Notices tagged with quantum

BeesGNU (kindcomputer@gnusocial.no)'s status on Sunday, 14Apr2019 14:04:16 PDT BeesGNU Futureproof cybersecurity: Addressing implementation challenges in #quantum #cryptography
https://gnusocial.no/url/1406403 
Dr. Roy Schestowitz (罗伊) (schestowitz@pleroma.site)'s status on Saturday, 30Mar2019 01:42:35 PDT Dr. Roy Schestowitz (罗伊) Error mitigation extends the computational reach of a noisy #quantum #processor
https://www.nature.com/articles/s4158601910407 full paper. New. 
Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Friday, 15Mar2019 23:11:55 PDT Hacker News ( unofficial ) Experimental rejection of observerindependence in the quantum world
The scientific method relies on facts, established through repeated measurements and agreed upon universally, independently of who observed them. In quantum mechanics, the objectivity of observations…
Article word count: 146HN Discussion: https://news.ycombinator.com/item?id=19404873
Posted by lisper (karma: 32554)
Post stats: Points: 124  Comments: 62  20190315T22:23:21Z#HackerNews #experimental #observerindependence #quantum #rejection #the #world
Article content:
(Submitted on 13 Feb 2019)
Abstract: The scientific method relies on facts, established through repeated measurements and agreed upon universally, independently of who observed them. In quantum mechanics, the objectivity of observations is not so clear, most dramatically exposed in Eugene Wignerʼs eponymous thought experiment where two observers can experience fundamentally different realities. While observerindependence has long remained inaccessible to empirical investigation, recent nogotheorems construct an extended Wignerʼs friend scenario with four entangled observers that allows us to put it to the test. In a stateoftheart 6photon experiment, we here realise this extended Wignerʼs friend scenario, experimentally violating the associated Belltype inequality by 5 standard deviations. This result lends considerable strength to interpretations of quantum theory already set in an observerdependent framework and demands for revision of those which are not.
From: Martin Ringbauer PhD [[1]view email]
[v1] Wed, 13 Feb 2019 19:00:07 UTC (1,967 KB)References
Visible links
1. https://arxiv.org/showemail/7fb3c799/1902.05080HackerNewsBot debug: Calculated post rank: 103  Loop: 137  Rank min: 100  Author rank: 51

Dr. Roy Schestowitz (罗伊) (schestowitz@joindiaspora.com)'s status on Saturday, 12Jan2019 05:42:13 PST Dr. Roy Schestowitz (罗伊) #IBM and #RedHat : #Quantum Hype, #ORNL and Red Hat #Ansible Tower
http://www.tuxmachines.org/node/119432 
Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Thursday, 03Jan2019 13:11:59 PST Hacker News ( unofficial ) How Space and Time Could Be a Quantum ErrorCorrecting Code
The same codes needed to thwart errors in quantum computers may also give the fabric of spacetime its intrinsic robustness.
Article word count: 2422HN Discussion: https://news.ycombinator.com/item?id=18817410
Posted by _Microft (karma: 1142)
Post stats: Points: 141  Comments: 42  20190103T18:08:20Z#HackerNews #and #code #could #errorcorrecting #how #quantum #space #time
Article content:
In 1994, a mathematician at AT&T Research named [1]Peter Shor brought instant fame to “quantum computers” when he [2]discovered that these hypothetical devices could quickly factor large numbers — and thus break much of modern cryptography. But a fundamental problem stood in the way of actually building quantum computers: the innate frailty of their physical components.
Unlike binary bits of information in ordinary computers, “qubits” consist of quantum particles that have some probability of being in each of two states, designated 0⟩ and 1⟩, at the same time. When qubits interact, their possible states become interdependent, each one’s chances of 0⟩ and 1⟩ hinging on those of the other. The contingent possibilities proliferate as the qubits become more and more “entangled” with each operation. Sustaining and manipulating this exponentially growing number of simultaneous possibilities are what makes quantum computers so theoretically powerful.
But qubits are maddeningly errorprone. The feeblest magnetic field or stray microwave pulse causes them to undergo “bitflips” that switch their chances of being 0⟩ and 1⟩ relative to the other qubits, or “phaseflips” that invert the mathematical relationship between their two states. For quantum computers to work, scientists must find schemes for protecting information even when individual qubits get corrupted. What’s more, these schemes must detect and correct errors without directly measuring the qubits, since measurements collapse qubits’ coexisting possibilities into definite realities: plain old 0s or 1s that can’t sustain quantum computations.
In 1995, Shor followed his factoring algorithm with another stunner: [3]proof that “quantum errorcorrecting codes” exist. The computer scientists [4]Dorit Aharonov and [5]Michael BenOr (and other researchers working independently) [6]proved a year later that these codes could theoretically push error rates close to zero. “This was the central discovery in the ’90s that convinced people that scalable quantum computing should be possible at all,” said [7]Scott Aaronson, a leading quantum computer scientist at the University of Texas — “that it is merely a staggering problem of engineering.”
Now, even as small quantum computers are materializing in labs around the world, useful ones that will outclass ordinary computers [8]remain years or decades away. Far more efficient quantum errorcorrecting codes are needed to cope with the daunting error rates of real qubits. The effort to design better codes is “one of the major thrusts of the field,” Aaronson said, along with improving the hardware.
But in the dogged pursuit of these codes over the past quartercentury, a funny thing happened in 2014, when physicists found evidence of a deep connection between quantum error correction and the nature of space, time and gravity. In Albert Einstein’s general theory of relativity, gravity is defined as the fabric of space and time — or “spacetime” — bending around massive objects. (A ball tossed into the air travels along a straight line through spacetime, which itself bends back toward Earth.) But powerful as Einstein’s theory is, physicists believe gravity must have a deeper, quantum origin from which the semblance of a spacetime fabric somehow emerges.
That year — 2014 — three young quantum gravity researchers came to an astonishing realization. They were working in physicists’ theoretical playground of choice: a [9]toy universe called “antide Sitter space” that works like a hologram. The bendy fabric of spacetime in the interior of the universe is a projection that emerges from entangled quantum particles living on its outer boundary. [10]Ahmed Almheiri, [11]Xi Dong and [12]Daniel Harlow did calculations suggesting that this holographic “emergence” of spacetime works just like a quantum errorcorrecting code. They [13]conjectured in the Journal of High Energy Physics that spacetime itself is a code — in antide Sitter (AdS) universes, at least. The paper has triggered a wave of activity in the quantum gravity community, and new quantum errorcorrecting codes have been discovered that capture more properties of spacetime.
[14]John Preskill, a theoretical physicist at the California Institute of Technology, says quantum error correction explains how spacetime achieves its “intrinsic robustness,” despite being [15]woven out of fragile quantum stuff. “We’re not walking on eggshells to make sure we don’t make the geometry fall apart,” Preskill said. “I think this connection with quantum error correction is the deepest explanation we have for why that’s the case.”
The language of quantum error correction is also starting to enable researchers to probe the mysteries of black holes: spherical regions in which spacetime curves so steeply inward toward the center that not even light can escape. “Everything traces back to black holes,” said Almheiri, who is now at the Institute for Advanced Study in Princeton, New Jersey. These paradoxridden places are where gravity reaches its zenith and Einstein’s general relativity theory fails. “There are some indications that if you understand which code spacetime implements,” he said, “it might help us in understanding the black hole interior.”
As a bonus, researchers hope holographic spacetime might also point the way to scalable quantum computing, fulfilling the longago vision of Shor and others. “Spacetime is a lot smarter than us,” Almheiri said. “The kind of quantum errorcorrecting code which is implemented in these constructions is a very efficient code.”
So, how do quantum errorcorrecting codes work? The trick to protecting information in jittery qubits is to store it not in individual qubits, but in patterns of entanglement among many.
As a simple example, consider the threequbit code: It uses three “physical” qubits to protect a single “logical” qubit of information against bitflips. (The code isn’t really useful for quantum error correction because it can’t protect against phaseflips, but it’s nonetheless instructive.) The 0⟩ state of the logical qubit corresponds to all three physical qubits being in their 0⟩ states, and the 1⟩ state corresponds to all three being 1⟩’s. The system is in a “superposition” of these states, designated 000⟩ + 111⟩. But say one of the qubits bitflips. How do we detect and correct the error without directly measuring any of the qubits?
The qubits can be fed through two gates in a quantum circuit. One gate checks the “parity” of the first and second physical qubit — whether they’re the same or different — and the other gate checks the parity of the first and third. When there’s no error (meaning the qubits are in the state 000⟩ + 111⟩), the paritymeasuring gates determine that both the first and second and the first and third qubits are always the same. However, if the first qubit accidentally bitflips, producing the state 100⟩ + 011⟩, the gates detect a difference in both of the pairs. For a bitflip of the second qubit, yielding 010⟩ + 101⟩, the paritymeasuring gates detect that the first and second qubits are different and first and third are the same, and if the third qubit flips, the gates indicate: same, different. These unique outcomes reveal which corrective surgery, if any, needs to be performed — an operation that flips back the first, second or third physical qubit without collapsing the logical qubit. “Quantum error correction, to me, it’s like magic,” Almheiri said.
