Quantum computing could revolutionize nuclear and particle physics

#Quantumcomputing could revolutionize nuclear and particle physics 14 Apr 2018 Hamish Johnston Lowering barriers: commercial quantum-computing services will be a boon to nuclear and particle physics (Courtesy: H Johnston) The theme of this year’s April Meeting of the American Physical Society is the “Feynman Century” because the iconoclastic, Nobel-prize-winning physicist was born in 1918. This morning at a special session devoted to Feynman, quantum computing expert Christopher Monroe of the University of Maryland spoke about early contributions to quantum computing that were made by Feynman before his untimely death in 1988. That theme continued in an afternoon session at the conference where nuclear and particle physicists discussed how quantum computers could be applied to their work. A huge challenge to those studying the physics of quarks (quantum chromodynamics or QCD) is that it takes vast amounts of computing power just to calculate the properties of relatively simple systems. Low barrier to entry Quantum computers, which (at least in principle), can solve certain problems much more efficiently than conventional computers could offer a way forward. Earlier this year we reported what is probably the first-ever nuclear physics calculation done using quantum computers – the binding energy of the deuteron. @ThomasPapenbrock of @Oak Ridge National Lab in Tennessee explained how commercial #cloud #quantumcomputing services from @IBM and @Rigetti had made this calculation possible, pointing out that the barrier to entry to quantum computing is very low thanks to these services. He was followed by Martin Savage of the University of Washington, who is an expert in lattice QCD, which requires mind-boggling amounts of computer power. He pointed out that the QCD community already relies on large computing infrastructures that are created and maintained by both physicists and computing experts. A similar technological and human infrastructure, he believes, must be created for lattice QCD quantum computing. Solving the “sign problem” Quantum computers could play crucial roles in solving the “sign problem” in lattice QCD, which makes calculations increasingly difficult as the number of particles increases. They could also be used to calculate the dynamical evolution of a system, charting particle interactions in a collider, for example. The IBM and Rigetti quantum computers used to perform the first nuclear calculation had 16 and 19 qubits respectively, so my jaw dropped when Savage said that about 4 million qubits would be needed to do a lattice QCD better than state-of-the-art conventional computers. And my jaw dropped even further when he said that experts in the industry didn’t seem to think this would be a problem!

https://physicsworld.com/a/quantum-computing-could-revolutionize-nuclear-and-particle-physics/

Rigetti Rolls Out Latest Forest Quantum Developer Environment

@RigettiComputing last week reported upgrading its #Forest developer environment for #quantumcomputing. Forest provides developers with access to Rigetti’s cloud-based quantum simulator the quantum #virtualmachine ( #QVM ) as well as access to its quantum hardware systems. According to the company the latest version has improved tools for debugging and optimizing quantum programs. Founded in 2013 and based Berkeley, Calif., Rigetti bills itself as “a full-stack quantum computing company.” It drew attention in December with claims of being the first to solve an unsupervised machine learning problem on a gate model quantum computer. “We did this by connecting one of our recent superconducting quantum processors, a 19-qubit system, to our software platform, Forest. In the ten weeks since then, researchers have already used Forest to train neural networks, program benchmarking games, and simulate nuclear physics,” wrote Rigetti software developer Will Zeng in a blog announcing the Forest 1.3 upgrade. Key improvements in version 1.3, reported Zeng, include: Access to the compiler through a dedicated API that allows users to experiment with compiling programs to different hardware architectures. Test programs on the improved QVM that more accurately mimics actual quantum hardware, accelerating development time. “We’ve released preconfigured noise models based on the behavior of our QPU,” wrote Zeng. Post-testing it is now easier to port programs to the QPU which now supports the .run pyQuil command. Readout fidelity has been improved with a toolset that compensates for readout errors in the QPU; Rigetti says this “can dramatically improve” the performance of programs. “In any given quantum program, researchers make multiple calls to our API. In these circumstances, network latency can become an issue and slow down overall execution time. In Forest 1.3 we provide a 2x speedup in cloud job execution on our QPUs. This makes running hybrid classical/quantum algorithms with many API calls much faster. We’ve also made a new two-qubit gate instruction available on our 19Q-Acorn quantum processor: CPHASE(theta). This introduces our first parameterized two-qubit gate, offering more powerful and flexible entanglement between qubits,” according to Rigetti.

