ORNL neutrons add advanced polarization capability for measuring magnetic materials
Understanding magnetism at its most fundamental level is vital to developing more powerful electronics, but materials with more complex magnetic structures require more complex tools for studying them--powerful tools simply referred to as "neutrons."
Two of the world's most powerful sources for neutron scattering at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) are getting upgrades. Adding an advanced capability called spherical neutron polarimetry will enable researchers using ORNL's High Flux Isotope Reactor (HFIR) and Spallation Neutron Source (SNS) to make measurements of materials featuring exotic magnetic structures and quantum states that were previously inaccessible in the United States.
"Neutrons are ideal for studying magnetic phenomena," said ORNL post-masters researcher Nicolas Silva. "They're electrically neutral, or have no charge, and exhibit magnetic moments, which sort of make them like tiny magnets themselves."
When neutrons pass through a material and scatter off magnetic fields generated by a material's atoms, they paint an atomic portrait or even a 3D model of the material's atomic arrangement and reveal how the atoms within the system are behaving.
Neutrons have a "spin," or orientation, like the north and south poles of refrigerator magnets. In a typical neutron beam, the neutrons within the beam have spins that are arranged randomly. Measuring certain highly dynamic or complex magnetic systems, however, requires more uniformity, which is provided by a polarized neutron beam in which each neutron spin is aligned in parallel and with the same orientation.
"Neutron polarization filters allow us to see through the stuff we don't want to see that might be muddying up the signal we're interested in," said instrument scientist Barry Winn. "Similar to how polarized lenses allow anglers to see fish swimming below that would be otherwise blocked by the water's reflection."
Neutrons will change their spins in predictable ways when they scatter. Using a polarized beam enables researchers to better understand what's happening in a material by establishing the neutron spin before and measuring the neutron spin after the beam strikes the sample. For example, a neutron's spin could be flipped in the opposite direction during scattering.
"In the US, most of the measurements we've been doing with polarized neutrons until now have been based on whether the neutron, after being scattered from the material or its magnetic field, gets rotated 180 degrees or preserves its orientation. We call that spin-flip and non-spin-flip," said Winn.
"But there's a problem with that. If we get any scattering off the sample that's something other than a non-spin-flip or spin-flip--or something other than 0 and 180 degrees--then the strategy blows up in our face."
The strategy works well for conventional magnetic materials such as ferromagnets and antiferromagnets, in which all the magnetic atoms are pointing either in the same direction or in alternate directions, but remain parallel to their neighbors. However, the strategy does not work for more complex magnetic structures.
For example, the technique is limited when it comes to investigating exotic particles such as skyrmions--quasi-particles that exhibit chiral motion, or tangled vortices, or whirlpools of asymmetric field lines. Such particles provide exciting potential for materials used in advanced data storage and quantum computing applications.
To tackle the problem, polarization scientist Peter Jiang is leading an ORNL team including Winn and Silva in a laboratory directed research and development project to develop spherical neutron polarimetry for multiple ORNL beamlines. The technology will enable neutron measurements of materials that don't conform to the traditional spin-flip and non-spin-flip domains, or, in other words, will enable researchers to see the dynamical magnetic behavior that exists in between.
"The traditional techniques are not sophisticated enough to study certain complex magnetic systems," said Jiang. "Now, we're no longer restricted to spin-flips. This allows us to look at magnetic arrangements that we weren't able to figure out before."
Spherical neutron polarimetry has been used in Europe, and now Jiang and the ORNL team are adapting the technology to instruments at SNS and HFIR. They're building the technology based on ongoing research conducted by Tianhao Wang, first as a graduate student at Indiana University, Bloomington, and later as a postdoctoral research on the ORNL team.
The basic technology incorporates additional optical devices installed on both the incoming beam that hits the sample--the incident beam--and the outgoing beam that scatters off it, which enables measurements of scattered neutrons oriented in any direction. The ORNL technology builds on previous prototype designs and will offer several innovations.
With the ORNL spherical neutron polarimetry devices, the scattered beam trajectory need not be in line with the incident beam but instead can be angled around the sample.
"That means if the neutron doesn't experience a full flip, we can adjust the field on the other end, or move the apparatus to detect neutrons scattering in different directions," explained Silva.
The team also developed two independent cooling systems to enable researchers to study how magnetic structures change as a function of temperature. The first system cools two spherical neutron polarization components located on either side of the sample to make them superconducting. The second system introduces an extra cryostat with liquid helium auto-refilling capability that allows researchers to more easily explore materials under a range of temperatures without interfering with the temperatures required for superconductivity in the first system.
Finally, the spherical neutron polarimetry devices are made with more efficient materials. Whereas previous designs use niobium for the superconducting sheets, the new design uses an yttrium-barium-copper-oxide (YBCO) that superconducts at 93 Kelvin (-292° F), a significantly higher temperature than its niobium predecessor. Additionally, the superconducting films are coupled with Mu-metal yokes that combine to shield all other magnetic fields and establish a zero field around the sample to study the materials' spins in their natural state.
"Reaching superconductivity requires a significant amount of cooling power. Niobium needs to be cooled to below 10 K to maintain superconductivity, so the European designs required extensive cooling systems that had to be manually refilled with liquid helium often," said Jiang.
"With the high-temperature YBCO films, we can use a single-stage closed-cycle refrigerator to cool the film to far below its critical temperature, so we're not worried about any loss in superconductivity. And, with the added liquid helium autofill system for the cryostat and the closed-cycle refrigeration system, the device will be easier to use and more efficient."
What's more, the system is compact by comparison with previous systems--the high-temperature superconductors that negate the need for a large cooling system make it mobile.
"If anything, there's a testament to how portable the device is. We've moved it to the nuclear reactor at the University of Missouri, then back to HFIR, and from HFIR to SNS," said Silva. "I've put it together and taken it apart multiple times, and each time I've found easier ways to connect the pieces--just little quality-of-life changes we're making to enhance its utility."
The system has been successfully tested, wherein full polarization measurements were made using several known materials including silicon, manganese-oxide, and bismuth-iron-oxide.
The team plans to implement the system at HFIR's PTAX triple axis spectrometer and the GP-SANS diffractometer, which will be optimized for the reactor's steady-state neutron beam, with full capabilities expected by the end of 2020.