The best errorcorrecting codes can typically recover all of the encoded information from slightly more than half of your physical qubits, even if the rest are corrupted. This fact is what hinted to Almheiri, Dong and Harlow in 2014 that quantum error correction might be related to the way antide Sitter spacetime arises from quantum entanglement.
It’s important to note that AdS space is different from the spacetime geometry of our “de Sitter” universe. Our universe is infused with positive vacuum energy that causes it to expand without bound, while antide Sitter space has negative vacuum energy, which gives it the hyperbolic geometry of one of M.C. Escher’s Circle Limit designs. Escher’s tessellated creatures become smaller and smaller moving outward from the circle’s center, eventually vanishing at the perimeter; similarly, the spatial dimension radiating away from the center of AdS space gradually shrinks and eventually disappears, establishing the universe’s outer boundary. AdS space gained popularity among quantum gravity theorists in 1997 after the renowned physicist [16]Juan Maldacena discovered that the bendy spacetime fabric in its interior is “holographically dual” to a quantum theory of particles living on the lowerdimensional, gravityfree boundary.
In exploring how the duality works, as hundreds of physicists have in the past two decades, Almheiri and colleagues noticed that any point in the interior of AdS space could be constructed from slightly more than half of the boundary — just as in an optimal quantum errorcorrecting code.
In their paper conjecturing that holographic spacetime and quantum error correction are one and the same, they described how even a simple code could be understood as a 2D hologram. It consists of three “qutrits” — particles that exist in any of three states — sitting at equidistant points around a circle. The entangled trio of qutrits encode one logical qutrit, corresponding to a single spacetime point in the circle’s center. The code protects the point against the erasure of any of the three qutrits.
Of course, one point is not much of a universe. In 2015, Harlow, Preskill, Fernando Pastawski and Beni Yoshida [17]found another holographic code, nicknamed the HaPPY code, that captures more properties of AdS space. The code tiles space in fivesided building blocks — “little Tinkertoys,” said [18]Patrick Hayden of Stanford University, a leader in the research area. Each Tinkertoy represents a single spacetime point. “These tiles would be playing the role of the fish in an Escher tiling,” Hayden said.
In the HaPPY code and other holographic errorcorrecting schemes that have been discovered, everything inside a region of the interior spacetime called the “entanglement wedge” can be reconstructed from qubits on an adjacent region of the boundary. Overlapping regions on the boundary will have overlapping entanglement wedges, Hayden said, just as a logical qubit in a quantum computer is reproducible from many different subsets of physical qubits. “That’s where the errorcorrecting property comes in.”
“Quantum error correction gives us a more general way of thinking about geometry in this code language,” said Preskill, the Caltech physicist. The same language, he said, “ought to be applicable, in my opinion, to more general situations” — in particular, to a de Sitter universe like ours. But de Sitter space, lacking a spatial boundary, has so far proven much harder to understand as a hologram.
For now, researchers like Almheiri, Harlow and Hayden are sticking with AdS space, which shares many key properties with a de Sitter world but is simpler to study. Both spacetime geometries abide by Einstein’s theory; they simply curve in different directions. Perhaps most importantly, both kinds of universes contain black holes. “The most fundamental property of gravity is that there are black holes,” said Harlow, who is now an assistant professor of physics at the Massachusetts Institute of Technology. “That’s what makes gravity different from all the other forces. That’s why quantum gravity is hard.”
The language of quantum error correction has provided a new way of describing black holes. The presence of a black hole is defined by “the breakdown of correctability,” Hayden said: “When there are so many errors that you can no longer keep track of what’s going on in the bulk [spacetime] anymore, you get a black hole. It’s like a sink for your ignorance.”
Ignorance invariably abounds when it comes to black hole interiors. Stephen Hawking’s 1974 epiphany that black holes radiate heat, and thus eventually evaporate away, triggered the infamous [19]“black hole information paradox,” which asks what happens to all the information that black holes swallow. Physicists need a quantum theory of gravity to understand how things that fall in black holes also get out. The issue may relate to cosmology and the birth of the universe, since expansion out of a Big Bang singularity is much like gravitational collapse into a black hole in reverse.
AdS space simplifies the information question. Since the boundary of an AdS universe is holographically dual to everything in it — black holes and all — the information that falls into a black hole is guaranteed never to be lost; it’s always holographically encoded on the universe’s boundary. Calculations suggest that to reconstruct information about a black hole’s interior from qubits on the boundary, you need access to entangled qubits throughout roughly threequarters of the boundary. “Slightly more than half is not sufficient anymore,” Almheiri said. He added that the need for threequarters seems to say something important about quantum gravity, but why that fraction comes up “is still an open question.”
In Almheiri’s first claim to fame in 2012, the tall, thin Emirati physicist and three collaborators [20]deepened the information paradox. Their reasoning suggested that information might be prevented from ever falling into a black hole in the first place, by a “firewall” at the black hole’s event horizon.
Like most physicists, Almheiri doesn’t really believe black hole firewalls exist, but finding the way around them has proved difficult. Now, he thinks quantum error correction is what stops firewalls from forming, by protecting information even as it crosses black hole horizons. In [21]his latest, solo work, which appeared in October, he reported that quantum error correction is “essential for maintaining the smoothness of spacetime at the horizon” of a twomouthed black hole, called a wormhole. He speculates that quantum error correction, as well as preventing firewalls, is also how qubits escape a black hole after falling in, through strands of entanglement between the inside and outside that are themselves like miniature wormholes. This would resolve Hawking’s paradox.
This year, the Department of Defense is [22]funding research into holographic spacetime, at least partly in case advances there might spin off more efficient errorcorrecting codes for quantum computers.
On the physics side, it remains to be seen whether de Sitter universes like ours can be described holographically, in terms of qubits and codes. “The whole connection is known for a world that is manifestly not our world,” Aaronson said. In [23]a paper last summer, Dong, who is now at the University of California, Santa Barbara, and his coauthors [24]Eva Silverstein and Gonzalo Torroba took a step in the de Sitter direction, with an attempt at a primitive holographic description. Researchers are still studying that particular proposal, but Preskill thinks the language of quantum error correction will ultimately carry over to actual spacetime.
“It’s really entanglement which is holding the space together,” he said. “If you want to weave spacetime together out of little pieces, you have to entangle them in the right way. And the right way is to build a quantum errorcorrecting code.”
Ancient Turing Pattern Builds Feathers, Hair — and Now, Shark Skin
References
Visible links
1. http://wwwmath.mit.edu/~shor/
2. https://arxiv.org/pdf/quantph/9508027.pdf
3. https://journals.aps.org/pra/abstract/10.1103/PhysRevA.52.R2493
4. http://www.cs.huji.ac.il/~doria/
5. https://www.cs.huji.ac.il/~benor/
6. https://arxiv.org/abs/quantph/9611025
7. https://www.scottaaronson.com/
8. https://www.quantamagazine.org/theeraofquantumcomputingishereoutlookcloudy20180124/
9. https://www.quantamagazine.org/alberteinsteinhologramsandquantumgravity20181114/
10. https://www.ias.edu/scholars/ahmedalmheiri
11. http://www.physics.ucsb.edu/people/xidong
12. http://web.mit.edu/physics/people/faculty/harlow_daniel.html
13. https://arxiv.org/abs/1411.7041
14. http://www.theory.caltech.edu/people/preskill/
15. https://www.quantamagazine.org/tensornetworksandentanglement20150428/
16. https://www.quantamagazine.org/juanmaldacenaponderingquantumgravitybythepond20170623/
17. https://arxiv.org/abs/1503.06237
18. https://web.stanford.edu/~phayden/
19. https://www.quantamagazine.org/stephenhawkingsblackholeparadoxkeepsphysicistspuzzled20180314/
20. https://arxiv.org/abs/1207.3123
21. https://arxiv.org/abs/1810.02055
22. https://grantbulletin.research.uiowa.edu/fy2019defensemultidisciplinaryresearchprogramuniversityresearchinitiativemuriwhitepaper
23. https://link.springer.com/article/10.1007%2FJHEP07%282018%29050
24. https://sitp.stanford.edu/people/evasilversteinHackerNewsBot debug: Calculated post rank: 108  Loop: 140  Rank min: 100  Author rank: 53

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Sunday, 23Dec2018 00:11:48 PST Hacker News ( unofficial ) President Trump has signed a $1.2B law to boost US quantum tech
The new National Quantum Initiative Act will give America a national masterplan for advancing quantum technologies.
Article word count: 244HN Discussion: https://news.ycombinator.com/item?id=18744464
Posted by jonbaer (karma: 42115)
Post stats: Points: 145  Comments: 37  20181223T04:37:56Z#HackerNews #12b #boost #has #law #president #quantum #signed #tech #trump
Article content:
The new National Quantum Initiative Act will give America a national masterplan for advancing quantum technologies.