https://www.hpcwire.com/2018/02/27/rigetti-rolls-latest-forest-quantum-developer-environment/

Link to Rigetti blog:
https://medium.com/rigetti/forest-1-3-upgraded-developer-tools-improved-stability-and-faster-execution-561b8b44c875

Rigetti has a 19 qubit quantum computing system and it runs unsupervised machine learning

@Rigetti has demonstrated unsupervised machine learning using #19Q, their new 19-#qubit general purpose superconducting quantum processor. We did this with a quantum/classical #hybridalgorithm for clustering developed at Rigetti. Arxiv – Unsupervised Machine Learning on a Hybrid #QuantumComputer #Machinelearning techniques have led to broad adoption of a statistical model of computing. The statistical distributions natively available on quantum processors are a superset of those available classically. Harnessing this attribute has the potential to accelerate or otherwise improve machine learning relative to purely classical performance. A key challenge toward that goal is learning to hybridize classical computing resources and traditional learning techniques with the emerging capabilities of general purpose quantum processors. Here, we demonstrate such hybridization by training a 19-qubit gate model processor to solve a clustering problem, a foundational challenge in unsupervised learning. We use the quantum approximate optimization algorithm in conjunction with a gradient-free Bayesian optimization to train the quantum machine. This quantum/classical hybrid algorithm shows robustness to realistic noise, and we find evidence that classical optimization can be used to train around both coherent and incoherent imperfections.

https://www.google.com/amp/s/www.nextbigfuture.com/2017/12/rigetti-has-a-19-qubit-quantum-computing-system-and-it-runs-unsupervised-machine-learning.html/amp#ampshare=https://www.nextbigfuture.com/2017/12/rigetti-has-a-19-qubit-quantum-computing-system-and-it-runs-unsupervised-machine-learning.html

Term Sheet Rigetti -- Wednesday, October 25

TERM SHEET: You led Rigetti Computing’s Series A round. The quantum computing company has raised nearly $70 million in venture funding. Why did you decide to invest? PANDE: @Rigetti has a full-stack operation where they create their own chips, they build their own computers, and they write their own software and applications. That full-stack approach will be critically important in this juncture of quantum computing where we are still working to design the first machines. Many companies, like @Google and @Intel, feel that there is huge opportunity for #quantumcomputing right now to be able to do certain tasks dramatically faster than the way traditional computers could work. People have been talking about quantum computing for at least 20 years, but I think we’re now seeing a big shift. For the last two decades, we had been trying to work out the fundamental science of quantum computing, and right now, a lot of the scientific advances are done. The next steps are to figure out engineering advances. In other words, we’re asking questions about how we scale up chips, rather than how we build the fundamental devices themselves. More and more companies are working on building a brain-computer interface, which would allow the mind to connect with artificial intelligence. Facebook is building a BCI that would let people type with their mind, and Elon Musk launched Neuralink to create devices that can be implanted in the brain. What do you think about the future of these innovations? I think it’s a super exciting area. This is another example where tech is meeting biology in stride. There are so many more advances than our understanding of neuroscience and the brain that happened just in the last five or 10 years. It’s very natural to apply machine learning to this brain-computer interface because the computer will have to understand and decode our thoughts — and that’s something that would be very hard to achieve without machine learning. This is very much the topic of science fiction past, but like any other technology, it will start off simple and evolve from there. The simple things will have a huge impact on human health. A brain computer interface could dramatically change the lives of quadriplegics and paraplegics, for example. As the technology gathers some scale and as machine learning gets stronger, these innovations will get closer and closer to your science fiction dreams. The cancer immunotherapy market is projected to reach $111.23 billion by 2021 and there’s been plenty of VC activity there lately. Why are we seeing more of that now? The oncology area is super interesting to me because many of us have felt that drugs don’t really kill or cure people — your immune system cures you. Even something like an antibiotic, which will kill a bacteria in a Petri dish, is largely administered in doses designed to weaken the bacteria so that your immune system can go after it. The immune system is really key. Machine learning will come into play more and more, and we’re seeing biology companies add a layer of tech that will help them accelerate innovation. What are some interesting innovations going on in the biotech space that Term Sheet readers should know about? There are a couple of different spaces that are getting interesting. One is diagnostics. We’re seeing diagnostics companies use AI to create new tests that have much higher accuracy, much lower cost, and typically diagnose things much earlier. In a sense, the genome sequencer is almost like a smartphone. With that one piece of hardware, you can run many different types of tests. That trend is very much blowing up. What do the next 10 years look like? Will we all have brain implants and be able to edit our genes as we wish? I think a lot of medicine will become like dentistry. Dentistry is a great example of doing things preventively. When you get an X-ray, for example, maybe you have a cavity and you just treat it. It’s not like you’re waiting until you’re 80 years old to finally see the dentist. I think in 10 years, we’ll have cancer tests such that you take them once or twice a year, find out you have early-stage cancer, take care of it early, and move on. The big trend is that we’ve got all these new data sources and machine learning to take advantage of and the ability do something actionable.
http://fortune.com/2017/10/25/term-sheet-wednesday-october-25/