Subsequently, the team will develop a similar spherical neutron polarimetry device exclusively for the HYSPEC instrument at SNS which will make it the only instrument in the world that couples a super-mirror array and wide-angle capability. The device will also benefit from the unique capabilities enabled by the SNS pulsed-source accelerator.
"In the meantime," said Winn, "we're going to have a workhorse in PTAX that's going to knock our socks off."
HFIR and SNS are DOE Office of Science User Facilities. UT-Battelle LLC manages ORNL for the DOE Office of Science. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.--by Jeremy Rumsey


Quantum computer emulated by a classical system
(Phys.org)—Quantum computers are inherently different from their classical counterparts because they involve quantum phenomena, such as superposition and entanglement, which do not exist in classical digital computers. But in a new paper, physicists have shown that a classical analog computer can be used to emulate a quantum computer, along with quantum superposition and entanglement, with the result that the fully classical system behaves like a true quantum computer.
Physicist Brian La Cour and electrical engineer Granville Ott at Applied Research Laboratories, The University of Texas at Austin (ARL:UT), have published a paper on the classical emulation of a quantum computer in a recent issue of The New Journal of Physics. Besides having fundamental interest, using classical systems to emulate quantum computers could have practical advantages, since such quantum emulation devices would be easier to build and more robust to decoherence compared with true quantum computers.
"We hope that this work removes some of the mystery and 'weirdness' associated with quantum computing by providing a concrete, classical analog," La Cour told Phys.org. "The insights gained should help develop exciting new technology in both classical analog computing and true quantum computing."
As La Cour and Ott explain, quantum computers have been simulated in the past using software on a classical computer, but these simulations are merely numerical representations of the quantum computer's operations. In contrast, emulating a quantum computer involves physically representing the qubit structure and displaying actual quantum behavior. One key quantum behavior that can be emulated, but not simulated, is parallelism. Parallelism allows for multiple operations on the data to be performed simultaneously—a trait that arises from quantum superposition and entanglement, and enables quantum computers to operate at very fast speeds.
To emulate a quantum computer, the physicists' approach uses electronic signals to represent qubits, in which a qubit's state is encoded in the amplitudes and frequencies of the signals in a complex mathematical way. Although the scientists use electronic signals, they explain that any kind of signal, such as acoustic and electromagnetic waves, would also work.
Even though this classical system emulates quantum phenomena and behaves like a quantum computer, the scientists emphasize that it is still considered to be classical and not quantum.
"This is an important point," La Cour explained. "Superposition is a property of waves adding coherently, a phenomenon that is exhibited by many classical systems, including ours.
"Entanglement is a more subtle issue," he continued, describing entanglement as a "purely mathematical property of waves."
"Since our classical signals are described by the same mathematics as a true quantum system, they can exhibit these same properties."
He added that this kind of entanglement does not violate Bell's inequality, which is a widely used way to test for entanglement.
"Entanglement as a statistical phenomenon, as exhibited by such things as violations of Bell's inequality, is rather a different beast," La Cour explained. "We believe that, by adding an emulation of quantum noise to the signal, our device would be capable of exhibiting this type of entanglement as well, as described in another recent publication."
In the current paper, La Cour and Ott describe how their system can be constructed using basic analog electronic components, and that the biggest challenge is to fit a large number of these components on a single integrated circuit in order to represent as many qubits as possible. Considering that today's best semiconductor technology can fit more than a billion transistors on an integrated circuit, the scientists estimate that this transistor density corresponds to about 30 qubits. An increase in transistor density of a factor of 1000, which according to Moore's law may be achieved in the next 20 to 30 years, would correspond to 40 qubits.
This 40-qubit limit is also enforced by a second, more fundamental restriction, which arises from the bandwidth of the signal. The scientists estimate that a signal duration of a reasonable 10 seconds can accommodate 40 qubits; increasing the duration to 10 hours would only increase this to 50 qubits, and a one-year duration would only accommodate 60 qubits. Due to this scaling behavior, the physicists even calculated that a signal duration of the approximate age of the universe (13.77 billion years) could accommodate about 95 qubits, while that of the Planck time scale (10-43 seconds) would correspond to 176 qubits.
Considering that thousands of qubits are needed for some complex quantum computing tasks, such as certain encryption techniques, this scheme clearly faces some insurmountable limits. Nevertheless, the scientists note that 40 qubits is still sufficient for some low-qubit applications, such as quantum simulations. Because the quantum emulation device offers practical advantages over quantum computers and performance advantages over most classical computers, it could one day prove very useful. For now, the next step will be building the device.
"Efforts are currently underway to build a two-qubit prototype device capable of demonstrating entanglement," La Cour said. "The enclosed photo [see above] shows the current quantum emulation device as a lovely assortment of breadboarded electronics put together by one of my students, Mr. Michael Starkey. We are hoping to get future funding to support the development of an actual chip. Leveraging quantum parallelism, we believe that a coprocessor with as few as 10 qubits could rival the performance of a modern Intel Core at certain computational tasks. Fault tolerance is another important issue that we studying. Due to the similarities in mathematical structure, we believe the same quantum error correction algorithms used to make quantum computers fault tolerant could be used for our quantum emulation device as well."
More information: Brian R. La Cour and Granville E. Ott. "Signal-based classical emulation of a universal quantum computer." New Journal of Physics. DOI: 10.1088/1367-2630/17/5/053017
Journal information: New Journal of Physics
© 2015 Phys.org


Introduction to Quantum Computing
Guest post by Anita Ramanan Software Development Engineer at Microsoft
Anita graduated from University College London in 2014 with an MSci in Natural Sciences: Atomic and Particle Physics and Physical Chemistry (TL;DR: Quantum Mechanics). Since then, she has been working at Microsoft and is now a Software Engineer focusing on the Internet of Things (particularly as it relates to healthcare), Xamarin, Power BI and now Quantum Computing.
The concept of quantum computing was famously discussed by Richard Feynman during his 1981 keynote delivery at the first ‘Physics of Computation’ conference (worth a read if you’re that way inclined) ( Feynman, 1982 ) . In his speech, he explored the difficulties of simulating complex quantum systems using classical computers and raised the suggestion that to accurately simulate quantum systems, we must strive to build quantum computers.