The news: The US president just signed into law [1]a bill that commits the government to providing $1.2 billion to fund activities promoting quantum information science over an initial fiveyear period. The new law, which was signed just as a partial US government shutdown began, will provide a significant boost to research, and to efforts to develop a future quantum workforce in the country.
The background: [2]Quantum computers leverage exotic phenomena from quantum physics to produce exponential leaps in computing power. The hope is that these machines will ultimately be able to outstrip even the most powerful classical supercomputers. Those same quantum phenomena can also be tapped to create [3]highly secure communications networks and other advances.
China, which has been investing heavily in quantum technology, sees the field as an opportunity to l[4]eapfrog the US. The European Union has also launched a €1 billion ($1.1 billion) quantum masterplan. America has a long history of investing in quantum science, but it’s lacked a comprehensive strategy for coordinating research efforts. The new legislation, which has strong bipartisan support in Congress, should help fix that.
The details: The act establishes a National Quantum Coordination Office that will be part of the White House Office of Science and Technology Policy. It also calls for the development of a multiyear strategic plan to help keep America at the forefront of the quantum revolution.
References
Visible links
1. https://www.congress.gov/bill/115thcongress/housebill/6227
2. https://www.technologyreview.com/s/610250/seriousquantumcomputersarefinallyherewhatarewegoingtodowiththem/
3. https://www.technologyreview.com/s/612342/usunhackablequantumnetworks/
4. https://www.technologyreview.com/s/612596/themanturningchinaintoaquantumsuperpower/HackerNewsBot debug: Calculated post rank: 109  Loop: 35  Rank min: 100  Author rank: 42

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Wednesday, 28Nov2018 11:11:58 PST Hacker News ( unofficial ) Amazon Quantum Ledger Database
Amazon QLDB is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log owned by a central trusted authority. Amazon QLDB tracks each…
Article word count: 384HN Discussion: https://news.ycombinator.com/item?id=18553387
Posted by mcrute (karma: 409)
Post stats: Points: 140  Comments: 78  20181128T17:23:17Z#HackerNews #amazon #database #ledger #quantum
Article content:
Amazon QLDB is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log owned by a central trusted authority. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time.
Ledgers are typically used to record a history of economic and financial activity in an organization. Many organizations build applications with ledgerlike functionality because they want to maintain an accurate history of their applicationsʼ data, for example, tracking the history of credits and debits in banking transactions, verifying the data lineage of an insurance claim, or tracing movement of an item in a supply chain network. Ledger applications are often implemented using custom audit tables or audit trails created in relational databases. However, building audit functionality with relational databases is timeconsuming and prone to human error. It requires custom development, and since relational databases are not inherently immutable, any unintended changes to the data are hard to track and verify. Alternatively, blockchain frameworks, such as Hyperledger Fabric and Ethereum, can also be used as a ledger. However, this adds complexity as you need to setup an entire blockchain network with multiple nodes, manage its infrastructure, and require the nodes to validate each transaction before it can be added to the ledger.
Amazon QLDB is a new class of database that eliminates the need to engage in the complex development effort of building your own ledgerlike applications. With QLDB, your data’s change history is immutable – it cannot be altered or deleted – and using cryptography, you can easily verify that there have been no unintended modifications to your application’s data. QLDB uses an immutable transactional log, known as a journal, that tracks each application data change and maintains a complete and verifiable history of changes over time. QLDB is easy to use because it provides developers with a familiar SQLlike API, a flexible document data model, and full support for transactions. QLDB is also serverless, so it automatically scales to support the demands of your application. There are no servers to manage and no read or write limits to configure. With QLDB, you only pay for what you use.
Sign Up for the [1]Amazon QLDB preview today!
Looking to build a blockchain application? Learn more about Amazon Managed Blockchain [2]here.
Amazon QLDB uses a journal that tracks each application data change and maintains a complete and sequenced history of changes over time. Data on the journal cannot be deleted or modified. The full history of your database can be accessed and you can query and analyze the history to see how your data has changed over time.
With Amazon QLDB, you can trust that the history of changes to your application data is accurate. QLDB uses a cryptographic hash function (SHA256) to generate a secure output file of your data’s change history, known as a digest. The digest acts as a proof of your data’s change history, allowing you to look back and validate the integrity of your data changes.
Amazon QLDB is highly scalable and can execute 2 – 3X as many transactions than ledgers in common blockchain frameworks. Blockchain frameworks are decentralized so to execute a transaction, they require a majority of members of the network to reach consensus on the validity of the transaction. On the other hand, QLDB has a centralized design, allowing its transactions to execute without the need for multiparty consensus.
With Amazon QLDB, you don’t have to worry about provisioning capacity or configuring read and write limits. You create a ledger, define your tables, and QLDB automatically scales to support the demands of your application. To help you gain a better understanding of the operational health of your database, QLDB also allows you to monitor operational metrics such as readevents, writeevents, storage, etc.
Amazon QLDB’s familiar database capabilities make it easy to use. QLDB’s SQLlike API allows you to query, manage and update your data using SQL operators. QLDB’s documentoriented data model is flexible, enabling you to easily store and process both structured and semistructured data.
[3]ProductPageDiagram_AWSQuantum(2)
Banks often need a centralized ledgerlike application to keep track of critical data, such as credit and debit transactions across customer bank accounts. Instead of building a custom ledger which has complex auditing functionality, banks can use QLDB to easily store an accurate and complete record of all financial transactions.
Manufacturing companies often need to reconcile data between their supply chain systems to track the full manufacturing history of a product. A ledger database can be used to record the history of each transaction, and provide details of every individual batch of the product manufactured at a facility. In case of a product recall, manufacturers can use QLDB to easily trace the history of the entire production and distribution lifecycle of a product.
Insurance applications often need a way to better track the history of claim transactions. Instead of building complex auditing functionality using relational databases, insurance companies can use QLDB to accurately maintain the history of claims over their entire lifetime, and whenever a potential conflict arises, QLDB can also help cryptographically verify the integrity of the claims data (e.g., whether a claim was submitted accurately), making the application resilient against data entry errors and manipulation.
HR systems often have to track and maintain a record of an employee’s details such as payroll, bonus, benefits, performance history, and insurance. By implementing a systemofrecord application using QLDB, customers can easily maintain a trusted and complete record of the digital history of their employees, in a single place.
Retailers often need to access information on every stage of a productʼs supply chain, such as what location did the product come from, how many items of the product were shipped and to whom, who handled the shipment, etc. With QLDB, retail companies can look back and track the full history of inventory and supply chain transactions at every logistical stage of their products.
[4]600x400healthdirect_logo
Healthdirect Australia is a national, governmentowned, notforprofit organization that has been helping Australians manage their health and wellbeing for over a decade.
“Healthdirect Australia operates in a heavily regulated industry, and it is critical that our compliance data is correct and auditable. With Amazon QLDB we look forward to having a complete and verifiable history of every change in our system, making it simple to audit when and how we arrived at our current state. Regulatory compliance is fact of life for healthcare companies, and Amazon QLDB enables us to easily track the controls we have in place and understand how they have changed over time.”
Bruce Haefele  General Manager, Technology, Healthdirect Australia
[5]Smaato
Smaato, one of the leading online ad exchanges in the world, sees over 20 billion bid requests/day pass through their exchange.
“All participants in the online advertising and real time bidding market segments are interested in transparency throughout the entire lifecycle of an online ad auction, from the initial auction, to the winning bid, and ultimately through to the final impression delivery and who it’s actually shown to. Since we act as a centralized point of trust in this value chain, we’re really excited about Amazon QLDB. It offers a scalable, immutable, and cryptographically verifiable ledger that would allow us to maintain a complete and auditable record of all our auctions. We’re looking forward to collaborating with AWS and the Amazon Quantum Ledger Database team to bring more transparency to our ecosystem.”
Ragnar Kruse  CEO, Smaato
References
Visible links
1. https://pages.awscloud.com/QuantumLedgerDatabasepreview.html
2. https://aws.amazon.com/managedblockchain/HackerNewsBot debug: Calculated post rank: 119  Loop: 138  Rank min: 100  Author rank: 136

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Thursday, 15Nov2018 22:15:50 PST Hacker News ( unofficial ) The Case Against Quantum Computing
The proposed strategy relies on manipulating with high precision an unimaginably huge number of variables
HN Discussion: https://news.ycombinator.com/item?id=18462374
Posted by niccl (karma: 184)
Post stats: Points: 100  Comments: 71  20181115T19:25:06Z#HackerNews #against #case #computing #quantum #the
Article content:
[1]img[2]
Illustration: Christian GralingenQuantum computing is all the rage. It seems like hardly a day goes by without some news outlet describing the extraordinary things this technology promises. Most commentators forget, or just gloss over, the fact that people have been working on quantum computing for decades—and without any practical results to show for it.