First quantum computers need smart software

The world is about to have its first #quantumcomputers. The complexity and power of #quantumhardware, such as ion traps and #superconducting #qubits, are scaling up. Investment is flooding in: from governments, through the billion-dollar @EuropeanQuantumTechnologyFlagshipProgram, for example; from companies, including @Google, @IBM, @Intel and @Microsoft; and from venture-capital firms, which have funded start-ups. One such is ours, @RigettiComputing, which in June opened the first dedicated facility for making quantum integrated circuits: Fab-1 in Fremont, California. The vision is that commercial quantum- computing services will one day solve problems that used to be unimaginably hard, in areas from molecular design and machine learning to cybersecurity and logistics.1 The problem is how best to program these devices. The stakes are high — get this wrong and we will have experiments that nobody can use instead of technology that can change the world. Listen Reporter Lizzie Gibney talks to Will Zeng about quantum software. 00:00 Go to full podcast We outline three developments that are needed over the next five years to ensure that the first quantum computers can be programmed to perform useful tasks. First, developers must think in terms of 'hybrid' approaches that combine classical and quantum processors. For example, at Rigetti we have developed an interface called Quil2, which includes a set of basic instructions for managing quantum gates and classical processors and for reading and writing to and from shared memory. Second, researchers and engineers must build and use open-source software for quantum-computing applications. Third, scientists need to establish a quantum-programming community to nurture an ecosystem of software. This community must be interdisciplinary, inclusive and focused on applications. Hybrid systems Today's quantum programming differs from much previous theoretical work on algorithms; it is becoming more and more practical. Theoretical computer scientists have been developing potential algorithms for imagined quantum computers since the 1990s. Mathematician Peter Shor's famous code for breaking encryptions was one of the first; many more are listed in the Quantum Algorithm Zoo from the US National Institute of Standards and Technology (see go.nature.com/2inmtco). These algorithms are generally designed for big, noiseless quantum computers, which are unlike the devices that will be available within the next five years. These will have tens to thousands, not millions, of qubits, with little redundancy to correct for internal errors. They will calculate a limited range of things in a noisy way. For example, they will not be able to use Shor's algorithm to find the prime factors of large numbers. So their use must be targeted: they will not always beat conventional computers. Related stories Quantum software How quantum trickery can scramble cause and effect Commercialize quantum technologies in five years More related stories These limitations can be overcome by building quantum processors as 'accelerators' to boost the performance of conventional computers. A classical computer might, for example, optimize operations to compensate for noise in the quantum processor, or aggregate answers from sequences of short quantum programs. Such hybrid programming has been demonstrated in quantum chemistry3 and in optimization4. Algorithms that run on small, superconducting quantum processors have performed steps in calculating the ground states of materials and molecular systems, for example5, 6. Another algorithm has solved constrained optimization problems, which are common in areas such as machine learning, logistics and scheduling4. We've found, however, that it can be hard to predict the performance of hybrid algorithms. For example, the quality of the quantum subroutine in hybrid algorithms for chemistry can vary greatly depending on the system that is being simulated and the mathematical tricks used. So hybrid quantum-computing algorithms need to be studied empirically, as they are for machine learning. The way to find out how a system works is to build it, see what it does and back up any rules of thumb with mathematics later. This work will begin in earnest once the first quantum computers are available, and it will accelerate fast. To reach this stage, researchers must change their mindsets, and this could be hard. We will find that some past work has little utility. We've all seen talks on quantum algorithms whose complexities are peppered with huge exponents, meaning that they could take millions of years to complete. For the coming devices, such codes are so impractical as to be useless. Quantum programmers must care about practical details such as noise models and exact counts of logic gates. They will have to decide which qubits in the computer to use and how to deal with ranges of operational fidelities and low-level precisions that are foreign to most modern programmers. But the gain will be worth the pain. In turn, hardware designers need to be responsive to the choices and preferences of quantum programmers, so that their technology can become more useful. Open software Different classical computers behave similarly enough to enable software written for one to run on others. Early quantum computers will have their own nuances, and software for them will need to be bespoke. When each operation and instruction matters, generalized solutions need to be optimized, and software and hardware designed concurrently. Algorithms must be discovered numerically rather than algebraically, and developed using simulators and software rather than pens and paper. Innovative digital tools are needed for developing and testing algorithms, writing software and programming the devices. Quantum programmers should keep an eye on the underlying physics, so that they are aware of different types of noise in sequences of pulses, for example. Performance benchmarks, such as a suite of standard molecules to simulate, are also necessary. Differences between quantum and classical programming begin at the instruction level. Classical computers use Boolean logic — with basic operations such as AND, NOT, OR. Operations in quantum computations, such as multiplying tensors and matrices, are much more complex and result in unusual behaviour. For example, quantum information cannot be cloned exactly between processor registers; and reading the state of a quantum register alters the information stored in it.7 Hybrid software needs to handle all these behaviours simply enough for programmers to be able to code easily. The result will be a new programming paradigm, as object-oriented, probabilistic and distributed programming once were.  