Category Archives: Quantum Computer
Twitter’s hashtag just turned 10, and wouldn’t you know it, scientists just worked out a far better use for it – at the nanoscale.
See, it turns out that a criss-cross pattern of semiconducting nanowires is the perfect structure to help manipulate a particular type of quasiparticle into quantum bits.
This nano-hashtag structure at scales of a billionth of a metre should help the quasiparticles, known as Majorana fermions, be more easily formed into a qubit: the building blocks for quantum computers.
Majorana fermions are shown to be far more robust than existing qubit technology, and scientists developing this technology reckon it will lead to a new generation of quantum architecture that will end with a scalable, fault-tolerant universal quantum computer.
This kind of architecture is what Microsoft’s Station Q is looking for, and recently announced a multi-year partnership at the University of Sydney. One of the researchers involved with this new research is Dr Maja Cassidy, a senior researcher at Station Q Sydney, which is based at the University of Sydney Nanoscience Hub.
“Networks of nanowires are crucial to demonstrate how Majorana fermions interact through braiding,” said Dr Cassidy. “These will be a fundamental building block for topological quantum computation.”
Dr Cassidy worked on the research team while she was at TU Delft in the Netherlands.
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Scientists Just Found A Use For The Hashtag In Quantum Computing – Gizmodo Australia
Image credit: TriStar Pictures
Over the course of the hour, the discussion ranged from quantum computing and robotics to hacking and the ethics of creating sentient AI. Here are some of the highlights from the talk!
The Beginnings of AI: Early AI and Symbolic Reasoning
Joe Haldeman started off the discussion by talking about his experience with AI and symbolic reasoning courses during his college education, which covered philosophy, mathematics, and computer science. “I was studying AI before you guys were even born,” he joked.
He described writing out truth tables and learning quasi-algebraic logic, which allowed him to represent the “thought processes” of early computers. “I have faith in symbolic logic that I don’t have in natural language,” he said. “I won’t say it doesn’t lie, but when it lies, you can piece the truth out of it.”There’s something very elemental about writing out basic true-false equations for Haldeman: “I do know how to sit with a quasi-algebraic system and tease the truth out itit’s a feeling of power.”
Sentient AI and Quantum Computing
What gives a quantum computer its incredible, limit-breaking power is the qubit, which is analogous to the usual bits found in all computers, except that instead of a 1 and 0 state, a qubit can exist in a state that’s simultaneously 1 and 2 or neither.
This extra dimension allows for computational power that transcends current limits, opening the possibility for an artificial intelligence to grasp higher functions like self-awareness.
Hacking the Internet (of Things)
Paired with the introduction of hundreds of new, networked “smart” devices, from refrigerators to wearables to personal robots, the potential for hackers to take over a given device has grown exponentially in recent years.
Kelly cites the recent shutdown of a Facebook chatbot AIas an example of what happens when we lose control of AI: after trying to learn to communicate in English, the AI behind Facebook’s chat program decided that it would create its own, more efficient language, which was unintelligible to humans. When researchers realized what it was doing, they quickly shut it down.
The Promise of Sci-Fi and AI
As for the question of how close we are to realizing the kind of AI found in sci-fi and how safe we are from our darkest fears of robotic domination, Haldeman summed it up nicely: “This whole question shimmers between the uncomputable and the fictional. It’s a great place to start stories. These are existential stories-what is man? What are his computational limits?”
Stay tuned for more stories from Escape Velocity 2017!
The 4th International Conference on Quantum Technologies held in Moscow last month was supposed to put the spotlight on Google, who were preparing to give a lecture on a 49-qubit quantum computer they have in the works.
A morning talk presented by Harvard University’s Mikhail Lukin, however, upstaged that evening’s event with a small announcement of his own his team of American and Russian researchers had successfully tested a 51-qubit device, setting a landmark in the race for quantum supremacy.
Quantum computers are considered to be part of the next generation in revolutionary technology; devices that make use of the odd ‘in-between’ states of quantum particles to accelerate the processing power of digital machines.