[3]We’ve been told that quantum computers could “provide breakthroughs in many disciplines, including materials and drug discovery, the optimization of complex manmade systems, and artificial intelligence.” [4]We’ve been assured that quantum computers will “forever alter our economic, industrial, academic, and societal landscape.” [5]We’ve even been told that “the encryption that protects the world’s most sensitive data may soon be broken” by quantum computers. It has gotten to the point where many researchers in various fields of physics feel obliged to justify whatever work they are doing by claiming that it has some relevance to quantum computing.
Meanwhile, government research agencies, academic departments (many of them funded by government agencies), and corporate laboratories are spending billions of dollars a year developing quantum computers. On Wall Street, Morgan Stanley and other financial giants [6]expect quantum computing to mature soon and are keen to figure out how this technology can help them.
It’s become something of a selfperpetuating arms race, with many organizations seemingly staying in the race if only to avoid being left behind. Some of the world’s top technical talent, at places like Google, IBM, and Microsoft, are working hard, and with lavish resources in stateoftheart laboratories, to realize their vision of a quantumcomputing future.
In light of all this, it’s natural to wonder: When will useful quantum computers be constructed? The most optimistic experts estimate it will take 5 to 10 years. More cautious ones predict 20 to 30 years. (Similar predictions have been voiced, by the way, for the last 20 years.) I belong to a tiny minority that answers, “Not in the foreseeable future.” Having spent decades conducting research in quantum and condensedmatter physics, I’ve developed my very pessimistic view. It’s based on an understanding of the gargantuan technical challenges that would have to be overcome to ever make quantum computing work.
The idea of quantum computing first appeared nearly 40 years ago, in 1980, when the Russianborn mathematician Yuri Manin, who now works at the Max Planck Institute for Mathematics, in Bonn, first put forward the notion, albeit in a rather vague form. The concept really got on the map, though, the following year, when physicist Richard Feynman, at the California Institute of Technology, independently proposed it.
Realizing that computer simulations of quantum systems become impossible to carry out when the system under scrutiny gets too complicated, Feynman advanced the idea that the computer itself should operate in the quantum mode: “Nature isn’t classical, dammit, and if you want to make a simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy,” he opined. A few years later, Oxford physicist David Deutsch formally described a generalpurpose quantum computer, a quantum analog of the universal Turing machine.
The subject did not attract much attention, though, until 1994, when mathematician Peter Shor (then at Bell Laboratories and now at MIT) proposed [7]an algorithm for an ideal quantum computer that would allow very large numbers to be factored much faster than could be done on a conventional computer. This outstanding theoretical result triggered an explosion of interest in quantum computing. Many thousands of research papers, mostly theoretical, have since been published on the subject, and they continue to come out at an increasing rate.
The basic idea of quantum computing is to store and process information in a way that is very different from what is done in conventional computers, which are based on classical physics. Boiling down the many details, it’s fair to say that conventional computers operate by manipulating a large number of tiny transistors working essentially as onoff switches, which change state between cycles of the computer’s clock.
The state of the classical computer at the start of any given clock cycle can therefore be described by a long sequence of bits corresponding physically to the states of individual transistors. With N transistors, there are 2^N possible states for the computer to be in. Computation on such a machine fundamentally consists of switching some of its transistors between their “on” and “off” states, according to a prescribed program.
[8]Illustration: Christian Gralingen Illustration: Christian Gralingen
In quantum computing, the classical twostate circuit element (the transistor) is replaced by a quantum element called a quantum bit, or qubit. Like the conventional bit, it also has two basic states. Although a variety of physical objects could reasonably serve as quantum bits, the simplest thing to use is the electron’s internal angular momentum, or spin, which has the peculiar quantum property of having only two possible projections on any coordinate axis: +1/2 or –1/2 (in units of the Planck constant). For whatever the chosen axis, you can denote the two basic quantum states of the electron’s spin as ↑ and ↓.
Here’s where things get weird. With the quantum bit, those two states aren’t the only ones possible. That’s because the spin state of an electron is described by a quantummechanical wave function. And that function involves two complex numbers, α and β (called quantum amplitudes), which, being complex numbers, have real parts and imaginary parts. Those complex numbers, α and β, each have a certain magnitude, and according to the rules of quantum mechanics, their squared magnitudes must add up to 1.
That’s because those two squared magnitudes correspond to the probabilities for the spin of the electron to be in the basic states ↑ and ↓ when you measure it. And because those are the only outcomes possible, the two associated probabilities must add up to 1. For example, if the probability of finding the electron in the ↑ state is 0.6 (60 percent), then the probability of finding it in the ↓ state must be 0.4 (40 percent)—nothing else would make sense.
In contrast to a classical bit, which can only be in one of its two basic states, a qubit can be in any of a continuum of possible states, as defined by the values of the quantum amplitudes α and β. This property is often described by the rather mystical and intimidating statement that a qubit can exist simultaneously in both of its ↑ and ↓ states.
Yes, quantum mechanics often defies intuition. But this concept shouldn’t be couched in such perplexing language. Instead, think of a vector positioned in the xy plane and canted at 45 degrees to the xaxis. Somebody might say that this vector simultaneously points in both the x and ydirections. That statement is true in some sense, but it’s not really a useful description. Describing a qubit as being simultaneously in both ↑ and ↓ states is, in my view, similarly unhelpful. And yet, it’s become almost de rigueur for journalists to describe it as such.
In a system with two qubits, there are 2^2 or 4 basic states, which can be written (↑↑), (↑↓), (↓↑), and (↓↓). Naturally enough, the two qubits can be described by a quantummechanical wave function that involves four complex numbers. In the general case of N qubits, the state of the system is described by 2^N complex numbers, which are restricted by the condition that their squared magnitudes must all add up to 1.
While a conventional computer with N bits at any given moment must be in one of its 2^N possible states, the state of a quantum computer with N qubits is described by the values of the 2^N quantum amplitudes, which are continuous parameters (ones that can take on any value, not just a 0 or a 1). This is the origin of the supposed power of the quantum computer, but it is also the reason for its great fragility and vulnerability.
How is information processed in such a machine? That’s done by applying certain kinds of transformations—dubbed “quantum gates”—that change these parameters in a precise and controlled manner.
Experts estimate that the number of qubits needed for a useful quantum computer, one that could compete with your laptop in solving certain kinds of interesting problems, is between 1,000 and 100,000. So the number of continuous parameters describing the state of such a useful quantum computer at any given moment must be at least 2^1,000, which is to say about 10^300. That’s a very big number indeed. How big? It is much, much greater than the number of subatomic particles in the observable universe.
To repeat: A useful quantum computer needs to process a set of continuous parameters that is larger than the number of subatomic particles in the observable universe.
At this point in a description of a possible future technology, a hardheaded engineer loses interest. But let’s continue. In any realworld computer, you have to consider the effects of errors. In a conventional computer, those arise when one or more transistors are switched off when they are supposed to be switched on, or vice versa. This unwanted occurrence can be dealt with using relatively simple errorcorrection methods, which make use of some level of redundancy built into the hardware.
In contrast, it’s absolutely unimaginable how to keep errors under control for the 10^300 continuous parameters that must be processed by a useful quantum computer. Yet quantumcomputing theorists have succeeded in convincing the general public that this is feasible. Indeed, they claim that something called the threshold theorem proves it can be done. They point out that once the error per qubit per quantum gate is below a certain value, indefinitely long quantum computation becomes possible, at a cost of substantially increasing the number of qubits needed. With those extra qubits, they argue, you can handle errors by forming logical qubits using multiple physical qubits.
How many physical qubits would be required for each logical qubit? No one really knows, but estimates typically range from about 1,000 to 100,000. So the upshot is that a useful quantum computer now needs a million or more qubits. And the number of continuous parameters defining the state of this hypothetical quantumcomputing machine—which was already more than astronomical with 1,000 qubits—now becomes even more ludicrous.
Even without considering these impossibly large numbers, it’s sobering that no one has yet figured out how to combine many physical qubits into a smaller number of logical qubits that can compute something useful. And it’s not like this hasn’t long been a key goal.
In the early 2000s, at the request of the [9]Advanced Research and Development Activity (a funding agency of the U.S. intelligence community that is now part of Intelligence Advanced Research Projects Activity), a team of distinguished experts in quantum information established [10]a road map for quantum computing. It had a goal for 2012 that “requires on the order of 50 physical qubits” and “exercises multiple logical qubits through the full range of operations required for faulttolerant [quantum computation] in order to perform a simple instance of a relevant quantum algorithm….” It’s now the end of 2018, and that ability has still not been demonstrated.
[11]Illustration: Christian Gralingen Illustration: Christian Gralingen
The huge amount of scholarly literature that’s been generated about quantumcomputing is notably light on experimental studies describing actual hardware. The relatively few experiments that have been reported were extremely difficult to conduct, though, and must command respect and admiration.
The goal of such proofofprinciple experiments is to show the possibility of carrying out basic quantum operations and to demonstrate some elements of the quantum algorithms that have been devised. The number of qubits used for them is below 10, usually from 3 to 5. Apparently, going from 5 qubits to 50 (the goal set by the ARDA Experts Panel for the year 2012) presents experimental difficulties that are hard to overcome. Most probably they are related to the simple fact that 2^5 = 32, while 2^50 = 1,125,899,906,842,624.