Rigetti Computing Inside the clean room at Rigetti Computing's Fab-1 facility in Fremont, California. Quantum programmers must decide which aspects of the system are essential for them to consider and which they can skim over in practice. For example, executing a program on superconducting quantum processors requires instructions to be translated many times. Control and readout instructions are converted from digital to analog to quantum to analog to digital as they go from the control hardware to the qubits and back. Programmers don't want to have to deal with all the microwave engineering and physics, but they need to be aware of how these processes affect noise or the time it takes to run the code. They need tools to work directly with the devices, so that they can understand and exploit the trade-offs. Easy programming interfaces are crucial to making quantum computers widely usable; examples include Quil and OpenQASM8 from IBM. More sophisticated options still need to be added, such as optimizations for specific types of processors. Higher-level languages for writing and compiling quantum programs also need to be developed. It is important that all these tools are open source. Such a model was not available at the dawn of digital computing, but its power to speed innovation, as with Linux in the early days of the web, is essential for the quantum-programming community to grow quickly. We have made a start with our quantum-programming toolkit, Forest, which is written in Python, open source and accessible to anyone. It joins an exciting early ecosystem — much of it open source — developed by different academic and industrial research groups. Other examples are LIQUi|> (embedded in F#), Scaffold (C++), Quipper (Haskell), QGL (Python), ProjectQ (Python), QCL, QuIDDPro and Chisel-Q (Scala). Researchers must resist pressure to standardize tools prematurely or keep the high-level, exploratory parts of the programming stack proprietary. Build a community A new breed of quantum programmer is needed to study and implement quantum software — with a skillset between that of a quantum information theorist and a software engineer. Such programmers will understand how quantum devices operate well enough to instruct them and minimize problems. They will be able to build usable software and will have a deep knowledge of the mathematics of quantum algorithms and computation. Experts from fields in which the software will be applied must be closely involved if the code is to be truly useful. For example, chemists such as Alán Aspuru-Guzik at Harvard University in Cambridge, Massachusetts, drove interest in using hybrid algorithms in quantum-chemistry calculations. Researchers in other fields, especially in machine learning and optimization, should get on board. “We will find that some past work has little utility.” Advanced kinds of education are needed to train this new breed. Several centres are well positioned to draw together the interdisciplinary skills and tools needed to offer degrees in quantum-computer engineering: the Institute for Quantum Computing at the University of Waterloo in Canada, the Institute for Quantum Information and Matter at the California Institute of Technology in Pasadena, the quantum-engineering doctoral training centres in the United Kingdom, and QuSoft, the Dutch research centre for quantum software in Amsterdam. At Rigetti we have started a Junior Quantum Engineer programme for bachelor's degree students, which includes training in quantum programming. We have partnered with the Quantum Machine Learning accelerator at the Creative Destruction Lab (a technology-transfer centre that fosters start-ups) at the University of Toronto, Canada, to provide access to and support for Forest and other programming tools. Early-career quantum programmers have tremendous opportunities to become leaders of a transformational field. But they need support. Their supervisors must recognize that work on an open-source software project might delay their next pure research paper. They need industrial internships to gain a broader practical perspective. And they need institutional backing to work between the fields of software engineering and quantum physics. Next steps It is crucial that research on quantum-computing algorithms is tied more closely to research on the software that's used to implement them. First, funders should insist that theoretical work is implemented in software and, as much as possible, tested on hardware. Second, algorithm researchers must be explicit about the architecture they are targeting. They must show evidence of how algorithms will be practically implemented on different noisy systems. Third, funders and journal editors must establish standard ways to assess algorithm performance and resource requirements. This will enable hardware and software to improve together, and will sift out the most viable algorithms more quickly. Open-source tools should be used wherever possible, and publications should encourage the publication of code alongside results. Finally, the quantum-computing community must prioritize engagement with experts in areas such as simulation and machine learning. Quantum and classical programmers must collaborate more. We call on every current and aspiring quantum-algorithm researcher to present their work at a classical conference at least once in the next year. It falls to us to expand the community that will realize the incredible potential of quantum computing.