The truth is both fascinating and disappointing. It’s unlikely we’ll be playing Grand Theft Auto VR8K-3000 on a quantum-souped Playstation 7 any time soon. Sorry, folks.
Quantum computing isn’t all about swapping one kind of chip for a faster one.
What it does do is give us a third kind of bit where typical computers have only two. In quantum computing, we apply quantum superposition that odd cloud of ‘maybes’ that a particle occupies before we observe its existence cemented as one of two different states to solving highly complex computational problems.
While those kinds of problems are a long, tedious process that tax even our best supercomputers, a quantum computer’s “qubit” mix of 1s, 0s, and that extra space in between can make exercises such as simulating quantum systems in molecules or factorising prime numbers vastly easier to crunch.
That’s not to say quantum computing could never be a useful addition for your home desktop. But to even begin dreaming of the possibilities, there are a whole number of problems to solve first.
One of them is to ramp up a measly handful of qubits from less than 20 to something that can begin to rival our best classical supercomputers on those trickier tasks.
That number? About 50-odd, a figure that’s often referred to in rather rapturous terms as quantum supremacy.
The Harvard device was based on an array of super-cooled atoms of rubidium held in a trap of magnets and laser ‘tweezers’ that were then excited in a fashion that allowed their quantum states to be used as a single system.
The researchers were able to control 51 of these trapped atoms in such a way that they could model some pretty complex quantum mechanics, something well out of reach of your everyday desktop computer.
While the modelling was mostly used to test the limits of this kind of set-up, the researchers gained useful insights into the quantum dynamics associated with what’s called many-body phenomena.
Fortunately they were still able to test their relatively simpler discoveries using classical computers, finding their technique was right on the money.
The research is currently on the pre-publish website arXiv.com, awaiting peer review. But the announcement certainly has the quantum computing community talking about the possibilities and consequences of achieving such limits.
The magical number of 50 qubits is more like a relative horizon than a true landmark. Not much has changed in the world of quantum computing with the Harvard announcement, and we still have a long way to go before this kind of technology will be useful in making any significant discoveries.
Google’s own plan for a 49-qubit device uses a completely different process to Lukin’s, relying on multiple-qubit quantum chips that employ a solid-state superconducting structure called a Josephson junction.
They’ve proven their technology with a simpler 9-qubit version, and plan to gradually step up to their goal.
Without going into detail, each of the technologies has its pros and cons when it comes to scaling and reliability.
A significant problem with quantum computing will be how to make the system as reliable and error-free as possible. While classical computing can duplicate processes to reduce the risk of mistakes, the probabilistic nature of qubits makes this impossible for quantum calculations.
There’s also the question on how to connect a number of units together to form ever larger processors.
Which methods will address these concerns best in the long run is anybody’s guess.
“There are several platforms that are very promising, and they are all entering the regime where it is getting interesting, you know, system sizes you cannot simulate with classical computers,” Lukin said to Himanshu Goenka from International Business Times.
“But I think it is way premature to pick a winner among them. Moreover, if we are thinking about truly large scales, hundreds of thousands of qubits, systems which will be needed for some algorithms, to be honest, I don’t think anyone knows how to go there.”
It’s a small step on the road to a hundred thousand qubits, but it doesn’t make passing this milestone any less significant.
Happy 51, Harvard!
See the article here:
We’re About to Cross The ‘Quantum Supremacy’ Limit in Computing – ScienceAlert
Explaining the Most Recent Record for Quantum Computing: A 51-Qubit Quantum Computer Array – All About Circuits
Last month, a team of Russian and American scientists unveiled a quantum computer array with 51 qubits at the International Conference on Quantum Technologies in Moscow. Here’s a look at how they accomplished this new milestone with the use of cold atoms and lasers.
If you’re already familiar with quantum computing, I recommend skipping to the next section. If you’re not familiar with quantum computing, it is aptly named for its quantum properties. In quantum physics, particles do not have a defined location until they are observed. In classical computing, digital data is read in bits, which are 1s and 0s, or ON and OFF states, which we know as binary, which can be manipulated into different arrangements using various logic gates.