By contrast, the theory of quantum computing does not appear to meet any substantial difficulties in dealing with millions of qubits. In studies of error rates, for example, various noise models are being considered. It has been proved (under certain assumptions) that errors generated by “local” noise can be corrected by carefully designed and very ingenious methods, involving, among other tricks, massive parallelism, with many thousands of gates applied simultaneously to different pairs of qubits and many thousands of measurements done simultaneously, too.
A decade and a half ago, ARDA’s Experts Panel noted that “it has been established, under certain assumptions, that if a threshold precision per gate operation could be achieved, quantum error correction would allow a quantum computer to compute indefinitely.” Here, the key words are “under certain assumptions.” That panel of distinguished experts did not, however, address the question of whether these assumptions could ever be satisfied.
I argue that they can’t. In the physical world, continuous quantities (be they voltages or the parameters defining quantummechanical wave functions) can be neither measured nor manipulated exactly. That is, no continuously variable quantity can be made to have an exact value, including zero. To a mathematician, this might sound absurd, but this is the unquestionable reality of the world we live in, as any engineer knows.
Sure, discrete quantities, like the number of students in a classroom or the number of transistors in the “on” state, can be known exactly. Not so for quantities that vary continuously. And this fact accounts for the great difference between a conventional digital computer and the hypothetical quantum computer.
Indeed, all of the assumptions that theorists make about the preparation of qubits into a given state, the operation of the quantum gates, the reliability of the measurements, and so forth, cannot be fulfilled exactly. They can only be approached with some limited precision. So, the real question is: What precision is required? With what exactitude must, say, the square root of 2 (an irrational number that enters into many of the relevant quantum operations) be experimentally realized? Should it be approximated as 1.41 or as 1.41421356237? Or is even more precision needed? Amazingly, not only are there no clear answers to these crucial questions, but they were never even discussed!
While various strategies for building quantum computers are now being explored, an approach that many people consider the most promising, initially undertaken by the Canadian company DWave Systems and now being pursued by IBM, Google, Microsoft, and others, is based on using quantum systems of interconnected Josephson junctions cooled to very low temperatures (down to about 10 millikelvins).
The ultimate goal is to create a universal quantum computer, one that can beat conventional computers in factoring large numbers using Shor’s algorithm, performing database searches by a similarly famous [12]quantumcomputing algorithm that Lov Grover developed at Bell Laboratories in 1996, and other specialized applications that are suitable for quantum computers.
On the hardware front, advanced research is under way, with a [13]49qubit chip (Intel), a [14]50qubit chip (IBM), and a [15]72qubit chip (Google) having recently been fabricated and studied. The eventual outcome of this activity is not entirely clear, especially because these companies have not revealed the details of their work.
While I believe that such experimental research is beneficial and may lead to a better understanding of complicated quantum systems, I’m skeptical that these efforts will ever result in a practical quantum computer. Such a computer would have to be able to manipulate—on a microscopic level and with enormous precision—a physical system characterized by an unimaginably huge set of parameters, each of which can take on a continuous range of values. Could we ever learn to control the more than 10^300 continuously variable parameters defining the quantum state of such a system?
My answer is simple. No, never.
I believe that, appearances to the contrary, the quantum computing fervor is nearing its end. That’s because a few decades is the maximum lifetime of any big bubble in technology or science. After a certain period, too many unfulfilled promises have been made, and anyone who has been following the topic starts to get annoyed by further announcements of impending breakthroughs. What’s more, by that time all the tenured faculty positions in the field are already occupied. The proponents have grown older and less zealous, while the younger generation seeks something completely new and more likely to succeed.
All these problems, as well as a few others I’ve not mentioned here, raise serious doubts about the future of quantum computing. There is a tremendous gap between the rudimentary but very hard experiments that have been carried out with a few qubits and the extremely developed quantumcomputing theory, which relies on manipulating thousands to millions of qubits to calculate anything useful. That gap is not likely to be closed anytime soon.
To my mind, quantum computing researchers should still heed an admonition that IBM physicist Rolf Landauer made decades ago when the field heated up for the first time. He urged proponents of quantum computing to include in their publications a disclaimer along these lines: “This scheme, like all other schemes for quantum computation, relies on speculative technology, does not in its current form take into account all possible sources of noise, unreliability and manufacturing error, and probably will not work.”
About the Author
[16]Mikhail Dyakonov does research in theoretical physics at Charles Coulomb Laboratory at the University of Montpellier, in France. His name is attached to various physical phenomena, perhaps most famously [17]Dyakonov surface waves.
References
Visible links
1. https://spectrum.ieee.org/image/MzE3MDcyNw.jpeg
2. https://spectrum.ieee.org/image/MzE3MDcyNw.jpeg
3. https://www.research.ibm.com/ibmq/learn/whatisquantumcomputing/
4. https://www.microsoft.com/enus/quantum
5. https://www.meritalk.com/articles/getreadyforfirstquantumcomputertobreaktheinternet
6. https://www.barrons.com/articles/googleibmprimedforaquantumcomputingleapsaysmorganstanley1503602607
7. https://en.wikipedia.org/wiki/Shor%27s_algorithm
9. https://en.wikipedia.org/wiki/Disruptive_Technology_Office
10. https://qist.lanl.gov/pdfs/qc_roadmap.pdf
12. https://en.wikipedia.org/wiki/Grover%27s_algorithm
13. https://newsroom.intel.com/news/inteladvancesquantumneuromorphiccomputingresearch/
14. https://www03.ibm.com/press/us/en/pressrelease/53374.wss
15. https://ai.googleblog.com/2018/03/apreviewofbristleconegooglesnew.html
16. https://www.coulomb.univmontp2.fr/user/michel.dyakonov?lang=en
17. https://en.wikipedia.org/wiki/Dyakonov_surface_wavesHackerNewsBot debug: Calculated post rank: 90  Loop: 342  Rank min: 80  Author rank: 31

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Saturday, 22Sep2018 21:12:39 PDT Hacker News ( unofficial ) The Mathematics of Quantum Mechanics [pdf]
HN Discussion: https://news.ycombinator.com/item?id=18046343
Posted by sajid (karma: 5569)
Post stats: Points: 130  Comments: 46  20180922T15:26:10Z#HackerNews #mathematics #mechanics #pdf #quantum #the
HackerNewsBot debug: Calculated post rank: 102  Loop: 234  Rank min: 100  Author rank: 88 
Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Thursday, 13Sep2018 02:14:56 PDT Hacker News ( unofficial ) Yale researchers 'teleport' a quantum gate
Yale researchers have demonstrated one of the key steps in building modular quantum computers: “teleporting” a quantum gate between two qubits, on demand.
Article word count: 465HN Discussion: https://news.ycombinator.com/item?id=17974333
Posted by vtomole (karma: 315)
Post stats: Points: 119  Comments: 9  20180912T23:07:12Z#HackerNews #gate #quantum #researchers #teleport #yale
Article content:
[1]A network overview of the modular quantum architecture demonstrated in the new study.A network overview of the modular quantum architecture demonstrated in the new study.
Yale University researchers have demonstrated one of the key steps in building the architecture for modular quantum computers: the “teleportation” of a quantum gate between two qubits, on demand.
[2]The findings appear online Sept. 5 in the journal Nature.
The key principle behind this new work is quantum teleportation, a unique feature of quantum mechanics that has previously been used to transmit unknown quantum states between two parties without physically sending the state itself. Using a theoretical protocol developed in the 1990s, Yale researchers experimentally demonstrated a quantum operation, or “gate,” without relying on any direct interaction. Such gates are necessary for quantum computation that relies on networks of separate quantum systems — an architecture that many researchers say can offset the errors that are inherent in quantum computing processors.
Through the Yale Quantum Institute, a Yale research team led by principal investigator [3]Robert Schoelkopf and former graduate student Kevin Chou is investigating a modular approach to quantum computing. Modularity, which is found in everything from the organization of a biological cell to the network of engines in the latest SpaceX rocket, has proved to be a powerful strategy for building large, complex systems, the researchers say. A quantum modular architecture consists of a collection of modules that function as small quantum processors connected into a larger network.
Modules in this architecture have a natural isolation from each other, which reduces unwanted interactions through the larger system. Yet this isolation also makes performing operations between modules a distinct challenge, according to the researchers. Teleported gates are a way to implement intermodule operations.
“Our work is the first time that this protocol has been demonstrated where the classical communication occurs in realtime, allowing us to implement a ‘deterministic’ operation that performs the desired operation every time,” Chou said.
Fully useful quantum computers have the potential to reach computation speeds that are orders of magnitude faster than today’s supercomputers. Yale researchers are at the forefront of efforts to develop the first fully useful quantum computers and have done pioneering work in quantum computing with superconducting circuits.