Since then, the field of Quantum Computing has developed at a rapid pace, bringing us within touching distance of a true, physical realisation of a scalable quantum computer (more on this in future posts).
The most fundamental difference between a classical computer and a quantum one is the way in which the bit is realised. The bit (‘binary digit’) is the smallest possible unit of digital data. Classically, bits can only take one of two values at any one time: 0 or 1. A quantum bit (qubit) obeys the laws of quantum mechanics however, and can therefore exist in a superposition of both states 0 and 1 simultaneously.
(that is, the qubit has a 100% chance that it will be found in either of these two states at a particular time, and a 0% chance that it will be measured to be in any other state at that time).
We are able to rewrite our wavefunction |ψ〉 like so:
Now we have it in this form, we can visualise this superposition of states |0〉 and |1〉 using the Bloch Sphere:
Now any unitary transformation we do on |ψ〉 can be visualised as simply moving the point (marked |ψ〉) around the surface of the sphere. For example, if our state were |ψ〉 = |0〉, the point would sit on the z-axis at the location marked |0〉. Sadly, this visualisation can only be used for single qubit states, as there is no known (simple) generalisation that applies to multi-qubit systems. We will revisit the Bloch Sphere later on in this series.
A quantum computer can take advantage of superposition and entanglement* to perform certain calculations more efficiently than is believed to be possible for classical computers – for example, prime factorisation (Shor, 1997)and unstructured search problems (Grover, 1997 ) . Furthermore, these unique properties of quantum physics offer unique new applications such as quantum cryptography (Bennett & Brassard, 1984 ) . The next section describes the accepted requirements necessary to construct such a system.
*Superposition is the phenomenon where a quantum system exists as a probabilistic distribution of states a single qubit can exist in a superposed state such as Entanglement requires two or more qubits (or degrees of freedom, more generally) and is what Einstein famously described as ‘spooky action at a distance’ – the concept that the perturbation of one particle can affect the state of another regardless of distance or physical separation from one another (despite not allowing faster than light communication). One example of an entangled state is the Bell State .
Five (Plus Two) Criteria for Quantum Computing
In 2008, David DiVincenzo published five requirements (refined from his original 1996 paper) which a system must fulfil in order to qualify as a scalable quantum computer. These criteria will be used as a basis for discussion throughout this series of posts. I have provided a high-level summary below (for a proper discussion, please see the original paper):
1. The physical system must be scalable and the qubits must be well-known
You must be able to ‘scale up’ the system from a single qubit to the many qubits required for complex computation. A “well characterised” qubit is one that has well-known properties and interactions with the rest of the system.
2. We must be able to repeatedly prepare the qubits in a simple starting state (such as |000…〉 )
The system must be in a simple, accurately-known state at the start of computation. If you can’t repeatedly initialise the system in this simple starting state, it can’t really be considered a computer of any sort.
3. The system must survive long enough to perform operations on the qubits
For several reasons (such as interactions with external systems), it is difficult to maintain a system of qubits in a prepared state for long before they ‘decohere’ because of unwanted correlations emerging between the system and its unknown and uncontrolled surroundings. When a quantum system decoheres, the quantum bits follow a statistical distribution when measured of 0 or 1 rather than a quantum distribution. Once decohered, no quantum operations can be used to re-cohere the state. The time taken for our system to decohere must therefore be much longer than the time needed for gate operations.
4. We must be able to implement a ‘universal set’ of gates using our system
A ‘universal set’ contains all the gates needed to perform any quantum computation. At a minimum, we must be able to move single qubits to any position on the Bloch Sphere (using single-qubit gates), as well as introduce entanglement in the system (this requires a multi-qubit gate). For example, the Hadamard, phase, CNOT and gates form a universal set, from which any quantum computation (on any number of qubits) can be generated.
5. Measurement of specific qubits must be possible
One must be able to ‘read out’ the result of the computation by measuring the final state of specific qubits.
There are two additional requirements that refer to quantum communication – these requirements relate to quantum information processing:
1. The system must be able to reliably convert data stored in stationary (computational) qubits to networking (“flying”) qubits (e.g. photons) and back again.
2. The system must be able to reliably transmit flying qubits between specified points.
There are currently several different physical models for quantum computing in development, ranging from ion trap to photon-based to topological qubits and more. Any system developed to fulfil the role of a quantum computer must satisfy the five (plus two) criteria outlined above.
A handful of these candidate systems will be explored in a later blog post, but first we must familiarise ourselves with quantum gates and circuit diagrams, which will be the topic of my next blog post. I look forward to seeing you there!
Anita’s GitHub: https://github.com/anraman
Anita’s Personal Blog: https://whywontitbuild.com/
Anita’s LinkedIn: https://www.linkedin.com/in/anitaramanan/
Microsoft Quantum https://www.microsoft.com/quantum
Microsoft Quantum Development Kit https://www.microsoft.com/en-us/quantum/development-kit
Microsoft Quantum Blog https://cloudblogs.microsoft.com/quantum/


What are Quantum Computers
Truly strange and unexpected behaviors of subatomic particles have been discovered by science in the recent past – just the last about 50 years or so. Einstein’s work predicted some of this, but even Einstein couldn’t figure out if the theories were right or what it even meant IF it were true.
While we don’t yet fully understand why these things happen the way they do, some brilliant people have figured out ways to leverage these behaviors for something useful. They’re building totally new kinds of supercomputers called “Quantum Computers”. These machines use these strange subatomic behaviors to conduct logical operations in a totally new way.
What’s the big deal?
When scaled up just a little, it becomes physically impossible for traditional electronic computers to compare. Google recently announced that its quantum computer just achieved something called “quantum supremacy.” It claims to have processed a calculation in just 200 seconds that would have required the world’s most powerful supercomputers over 10,000 years to crunch. While there’s a debate on the use of the “quantum supremacy” term and technicalities of the results, it is nonetheless incredibly impressive!
How do quantum computers work?
To appreciate quantum computers, we should really look at the strange stuff they work with to do the amazing feats of calculation they can accomplish.
