Archive for the ‘Quantum Computer’ Category

Lighting up the ion trap – MIT News

Walk into a quantum lab where scientists trap ions, and you'll find benchtops full of mirrors and lenses, all focusing lasers to hit an ion trapped in place above a chip. By using lasers to control ions, scientists have learned to harness ions as quantum bits, or qubits, the basic unit of data in a quantum computer. But this laser setup is holding research back making it difficult to experiment with more than a few ions and to take these systems out of the lab for real use.

Now, MIT Lincoln Laboratory researchers have developed a compact way to deliver laser light to trapped ions. In a recent paper published in Nature, the researchers describe a fiber-optic block that plugs into the ion-trap chip, coupling light to optical waveguides fabricated in the chip itself. Through these waveguides, multiple wavelengths of light can be routed through the chip and released to hit the ions above it.

It's clear to many people in the field that the conventional approach, using free-space optics such as mirrors and lenses, will only go so far, says Jeremy Sage, an author on the paper and senior staff in Lincoln Laboratory's Quantum Information and Integrated Nanosystems Group. If the light instead is brought onto the chip, it can be directed around to the many locations where it needs to be. The integrated delivery of many wavelengths may lead to a very scalable and portable platform. We're showing for the first time that it can be done.

Multiple colors

Computing with trapped ions requires precisely controlling each ion independently. Free-space optics have worked well when controlling a few ions in a short one-dimensional chain. But hitting a single ion among a larger or two-dimensional cluster, without hitting its neighbors, is extremely difficult. When imagining a practical quantum computer requiring thousands of ions, this task of laser control seems impractical.

That looming problem led researchers to find another way. In 2016, Lincoln Laboratory and MIT researchers demonstrated a new chip with built-in optics. They focused a red laser onto the chip, where waveguides on the chip routed the light to a grating coupler, a kind of rumble strip to stop the light and direct it up to the ion.

Red light is crucial for doing a fundamental operation called a quantum gate, which the team performed in that first demonstration. But up to six different-colored lasers are needed to do everything required for quantum computation: prepare the ion, cool it down, read out its energy state, and perform quantum gates. With this latest chip, the team has extended their proof of principle to the rest of these required wavelengths, from violet to the near-infrared.

With these wavelengths, we were able to perform the fundamental set of operations that you need to be able to control trapped ions, says John Chiaverini, also an author on the paper. The one operation they didn't perform, a two-qubit gate, was demonstrated by a team at ETH Zrich by using a chip similar to the 2016 work, and is described in a paper in the same Nature issue. This work, paired together with ours, shows that you have all the things you need to start building larger trapped-ion arrays, Chiaverini adds.

Fiber optics

To make the leap from one to multiple wavelengths, the team engineered a method to bond a fiber-optic block directly to the side of the chip. The block consists of four optical fibers, each one specific to a certain range of wavelengths. These fibers line up with a corresponding waveguide patterned directly onto the chip.

Getting the fiber block array aligned to the waveguides on the chip and applying the epoxy felt like performing surgery. It was a very delicate process. We had about half a micronof tolerance and it needed to survive cooldown to4 kelvins, says Robert Niffenegger, who led the experiments and is first author on the paper.

On top of the waveguides sits a layer of glass. On top of the glass are metal electrodes, which produce electric fields that hold the ion in place; holes are cut out of the metal over the grating couplers where the light is released. The entire device was fabricated in the Microelectronics Laboratory at Lincoln Laboratory.

Designing waveguides that could deliver the light to the ions with low loss, avoiding absorption or scattering, was a challenge, as loss tends to increase with bluer wavelengths. It was a process of developing materials, patterning the waveguides, testing them, measuring performance, and trying again. We also had to make sure the materials of the waveguides worked not only with the necessary wavelengths of light, but also that they didn't interfere with the metal electrodes that trap the ion, Sage says.

Scalable and portable

The team is now looking forward to what they can do with this fully light-integrated chip. For one, make more, Niffenegger says. Tiling these chips into an array could bring together many more ions, each able to be controlled precisely, opening the door to more powerful quantum computers.

Daniel Slichter, a physicist at the National Institute of Standards and Technology who was not involved in this research, says, This readily scalable technology will enable complex systems with many laser beams for parallel operations, all automatically aligned and robust to vibrations and environmental conditions, and will in my view be crucial for realizing trapped ion quantum processors with thousands of qubits.