Quantum calculations are done via delicate bits of data called qubits, which are prone to errors. In experimental quantum systems, “logical” qubits are monitored by “ancillary” qubits in order to detect and correct errors immediately. “Our experiment is also the first demonstration of a twoqubit operation between logical qubits,” Schoelkopf said. “It is a milestone toward quantum information processing using errorcorrectable qubits.”
Coauthors of the study are current and former Yale graduate students Jacob Blumoff, Christopher Wang, Philip Reinhold, Christopher Axline, and Yvonne Gao; senior research scientist Luigi Frunzio; and professors Michel Devoret and Liang Jiang.
The Army Research Office and the Office for Naval Research supported the work.
References
Visible links
2. https://www.nature.com/articles/s415860180470y
3. https://rsl.yale.edu/node/148HackerNewsBot debug: Calculated post rank: 82  Loop: 205  Rank min: 80  Author rank: 30

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Saturday, 08Sep2018 08:19:01 PDT Hacker News ( unofficial ) Quantum computing is almost ready for business, startup says
In launching its own online service, the Berkeleybased upstart Rigetti aspires to be the Amazon of cloudbased quantum computing.
Article word count: 80HN Discussion: https://news.ycombinator.com/item?id=17935969
Posted by seancaptain (karma: 39)
Post stats: Points: 95  Comments: 39  20180907T17:17:04Z#HackerNews #almost #business #computing #for #quantum #ready #says #startup
Article content:
Quantum computing–which harnesses the peculiar physics of subatomic scale–long appeared to be a technology of the far future, maybe decades away. But the scifi tech is coming to market way sooner than was expected just a few years ago, due to new thinking on how to make use of its stillimperfect capabilities. Along with behemoths like Google and IBM, Berkeleybased company [1]Rigetti is making a bid to grab the market. And it may—at the moment at least—be in the lead.
Today the startup launches a project in the mold of Amazon Web Services (AWS) called Quantum Cloud Services. “What this platform achieves for the very first time is an integrated computing system that is the first quantum cloud services architecture,” says Chad Rigetti, founder and CEO of his namesake company. The dozen initial users Rigetti has announced include biotech and chemistry companies harnessing quantum technology to study complex molecules in order to develop new drugs.
Chad Rigetti [Photo: courtesy of Rigetti Computing]I met the physicist earlier this week to see what a quantum data center looks like. Next to each of several fat white cylinders holding the supercooled quantum computers are racks of traditional servers–constituting the quantumclassical hybrid systems that are bringing this bizarre physics to market.
The particular operations that the quantum end of the system can do, while still limited and errorprone, are nearly good enough to boost the performance of traditional computers beyond what they could do on their own–a coming milestone called quantum advantage. “My guess is this could happen anytime from six to 36 months out,” says Rigetti.
Socalled hybrid algorithms leveraging both systems are able to spot and correct some errors. And even imperfect results from quantum computers can be good enough in many cases, either flatout exceeding what traditional computer technology can do, or producing results faster or cheaper.
Rigetti has been playing this angle, creating a software development kit called [2]Forest (because it’s an ecosystem, says Chad) that allows programmers to access hybrid systems. Like other companies such as IBM, Rigetti has been allowing developers to access smallscale quantum computers online to essentially start working out how to program for them. The company learned a lot since opening this up in December 2017, says Rigetti. For instance, it originally tried providing just the quantum end of the system from its own data centers, linking it to traditional computers running on AWS. But the lag time between the systems, though minimal, was long enough to limit performance.
Hence the decision to provide all the computing in its own Berkeley data center. Rigetti is now inviting customers to apply for free access to these systems, toward the goal of developing a realworld application that achieves quantum advantage. As an extra incentive, the first to make it wins a $1 million prize. “What we want to do is focus on the commercial utility and applicability of these machines, because ultimately that’s why this company exists,” says Rigetti. (On that promise, the company has raised $119.5 million from investors, including Andreessen Horowitz.)
One of the cylinders that chills the quantum computer close to absolute zero. [Photo: Sean Captain]
Some important things have to happen in the meantime, and here’s where a little physics helps explain the challenge. One takeaway from quantum mechanics is that the very act of observing certain things on the subatomic scale affects their properties, such as the spin of an electron or the polarization of a photon. This leads to the bizarre conclusion that subatomic particles exist in all possible states at once until you actually take a measurement, at which point a single state emerges. That has mindblowing implications for computing.
Tufts of microwave cables descend down to the supercooled quantum chip at the bottom of Rigetti’s machine. [Image: courtesy of Rigetti Computing]The classical computers we all use today are based on bits of information: each can have a set value of zero or one. But a quantum bit, or qubit, could be both zero and one at the same time (and sometimes even more simultaneous values). The upshot for computing is that quantum mechanics allows a system to encode and compute problems with a vast multitude of different outcomes–represented by an amalgamation of qubits in all those simultaneous quantum states. Known as optimization problems, these start out really hard and quickly spiral towards impossible with classical digital computers.
A prime example of such problems, and a solid contender for demonstrating quantum advantage: designing new chemicals, such as drugs, by simulating all possible positions of all the elections in all the atoms of a theoretical new molecule. Several of the initial participants that Rigetti announced today are working in such simulated chemistry: Heisenberg, ProteinQure, and Qulab. (Another participant, Entropica Labs, is using quantum computing for genomics analysis.)
Finance provides other optimization problems. For instance: picking the best combination of stocks for an investment portfolio, considering everything that could likely happen to each stock, and all the combinations of everything in, say, a basket of 100. That could solve, or make, a lot of money. Another participant, QxBranch, is developing analytics for a number of fields, including finance. (Quantumtech giant IBM is [3]working with firms including Barclays and JP Morgan Chase.)
Rigetti’s hybrid system combines its quantum computer (the white cylinder) with traditional datacenter computers. [Photo: Sean Captain]
Sensitive bits
Qubits are very fragile. Rigetti and rivals like Google and IBM use superconducting technology, which requires cooling the systems in those big white cylinders close to absolute zero, then manipulating the qubits using microwaves. Those qubit readings aren’t always accurate.
But even imperfect quantum computing results are still valuable. “There are some problems where you don’t need the exact answer to the last bit,” says industry analyst Doug Finke. “Out of 5 billion possibilities, maybe you don’t get the very best answer, but maybe you get the second best, or the third best answer,” he says. In drug discovery, for instance, quantum computing might provide a solid short list of options to try out in the lab. In finance, it might provide a better portfolio than could otherwise be calculated.
Recognizing the value of good enough results has revolutionized quantum computing. “If you go back five, six years ago, quantum computing was very theoretical,” says Finke. “And people had developed a theory that said, well look, if we can develop quantum computers that have, you know, hundreds of thousands or millions of qubits, we can throw in all this error correction and we could solve this problem of the fact the qubits themselves are pretty unstable.”
But applications that can tolerate errors–and new hybrid algorithms better at spotting mistakes–mean that just a few dozen qubits could be enough to reach quantum advantage in some industries. “I think Rigetti may have a slight lead,” says Finke. “I think they recognized this potential maybe a little bit earlier than other people.”
The current system holds a 16qubit quantum chip. Rigetti plans to scale to 128 qubits in 2019. [Image: courtesy of Rigetti Computing]
The battle of the bits
Rigetti’s chips have 16 qubits each–certainly not enough, but it’s tiling them together into systems with 32, and eventually 128 qubits in the coming year. “We believe quantum advantage is possible when you get above 80 or 100 [qubits],” says Rigetti. His aim is to get people learning to develop applications on its 16qubit systems now so that they are ready to take advantage of the bigger machines when they come online.
Rigetti does not have the biggest quantum computer today. That honor goes to Google, which has announced a 72qubit system, according to a [4]scorecard that Doug Finke maintains. (IBM, meanwhile, has a system with 50 qubits, and [5]Intel has one with 49.)
[6]Rigetti’s plan for 128 qubits is the biggest anyone has announced so far, according to Finke. “The issue is, we don’t know what those other guys are shipping next year,” he says. Even harder to ascertain is how well those systems work: Not all qubits are equally accurate. “It turns out that qubit quality is actually even more important than the raw number of cubits,” says Finke.
In true quantum fashion, Rigetti appears to be ahead in bringing technology to market at this moment of observation. But with so many unknowns among the various players and technologies, many different outcomes for the industry all appear to be possible, simultaneously.
Updated: Due to incorrect information provided by Rigetti, this article originally stated a lower amount of funding raised.