The first is called “entanglement” now, but Einstein labeled it “spooky action at a distance”. Since it is still only partially understood and even then only by a small group of physicists, let’s try to get a basic understanding from a metaphor using coins within boxes instead of subatomic bits:
First, imagine you have two boxes, each with a coin inside. Opening box A, you will see the coin sitting flat in the bottom showing, let’s say “heads” up. Opening box B, you will see its coin also sitting flat in the bottom showing “heads”.
Now we’re going to put the lids on the boxes and shake them so that the coins wildly bounce around in the boxes. Set them down on the table. Open them up, and you’ll see box A now has “tails”, but box B still has “heads”. Repeat this over and over and over again any number of times and each time you open the boxes you’ll see either heads or tails with a 50/50 chance. Sometimes the coins in the boxes will match, sometimes they won’t. It’s a random probability.
This is classical physics of the comfortable world we know and have learned to expect. These coins are not entangled. If they were entangled, it would go more like this:
You look in box A and see heads, you look in box B and see tails. You close the boxes and shake them up as before. You open box A and see “heads”, and now open box B and see “tails”. You shake them up again. Box A shows “tails”, then box B shows “heads”. You repeat this over and over again any number of times and see that every single time the coin in box B is the opposite of box A. They NEVER show the same side.
This is weird, right? So you test it. You leave box B on the table and only shake box A. When you open them, box A shows “heads” and box B shows “tails”. Close and shake box A again – leaving box B on the table. Box A shows “tails” and box B shows… “heads”! But you didn’t shake that box. It just sat there on the table. How did the coin flip?!?
Maybe there’s some force field between the boxes, so you ship box B to your very good friend in Tokyo. While you’re on the phone, you both check your boxes. Yours (box A) shows “heads” and the one on the other side of the planet shows “tails”. You close and shake your box and check it again, while your friend leaves box B sitting still on the table. Now box A on your side of the planet shows “tails” and the one on the opposite side of the planet, undisturbed, shows “heads”.
The thing is, you could move these boxes to opposite sides of the universe and the effect would be the same. Entangled particles behave like this and there is no (known) physical connection between them, nor is there anything transferred between the two (again, not that we know of). The effect is instantaneous and not limited by the speed of light – which is a limit on absolutely everything else in the known universe.
The second strangeness is called “superposition”. This is where a particle can be in two opposing states simultaneously. In our box and coin analogy, it’s like that the coin is showing “heads” AND showing “tails” at the same time. So which one is it? The answer is “yes”.
…until you measure it. Once you look into the box, the coin resolves into either “heads” or “tails”. The very act of measurement effects this result; however, the coins are actually both heads and tails at the same instant in time. The direct observation or measurement resolves the state of the coin into showing either “heads” or “tails”, but apart from that measurement, we can only understand how it interacts with other things. We can hear it moving inside the box, maybe even feel it moving around tapping the sides or spinning at the bottom. We know it’s in there and we know it could be “heads” or “tails”, but we don’t know which one until we look at it.
Further, if the coins in box A and box B are entangled, we don’t know what either was until we look at one of them. Once we see just one, we know with certainty what both coins are showing.
If you have made it this far, congratulations – you’ve made it. There are more details to this strangeness in subatomic land, but this is enough to appreciate what is happening.
The classical type of computer we’ve used to build the internet, send a man to the moon and tweet our daily dissatisfaction with uses electrons to change the charge of something to be on or off (“1” or “0”). We call these things bits. The original Atari you might have played Pong on had 8-bits. The latest smartphone and laptop processors use 64-bits. Each of the bits can be only one of two different options. Quantum computers use the properties of superposition and entanglement to manipulate and observe special different types of bits called qubits (Kew-bits), which are not just on or off. Not just “1” or “0” anymore. Each of these qubits can be any number of options in specific probable combinations of “1” and “0” simultaneously. That gives them exponentially increased computing power.
Photo by Alexander Sinn on Unsplash
This is tremendously useful for the types of calculations that require parallel usage of many of the bits in a computer for each step of a process. Since a quantum computer can manipulate each bit in far more than just two states (on or off, 1 or 0), it can do things in a single step that might take a classical computer many thousands or millions of steps. This means that complex problems solving for a wide variety of possibilities like chemical molecular engineering, global logistics optimization, data security, cryptography, and artificial intelligence can get a HUGE boost from this technology. Really, more accurately, it makes some of these things possible that were practically impossible even for the greatest supercomputers on the planet.
Unfortunately, this does nothing for streaming videos, sending emails, or playing video games – the things most of society use computers for. They just don’t get any advantage from parallel processing of complex algorithms. Quantum computers are destined to have a big impact on humanity, but not by virtue of your download speeds.
What if I DO use computers to engineer molecules, encrypt data, or optimize logistics? What if I am constructing the next AI engine that will take over the world?
You’re in luck! Researchers at IBM have built multiple 20-qubit quantum computer systems and made them available to the public – for free! These computers are some of the most advanced technology available to humanity. It has taken millions of dollars to develop and built and has to be meticulously and cryogenically maintained by a highly-skilled crew of technicians, and you can use it at absolutely no cost at all. It’s called the IBM Q Experience. With this program, IBM invites anyone interested to set up a free account, learn the new programming language (called “Quiskit”) and run your very own programs on their quantum systems. Check out the IBM Q Experience here.
CDN Inc. is a product design and engineering firm that can adapt easily to your project needs; engineering, industrial design, prototyping & manufacturing.


Radio is made from atomic-scale defects in diamond
Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have made the world’s smallest radio receiver – built out of an assembly of atomic-scale defects in pink diamonds.
This tiny radio — whose building blocks are the size of two atoms — can withstand extremely harsh environments and is biocompatible, meaning it could work anywhere from a probe on Venus to a pacemaker in a human heart.
The radio uses tiny imperfections in diamonds called nitrogen-vacancy (NV) centers. To make NV centers, researchers replace one carbon atom in a tiny diamond crystal with a nitrogen atom and remove a neighboring atom — creating a system that is essentially a nitrogen atom with a hole next to it. NV centers can be used to emit single photons or detect very weak magnetic fields. They have photoluminescent properties, meaning they can convert information into light, making them powerful and promising systems for quantum computing, phontonics and sensing.
