Daily Archives: October 8, 2019

This is an edition ofQuintessence, a series about fundamental ideas in science.

Earlier this year, IBM unveiled a much-hyped device at the Consumer Electronics Show in Las Vegas: a black cylinder hanging at the centre of a locked glass cage. Light from a wide panel on top bounced off of it. It was a simulacrum of a computing machine but it looked futuristic, even too futuristic. It didnt look like any normal computer because it wasnt.

It is easy to forget that even the simplest function on any electronic device in use today is the result of many, many transistors working in tandem. A keystroke, a button press, a single tap all of it is about current flowing in and out of a transistor, the modern version of which was invented 72 years ago.

A bit, which is the most fundamental unit of computing, refers to the state of a transistor. If the transistor is on, the bit has a value of 1; if the transistor is off, the bit has a value of 0. Every instruction fed to a computer and output derived from it is a pattern of such 0s and 1s. The computers hardware and software manipulate these patterns using a set of rules called Boolean algebra. All this logic flows at blazing speeds, enabling humankind to make rapid technological advancements.

But even though transistors have become incredibly sophisticated over the years and engineers have become able to cram billions of them on tinier and tinier chips, they are still classical objects. The kind of computing they lend themselves to uses the simpler principles of classical physics and is therefore limited by the limitations of these principles. The deepest of them is this: transistors can either be on or off. A bit can only assume one value at a time.

This is where IBMs sleek machine turns the curve. Enclosed in the black cylinder lies the soul of a quantum computer, in the form of a chip that taps into a more esoteric set of computing possibilities.

Also read:A Walk With Steven Kivelson Through the Realm of Strange Materials

Quantum computers are devices that manipulate quantum bits, or qubits. But instead of using a transistor to perform this function, quantum computers directly encode this information onto elementary particles like electrons and photons, or even entire atoms. These particles are thus part of a quantum computers hardware, and because they play by quantum rules, they execute their functions as qubits through strange quantum mechanical effects.

One of these effects is superposition. Where a classical bit has two states 0 and 1 a qubit also has a state where it is neither 0 nor 1 (but not a third value). This value is a fuzzy combination of two states, like 40% 1 and 60% 0. That is, if a classical bit is like a mechanical switch that can be turned on or off, a qubit can be on and off at the same time but neither completely on or off. Classical objects cannot be in such superposition.

Most people have already heard of such behaviour in the form of the Schrdingers cat thought experiment. A cat and a bowl of poison are placed in a sealed box. Until an observer opens the box and checks, the cat a metaphor for subatomic particles is to be considered both dead and alive. Similarly, an unobserved electron can have a spin pointing up and down at the same time, but the moment it is observed, it defaults to one of the two states: either up or down.

Next, two classical bits can have one of the following four configurations: {0, 0}, {1, 1}, {1, 0} and {0, 1}. But two qubits can exist in a mixture of all four at the same time; indeed, N number of qubits can exist as a simultaneous mixture of 2N states, each one representing a possible solution. Put differently, instead of tackling a problem by pursuing one solution at a time like a classical computer, a quantum computer can pursue multiple potential solutions at once and, presumably, arrive at the optimal one faster.

Superposition explains how a single qubit can be more powerful than a single bit. To understand how multiple qubits can work together as computers will require physicists use another concept called entanglement.

Quantum entanglement establishes strong ties between particles such that if one particle changes in a particular way, the other one also changes in a corresponding way. For example, if two qubits are entangled and one of them reveals its state, the other qubit automatically reveals its state as well.

Though the components of a quantum computers are markedly different from those of a classical computer, they still have to behave like computers, including processing instructions and producing an answer to a question in a predictable amount of time. This means a set of entangled qubits in superposition should ultimately collapse into a meaningful configuration of 1s and 0s on demand.