http://www.nature.com/news/first-quantum-computers-need-smart-software-1.22590

Computing about to take a quantum leap

#Quantumcomputing can make computers work a lot faster In theory, #quantumcomputers could be vastly superior to regular or “classical” computers in performing certain kinds of tasks, but it’s been hard to build one Creating breakthrough medicines in just months instead of scores of years spent in complex research, cracking the toughest computer code in just minutes, creating fool proof financial models for capital markets that analyse trends and execute trades at lightning speeds or forecasting weather with absolute precision, with marginal possibilities of error. These are no longer in the realms of distant possibilities but could turn into reality in just a couple of years, as quantum computing research progresses rapidly with the promise of workable versions being available as early as end of this year.According to MarketWatch, the quantum computing market is projected to top $5bn by 2020, not quite the big leagues yet. It attracted $147 million in venture capital in the last three years alone, and $2.2 billion in government funding globally, according to a Deloitte analysis. Barclays and Goldman Sachs are investigating the use of quantum computing in areas such as "portfolio optimization, asset pricing, capital project budgeting and data security. From fintech, to big data, to hardware design, cybersecurity, general analysis services, information and systems modelling, biotechnology, and a host of other sectors, once quantum computing gains sufficient traction, this would unleash the next wave of disruption along with artificial intelligence.

https://www.nationalheraldindia.com/science-tech/computing-about-to-take-a-quantum-leap