Radios have five basic components — a power source, a receiver, a transducer to convert the high-frequency electromagnetic signal in the air to a low-frequency current, speaker or headphones to convert the current to sound and a tuner.
In the Harvard device, electrons in diamond NV centers are powered, or pumped, by green light emitted from a laser. These electrons are sensitive to electromagnetic fields, including the waves used in FM radio. When NV center receives radio waves it converts them and emits the audio signal as red light. A common photodiode converts that light into a current, which is then converted to sound through a simple speaker or headphone.
An electromagnet creates a strong magnetic field around the diamond, which can be used to change the radio station, tuning the receiving frequency of the NV centers.
Shao and Loncar used billions of NV centers in order to boost the signal, but the radio works with a single NV center, emitting one photon at a time, rather than a stream of light.
The radio is extremely resilient, thanks to the inherent strength of diamond. The team successfully played music at 350 degrees Celsius — about 660 Fahrenheit.
“Diamonds have these unique properties,” said Loncar. “This radio would be able to operate in space, in harsh environments and even the human body, as diamonds are biocompatible.”
Receive an email update when we add a new NANOELECTRONICS article.
The Latest on: Nanoelectronics
via Google News
The Latest on: Nanoelectronics
- Two walls may beat one for solar-panel nanotubeson March 30, 2020 at 5:08 pm
This affects how suitable nested nanotube pairs may be for nanoelectronics applications, especially photovoltaics. In a 2002 study, the Rice University lab of materials theorist Boris Yakobson ...
- Double-walled nanotubes have electro-optical advantageson March 27, 2020 at 1:38 pm
This affects how suitable nested nanotube pairs may be for nanoelectronics applications, especially photovoltaics. The theoretical research by Yakobson's Brown School of Engineering group appears in ...
- About the Laboratoryon March 26, 2020 at 5:00 pm
Conducting research for the next generation of nanoelectronics and nanophotonics requires interdisciplinary collaboration between experts in solid mechanics, structural analysis, materials, ...
- Center for Photonics, Electromagnetics and Nanoelectronics (CPEN)on March 25, 2020 at 5:00 pm
The Center for Photonics, Electromagnetics and Nanoelectronics (CPEN) is to conduct innovative research in novel electromagnetic materials (metamaterials), optoelectronic devices, biosensors, antennas ...
- Scientists develop platform for building nanoelectronics and quantum processorson March 24, 2020 at 7:54 am
Scientists of Far Eastern Federal University (FEFU, Vladivostok, Russia) together with colleagues from the Chinese Academy of Sciences (Beijing) have designed a platinum-cobalt-magnesium oxide ...
- 500 microkelvin nanoelectronicson March 20, 2020 at 3:19 am
Pushing the low temperature limit of refrigerators beyond milli-kelvin regime holds the promise for new discoveries in the nano-electronic devices. Here, Sarsby et al. achieve 500 micro-kelvin ...
- How Can Polymers Push the Boundaries of Nanoelectronics?on March 19, 2020 at 5:00 pm
The fields of organic electronics and nanoelectronics have been undergoing significant advancements in recent years as scientists seek to miniaturize electronic devices via a number of routes.
- Millimetre-scale transceiver boosts ingestible sensorson March 19, 2020 at 5:12 am
Bachmann, who serves as programme manager for the Sensitive Networks project at Imec’s laboratory in Eindhoven, Netherlands, explains that these goals are only achievable thanks to an ongoing ...
- Obtaining and observing single-molecule magnets on the silica surfaceon March 3, 2020 at 8:26 am
The work carried out by a team led by Lukasz Laskowski from the Department of Molecular Engineering and Nanoelectronics of the Institute of Nuclear Physics of the Polish Academy of Sciences which ...
via Bing News


Try a quick experiment: Take two flashlights into a dark room and shine them so that their light beams cross. Notice anything peculiar? The rather anticlimactic answer is, probably not. That’s because the individual photons that make up light do not interact. Instead, they simply pass each other by, like indifferent spirits in the night.
But what if light particles could be made to interact, attracting and repelling each other like atoms in ordinary matter? One tantalizing, albeit sci-fi possibility: light sabers — beams of light that can pull and push on each other, making for dazzling, epic confrontations. Or, in a more likely scenario, two beams of light could meet and merge into one single, luminous stream.
It may seem like such optical behavior would require bending the rules of physics, but in fact, scientists at MIT, Harvard University, and elsewhere have now demonstrated that photons can indeed be made to interact — an accomplishment that could open a path toward using photons in quantum computing, if not in light sabers.
In a paper published today in the journal Science, the team, led by Vladan Vuletic, the Lester Wolfe Professor of Physics at MIT, and Professor Mikhail Lukin from Harvard University, reports that it has observed groups of three photons interacting and, in effect, sticking together to form a completely new kind of photonic matter.
In controlled experiments, the researchers found that when they shone a very weak laser beam through a dense cloud of ultracold rubidium atoms, rather than exiting the cloud as single, randomly spaced photons, the photons bound together in pairs or triplets, suggesting some kind of interaction — in this case, attraction — taking place among them.
While photons normally have no mass and travel at 300,000 kilometers per second (the speed of light), the researchers found that the bound photons actually acquired a fraction of an electron’s mass. These newly weighed-down light particles were also relatively sluggish, traveling about 100,000 times slower than normal noninteracting photons.
Vuletic says the results demonstrate that photons can indeed attract, or entangle each other. If they can be made to interact in other ways, photons may be harnessed to perform extremely fast, incredibly complex quantum computations.
“The interaction of individual photons has been a very long dream for decades,” Vuletic says.
Vuletic’s co-authors include Qi-Yung Liang, Sergio Cantu, and Travis Nicholson from MIT, Lukin and Aditya Venkatramani of Harvard, Michael Gullans and Alexey Gorshkov of the University of Maryland, Jeff Thompson from Princeton University, and Cheng Ching of the University of Chicago.