Quantum computing combines concepts from classical computing and quantum mechanics to make qubits (a shortened nickname for “quantum bits”). Unlike classical bits, qubits can be a 1 or 0 at the same time, much like Schrodinger’s cat, which is in a state of flux until observed. So,four bits have 16 possible combinations (24), whereas four qubits can be in every possible combination at the same time until they are observed. This allows a quantum computer to perform every possible calculation at the same time. A quantum algorithm reduces the time required for large calculations by the square root of the number of entries being searched.
Quantum computers are not practical for most tasks handled by personal computers, but they excel at large-scale calculations such as searching databases, running simulations, and even breaking encryptions. The video below is the simplest explanation of quantum computing I have seen so far.
It seems like every few months, quantum computing reaches a new milestone. Last month, at the International Conference on Quantum Technologies in Moscow, attendees and reporters gathered in mass for Professor John Martinis’ presentation of a chip embedded with 49 qubits. Instead, in a fashion that reminds me of Steve Harvey announcing the Miss Universe pageant, Mikhail Lukin, a Harvard professor and co-founder of the Russian Quantum Centermade his own announcement and stole the show.
Lukin’s team had successfully created the world’s most powerful, functional quantum computer to date, which runs on 51 qubits. The device was tested successfully at Harvard, where it solved physics problems that silicon chip-based supercomputers were struggling with.
Most quantum computers have been designed using superconductors and even semiconductors. Martinis’ 49-qubit chip was constructed in this fashion. Since traditional semiconductor materials are reaching their limits, Lukin’s team took a different approach.
The 51-qubit machine uses “cold atoms” in place of qubits that are locked onto laser cells. Cold atom physics is the discipline of studying atoms at incredibly low temperatures (.0000001 degrees Kelvin) in order to recreate quantum conditions. Cooling atoms to temperatures near absolute zero slows their movement down and makes them easier to observe. The video below gives an introduction to cold atom physics (starting at 1:35). After that, we’ll get into the biggest question I had about all of this:
How the heck do super-cooled atoms with lasers shining through them make a computer?
Lukin’s team wrote a research paper (PDF) explaining the experiment they set up. After sifting through the equations, I arrived at the data-reading mechanism. The setup consists of a linear array101 evenly spaced “optical tweezers”, which are generated by feeding a multi-tone RF signal into an into an acousto-optic deflector.
In simpler terms, they shine a laser beam through a vacuum tube and take fluorescence images(a type of laser scanning microscopy) of the atoms as they change betweenpositions. The “traps” that control the position of the atoms are programmable, which allows this super cooledvacuum tube with a laser shooting through it to function like a quantum computer.
As computing devices become ever smaller, engineers have been teaming up with scientists from other disciplines like physics and biology to make some outside-the box computing devices. Although it’s unlikely that any of these will end up in personal devices anytime soon (or ever), it always reminds me that a computer is just a device that calculates problems, and what our concept of a “computer” will look like in 100 years might just be beyond our current levels of comprehension.
If you’d like to learn more about quantum computing, I’ve compiled some resources below along with some of my favorite outlandish non-silicon computers!
Featured image used courtesy of Kurzgesagt
Quantum-encrypted communication and quantum computing promise to be safer and more secure ways of communicating, but a variety of challenges are keeping these goals from being achieved.
But new research has taken us an inch closer to the goal.
Quantum communication involves the sharing of quantum information over long distances. But in order to crack this, first the concept of quantum memory needs to be addressed.
Quantum memory means an interaction between light and matter that allows quantum information, stored in light, to be retrieved in a similar way to the memory in a classical computer.
Previous attempts at building a quantum memory storage system have been too big to be of use at the scale needed, the size of a computer chip.
Now researchers in China and the US have come up with quantum storage box, small enough to be used on a chip. The device is a nano-sized cavity, around one thousandth of a millimetre, filled with the element neodymium inside a crystal structure. The paper is published in the journal Science.
The photons are stored in an ensemble of rare-earth neodymium atoms, says Andrei Faraon, from the California Institute of Technology, and co-author on the paper.