An advantage of this laser-integrated chip is that it's inherently resistant to vibrations. With external lasers, any vibration to the laser would cause it to miss the ion, as would any vibrations to the chip. Now that the laser beams and chip are coupled together, the effects of vibrations are effectively nullified.

This stability is important for the ions to sustain coherence, or to operate as qubits long enough to compute with them. It's also important if trapped-ion sensors are to become portable. Atomic clocks, for example, that are based on trapped ions could keep time much more precisely than today's standard, and could be used to improve the accuracy of GPS, which relies on the synchronization of atomic clocks carried on satellites.

We view this work as an example of bridging science and engineering, that delivers a true advantage to both academia and industry, Sage says. Bridging this gap is the goal of the MIT Center for Quantum Engineering, where Sage is a principal investigator.We need quantum technology to be robust, deliverable, and user-friendly, for people to use who aren't PhDs in quantum physics, Sage says.

Simultaneously, the team hopes that this device can help push academic research. We want other research institutes to use this platform so that they can focus on other challenges like programming and running algorithms with trapped ions on this platform, for example. We see it opening the door to further exploration of quantum physics, Chiaverini says.

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Lighting up the ion trap - MIT News

U of T and Fujitsu extend agreement to collaborate on cutting-edge computing research – News@UofT

The University of Toronto and Japans Fujitsu Laboratories Ltd. have agreed to renew, for three years, a partnership that seeks to advance innovative computing research projects with wide-scale applications.

The partnership extension was marked this week by a transglobal videoconference that included Fujitsu CEO Hirotaka Hara and U of T President Meric Gertler, as well as other senior leaders and researchers.

The group discussed the progress of the partnership which launched in 2018 and involved the establishment of the Fujitsu Co-Creation Research Laboratory at U of Ts Myhal Centre for Engineering Innovation & Entrepreneurship and what can be achieved in the future.

Fujitsu is one of the worlds most admired companies and Fujitsu Laboratories is a major engine of research and development in leading innovation clusters around the world including Beijing, Silicon Valley, London and now, of course, Toronto, President Gertler said during the videoconference.

The University of Toronto and our department of electrical and computer engineering both enjoy very high rankings globally, and we are the academic anchor of an impressive innovation ecosystem here in the Toronto region.

Since its launch, the Fujitsu Co-Creation Research Laboratory has been credited with such major advancements as the advent of the Digital Annealer, a computing architecture that is inspired by quantum principles and can carry out operations beyond the scope of conventional computers, opening up potential applications in health care, drug discovery, finance, logistics, transportation and more.

Fujitsu also launched an R&D centre in Toronto in 2017 as part of its partnership with the university.

President Gertler said the collaboration between U of T and Fujitsu is testament to the richness of Torontos technology and innovation ecosystem.

Toronto is increasingly recognized as a global investment destination, he said. The University of Toronto is a major factor in shaping that status and making Toronto so attractive, and the presence of Fujitsu Laboratories has helped raise this attractiveness even further

Hara, who was appointed the CEO of Fujitsu Laboratories in 2019, said the company is excited about its ongoing association with U of T and the potential research outcomes of the partnership.

As a global brand, Fujitsu is always looking for innovative solutions to real-world problems, he said. Through this partnership, we have the opportunity to work with world-class researchers to contribute to social impact.

He added that the Fujitsu Co-Creation Research Laboratory was responsible for important developments.

The Digital Annealer is a great example of the exciting technology we have been developing together. Therefore, we would like to engage in future research of the Digital Annealer with U of T with greater outcomes.

The partnership between U of T and Fujitsu Labs can be traced back more than two decades to 1998, when Professor Ali Sheikholeslami, then a PhD student in electrical engineering at U of T, did a six-week internship at Fujitsu Labs.

Following the internship, Sheikholeslami continued to work with Fujitsu Lab researchers, and a formal collaboration was established after Sheikholeslami was hired as a faculty member at the Edward S. Rogers. Sr. department of electrical and computer engineering in the Faculty of Applied Science & Engineering.