Biggering and biggering
Vuletic and Lukin lead the MIT-Harvard Center for Ultracold Atoms, and together they have been looking for ways, both theoretical and experimental, to encourage interactions between photons. In 2013, the effort paid off, as the team observed pairs of photons interacting and binding together for the first time, creating an entirely new state of matter.
In their new work, the researchers wondered whether interactions could take place between not only two photons, but more.
“For example, you can combine oxygen molecules to form O2 and O3 (ozone), but not O4, and for some molecules you can’t form even a three-particle molecule,” Vuletic says. “So it was an open question: Can you add more photons to a molecule to make bigger and bigger things?”
To find out, the team used the same experimental approach they used to observe two-photon interactions. The process begins with cooling a cloud of rubidium atoms to ultracold temperatures, just a millionth of a degree above absolute zero. Cooling the atoms slows them to a near standstill. Through this cloud of immobilized atoms, the researchers then shine a very weak laser beam — so weak, in fact, that only a handful of photons travel through the cloud at any one time.
The researchers then measure the photons as they come out the other side of the atom cloud. In the new experiment, they found that the photons streamed out as pairs and triplets, rather than exiting the cloud at random intervals, as single photons having nothing to do with each other.
In addition to tracking the number and rate of photons, the team measured the phase of photons, before and after traveling through the atom cloud. A photon’s phase indicates its frequency of oscillation.
“The phase tells you how strongly they’re interacting, and the larger the phase, the stronger they are bound together,” Venkatramani explains. The team observed that as three-photon particles exited the atom cloud simultaneously, their phase was shifted compared to what it was when the photons didn’t interact at all, and was three times larger than the phase shift of two-photon molecules. “This means these photons are not just each of them independently interacting, but they’re all together interacting strongly.”
The researchers then developed a hypothesis to explain what might have caused the photons to interact in the first place. Their model, based on physical principles, puts forth the following scenario: As a single photon moves through the cloud of rubidium atoms, it briefly lands on a nearby atom before skipping to another atom, like a bee flitting between flowers, until it reaches the other end.
If another photon is simultaneously traveling through the cloud, it can also spend some time on a rubidium atom, forming a polariton — a hybrid that is part photon, part atom. Then two polaritons can interact with each other via their atomic component. At the edge of the cloud, the atoms remain where they are, while the photons exit, still bound together. The researchers found that this same phenomenon can occur with three photons, forming an even stronger bond than the interactions between two photons.
“What was interesting was that these triplets formed at all,” Vuletic says. “It was also not known whether they would be equally, less, or more strongly bound compared with photon pairs.”
The entire interaction within the atom cloud occurs over a millionth of a second. And it is this interaction that triggers photons to remain bound together, even after they’ve left the cloud.
“What’s neat about this is, when photons go through the medium, anything that happens in the medium, they ‘remember’ when they get out,” Cantu says.
This means that photons that have interacted with each other, in this case through an attraction between them, can be thought of as strongly correlated, or entangled — a key property for any quantum computing bit.
“Photons can travel very fast over long distances, and people have been using light to transmit information, such as in optical fibers,” Vuletic says. “If photons can influence one another, then if you can entangle these photons, and we’ve done that, you can use them to distribute quantum information in an interesting and useful way.”
Going forward, the team will look for ways to coerce other interactions such as repulsion, where photons may scatter off each other like billiard balls.
“It’s completely novel in the sense that we don’t even know sometimes qualitatively what to expect,” Vuletic says. “With repulsion of photons, can they be such that they form a regular pattern, like a crystal of light? Or will something else happen? It’s very uncharted territory.”
This research was supported in part by the National Science Foundation.


Nothing is more frustrating than watching that circle spinning in the centre of your screen, while you wait for your computer to load a programme or access the data you need.
Now a team from the Universities of Sheffield and Leeds may have found the answer to faster computing: sound. The research – published in Applied Physics Letters – has shown that certain types of sound waves can move data quickly, using minimal power.
The world’s 2.7 zettabytes (2.7 followed by 21 zeros) of data are mostly held on hard disk drives: magnetic disks that work like miniaturised record players, with the data read by sensors that scan over the disk’s surface as it spins. But because this involves moving parts, there are limits on how fast it can operate.
For computers to run faster, we need to create “solid-state” drives that eliminate the need for moving parts – essentially making the data move, not the device on which it’s stored. Flash-based solid-state disk drives have achieved this, and store information electrically rather than magnetically. However, while they operate much faster than normal hard disks, they last much less time before becoming unreliable, are much more expensive and still run much slower than other parts of a modern computer – limiting total speed.
Creating a magnetic solid-state drive could overcome all of these problems. One solution being developed is ‘racetrack memory’, which uses tiny magnetic wires, each one hundreds of times thinner than a human hair, down which magnetic “bits” of data run like racing cars around a track. Existing research into racetrack memory has focused on using magnetic fields or electric currents to move the data bits down the wires. However, both these options create heat and reduce power efficiency, which will limit battery life, increase energy bills and CO2 emissions.
Dr Tom Hayward from the University of Sheffield and Professor John Cunningham from the University of Leeds have together come up with a completely new solution: passing sound waves across the surface on which the wires are fixed. They also found that the direction of data flow depends on the pitch of the sound generated – in effect they “sang” to the data to move it.
The sound used is in the form of surface acoustic waves – the same as the most destructive wave that can emanate from an earthquake. Although already harnessed for use in electronics and other areas of engineering, this is the first time surface acoustic waves have been applied to a data storage system.
The Latest on: Faster computing
via Google News
The Latest on: Faster computing
- Future quantum computers may pose threat to today's most-secure communicationson April 8, 2020 at 9:18 pm
Quantum computers that are exponentially faster than any of our current classical computers and are capable of code-breaking applications could be available in 12 to 15 years, posing major risks to ...
- Why the Market Dropped so Fast - The Reich Reporton April 8, 2020 at 9:00 pm
In March, the stock market fell 30% in only 18 days which was by far the fastest 30% drop in history. By comparison, the next fastest was 55 days in ...
- A student created a computer program that tells you when an Amazon Fresh or Whole Foods delivery slot opens upon April 8, 2020 at 5:23 pm
It's almost impossible to find a delivery time slot using Amazon Fresh or Whole Foods right now. But a script can alert you when one opens up. Here's how to use it.