References
Visible links
1. https://www.rigetti.com/
2. https://www.rigetti.com/products
3. https://www.cnbc.com/2017/12/14/ibmteamsupwithsamsungjpmorgantodevelopquantumcomputing.html
4. https://quantumcomputingreport.com/scorecards/qubitcount/
5. https://www.fastcompany.com/40514189/intelnewchipaimsforquantumsupremacy
6. https://medium.com/rigetti/therigetti128qubitchipandwhatitmeansforquantumdf757d1b71eaHackerNewsBot debug: Calculated post rank: 76  Loop: 473  Rank min: 60  Author rank: 97

Dr. Roy Schestowitz (罗伊) (schestowitz@pleroma.site)'s status on Thursday, 02Aug2018 23:01:07 PDT Dr. Roy Schestowitz (罗伊) Outofprocess extensions to finally debut on Linux in Firefox #quantum 63 https://www.neowin.net/news/outofprocessextensionstofinallydebutonlinuxinfirefoxquantum63 about time. #mozilla #firefox #gnu #linux 
Dr. Roy Schestowitz (罗伊) (schestowitz@joindiaspora.com)'s status on Thursday, 02Aug2018 23:00:53 PDT Dr. Roy Schestowitz (罗伊) Outofprocess extensions to finally debut on Linux in Firefox #Quantum 63 https://www.neowin.net/news/outofprocessextensionstofinallydebutonlinuxinfirefoxquantum63 about time. #mozilla #firefox #gnu #linux

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Tuesday, 31Jul2018 11:11:58 PDT Hacker News ( unofficial ) Teenager Finds Classical Alternative to Quantum Recommendation Algorithm
18yearold Ewin Tang has proven that classical computers can solve the “recommendation problem” nearly as fast as quantum computers. The result eliminates one of the best examples of quantum speedup.
Article word count: 693HN Discussion: https://news.ycombinator.com/item?id=17654220
Posted by okket (karma: 27620)
Post stats: Points: 215  Comments: 43  20180731T15:48:48Z#HackerNews #algorithm #alternative #classical #finds #quantum #recommendation #teenager
Article content:
A teenager from Texas has taken quantum computing down a notch. In a paper [1]posted online earlier this month, 18yearold Ewin Tang proved that ordinary computers can solve an important computing problem with performance potentially [2]comparable to that of a quantum computer.
In its most practical form, the “recommendation problem” relates to how services like Amazon and Netflix determine which products you might like to try. Computer scientists had considered it to be one of the [3]best examples of a problem that’s exponentially faster to solve on quantum computers — making it an important validation of [4]the power of these futuristic machines. Now Tang has stripped that validation away.
“This was one of the most definitive examples of a quantum speedup, and it’s no longer there,” said Tang, who graduated from the University of Texas, Austin, in spring and will begin a Ph.D. at the University of Washington in the fall.
In 2014, at age 14 and after skipping the fourth through sixth grades, Tang enrolled at UT Austin and majored in mathematics and computer science. In the spring of 2017 Tang took a class on quantum information taught by [5]Scott Aaronson, a prominent researcher in quantum computing. Aaronson recognized Tang as an unusually talented student and offered himself as adviser on an independent research project. Aaronson gave Tang a handful of problems to choose from, including the recommendation problem. Tang chose it somewhat reluctantly.
“I was hesitant because it seemed like a hard problem when I looked at it, but it was the easiest of the problems he gave me,” Tang said.
The recommendation problem is designed to give a recommendation for products that users will like. Consider the case of Netflix. It knows what films you’ve watched. It knows what all of its other millions of users have watched. Given this information, what are you likely to want to watch next?
You can think of this data as being arranged in a giant grid, or matrix, with movies listed across the top, users listed down the side, and values at points in the grid quantifying whether, or to what extent, each user likes each film. A good algorithm would generate recommendations by quickly and accurately recognizing similarities between movies and users and filling in the blanks in the matrix.
In 2016 the computer scientists [6]Iordanis Kerenidis and [7]Anupam Prakash [8]published a quantum algorithm that solved the recommendation problem exponentially faster than any known classical algorithm. They achieved this quantum speedup in part by simplifying the problem: Instead of filling out the entire matrix and identifying the single best product to recommend, they developed a way of sorting users into a small number of categories — do they like blockbusters or indie films? — and sampling the existing data in order to generate a recommendation that was simply good enough.
At the time of Kerenidis and Prakash’s work, there were only a few examples of problems that quantum computers seemed to be able to solve exponentially faster than classical computers. Most of those examples were specialized — they were narrow problems designed to play to the strengths of quantum computers (these include the “forrelation” problem Quanta [9]covered earlier this year). Kerenidis and Prakash’s result was exciting because it provided a realworld problem people cared about where quantum computers outperformed classical ones.
“To my sense it was one of the first examples in machine learning and big data where we showed quantum computers can do something that we still don’t know how to do classically,” said Kerenidis, a computer scientist at the Research Institute on the Foundations of Computer Science in Paris.
Kerenidis and Prakash proved that a quantum computer could solve the recommendation problem exponentially faster than any known algorithm, but they didn’t prove that a fast classical algorithm couldn’t exist. So when Aaronson began working with Tang in 2017, that was the question he posed — prove there is no fast classical recommendation algorithm, and thereby confirm Kerenidis and Prakash’s quantum speedup is real.
“That seemed to me like an important ‘t’ to cross to complete this story,” said Aaronson, who believed at the time that no fast classical algorithm existed.
References
Visible links
1. https://arxiv.org/abs/1807.04271
2. https://www.quantamagazine.org/quantumcomputersstruggleagainstclassicalalgorithms20180201/
3. https://www.quantamagazine.org/joboneforquantumcomputersboostartificialintelligence20180129/
4. https://www.quantamagazine.org/theeraofquantumcomputingishereoutlookcloudy20180124/
5. https://www.scottaaronson.com/
6. https://www.irif.fr/~jkeren/jkeren/Iordanis_Kerenidis.html
7. https://simons.berkeley.edu/people/anupamprakash
8. https://arxiv.org/abs/1603.08675
9. https://www.quantamagazine.org/finallyaproblemthatonlyquantumcomputerswilleverbeabletosolve20180621/HackerNewsBot debug: Calculated post rank: 157  Loop: 155  Rank min: 100  Author rank: 68

Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Sunday, 22Jul2018 18:15:11 PDT Hacker News ( unofficial ) Getting Started with Quantum Computing in Python
Quantum computers might sound a bit exotic and far into the future, but in reality, they are now accessible in the cloud or through emulators for everyone to write quantum code. In this tutorial, we’l
Article word count: 1173HN Discussion: https://news.ycombinator.com/item?id=17586518
Posted by chriotte (karma: 56)
Post stats: Points: 131  Comments: 30  20180722T13:02:42Z#HackerNews #computing #getting #python #quantum #started #with
Article content:
Quantum computers might sound a bit exotic and far into the future, but in reality, they are now accessible in the cloud or through emulators for everyone to write quantum code. In this tutorial, we’ll go through how you can program a simple quantum computer to generate random numbers.
This example can be done on any emulator or quantum computer. For this blog post, the free and open source Python library [1]ProjectQ is used.
ProjectQ can emulate a quantum computer on any CPU, or connect to [2]IBMs quantum computer as a backend.
To get started, just install ProjectQ through pip or [3]follow their installation guideProgramming a quantum computer
Programming a quantum program is a bit different from what we are used to when creating classical programs, we have to dive down in the levels of computer abstractions and use logic gates to manipulate data, along the same mindset Alan Turing used when creating his famous Turing Machine, which describes a classical machine doing classical computations on classical bits.A Quantum Turing machine describes a computer that can perform quantum computations on a Qubit, where quantum computations refers to applying quantum logic gates such as PauliX, CNOT etc to Qubits.
This means that any program possible to write on a classical computer is possible to run on a quantum computer, and vice versa, but this [4]doesn’t mean any program will be more effective on a quantum computer, in fact, a range of programs will run slower on a quantum computer and a quantum computer will have to work in parallel with a classical computer to handle computations where the classical computer falls short, computations such as matrix multiplication or finding prime factors to break [5]cryptography.
Creating a random generator with quantum gates
Creating a (pseudo) random number is one of the first things taught in computer science courses. Usually, it involves importing a premade library called something along the lines of ‘Random’ and then just call the appropriate function.
In quantum computing we’re not yet at this level of abstraction, but to create a random number is almost as easy by using quantum logic gates.Quantum gates are similar to the logic gates we know from classical computing. Eg AND, OR, NAND, XOR etcFor those not familiar with the concept, logic gates are a set of input and output used to manipulate an input through boolean functions.
For example, if we feed the OR gate two numbers where one or both is one, the output will be True, if we feed the input two zeros the output will be false.
The table above shows the truth table of an OR gate, where A and B are inputs and Q is the output. Imagine that a door only opens when a lamp is lit and will stay closed when both lamps are turned off.
Logic gates can be used to compute any operation, and in quantum computing, we can use the logic gate called Hadamard to create a random number (1 or 0).
The Hadamard gate takes one input, and maps the output with a equal probability of being 1 or 0, i.e. create a superposition where the input can be either 1 or 0 at the same time.The basis state 0⟩ is mapped to:$$\frac{0\rangle + 1\rangle}{\sqrt{2}}$$The basis state 1⟩ is mapped to:
$$\frac{0\rangle  1\rangle}{\sqrt{2}}$$
The Hadamard gate is represented by the Hadamard matrix which shows that the rows are [6]mutually orthogonal.$$H = \frac{1}{\sqrt{2}} \begin{bmatrix}1 & 1 \ 1 & 1\end{bmatrix}
$$
[7]Read more about the Hadamard matrix and other Quantum Logic gates on Wikipedia
Essentially, the Hadamard gate flips a coin and while the coin is in the air, it’s in a superposition in the sense that the coin can be both head and tail until it falls back down and we glance down at it  the human way of measuring the state of the coin.