Today, Sheikholeslami is the head of the Fujitsu Co-Creation Research Laboratory, which has engaged more than 10 faculty members and 25 graduate students and post-doctoral researchers from fields ranging from electrical, computer, mechanical and industrial engineering to medicine, finance and statistics.

In collaboration with Fujitsu, Sheikholeslami said the researchers aim to improve the speed, accuracy and flexibility of the Digital Annealer technology. He added quantum computing is another promising avenue.

We would like to collaborate with Fujitsu and expand our collaboration into the area of quantum computing, Sheikholeslami said. As you know, a quantum computer is a natural extension of the Digital Annealer.

What we would like to do is build quantum computing systems in the near future. We have a lot of expertise at U of T all the expertise that it takes to build this quantum processing unit. We have expertise in physics, hardware, algorithm and in software. We will be discussing the possible collaboration.

Sheikholeslami said U of T and Fujitsu have applied for or are in the process of applying for patents on a range of inventions.More inventions are in the making, and theres a possibility now of U of T and Fujitsu co-creating startups for the first time, he said.

In his closing remarks, President Gertler lauded the progress achieved by the partnership and highlighted that the best is yet to come.

As the platform expands now to include even more disciplines, no doubt it will enable even greater accomplishments in the years to come, he said. I, for one, will be truly delighted to follow its progress.

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U of T and Fujitsu extend agreement to collaborate on cutting-edge computing research - News@UofT

For Thomas Searles, a passion for people and science at HBCUs and MIT – MIT News

When Thomas Searles was assigned a book report in the first grade, he initially had trouble choosing a topic. He really didnt like fiction books. After a bit of indecision, he chose to write his report on a book about Black astronauts. Though he didnt realize it at the time, his journey to becoming a physicist at MIT had just begun.

I looked in the book, and there was Ronald E. McNair, who happens to be an MIT alum, randomly; he got his PhD here, Searles says. And it said that he was a laser physicist. So, I said, Well, that's what I'm going to be, because I want to be an astronaut.

Searles is now a member of the 2020-21 Martin Luther King (MLK) Visiting Professors and Scholars Program cohort at MIT. Since 1995, the MLK Scholars Program has brought in a total of 67 visiting professors and 21 visiting scholars from across all academic disciplines. Individuals of any underrepresented minority group are eligible to apply, and scholars are selected for their contributions both to their fields and their potential contributions to MIT.

It's something that was always on my radar as a young Black scientist, Searles said. It was something that was on my five- to 10-year plan.

Searles is currently an associate professor in the Department of Physics at Howard University, a historically Black college and university (HBCU) located in Washington. There, he established a new research program in applied and materials physics. He is also the director of a new academic partnership between IBM and 13 other HBCUs called the IBM-HBCU Quantum Center.

Searles research career began as an undergraduate in mathematics and physics at Morehouse College, a HBCU in Atlanta. Before graduating in 2005, he worked in an optics lab, examining the properties of light and its interactions with matter.

A lot of us had an interest in optics, because that was the only experimental lab that we had at Morehouse at the time, Searles says. So naturally, I applied to graduate schools that were optics-related.

That interest led him to pursue his PhD in applied physics in the Department of Electrical and Computer Engineering at Rice University in Houston, Texas, from which he graduated. Before graduating in 2011, he studied light-matter interactions, and completed a thesis about the magneto-optical properties of carbon nanotubes, tiny cylinders comprised of a single layer of carbon atoms. Carbon nanotubes are extremely strong, lightweight, and electrically conductive, making them promising for a variety of applications.

In 2015, Searles started at Howard University. I wanted to go back and work at an HBCU. I thought of my experience working in the Morehouse optics lab and how they kind of shaped my experience, Searles says. So then I was like, What can I do that's different from everyone else that will also provide opportunities to a lot of Black students? So, I set out to start a terahertz experimental lab, knowing that it was going to be difficult. And it was difficult. But we were able to do it.

In the terahertz spectroscopy lab at Howard University, researchers work with matter that has a large wavelength, and a frequency between several hundred gigahertz and several terahertz. During the first so-called quantum age in the mid-1900s, silicon was the new, exciting material used to develop transistors. Now, researchers in fields like chemistry and physics are on the hunt for the next material to be a platform for a new generation of quantum technologies.

