of customers as feasible that implies production it easy to use, and also readily available to business with even more restricted source, as cloud-based Quantum-Computing- as-a-Service (QCaaS).

According to Heather Wells, the IDCs elderly study expert of Infrastructure Systems, Platforms, and also Technology, Quantum computing is the future market and also facilities disruptor for companies wanting to make use of big quantities of information, expert system, and also artificial intelligence to speed up real-time company knowledge and also introduce item growth.

Many organizations from many industries are already experimenting with its potential.

These understandings more mention one of the most prominent applications and also methods of quantum innovation, that include cloud-centric quantum computing, quantum networks, facility quantum formulas, and also crossbreed quantum computing which takes in 2 or even more adaptions of quantum technological opportunities.

The future appears significantly encouraging for quantum computing mass fostering, nonetheless, those business creating should act rapidly to make its very early power easily accessible to organisations in order to protect the financial investment to drive the innovations real future possibility.

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The future's bright for quantum computing but it will need big backing - The Union Journal

If you dont believe our world of work is changing, you must either have your head stuck in the ground or had one too many conferences cancelled due to the coronavirus.

The platform economy is alive and well and has shaped our personal and business lives for at least the last decade. Up until recently platforms have been built on the foundation of SMAC technologies - Social, Mobile, Analytics and Cloud - and with great effect. One only has to look at the worlds most valuable companies including Apple, Amazon, Alphabet, and Alibaba. These companies have fully embraced the SMAC stack and have created levels of economic value, the like of which has seldom been seen in history.

However, change has arrived and for companies to remain competitive, SMAC no longer fits the bill.

Today, organisations are pivoting their businesses around ABEQ: artificial intelligence, blockchain (or distributed ledgers), enhanced reality and quantum computing. Of course, the most divisive of these technologies is artificial intelligence (AI).Business leaders, politicians and modern day soothsayers are all weighing in on the impact of this technology, with many believing AI will replace vast swathes of the modern workforce leaving us with a ruling elite.

One just has to look at the media to realise the state of paranoia. The percentage of jobs feared to be lost in the face of AI range from 25% to 47%. Even at the lower end, these estimates would cripple global economies and would lead to mass unemployment and potentially global unrest. However, how accurate are they?

We at Cognizants Center for the Future of Work (CFoW) believe that many of these studies fail to realise one key element that has defined all three of the last industrial revolutions. New technologies lead to new job creation. Our findings indicate that digital technologies will result in 13% new job creation, mitigating the 12% of job replacement these technologies will cause. In addition, 75% of jobs will remain but be drastically enhanced by man-machine collaboration. Yes, the disruption of these jobs will cause short- to medium-term impacts to many workers, but it is far from the doomsday scenario painted by many futurists.

The next question is: what will these new jobs be? Cognizants CFoW sought to understand exactly that and studied the latest macro, micro and socio economic trends, resulting in two report: 21 Jobs of the Future and 21 More Jobs of the Future.

These two reports name the exact jobs that will likely emerge in the future, and provide a timescale and tech centricity of when and how these jobs will occur. Spoiler: not all jobs of the future will require massive technical expertise. Instead, jobs will pivot around three core pillars that are currently shaping modern society: coaching, caring and connecting.

Heres why:

Ultimately, it is very easy to be caught up in the dystopian fear of the unknown future. However, instead we need to have a fascination with the unknown.

About the authorMicheal Cook is senior manager responsible for developing thought leadership in Cognizants EMEA Center for the Future of Work - a fulltime think tank of Cognizant Technical Services. Now based in London, Michael was born in Johannesburg and earned his Bachelors of Economics and Econometrics and Post Graduate qualification of International Trade and Development from the University of Johannesburg.

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No, AI won't steal your job. Here's why. - ITWeb