Our quantum random generator outlined in a few simple steps together with the coin analogy
1. Create a new Qubit
* Fishing a coin out of our pocket
2. Applying a Hadamard gate to the Qubit to put it into a superposition of equal probability of being 0 and 1.
* Tossing our coin in the air, it can now be either heads or tail.
3. Measuring the Qubit
* The coin has finally landed and settled, its time to look at it to see if its head or tail.
Start by importing projectQ along with the Hadamard gate and the measuring function. We’re using projectQ in this tutorial, but the same approach can be followed in other libraries and systems as well, the code syntax will be a bit different, but the theory will be the same.
1 from projectq.ops import H, Measure
2 from projectq import MainEngineInitialise the backend, we’re using the emulator, but you can also use eg IBMs quantum computer.
Then Create a new Qubit to apply computations on.1 quantum_engine = MainEngine()
2 qubit = quantum_engine.allocate_qubit()We now have a Qubit that initialised and ready to be turned into superposition. Remeber the coin analogy here, where we picked up a coin and now is ready to throw it in the air.
We’re then applying the Hadamard gate to the Qubit, this refers to the step where we toss the coin up in the air.
The syntax to do this will vary between each library and tool but in ProjectQ it’s simply done in the following way.One interesting thing to pay attention to here is that we’re applying the gate directly to the Qubit and not creating a copy. This is because unlike classical bits, Qubits cannot be copied due to fundamental laws of physics.
However, its possible to [8]teleport a quantum state from one location to another, but this is something for the next tutorial.With the Qubit in a superposition, we can now measure it, this refers to the step where the coin has landed and settled on the table and its time to have a look whether its head or tail.
In projectQ the measuring is done with the following command.The measured qubit can now be printed and will return either 0 or 1.
Tidying this all up in a complete Python code along with a for loop that demonstrates the randomness of our coin toss.
1
2
3 from projectq.ops import H, Measure
4 from projectq import MainEngine
5 """
6 This Function creates a new qubit,
7 applies a Hadamard gate to put it in superposition,
8 and then measures the qubit to get a random
9 1 or 0.
10
11 """
12 def get_random_number(quantum_engine):
13 qubit = quantum_engine.allocate_qubit()
14 H  qubit
15 Measure  qubit
16 random_number = int(qubit)
17 return random_number
18 random_numbers_list = []
19 for i in range(10):
20
21 quantum_engine = MainEngine()
22
23 random_numbers_list.append(get_random_number(quantum_engine))
24 quantum_engine.flush()
25 print(ʼRandom numbersʼ, random_numbers_list)
26
27
28Some outputs from the random generator.
1
2
3 Run 1: Random numbers [1, 1, 1, 1, 1, 0, 0, 1, 0, 0]
4 Run 2: Random numbers [0, 0, 1, 0, 0, 0, 1, 0, 0, 1]
5 Run 3: Random numbers [0, 0, 1, 0, 0, 0, 1, 0, 0, 1]
6 Run 4: Random numbers [1, 0, 1, 1, 0, 0, 1, 1, 0, 0]
7 Run 5: Random numbers [1, 1, 1, 1, 1, 1, 0, 1, 1, 0]
8
9This was a simple introduction to creating a random generator with quantum gates in Python. Feel free to post any comments, concerns or questions in the comment field below.
Read more about the author [9]Christopher Ottesen
Share[10]Comments[11]Older
Transform an iPad into a digital photo frame for £10 (no tools)References
Visible links
1. https://github.com/ProjectQFramework/ProjectQ
2. https://projectq.readthedocs.io/en/latest/projectq.setups.html?highlight=ibm
3. http://projectq.readthedocs.io/en/latest/tutorials.html#gettingstarted
4. https://arxiv.org/abs/1501.00011
5. https://en.wikipedia.org/wiki/Shor%27s_algorithm
6. https://en.wikipedia.org/wiki/Hadamard_matrix
7. https://en.wikipedia.org/wiki/Quantum_logic_gate
8. https://en.wikipedia.org/wiki/Quantum_teleportation
9. http://dataespresso.com/ChristopherOttesen/index.html
10. http://dataespresso.com/en/2018/07/22/TutorialGeneratingrandomnumberswithaquantumcomputerPython/#comments
11. http://dataespresso.com/en/2018/02/13/transformanipadintoadigitalphotoframefor10notools/HackerNewsBot debug: Calculated post rank: 97  Loop: 282  Rank min: 80  Author rank: 280

Dr. Roy Schestowitz (罗伊) (schestowitz@joindiaspora.com)'s status on Tuesday, 03Jul2018 22:07:44 PDT Dr. Roy Schestowitz (罗伊) Dark Theme Darkening: Better Theming for #Firefox #Quantum https://hacks.mozilla.org/2018/07/darkthemedarkeningbetterthemingforfirefoxquantum/ #mozilla #freesw

Dr. Roy Schestowitz (罗伊) (schestowitz@pleroma.site)'s status on Tuesday, 03Jul2018 22:07:43 PDT Dr. Roy Schestowitz (罗伊) Dark Theme Darkening: Better Theming for #firefox #quantum https://hacks.mozilla.org/2018/07/darkthemedarkeningbetterthemingforfirefoxquantum/ #mozilla #freesw 
Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Friday, 29Jun2018 10:11:27 PDT Hacker News ( unofficial ) I'm Scott Aaronson, quantum computing/computational complexity researcher. AMA
Hey HN,
We recently recorded a podcast (https://blog.ycombinator.com/scottaaronsononcomputationalcomplexitytheoryandquantumcomputers/) where I discussed my research, AI, and advice for nerds in general or people who want careers in science.
We covered many but not all of the questions submitted over the internet so AMA!
HN Discussion: https://news.ycombinator.com/item?id=17425377
Posted by ScottAaronson (karma: 73)
Post stats: Points: 133  Comments: 59  20180629T16:15:20Z#HackerNews #aaronson #ama #complexity #computational #computing #quantum #researcher #scott
HackerNewsBot debug: Calculated post rank: 108  Loop: 54  Rank min: 100  Author rank: 73 
Hacker News ( unofficial ) (hackernews@pod.jpope.org)'s status on Monday, 09Apr2018 16:30:07 PDT Hacker News ( unofficial ) Top 10 HackerNews posts
Next generation video: Introducing AV1
Post stats: HN Link: link  Posted by: TDLinux  Points: 361  Comments: 97  20180409T19:26:08ZColor: From Hex codes to Eyeballs
Post stats: HN Link: link  Posted by: markdog12  Points: 149  Comments: 9  20180409T18:31:41ZZfsbug causes data loss on Linux
Post stats: HN Link: link  Posted by: heinrichhartman  Points: 16  Comments: 0  20180409T22:46:47ZWeirdstuff Warehouse is closed
Post stats: HN Link: link  Posted by: kevbin  Points: 72  Comments: 50  20180409T21:47:51ZA Quantum Computer Simulator in 150 Lines of Python
Post stats: HN Link: link  Posted by: adamisntdead  Points: 43  Comments: 15  20180409T21:32:53ZIt’s time for thirdparty data brokers to emerge from the shadows
Post stats: HN Link: link  Posted by: worez  Points: 20  Comments: 3  20180409T21:38:55ZThe Mathematics of 2048: Optimal Play with Markov Decision Processes
Post stats: HN Link: link  Posted by: jonbaer  Points: 103  Comments: 8  20180409T04:20:49ZElm at Pacific Health Dynamics
Post stats: HN Link: link  Posted by: mordrax  Points: 75  Comments: 12  20180409T11:02:14ZUber enters dockless bike wars with Jump acquisition
Post stats: HN Link: link  Posted by: eplanit  Points: 128  Comments: 199  20180409T17:34:48ZAndroid container in Chrome OS
Post stats: HN Link: link  Posted by: navigaid  Points: 293  Comments: 101  20180409T15:21:56ZTags: #HackerNews #TopPosts #150 #2048 #a #acquisition #android #at #av1 #bike #brokers #causes #chrome #closed #codes #color #computer #container #data #decision #dockless #dynamics #elm #emerge #enters #eyeballs #for #from #generation #health #hex #in #introducing #is #its #jump #lines #linux #loss #markov #mathematics #next #of #on #optimal #os #pacific #play #processes #python #quantum #shadows #simulator #the #thirdparty #time #to #uber #video #warehouse #wars #weirdstuff #with #zfsbug

Antón (antonlopez@mastodon.social)'s status on Friday, 26Jan2018 01:53:30 PST Antón Acaba de aterrizar de golpe la versión 58 de #Firefox en #Trisquel (a través de #Abrowser), la primera versión de #Quantum que llega al repositorio del sistema. So far so good!! El único detalle que me ha disgustado es que los marcadores ahora están mas espaciados, y ya no me caben en la barra. 🙂