This is tricky. A qubit has a finite probability of existing in one of two quantum states. So N qubits have a tendency to randomly settle into any one of the 2N possible states when measured. Eight qubits, for example, could settle into one of 256 states. To get around this problem, the qubits have to be subtly manipulated to increase their probability of chasing the correct, or more desirable, paths.

Scientists achieve this by orchestrating the qubits in such a way that the signatures of the undesirable quantum states cancel out and the right ones add up. This is how some quantum computing algorithms that can take advantage of this technique based on the idea of interference from high-school physics vastly outperform their classical counterparts.

Also read:The DIY Experiment That Captures All the Mystery of Quantum Physics

For example, a quantum computer could use Grovers algorithm named for the computer scientist Lov Kumar Grover to sift through very large, unstructured databases to find a specific entry faster than a classical machine can. Using Peter Shors algorithm, a quantum computer can find the factors of large integers that are prime numbers much faster than the best algorithms classical computers use.

Scientists in various fields would also like to understand how complex molecules behave and interact with each other. Powerful supercomputers struggle with the dynamics of such many-body interactions, but quantum computers are expected to have a knack for them because theyre networks of particles themselves.

On the flip side, quantum computers arent always better. There are many problems for which classical computers arent efficient and quantum computers arent either. In most of these cases, increasing the scale of the problem exponentially increases the amount of time the computer needs to find the answer. Quantum computers might be able to crack some of them efficiently but not some others.

It would also be prudent to move our eyes away from the horizon of infinite possibilities and towards the mountain range standing in the way. Qubits must be stable and work well together for a computer to compute. This is easier said than done. Multiple qubits in a superposition, and entangled with each other, tend to be quite fragile. Even the smallest physical vibration can destroy their collective coherence, interfere with their quantum nature and induce large errors that can render the machine useless.

Different research groups around the world have stretched engineering to its bleeding edge to prevent such decoherence. IBMs monolithic quantum computer cools its superconducting chip which carries the qubits to about 0.01 K, or 270-times colder than outer space.

Late last month, a paper quietly appeared on and promptly disappeared from a NASA website. In the paper, scientists from Google claimed to have performed a computing task way out of reach of even the best conventional computers using a quantum computer.

The company hasnt issued an official comment or shared a peer-reviewed paper. According to various news reports, its 53-qubit machine performed a purpose-built task a computation in about 200 seconds when a powerful classical machine would have required millennia. If independent experts are able validate Googles claim, it will be the first time a quantum computer will have surpassed a classical machine at a specific task.

That said, we are still decades away from a practically useful quantum computer. The transistor reigns supreme for now.

The author would like to thank Vedangi Pathak and Kevin Dsouza for discussions about quantum mechanics and computing.

Ronak Guptais doing a PhD in fluid mechanics at the University of British Columbia, Vancouver.

View post:

What on Earth Is a Quantum Computer, and Why Should You Care? - The Wire

It's been a while now and Goldman Sachs still doesn't seem to have anyone to lead its proposed 'quantum computing research' team.

The firm doesn't date stamp its jobs, but we first noticed it was advertising for a 'Leader of a new quantum computing research team in R&D Engineering,' in early September. One month on, there don't seem to have been any takers: the job is still open.

Goldman didn't respond to a query on the role. It might be simply that the firm is taking its time and interviewing every quantum computing expert on the market. Or it might just be that quantumpeople are hard to come by.

Goldman's requirements are fairly precise: it wants someone who can identify applications for quantum computing across Goldman, who can talk to clients about quantum computing, who can liaise with Goldman staff and academics,and who can lead thenew quantum computingresearch team. The ideal would seem to be a quantum computingPhDwith client facing skills, which might be hard to come by.

In the meantime, Goldman has at least one quantum computing research already.Rajiv Krishnakumar joined the firm in March 2018 and began work in the Franchise Analytics Strategy and Technology (FAST) team, which is tasked with applying machine learning across the firm.Krishnakumar moved into the quantum computing research team as an associate last month. It probably helps that he has a PhD in applied physics from Stanford and that he spent six months as a postdoctoral research fellow in machine learning at Caltech.