The primary goal is to study materials for new computers, making them either safer, faster, or more secure, Searles says. This whole idea of quantum computing is what we're focusing our lab on, moving towards this idea of quantum advantage.

Quantum computing relies upon the use of quantum materials which have unique electronic and magnetic properties to build faster, stronger, and more powerful computers. Such machines are likely to provide this quantum advantage for new developments in medicine, science, finance, chemistry, and many other fields.

In 2016, Searles met MIT associate professor of physics and Mitsui Career Development Professor in Contemporary Technology Joseph Checkelsky at an event through the National Science Foundation Center for Integrated Quantum Materials.

The idea was to try to find people that we wanted to collaborate and work with, Checkelsky says. And I think I even wrote down in my notepad Thomas' name and put a big underline that I should work with this guy. Searles says the best thing that can ever happen to a spectroscopist like himself is to find a crystal-growth person that provides samples, who you also really vibe with and like as a person. And that person for me has been Joe. The two have been collaborating ever since.

Checkelskys lab works to discover new crystalline materials that enable quantum phenomena. For instance, one material that has previously been of interest to Checkelsky is a kagome crystal lattice, a 2D arrangement of iron and tin molecules. Both Checkelsky and Searles are interested in applying a branch of mathematics called topology to solids, particularly semimetals.

One of the roles Thomas plays is to examine the optical properties of these new systems to understand how light interacts with quantum materials, Checkelsky says. Its not only fundamentally important, it can also be the bridge that connects to new technologies that interfaces light with quantum science.

Searles expertise on the optics side of the research enables him to identify which materials are ideal for further study, while Checkelskys group is able to synthesize materials with certain properties of interest.

It's a cycle of innovation where his lab knows how it can be tested and my lab knows how to generate the material, Checkelsky says. Each time we get through the cycle is another step toward answering questions in fundamental science that can also bring us to new platforms for quantum technology.

Checkelsky nominated Searles for the MLK Scholars Program in hopes of further expanding their academic partnership. He now serves as Searles host researcher through the program.

I hope to extend my collaboration with Joe to not only [explore] this condensed matter, experimental side of my group, but to expand this into Lincoln Laboratory and the quantum information portion that MIT has, Searles says. I think that's critical, research-wise.

In addition to their research goals, Searles and Checkelsky are excited to strengthen the general connection between MIT and Howard.

I think there are opportunities for Thomas to see, for example, the graduate school process in our department, Checkelsky says. Along the same lines, it is a great opportunity for MIT and our department to learn more how to connect to the people and science within HBCUs. It is a great chance for information to flow both ways.

Searles also hopes to encourage more HBCU students to pursue graduate study at MIT. The goal of increasing the number of qualified applicants [from HBCUs] I think that's something that I can measure metrically from the first year, Searles says. And if there's anything that I can do to help with that number, I think that would be awesome.

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For Thomas Searles, a passion for people and science at HBCUs and MIT - MIT News

Outlook on the AI Chipsets Global Market to 2025 – More than 83% of Global Chipsets Will be AI-equipped – PRNewswire

DUBLIN, Nov. 6, 2020 /PRNewswire/ -- The "AI Chipsets for Wireless Networks and Devices, Cloud and Next Generation Computing, IoT, and Big Data Analytics 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.

This report evaluates leading market players across the AI chipsets ecosystem, technology strategies, and solution plans. This includes leveraging AI chipsets for support of various emerging and disintermediating technology areas such as edge computing, 5G, and blockchain systems. Additional areas addressed include AI support of emerging computing technologies including edge platforms and servers.

This report also assesses applications and service support scenarios for AI chipsets across almost all major industry verticals. The report provides forecasts for AI chipset hardware, embedded software, professional service, deployment platforms, and applications for every major industry vertical as well as regional and country forecasts for 2020 to 2025. The report also provides exclusive recommendations for stakeholders within the AI chipsets ecosystem.

The AI chipset marketplace is poised to transform the entire embedded system ecosystem with a multitude of AI capabilities such as deep machine learning, image detection, and many others. With 83% of all chipsets globally shipping AI-equipped, over 57% of all electronics will have some form of embedded intelligence by 2025. This will also be transformational for existing critical business functions such as identity management, authentication, and cybersecurity.