- These devices should be your go-to computing options for remote learningon April 8, 2020 at 11:01 am
As school districts across the country shut down, some for the rest of the school year, a lot of them have switched to some form of remote learning. With online teaching, kids are still able to ...
- Sen. Sherrod Brown, other Democrats, ask Labor Department to help states get unemployment help out fasteron April 8, 2020 at 10:39 am
Vendors that support those systems expanded it with more computer network servers and hardware to deal with increased traffic ... and to reach out workforce agencies in all 50 states to determine how ...
- Mobile Computing Devices Market- Future Scope, Demands And Projected Market Growth Till 2027on April 8, 2020 at 8:58 am
The global mobile computing devices market is segmented into type such as smartphones, tablets, laptops, wearable ...
- Unisys Always-On Access™ Powered by Stealth™ Provides Fast, Encrypted Remote Access for Workerson April 8, 2020 at 5:00 am
Unisys Corporation (NYSE: UIS) today announced the general availability of Unisys Always-On Access™ (AOA), powered by Stealth™, its award-winning Unisys Stealth® security software that provides ...
- Honeywell’s Big Bet on Trapped Ion Quantum Computingon April 7, 2020 at 5:23 pm
but a decade ago the high-tech conglomerate better known for its control systems waded deliberately into the then calmer quantum computing (QC) waters. Fast forward to March when Honeywell announced ...
- This tiny hybrid power adapter and hub fast charges multiple Apple devices and supports 5Gbps data transfer!on April 7, 2020 at 1:15 pm
SuperHub uses USB 3.1 with 5Gbps data transfer, 10 times faster than traditional USB 2.0. You can transfer a movie in seconds. Perfect for anyone who doesn’t like waiting. Transferring photos from ...
- Want a Virus Disaster Loan Fast? Easy, If You’re in Californiaon April 7, 2020 at 12:15 pm
When it comes to federal aid for small businesses, U.S. states are learning a lesson already familiar to shoppers on the hunt for toilet paper: It pays to be first in line.
via Bing News


This is the era of Artificial Intelligence (AI). Every industry is trying to automate processes and predict the market and future demands using AI, Machine Learning, Deep Learning, and a clutch of new technologies. To be able to differentiate between all these much-hyped terms, let us understand what each of them stands for.
So, what is Artificial Intelligence?
Techopedia says that Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machines that can work and react like humans eventually. Some of the activities AI is designed for are learning, planning, speech recognition, and problem-solving.
Of course, machines can work and react like humans only if they have access to abundant information. AI should have access to categories, objects, properties, and the relationship between them to be able to initiate reasoning, make decisions, and plan to act. All the processes of rationalizing, categorizing, and training the machines to make human-like decisions and act accordingly are made possible by the combination of Machine Learning, Deep Learning, convolution learning algorithms, etc.
So, AI is a superset of all the other terms. Each of these terms refers to a specific application of AI. Each is equally important for AI to work with high efficiency and accuracy.
Now, let us look at what the terms Machine Learning and Deep Learning mean.
Machine Learning is an application of AI that uses data analytics techniques and computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Machine Learning algorithms can automatically learn and improve from experience without being explicitly programmed. These algorithms are built to do things by understanding labeled data, then use it to produce further outputs with more sets of data.
Machine Learning develops computer programs that can access data and self-learn. Some real-time examples of Machine Learning are virtual personal assistants, video surveillance, email spam and malware filtering, and online customer support.
Deep Learning, on the other hand, is a subset of Machine Learning that is capable of learning from massive volumes of unsupervised data, which may be unstructured or unlabeled. It is also termed as Deep Neural Learning or Deep Neural Networks. Some examples of Deep Learning at work are autonomous vehicles, image processing, etc.
Deep Learning allows us to train an AI by giving a set of inputs and predicting the output. AI is trained by using both supervised and unsupervised learning. Academic publications claim that Deep Learning uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers identify the dimensions of the image, while higher layers identify whether the object is a letter, a human face, or an animal.
Deep Learning has been significantly successful for two reasons. One reason is that a deep neural network (DNN) has the capacity to store information from large data sets. The other reason is that many Machine Learning algorithms can suffer from bottlenecks when it comes to creating features. Features are the input parameters of the training examples that enable a particular Machine Learning algorithm to pick up data.
So, we can conclude that Machine Learning is a subset of AI and Deep Learning is a subset of Machine Learning.
It is important to understand how Artificial Intelligence, Machine Learning, and Deep Learning relate to each other and simulate human intelligence. It is also key to know how they incrementally build on each other. Each of them has different data requirements, level of complexity, transparency, and limitations. Each of them is different with respect to the types of problems each can solve, even when the context that tests the skill required to get a specific model up and running is the same or similar. Even though they solve different business case problems, these three terms are tightly linked.
Choosing which one to use for a particular scenario is driven by various factors. For example, the first parameter of interest will be the amount of data available for use and the performance of the model when that data is scaled up. With an increase in the data volume, the parameters get tuned well and any bias in the model gets reduced.
In another instance, suppose we want to analyze the data on a day-to-day basis; like, say the stock market for day traders. Machine Learning models will perform better than Deep Learning models in such scenarios where the amount of data is smaller. So, there is no distinct line where one stops and the other takes over.
The advances made by researchers at DeepMind, Google Brain, Open AI, and various universities are startling. AI can now solve problems that humans can’t. And AI is changing faster than can be imagined. The power of AI grows with the power of computational hardware, and advances in a computational capacity like quantum computing or higher-quality chips.
Interestingly, the simulation of human intelligence (sometimes called Machine Intelligence) is a combination of all the three terms working together. When they come together, they enable machines to predict, classify, learn, plan, reason, and/or perceive like humans.
Latest posts by Calsoft Inc (see all)
- ServiceNow Integration to Accelerate Your Business Growth - April 1, 2020
- 6-Stage BCP at Calsoft – Ensuring Business Continuity for Customers and Partners - March 31, 2020
- Parallel Linux Utilities for NFS - February 27, 2020


Viewpoint: Unlocking the Hidden Information in Starlight
A provocative new result  by Mankei Tsang, Ranjith Nair, and Xiao-Ming Lu of the National University of Singapore suggests that a long-standing limitation to the precision of astronomical imaging, the Rayleigh criterion, proposed in 1879  is itself only an apparition. Using quantum metrology techniques, the researchers have shown that two uncorrelated point-like light sources, such as stars, can be discriminated to arbitrary precision even as their separation decreases to zero.