Goldman Sachs seems to be unusual among banks in having a dedicated quantum computing research team. Others are certainly interested though -Roland Fejfar, head of technology business development at Morgan Stanley for EMEA and APAC, says on LinkedIn that one of his tasks is to look at 'disruptive technology' like quantum systems. And JPMorgan hired a quantum research scientist on a salary of $150k in New York in March 2019, according to the H1B Visa database.

Google claimed last month that it had built a quantum computer that could perform a calculation in three minutes and 20 seconds instead of the 10,000 years it would take the fastest traditional computer. When and if quantum computers become usable, experts have warned that all existing systems of encryption will become instantly meaningless.

Google is also looking for quantum recruiting talent. It's currently advertising nine roles, seven of which are related to quantum artificial intelligence (AI). All are in California. Goldman, by comparison, wants its quantum researchers in New York City - which may be a harder sell.

Have a confidential story, tip, or comment youd like to share? Contact: sbutcher@efinancialcareers.com in the first instance. Whatsapp/Signal/Telegram also available.

Bear with us if you leave a comment at the bottom of this article: all our comments are moderated by human beings. Sometimes these humans might be asleep, or away from their desks, so it may take a while for your comment to appear. Eventually it will unless its offensive or libelous (in which case it wont.)

Read this article:

Goldman Sachs can't seem to find anyone to lead its quantum computing team - eFinancialCareers

Todays conventional computers may soon reach their limits. Moores law, which predicts that it takes about two years to double the power of computers, is expected to reach a dead end soon. Dealing with more complex problems ahead needs quantum computers, where individual atoms store and process information.Serge Haroche,a French physicist who won the Nobel Prize in 2012 for his work on manipulating individual quantum systems, discussed the subject withShimona Kanwarduring a recent visit to India.

August saw an agreement between India and France on cooperation in the fields of quantum computing. Where do you see this association headed?

I think laboratory scale classical computing is a matter of technology and one can see in what direction it is going.Quantum computing is still a question of basic science. So, you cannot predict if and when it will lead to practical applications. There is big progress in quantum computing, quantum communication and quantum meteorology using quantum devices. There are laboratories working on these in India and France.

One way to combat the quantum decoherence is supercooling, but its impractical for commercialisation. What kind of solution will emerge?

There are methods called quantum error correction to combat decoherence. This works on paper but not to the level of precision which is required to get a quantum computer. It means if you get a practical device, it will be very cold and not like a laptop which everybody can have at home. There are lot of challenges to scale up to a size which is useful. There is lot of hype in the field of quantum devices. Many private companies which are involved in this want to make profit. Quantum computing is trapped between hype and hope. A hope that one day it will lead to something useful. But as always in science you get surprises and what will be useful is may be not what we expect now.It is very rare in science where you have a path which leads you to a discovery that is predicted 20-30 years ahead of time. For the time being, quantum computing is basic science and not applied yet. Those who promise quantum computers are overselling, I think.

Why are researchers not motivated to pursue basic science?

I think basic science is background. You cannot have applied if there is no basic. Basic science requires a lot of time. Before it gives us application, it takes a very long time. For instance, the first idea of the laser wasgiven by Einstein in 1916 and the first laser came up in 1960. It took 44 years between the basic discovery and the invention. Sometimes it takes less time, but on an average 10-20 years. The big problem we have as scientists is to make sure that people will give us money and keep patience, and not ask for short term results. You need to build an atmosphere of trust and give time to basic science to develop.I think one of the problems is that there are not many positions in basic science. If you do not nourish basic science, you will not have good applied science.

Can we have Chinas model where there is huge investment in science?

China invests a huge amount and has made advances in science in the last 20 years. They have very good science institutes. They do good science in physics and biology. China has one problem and that is lack of freedom. I think science cannot be disconnected from humanities. A good scientist needs to have freedom of soul, freedom to choose his topic and to work with passion. A good scientist is driven by his/her own curiosity. I am sure if China gives more freedom to its researchers, it will be much more productive than it is now.