Quantum leap: Obstacles remain, but a revolution in computing is almost here

Salesman Tom is about to hit the road for a monthlong journey to visit customers and sales prospects, a trip that will take him to 50 destinations across the United States. Tom needs to optimize the trip by three priorities. He must first maximize the time he spends with each client. Second, he must complete the trip in the shortest possible distance. Finally, he needs to do it at the lowest possible cost. This variation on the classic “traveling salesman problem” would take one of today’s off-the-shelf computer servers decades to solve definitively, if it could solve the problem at all. A quantum computer could knock it off in seconds. Route optimization is one of the sweet spots of this technology, but so are modeling chemical compounds, spotting patterns in DNA sequences, optimizing financial portfolios and forecasting weather. Fleets of autonomous vehicles will be managed far more safely and efficiently by quantum computers than by traditional ones. The technology could revolutionize risk analysis in the insurance industry. In short, the more complex the problem and the greater the number of variables involved, the better #quantumcomputing looks. That’s one reason the field has acquired an almost mythical aura over the more than 35 years that scientists have pursued it. Now, there’s mounting evidence that quantum computing is tantalizingly close to reality. Investment capital is flowing into the market, and some quantum computer developers are talking about showing prototypes of supercomputer-grade systems as soon as next year. Quantum startup #IonQ Inc. said last month that it has raised $22 million toward its goal of producing general-purpose quantum processors within 12 months. It’s the second startup in this field to score significant funding this year. #Rigetti Computing Inc. said in March that it has raised $64 million and expects to demonstrate a machine next year that will outperform the world’s largest supercomputers in some tasks. #IBM Corp. put a #quantumprocessor on its #publiccloud in March and invited researchers to experiment with it. The move closely followed an announcement by #DWave Systems Inc. that it sold a giant quantum machine to an unspecified customer for $15 million. And in July, a report said #Google Inc. also plans to offer researchers access to its new quantum computing technology via the cloud. The pace of activity this year is all the more remarkable given that, as recently as three years ago, experts were debating whether quantum computers could ever even be built. The consensus now is that it’s just a matter of time, and not that much time either. “The major obstacles toward coherent, capable systems are pretty much worked out,” said Andrew Bestwick, director of engineering at Rigetti. “The major challenges now are how to take something that’s been demonstrated on small systems and implement it in a highly scalable form.” That could also open up a lucrative new market. Currently it’s small, but it’s growing quickly. Market Research Future expects quantum computer sales to surge 24 percent annually to nearly $2.5 billion in 2022. Market Research Media Ltd. is even more optimistic, forecasting $5 billion in annual sales in 2020. Good timing The timing couldn’t be better. Moore’s Law, the doubling of the number of transistors on a square inch of silicon every two years, has propelled the computer industry for more than 50 years, but it appears to be finally running out of steam. Quantum computers could kick off a new era of growth in computing power. It’s badly needed. The Internet of Things promises to make networks dramatically more complex, requiring entirely new approaches to network management, a task well-suited to quantum technology. Then there’s the explosion of IoT data that will require new approaches to analyzing it all. And organizations that are searching for new efficiencies and revenue in the quest for digital transformation will find that quantum opens up vast new opportunities. Does that mean it’s time to hang a “for sale” sign on those Intel servers? Not yet, if ever. People on the front lines of quantum computer development say users can expect to see tangible benefits within the next few years, but the vaunted machines that can crack 256-bit encryption codes in seconds are still a decade or more away. D-Wave is the only company that’s currently shipping a commercial quantum computer, and experts dispute whether its technology is actually “true” quantum. IBM, Google and Microsoft Corp. are among the big players that have established their own initiatives, but the market is fragmented, leaderless and still squabbling over architectural details. But it’s not too early to start thinking about the problems that quantum technology could tackle: tasks of exponential complexity such as optimizing transportation routes, mapping molecular interactions, optimizing stock market portfolios and forecasting the weather. That requires understanding how quantum computing is different. Of bits and qubits The principles of quantum mechanics are so baffling to the average person that experts tend to fall back to describing the computing technology in terms of what it isn’t, which is traditional computing. Mainstream digital computers are based on binary arithmetic, in which numbers are expressed as combinations of ones and zeros. Performing calculations using these bits, or binary digits, is painfully slow, but it has the advantage of lending itself well to transistors, which exist in either an on or an off state. When you throw enough transistors at a problem, they can do that binary arithmetic at blinding speed. That’s how binary digital computers work.
https://siliconangle.com/blog/2017/08/04/quantum-leap-obstacles-remain-revolution-computing-almost/