Inside, the atoms are trapped in a crystal called yttrium orthovanadate (YVO4). The ensemble is small, and by itself would not be able to absorb the photons, says Faraon. This is why we make an optical cavity, or resonator, in the YVO crystal, that enhances the interaction between the atoms and the light, so the absorption of photons by the atoms becomes efficient.
Dr. Tian Zhong
To store the photons, the cavity is prepared in a special way using a sequence of laser pulses. This preparation means that after the photons are absorbed they are automatically re-emitted after a certain short amount of time, or 75 nanoseconds to be precise. To implement a quantum memory using this device, we store photons that are shaped as two pulses, early and late pulse, says Faraon.
Quantum mechanically the photon exist in a superposition of early and late. This means they exist as a combination of the two phases at the same time. After the pulses are retrieved, it closely resembles the stored pulses, meaning the memory works.
Faraon hopes this new device, which is much smaller than anything made previously, will help us to crack quantum communication. In the future it could be used to transfer information at the quantum level at long distances via optical fibres, Faraon says. A quantum memory is essential in most schemes to transfer quantum information at long distances.
Quantum-encrypted communication would be much more secure than the mathematical algorithms used currently. This is because of the properties of quantum mechanics called Heisenbergs uncertainty principle.
Currently, information can be encrypted with techniques based on mathematical algorithms. It is difficult to figure out the exact algorithm used to encrypt a piece of data, making the approach largely safe for now.
However, experts anticipate computers powerful enough to crack the codes will surface in the next 10 to 20 years. This development would mean current encryption methods would be redundant as they could easily be broken.
Last year, researchers at Chatham House’s International Security Department said satellites and other space communications technology are at significant risk from hackers and cyber attacks.
But there is a potential solution and this is where quantum mechanics comes into it. Heisenbergs uncertainty principle means the act of observing a particle creates certain changes in its behaviour.
Specifically, it means we cannot know both the momentum and position of a particle to the same degree of certainty at once. Quantum encryption uses this to create encoded data in the form of light that, if intercepted, will change its behaviour. This can alert the people communicating that the security key is not safe to use.
Today the Universities Space Research Association (USRA) announced it has upgraded its current quantum annealing computer to a D-Wave 2000Q system. The computer offers the promise for solving challenging problems in a variety of applications including machine learning, scheduling, diagnostics, medicine and biology among others.
The newly upgraded system, which resides at the NASA Advanced Supercomputing Facility at NASAs Ames Research Center, has 2031 quantum bits (qubits) in its working graphnearly double the number of qubits compared to the previous processor. It has several system enhancements that enable more control over the adiabatic quantum computing process allowing it to solve larger and more complex optimization problems than were previously possible.
The Quantum AI Lab, in its first four years of operation, has supported diverse research by industry, academia and government research organizations, said Dr. David Bell, Director of the USRA Research Institute for Advanced Computer Science. This has included research on the use of quantum computing for a range of applications including machine learning, planning and scheduling, diagnostics, medicine, biology, and finance.
Scientists in the Quantum AI Lab have directly collaborated with researchers from academia and industry, and through NASA, the Quantum AI Lab serves as a resource for multiple government research organizations to test and evaluate quantum computing approaches and applications using state-of-the-art quantum computers.
Dr. Eugene Tu, Center Director at NASAs Ames Research Center stated that Scientists at NASA Ames continue to explore the potential of quantum computingand quantum annealing algorithms in particularto aid in the many challenging computational problems involved in NASA missions. He further affirmed that NASA looks forward to advancements in this technology to achieve these goals.
The D-Wave 2000Q system is the first installation in the United States. With this third generation processor up and running in the lab, USRA has also released a new Request for Proposals (RFP) to use the computer. As part of USRAs management of the science operations for the Quantum AI Lab, USRA is able to allocate 20% of the computing time free of cost to university and industrial research organizations. Details of this research opportunity are available online.
Google has a team of experimentalists and theorists focused on making practical quantum computing a reality, said Sergio Boixo, Tech Lead of the Quantum AI Theory Group at Google. Quantum annealers are one of the platforms that we are investigating, and we are cautiously optimistic that phenomena in quantum physics, such as many-body delocalization, will unlock the potential of quantum enhanced optimization.