CERN (European Organisation for Nuclear Research) has been a successful example of science diplomacy. What advice would it give?

CERN is a very big project as it involves thousands of researchers from all over the world. This is necessary as it is a project in high energy physics and requires huge instruments like big accelerators which no single country can manage. However, my area of working is in small scale physics. As a policy, small-scale physics is very interesting as it brings PhD students hands on with physics and they are responsible for their own project.So, it is a very different way of training people. I think for a country like India, this small-scale physics is good becauseit allows it to develop science in different institutes.A big project like CERN or space agency projects are interesting for governments as it gives them bigger media attention. I think the work you do on smaller scale, even if it does not attract attention, is more fruitful.

DISCLAIMER : Views expressed above are the author's own.

Read the original:

'Quantum computing's trapped between hype and hope in science you get surprises and what's useful may not be what we expect now' - Times of India

This book shows how our understanding of quantum physics is mostly, but not entirely, correct

Cover: Six Impossible Things by John Gribbin(Credit: Icon Books Ltd, 2019).

Quantum physics is so bizarre that even physicists dont understand it. According to the rules of quantum physics, a cat can be alive and dead at the same time, an electron can be in two places simultaneously, and a subatomic particle can also be a wave. But nobody has ever been able to provide a rational explanation for how these contradictory and seemingly impossible things can be true. Basically, the subatomic world is so strange that even Einstein could only shrug and describe it as spooky action at a distance.

Undaunted, British science writer and astrophysicist John Gribbin wrote a small book, Six Impossible Things (Icon Books; 2019: Amazon US / Amazon UK) that succinctly summarizes six of the foremost hypotheses that seek to explain the mysteries of the subatomic world. This slim hardcover is an elegant and accessible attempt to explain quantum physics to the nonspecialist, and is one of the six books included on the shortlist for the Royal Society Insight Investment popular Science Book Prize for 2019.

Six of the books ten chapters correspond to each of the six most popular explanations that may underlie quantum physics weirdness: All of them are crazy, and some are more crazy than others, Dr. Gribbin notes early in his book, but in this world crazy does not necessarily mean wrong, and being more crazy does not necessarily mean more wrong (p. xvii). The six impossible things include the Copenhagen Interpretation, the Pilot Wave Interpretation, the Many Worlds Interpretation, the Decoherence Interpretation, the Ensemble Non-Interpretation (also known as the Statistical Interpretation), and the Timeless Transactional Interpretation.

The chapters are quite short (as is the book itself), and include black-and-white diagrams and images of the main proponents of each hypothesis along with a condensed and readable description of that hypothesis. Professor Gribbins coherent summary of each hypothesis makes these incredibly complex ideas, puzzled over by physics leading minds since the late 1920s, into something that may be vaguely comprehensible to most readers, even if these ideas remain firmly embedded in the realm of the fantastic for physicists as well as for mere mortals.

Beside the fact that simply reading this book gave me incredible dreams, I was most tantalized during my waking hours by Dr. Gribbins too brief description of quantum computing, a fascinating technological advancement that cannot come too soon, in my opinion. But more interesting even than quantum computing (and its concomitant cybersecurity) is coming to a firm understanding about the reason that it works and that remains elusive.

The book lacks maths, so if you are skittish about mathematics, or lack a maths background, you will breathe a sigh of relief to discover that this book is still quite comprehensible well, as intelligible as quantum physics can be made.

If quantum physics still leaves you breathless, Professor Gribbin also summarizes each of these six impossible interpretations in a neat, and amusing, single sentence. Highly recommended for students of the sciences and fans of science fiction, as well as for anyone who is curious to understand the strange world of quantum physics.

Read the rest here:

Six Impossible Things: The Mysteries Of The Subatomic World - Forbes