Multi-processor AI chipsets learn from the environment, users, and machines to uncover hidden patterns among data, predict actionable insights and perform actions based on specific situations. AI chipsets will become an integral part of both AI software/systems as well as critical support of any data-intensive operation as they drastically improve processing for various functions as well as enhance overall computing performance. This will be a boon for many aspects of ICT ranging from decision support and data analytics to product safety and system optimization.

Consumers will realize benefits indirectly through improved product and service performance such as device and cloud-based gaming. Enterprise and industrial users will benefit through general improvements in automated decision-making, especially in the areas of robotic process automation, decision support systems, and overall data management. AI chipsets will be particularly useful for business edge equipment for real-time data analytics and store versus processing decisions.

Select Report Findings:

Key Topics Covered:

1. Executive Summary

2. Research Overview2.1 Research Objectives2.2 Select Findings

3. AI Chipsets Introduction3.1 AI Chipsets3.1.1 Chipset Components3.1.2 General Purpose Applications3.2 AI Systems3.3 Market Dynamics Analysis3.4 AI Investments3.5 Competitive Market

4. Technologies, Solutions, and Markets4.1 Chipsets Technology and Products4.2 AI Technology4.2.1 Machine Learning4.2.2 Machine Learning APIs4.2.3 Deep Machine Learning4.2.4 Natural Language Processing4.2.5 Computer Vision4.2.6 Voice Recognition4.2.7 Context Awareness Computing4.2.8 Neural Networks4.2.9 Facial Recognition4.3 Deployment Platform4.4 IoT Sector4.5 Applications in Industry Verticals4.6 Regional Markets4.7 Value Chain4.8 5G Network and Edge Computing4.9 Cloud Computing and Data Analytics4.10 Industry 4.0 and Factory Automation4.11 Autonomous Networks4.12 Blockchain Networks4.13 Quantum Computing4.14 Machine Intelligence4.15 Nanoscale Technology4.16 Mobile Network Operators

5. Company Analysis5.1 NVIDIA Corporation5.2 IBM Corporation5.3 Intel Corporation5.4 Samsung Electronics Co Ltd.5.5 Microsoft Corporation5.6 Baidu Inc.5.7 Qualcomm Incorporated5.8 Huawei Technologies Co. Ltd.5.9 Fujitsu Ltd.5.10 Softbank Group Corp. (ARM Limited)5.11 Apple Inc.5.12 Amazon Inc. (AWS)5.13 SK Telecom5.14 Inbenta Technologies Inc.5.15 Microchip Technology Inc.5.16 Texas Instruments Inc.5.17 Advanced Micro Devices (AMD) Inc.5.18 XILINX Inc.5.19 Micron Technology5.20 AIBrain Inc.5.21 General Vision Inc.5.22 Sentient Technologies Holdings Limited5.23 Graphcore5.24 Analog Devices Inc.5.25 Cypress Semiconductor Corp5.26 Rohm Semiconductor5.27 Semtech Corporation5.28 NXP Semiconductors N.V.5.29 STMicroelectronics5.30 MediaTek Inc.5.31 Renesas Electronics Corporation5.32 ZTE Corporation5.33 NEC Corporation5.34 Broadcom Corporation5.35 Integrated Device Technology (IDT) Inc.5.36 Toshiba Corporation5.37 Adapteva Inc.5.38 Applied Materials Inc.5.39 Bitmain Technologies Inc.5.40 Cambricon Technologies Corporation Limited5.41 DeePhi Tech5.42 Gyrfalcon Technology Inc.5.43 Horizon Robotics5.44 Mythic5.45 Tenstorrent Inc.5.46 Wave Computing5.47 Mellanox Technologies5.48 Koniku5.49 Numenta Inc.5.50 Imagination Technologies Limited5.51 Synopsys Inc.5.52 SenseTime5.53 Marvell Technology Group Ltd.5.54 Cadence Design Systems Inc.5.55 Rockchip5.56 VeriSilicon Limited5.57 Knuedge Inc.5.58 KRTKL Inc.5.59 Shanghai Think-Force Electronic Technology Co. Ltd.5.60 SK Hynix Inc.5.61 Taiwan Semiconductor Manufacturing Company Limited (TSMC)5.62 Alphabet (Google)5.63 Thinci5.64 LG Corporation5.65 SambaNova Systems5.66 Groq5.67 Kalray5.68 Facebook5.69 Almotive5.70 AnotherBrain5.71 BrainChip Holdings5.72 Cerebras Systems5.73 Chipintelli5.74 Tesla (DeepScale)5.75 Kneron5.76 NovuMind5.77 ThinkForce5.78 Vathys5.79 Nervana Systems5.80 Barefoot Networks5.81 Alibaba Group5.82 Megvii5.83 HPE5.84 Dell Inc. (Dell EMC)5.85 Western Digital5.86 Habana5.87 Nokia