Through these collaborative efforts, the Quantum AI Lab team is continuing to explore ways that quantum annealing computers might significantly improve the capability for organizations to find high quality solutions for a range of complex optimization problems, and to enhance the performance of machine learning systems. The objective is to identify the best approaches for achieving significant speed up as compared to the capabilities of the best known algorithms that run on classical computers.
The collaboration between NASA and USRA builds on a thirty-year history of collaboration between the USRA Research Institute for Advanced Computer Science (RIACS) and the NASA Ames Research Center, which started with a focus on supercomputing and artificial intelligence, and has extended that focus to include the intersection of quantum computing and artificial intelligence.
Founded in 1969, under the auspices of the National Academy of Sciences at the request of the U.S. Government, the Universities Space Research Association (USRA) is a nonprofit corporation chartered to advance space-related science, technology and engineering.
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Quantum computers, at long last, finally seem to be coming into their own with the promise of being far superior from its competition by years end. But this leads to one big problem, very few people actually know how to work them, let alone program them. So what is quantum programming anyhow? Quantum programming is a set of programming languages that express quantum algorithms using high-level constructs. It is through these complex algorithms, that conventional computers arent capable of handling, that set apart quantum computers from the rest. A normal computer is based on bits, which are variables that just have two possible values (0 or 1/true or false), where as quantum computers are also based on these two variables, but where it differs is that they are also based on qubits. These qubits mean that between these two variables there can exist many other variables which we call superposition states.
These states can be summed up as values that exist part way between. After Google unveiled a new quantum chip design, the bold claim of quantum computers achieving quantum supremacy was born. This phrase, summed up, means to perform tasks and calculations that are physically impossible for a normal computer to achieve. There is a lot of conventional belief that quantum programming is a drastically complex process, that very few scientists are able to understand. But with companies such as IBM and Google already having created quantum devices, the time to start looking into quantum programming as a mainstream action is now.
So where to begin looking if you are interested or curious about learning quantum programming? Well for starters it may be relieving to learn that you do not need a quantum computer in order to begin learning quantum programming. Simple quantum programs can easily be simulated on a normal computer. QISKit offers developers a way to explore IBMs Quantum Experience. It uses a Python interface which enables a user to experiment with and work with quantum circuits.
One of the main reasons that are stopping the growth of quantum programmers is the lack of accessibility to quantum computers. Where as you can start on a normal computer with APIs offered through different sites, quantum computers are likely to behave quite different than normal computers. This is a problem facing quantum programming since until there are tangible incentives for someone to learn the skill and until the software to run it is more widely available, it is hard to convince programmers to invest time and their skills into learning quantum programming especially since, unlike other programming languages, a knowledge in quantum programming doesnt necessarily transfer over to other programming languages where most programming languages have significant overlap amongst each other.
Also, with all these advancements and leading edge technologies that exist within quantum programming, its not a surprising fact that programming a quantum computer is much harder than that of conventional computers. To start, building a quantum algorithm is far more complex than what most programmers are used to seeing, which means an understanding of quantum physics, which is what will give qubits their properties, is recommended. While it is not a requirement to have a degree in the field, a basic understanding will definitely help since it is a far departure from that of normal computer programming.
These are some of the problems that the companies building these machines have recognized, which is why we have recently started seeing services offered where anyone can go and start using quantum computing through APIs and other programs. Also, these companies are really investing a lot of effort into making the programming framework in a way that will allow programmers to use them efficiently. Where as, just like any new technology, there are risks involved into dedicating time and effort into learning quantum programming, the potential pay off in being on the cutting edge in the next major evolution of programming offers huge incentives. Even if quantum computers only perform at a marginally faster speed than a normal computer, it will be enough of an incentive for most companies and users to switch to quantum computing in the future. Though if early indications are reliable, quantum computing should far surpass that of its predecessors. The opportunity to learn quantum programming now, and get a head start on other programmers, is not only a great opportunity but the tools necessary to help you get started are already readily available to you.