Rigetti Computing Appoints General Peter Pace to Board of Directors

BERKELEY, Calif., July 13, 2017 /PRNewswire/ -- #RigettiComputing, a full-stack #quantumcomputing company, announced the appointment of retired Marine Corps General Peter Pace to its board of directors. General Pace served as the 16th Chairman of the Joint Chiefs of Staff from 2005-2007. "It's an honor to have General Pace join our board as an independent director. He has deep experience in leading mission-driven organizations," said Chad Rigetti, founder and CEO, Rigetti Computing. "I have come to know him over the past several months and have seen firsthand the integrity, intellect, and warmth that make him a great person and an accomplished leader." With more than four decades of leadership, strategic, and long-term planning experience, General Pace has demonstrated success in both military and business arenas. He currently holds leadership positions as a board member or advisor for several corporations and non-profit organizations. "I'm delighted to have the opportunity to work with the Rigetti Computing team," said General Pace. "I am genuinely impressed with their innovation in quantum computing, and I look forward to supporting their mission to build the world's most powerful computer." General Pace joins Chad Rigetti, CEO, Rigetti Computing; Charlie Songhurst, angel investor; and Vijay Pande, general partner, Andreessen Horowitz, on Rigetti Computing's board of directors. About Rigetti Computing Rigetti Computing is a full-stack quantum computing company. The company designs and manufactures quantum chips and builds software to integrate its systems with cloud infrastructure. Rigetti's Forest is the world's first full-stack programming and execution environment for quantum/classical computing. Currently in public beta, Forest can be used to develop and run quantum algorithms over the cloud. Rigetti Computing was recently recognized by MIT Technology Review as one of the world's 50 Smartest Companies. Rigetti Computing was founded in 2013 and is based in Berkeley, Calif. and Fremont, Calif.

http://www.prnewswire.com/news-releases/rigetti-computing-appoints-general-peter-pace-to-board-of-directors-300487612.html

SoftBank’s $100 Billion Vision Fund Eyes Quantum Computing

#SoftBank Group Corp.’s $100 billion Vision Fund is scouting for possible investments in #quantumcomputing, an experimental science being researched by companies such as #Google and #IBM to succeed current computer processor technology. Shu Nyatta, who helps invest money for the fund, said the group wanted to find and back the company whose quantum computing hardware or software that runs atop it would become the “de facto industry standard.” “We are happy to invest enough to create that standard around which the whole industry can coalesce,” Nyatta said, speaking during a panel discussion at a conference on quantum computing in Munich Thursday

The Vision Fund, which has attracted investment from the Public Investment Fund of Saudi Arabia, Apple Inc. and other large institutional backers, is investing in cutting edge technologies from virtual reality to the Internet of Things. It recently invested $500 million for a minority stake in Improbable, a London-based simulation and virtual reality software startup, that has few customers and little revenue.

Quantum Science

Once considered purely theoretical, researchers have made strides in building functioning quantum computers based around a number of different designs and approaches. 

International Business Machines Corp ( #IBM )., #Alphabet Inc.’s #Google and #RigettiComputing, a San Francisco-based #quantumcomputing startup, have created working machines around one method, while #IonQ, a spin-out from the University of Maryland and Duke University, is working on technologies based on another. Microsoft is backing a third architecture, but has yet to create a working machine. 

#DWave, a Canadian company, is the only firm to sell quantum computers today. D-Wave’s system is based around yet another architecture, but its machine can only solve a limited set of problems compared to those Google, IBM and the others have been working on.

In conventional computing, information is encoded in bits that can have a value of either 0 or 1. In quantum computing, information is encoded in qubits that take advantage of quantum mechanical principals such as superposition, which allows the qubit to be both 0 and 1 simultaneously. In theory, a quantum computer could tackle complex problems in seconds or minutes that would take a conventional supercomputer many hours or days to complete.