6. AI Chipsets Market Analysis and Forecasts 2020 - 20256.1 Global AI Chipsets Market 2020 - 20256.1.1 Total Global Market Size 2020 - 20256.1.2 Market by Segment 2020 - 20256.1.3 Market by Deployment Platform 2020 - 20256.1.4 Market by Application 2020 - 20256.1.5 Market by Industry Vertical 2020 - 20256.1.6 Market by AI in Consumer, Enterprise, Industrial and Government 2020 - 20256.1.7 Market in 5G Networks 2020 - 20256.1.8 Market in Edge Computing Networks 2020 - 20256.1.9 Market in Cloud Computing 2020 - 20256.1.10 Market in Quantum Computing 2020 - 20256.1.11 Market in Big Data Analytics 2020 - 20256.1.12 Market in IoT 2020 - 20256.1.13 Market in Blockchain Networks 2020 - 20256.2 Regional AI Chipsets Market 2020 - 20256.2.1 Market by Region 2020 - 20256.2.1.1 North America Market 2020 - 20256.2.1.2 Europe Market 2020 - 20256.2.1.3 Asia Pacific Market 2020 - 20256.2.1.4 Middle East and Africa Market 2020 - 20256.2.1.5 Latin America Market 2020 - 20256.3 AI Chipsets Deployment Forecast 2020 - 20256.3.1 Total Global Deployment 2020 - 20256.3.2 Deployment by Segment 2020 - 20256.3.2.1 Deployment by Product 2020 - 20256.3.2.2 Deployment by Technology 2020 - 20256.3.2.3 Deployment by Processor Type 2020 - 20256.3.3 Deployment by Platform 2020 - 20256.3.3.1 Deployment by IoT Device 2020 - 20256.3.3.1.1 Deployment by Wearable Device 2020 - 20256.3.3.1.2 Deployment by Healthcare Device 2020 - 20256.3.3.1.3 Deployment by Smart Appliances 2020 - 20256.3.3.1.4 Deployment by Industrial Machines 2020 - 20256.3.3.1.5 Deployment by Entertainment Device 2020 - 20256.3.3.1.6 Deployment by Security Device 2020 - 20256.3.3.1.7 Deployment by Network Device 2020 - 20256.3.3.1.8 Deployment by Connected Vehicle Device 2020 - 20256.3.3.1.9 Deployment by Smart Grid Device 2020 - 20256.3.3.1.10 Deployment by Military Device 2020 - 20256.3.3.1.11 Deployment by Energy Management Device 2020 - 20256.3.3.1.12 Deployment by Agriculture Specific Device 2020 - 20256.3.3.2 Deployment by Non-IoT Device 2020 - 20256.3.3.3 Deployment by IoT "Things" 2020 - 20256.3.4 Deployment by AI Technology 2020 - 20256.3.4.1 Deployment by AI Technology Type 2020 - 20256.3.4.2 Deployment by Machine Learning Technology 2020 - 20256.3.5 Deployment by Application 2020 - 20256.3.6 Deployment by Industry Vertical 2020 - 20256.3.7 Deployment by Price Range 2020 - 20256.3.8 Deployment by Region 2020 - 20256.3.8.1 North America Deployment by Country 2020 - 20256.3.8.2 Europe Deployment by Country 2020 - 20256.3.8.3 Asia Pacific Deployment by Country 2020 - 20256.3.8.4 Middle East and Africa Deployment by Country 2020 - 20256.3.8.5 Latin America Deployment by Country 2020 - 2025