More News to Read
Google’s John Martinis Believes Quantum Computing Threat to Be Long Way Off – Bitcoin News (press release)
At a recent crypto event, Googles John Martinis addressed the hypothetical threats posed by quantum computing, stating that we are still many years from being able to realize quantum computers. Concerns regarding the threat quantum computing may pose to RSA encryption has been increasingly discussed within the cryptocurrency community in recent years.
Also Read:Antonopoulos Details Bitcoins Two Layers of Protection Against Quantum Computing
Googles John Martinis recently rejected the notion that quantum computing may pose a direct threat to cryptocurrency in the near future. Speaking at the University of California Santa Barbara as part of the Crypto 2017 event, Martinis says he believes it will take at least a decade until quantum computing may be realized, stating that building such is really, really hard, way harder than building a classical computer.
The perceived threat posed by quantum computing is that it may be able to break RSA encryption and digital signatures. That would mean you could forge transactions, and steal coins, stated Bernardo David, a cryptography expert from the Tokyo Institute of Technology. Martin Tomlinson, a professor in the Security, Communications and Networking Research Centre at Plymouth University, articulated the hypothetical threat that quantum computing may pose to bitcoin in a 2016 interview with MSN. If you have a quantum computer then youre able to just basically calculate the private key from the public key it would take just a minute or two. So by learning all the private keys using a quantum computer, youd have access to all the bitcoin thats available.
In refuting the threat, Martinis has pointed to an instability of quantum bits (qubits) which are the counterpart to bits in classical computing. Martinis describes qubits as resembling a three-dimensional version of bits, which rather than representing a strict, binary 1 or 0 (as is the case with bits), qubits can simultaneously represent both values in a dynamic array of superpositions. As such, Martinis argues that popular perception that competing quantum computing research labs are engaging in a race to produce the most qubits is inaccurate, stating that of equal importance is a labs ability to reduce the number of qubit errors that are generated.
Despite the distant nature of the hypothetical threat posed by quantum computing, many cryptocurrency developers are actively seeking to address such. QRL, or Quantum Resistant Ledger, is an altcoin that was developed with evading quantum computing as its principal stated utility. The Russian Quantum Centre has also stated its intention to expand its research in quantum proof blockchain solutions. These endeavors indicate the cryptocurrency community is taking the perceived threat seriously, well before quantum computing has become a reality.
Do you think that quantum computing poses a threat to bitcoin and cryptocurrency? Or do you think that developers will have the capacity to evade quantum computings threat by the time such has been realized? Share your thoughts in the comments section below!
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Have you seen our newwidget service? It allows anyone to embed informative Bitcoin.com widgets on their website. Theyre pretty cool and you can customize by size and color. The widgets include price-only, price and graph, price and news, forum threads. Theres also a widget dedicated to our mining pool, displaying our hash power.
The University of New South Wales (UNSW) is set to commercialize its quantum computing technology with the launch of what is being touted as Australias first quantum computing company.
The A$83 million (US$66 million) venture, from which the new company, Silicon Quantum Computing Pty Ltd, has emerged, has received backing from UNSW itself, which has contributed A$25 million, as well as the Commonwealth Bank of Australia and Telstra, which are contributing A$14 million and A$10 million, respectively.
This is in addition to the backing by the Australian Federal Government which, through its National Innovation and Science agenda, which is also investing A$25 million in the next five years in the company, and the NSW governments contribution, which is putting up A$8.7 million.
The creation of the new company is intended to help drive the development and commercialization of a 10-qubit quantum integrated circuit prototype in silicon by 2022 as the forerunner to a silicon-based quantum computer.
The company will work alongside the Australian Research Council (ARC) Centre of Excellence for Quantum Computation and Communication Technology (CQC2T), operating from new laboratories within the Centres UNSW headquarters.
Speaking at an event to launch the company at UNSW on 23 August, chief researcher and board member, Professor Michelle Simmons, said that the three-way collaboration between government, industry and universities was unique internationally.
I know the rest of the world is watching us, Simmons said.
The creation of the new company is expected to see up to 40 staff hired, including 25 postdoctoral researchers, 12 PhD students, and lab technicians. Recruitment is already underway.