Gene Sequencing

Nyatta compared what needed to happen in quantum computing to what has happened in genomics, where Illumina Inc.’s gene sequencing technology has become the technology around which an entire ecosystem of companies has been built, or what has happened in artificial intelligence, where Nvidia Corp’s graphics processors have become the preferred hardware on which to run neural networks.

“We are happy to do it alone and at massive size to facilitate the future,” Nyatta said, speaking of SoftBank’s approach to investing in these frontier technologies.

https://www.bloomberg.com/news/articles/2017-06-23/softbank-s-100-billion-vision-fund-eyes-quantum-computing

THE QUANTUM COMPUTER FACTORY THAT’S TAKING ON GOOGLE AND IBM

A FEW YARDS from the stockpile of La Croix in the warehouse space behind startup #Rigetti Computing’s offices in Fremont, California, sits a machine like a steampunk illustration made real. Its steel chambers are studded with bolts, handles, and circular ports. But this monster is powered by electricity, not coal, and evaporates aluminum, not water—it makes superconducting electronics. Rigetti is using the machine and millions of dollars’ worth of other equipment housed here in hermetically sealed glass lab spaces to try and build a new kind of super-powerful computer that runs on quantum physics. It’s hardly alone in such an undertaking, though it is the underdog: #Rigetti is racing against similar projects at #Google, #Microsoft, #IBM, and #Intel. Every Bay Area startup will tell you it is doing something momentously difficult, but Rigetti is biting off more than most – it's working on quantum computing. All venture-backed startups face the challenge of building a business, but this one has to do it by making progress on one of tech's thorniest problems.

Rigetti, which has 80 employees, has raised nearly $70 million to develop quantum computers, which by encoding data into the physics apparent only at tiny scales should offer a, well, quantum leapin computing power. “This is going to be a very large industry—every major organization in the world will have to have a strategy for how to use this technology,” says Chad Rigetti, the company’s founder. The strapping 38-year-old physics PhD worked on quantum hardware at Yale and IBM before founding his own company in 2013 and taking it through the Y Combinator incubator better known for software startups like Dropbox.

No company is yet very close to offering up a quantum computer ready to do useful work existing computers can't. But Google has pledged to commercialize the technology within five years. IBM offers a cloud platform intended as a warmup for a future commercial service that lets developers and researchers play with a prototype chip located in Big Blue’s labs. After a few years of mostly staying quiet, Rigetti is now entering the fray. The company on Tuesday launched its own cloud platform, called Forest, where developers can write code for simulated quantum computers, and some partners get to access the startup's existing quantum hardware. Rigetti gave WIRED a peek at the new manufacturing facility in Fremont—grandly dubbed Fab-1—that just started making chips for testing at the company's headquarters in Berkeley.

The startup's founder, who has a rare fluency in both quantum information theory and Silicon Valley business-speak, says that being smaller than its giant competitors gives his company an advantage. “We’re pursuing this long-term objective with the urgency and product clarity of a startup,” says Rigetti. “That's something that large corporations aren’t culturally matched to do.” The urgency is existential: Google's effort is a hunt for a new line of business; Rigetti's a quest to have one at all.

https://www.wired.com/story/quantum-computing-factory-taking-on-google-ibm/

Rigetti Computing Raises $64 Million in Series A and B Funding, Led by Andreessen Horowitz and Vy Capital

BERKELEY, Calif., March 28, 2017 /PRNewswire/ -- #RigettiComputing, a leading #quantumcomputing start-up, announced it has raised $64 million in Series A and B funding. The Series A round of $24 million was led by Andreessen Horowitz. Vijay Pande, general partner at Andreessen Horowitz, has been appointed to Rigetti's Board of Directors, joining Rigetti CEO Chad Rigetti and angel investor Charlie Songhurst.

http://www.prnewswire.com/news-releases/rigetti-computing-raises-64-million-in-series-a-and-b-funding-led-by-andreessen-horowitz-and-vy-capital-300430164.html