7. Conclusions and Recommendations7.1 Advertisers and Media Companies7.2 Artificial Intelligence Providers7.3 Automotive Companies7.4 Broadband Infrastructure Providers7.5 Communication Service Providers7.6 Computing Companies7.7 Data Analytics Providers7.8 Immersive Technology (AR, VR, and MR) Providers7.9 Networking Equipment Providers7.10 Networking Security Providers7.11 Semiconductor Companies7.12 IoT Suppliers and Service Providers7.13 Software Providers7.14 Smart City System Integrators7.15 Automation System Providers7.16 Social Media Companies7.17 Workplace Solution Providers7.18 Large Businesses, SMBs, and Governments7.19 Future Market Direction

Companies Mentioned

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Outlook on the AI Chipsets Global Market to 2025 - More than 83% of Global Chipsets Will be AI-equipped - PRNewswire

Want to visit 2 million stars? Theres a shortcut, but youll need a warp drive – SYFY WIRE

Its towel day. Really. Someone has actually come up with a real hitchhikers guide to the galaxy, though there are no directions to the Restaurant at the End of the Universe.

The shortest path to take in order to visit 2 million stars has been found by mathematicians William Cook and Keld Helsgaun. They have finally solved a long-standing mystery. By analyzing data from the Gaia space telescope thatincluded 2,079,471 stars in our galaxy, they mapped the most efficient 3D route to hop over to each of them once, and the margin for error is extremely small. The only problem is that youd need a warp drive that could travel at the speed of light (at least), and were definitely not there yet. That, and you would probably have to be immortal or close to it, as the journey would still take about 100 million years.

Cook and Helsgaun were after the solution to the traveling salesman problem, which asks how you could take the shortest route between multiple destinations with only one stop at each. The journey would eventually bring you back to where you started from, which, in this scenario, is the sun. Helsgaun searched for ways to find better and better tours, while Cook was the one who proved guarantees on how short a tour could possibly be. The tours inform the guarantees while the guarantees help improve the tours. Figuring out how to do this with the stars in our galaxy puts it on the largest scale ever.

To make the rotating maps of the tours, we used the three.js Javascript 3D library, Cook told SYFY WIRE. The 3D positions of the stars were taken from a data set created at the Max Planck Institute for Astronomy. We used somewhere between 100 and 200 CPU years in the computations, running on a network of computers when they were not otherwise occupied.

The problem took up to 200 years of computing time that was compressed into just two years (it is faster when you dont have thousands of people trying to log onto the same network). Quantum computers could potentially speed up that process, but Cook has doubts. If someone could come up with technology advanced enough for a huge and extremely strong quantum computer, it could help with finding shorter tours, and quantum computing search could further help in shortening those routes. The problem is that were not technologically there yet. The quantum computers that do exist are just unable to support such an extreme dataset, let alone dream up every possible tour at once.

It is not at all clear that a quantum computer can help in solving large instances of the traveling salesman problem, Cook said. In particular, there is no indication that quantum computing can help substantially in finding a guarantee needed to prove a tour is shortest possible.

Gaia, whose mission is to make the largest and most precise 3D map of the Milky Way, has released data on the locations of 1.33 billion stars, so Cook and Helsgaun are now trying to figure out the shortest route between them. This is a dataset 500 times larger than the last. Their best result yet is over 15 trillion light years. So far, they can only guarantee that it is at most about a factor of 1.0038 longer than the shortest possible tour, which seems like nothing, but is a far greater margin for error than the factor of 0.0000074, which is 700 light years for that particular route. Not bad compared to the nearly hundred thousand the entire trip would take. Even then, Cook still wants to push it further.

We have found a set of rules (using parallel computing) that we hope will give a strong guarantee, but the hugescale of the problem makes it difficult to find thecombination of the rules that we need, he said. Thiscombinationtask is calledlinear programming it is the workhorse for the field of mathematical optimization. Our 1.33-billon star project is driving thecreation of LPalgorithms to handle examples nearly 1,000 times larger than waspreviously possible.

By the way, because Cook and Helsgaun believe that the 2 million-star tour could be done in even less time than the guarantee, they are offering a reward* of $50 for each parsec (3.26 light years) that can be saved by rearranging the route to those 2,079,471 stars, up to a $10,000 total. Just saying.

*It's legit. Cook personally asked your friendly neighborhood writer to spread the word about this.

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Want to visit 2 million stars? Theres a shortcut, but youll need a warp drive - SYFY WIRE