Silicon Quantum Computings board members include Simmons; Hugh Bradlow, Telstras Chief Scientist; David Whiteing, CBAs Chief Information Officer; and Glenys Beauchamp, Secretary of the Department of Industry, Innovation and Science.
The board will be chaired initially by corporate lawyer and company director, Stephen Menzies.
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Australian quantum computing outfit goes commercial – Networks Asia
An international team of researchers has fashioned a device from nanowires that may finally prove the existence oflong-theorized quasiparticles known asMajorana particles. Once these Majorana particles are identified and isolated, they could form the basis of a quantum bitor qubitthat would process information in a new kind of quantum computer with improvedstability.
Ever since 1937, when the Italian physicist Ettore Majorana first theorized the existence of the quasiparticle that takes his name, there has been much effort to prove that it really exists, withlittle to show for it. But this changed back in 2012, when researchers at Delft University of Technology (TU Delft)in the Netherlandssaw strong hints of Majoranas when they sent electrons into a semiconducting nanowire placed alongside superconducting material.
Since that 2012 Delft research, there have been a number of experiments that have reported evidence of Majoranas in a similar system. However, all of those experiments, including the original one at Delft, left open the possibility of alternative explanations for the results. So, unilnow, there has been no smoking-gun evidence of Majoranas, saidHao Zhang, a post-doc at TU Delft, in an e-mail interview with IEEE Spectrum.
There remained one definitive way to prove the existence of these Majorana particles and that was for them to exchange places along the nanowire, a phenomenon referred to as exchanging statistics of the particles. These statistics describe how the quantum mechanics of the system changewhen two indistinguishable particles switch places.
This exchange of places along the nanowire is also called braiding. These braids form the logic gates of topological quantum computers. However, no one could see how this braiding was possible because the act of getting the particles to pass each other in this nanowire would annihilate them.
If this braiding of these quasiparticles could somehow be artificially induced, researchers theorized,it would result in a far more stable method for quantum computing than employing trapped quantum particles. Thats because the systemwouldnt be susceptible to outside influences like thermal fluctuations.
In research described in the journal Nature, the researchers, from TU Delft, Eindhoven University of Technology in the Netherlands, and the University of California,Santa Barbara, created a hashtag-like device made from nanowires. It provided a four-way intersection in which two Majorana particles could exchange places in the nanowire-based structure without coming in contact with each other and annihilating each other.
These braiding experiments can give experimental results, which are unique to Majoranas, and cannot be mimicked by other alternative scenarios, says Zhang. Thus it can be treated as the smoking-gun evidence.
Braiding not only provides definitive evidence of Majoranas, but perhaps more importantly, it also proves the feasibility of topological quantum computing in which the fundamental assumption of their operation is based on the braiding phenomenon. In other words, the braiding not only proves the existence of Majoranas, but also provides the mechanism by which they could serve as the basis of a qubit for a topological quantum computer.
This means that the quantum information (qubit) can be stored and manipulated simply by braiding (swapping) of Majoranas. This process of braiding is supposed to be robust against error since the outcome only depends on the order of braiding operations, adds Zhang.
In the video below, you can see a description of how the Majoranas are formed from the combining of semiconductor nanowires with a superconductor material, and how once formed can be manipulated into serving as qubits in a topological quantum computer.
This robustness against error depends on Majornas ability to maintain superposition. In previous quantum computing proposals, the unpaired electrons of certain ions can assume either of two spin states, up or downor in terms of digital logic, 0 or 1. When these ions are hit with a microwave pulse, the unpaired electron can take on both the 0 and 1 state simultaneously. These two states constitute what is termed superposition.
Unil now, it has only been possible to maintain a superposition state for very short periods of time because the spin states of neighboring atoms quickly destroy the coherent state.This makesthe life of the qubit too short for it to perform the desired number of quantum computations.
This is the biggest advantage of Majorana qubit compared to other qubits, says Zhang. The Majorana qubit should have longer coherence time (robust against error) due to its topological protection.
Zhang says that they are already working on the engineering of a qubit based on these Majoranas that will involve the fabrication of a microwave pulse circuit.
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Elusive Majorana Particle Takes Major Step Towards Quantum Computing – IEEE Spectrum