Quantum metrology, a field that has existed since the late 1960s with the pioneering work of Carl Helstrom , is a peculiar hybrid of quantum mechanics and the classical estimation theory developed by statisticians in the 1940s. The methodology is a powerful one, quantifying resources needed for optimal estimation of elementary variables and fundamental constants. These resources include preparation of quantum systems in a characteristic (entangled) state, followed by judiciously chosen measurements, from which a desired parameter, itself not directly measurable, may be inferred.
In the context of remote sensing, for example, in the imaging of objects in the night sky, the ability to prepare a physical system in an optimal state does not exist. In the case of starlight, the typical assumption is that the source is classical thermal light, the state of maximum entropy or “uninformativeness.” Imaging such sources is plagued by the limits of diffraction when the objects are in close proximity. The wave-like nature of light causes it to spread as it moves through space, bending around obstacles, for example when traversing a telescope aperture. This results in a diffraction pattern described by a so-called point spread function (PSF) in the image plane. The Rayleigh criterion states that two closely spaced objects are just resolvable—that is, discernable from one another—when the center of the diffraction pattern, or peak of the PSF, of one object is directly over the first minimum of the diffraction pattern of the other. Roughly, the PSF maxima must be farther apart than their widths (Fig. 1).
Some astronomers say they are able to resolve objects that are slightly closer than the Rayleigh limit allows. Yet inevitably, as the angular separation between the objects decreases, the information that can be obtained about that separation using direct detection becomes negligible, and even the most optimistic astronomer, utilizing the most sophisticated signal-processing techniques, must admit defeat. Correspondingly, as the separation approaches zero, the minimum error on any unbiased estimation of the separation blows up to infinity, which has limited angular resolution in imaging since the time of Galileo. Typically, the mean-squared error on the estimation of a parameter scales with the number of repeated measurements or data points, , as . Even for a large error per measurement, any desired precision is attained by taking multiple data points. When, however, the lower bound on direct estimation of the separation is divergent because of the Rayleigh limit, the factor makes no impact. This is what Tsang and collaborators call Rayleigh’s curse.
Using a quantum metrology formalism to minimize the estimation error, the initial achievement of their work has been to show that there is no fundamental obstacle to the estimation of the separation of two PSFs in one dimension (that is, for sources that sit on a line). As the separation of two PSFs decreases to zero, the amount of obtainable information stays constant. This discovery is nicely summed up by Tsang, who says we should apologize to the starlight “as it travels billions of light years to reach us, yet our current technology and even our space telescopes turn out to be wasting a lot of the information it carries.”
It could be suggested that this is merely a theoretical proof; the quantum metrology formalism indicates that there is always an optimal measurement, which minimizes the estimation error for the separation parameter. Paradoxically, this optimal measurement can, however, depend on the value of the parameter. To obviate such concerns, Tsang and his colleagues propose a strategy, based on state-of-the-art quantum optics technology, that produces a minimal error in the estimation of the separation variable—counterintuitively, this error remains constant for all separation values, under the assumption that the PSFs have a Gaussian shape. The method, which the authors call spatial mode demultiplexing (SPADE), splits the light from the two sources into optical waveguides that have a quadratic refractive-index lateral profile. Mathematically, this SPADE measurement decomposes the overlapping PSFs (a real function in one dimension) into the complete basis of Hermite functions, just as a Fourier transform provides a decomposition of a real function into a superposition of sine and cosine terms. A posteriori, one may be tempted to use intuition to explain why this Hermite basis measurement seems not to suffer Rayleigh’s curse, but then again, were intuition forthcoming, the result may not have been hidden from view for so long. (This elusiveness relates to subtleties in the estimation of a single parameter extracted from the joint statistics of two incoherent light sources.)
One minor caveat of the approach is that full imaging of two point sources at positions and requires estimation of both separation and centroid parameters. SPADE is only optimal when the centroid parameter is already known to high precision. Centroid estimation, however, has no equivalent analog to the Rayleigh curse; it may be made via direct imaging. Errors can be reduced appropriately via the factor for data points with much greater than 1.
A second detail worth pondering is that this result utilized techniques from the quantum domain to reveal a classical result. (All of the physical assumptions about starlight admit a classical model.) The quantum metrology formalism has been used to optimally estimate a parameter, but no quantum correlations exist in the system for any value of that parameter, that is, for any angular separation of two stars. When no quantum correlations are present, the formalism will still indicate the best possible measurement strategy and the smallest achievable estimation error.
An added blessing of quantum metrology is that it allows the development of generalized uncertainty relationships, for example between temperature and energy for a system at equilibrium , or photon number and path-length difference between the two arms of an interferometer. The result of Tsang and his colleagues can be presented as another type of generalized uncertainty, between source separation and “momentum.” The mean-squared error associated with separation estimation scales inversely with the momentum (Fourier) space variance of the overlapping PSFs.
Regarding impact on the field, the authors’ study produced a flurry of generalizations and other experimental proposals. During the past six months there have been four proof-of-principle experiments, first in Singapore by Tsang’s colleague Alex Ling and collaborators , and then elsewhere in Canada and Europe [7–9]. A subsequent theory paper from researchers at the University of York  extends Tsang and colleagues’ theory result, which was for incoherent thermal sources such as starlight, to any general quantum state existing jointly between the two sources. This work exploits the roles of squeezing (of quantum fluctuations) and of quantum entanglement to improve measurement precision, extending applicability to domains in which control of the source light is possible, such as microscopy.
Tsang and his colleagues have provided a new perspective on the utility of quantum metrology, and they have reminded us that even in observational astronomy—one of the oldest branches of science—there are (sometimes) still new things to be learned, at the most basic level.
This research is published in Physical Review X.
- M. Tsang, R. Nair, and X.-M. Lu, “Quantum Theory of Superresolution for Two Incoherent Optical Point Sources,” Phys. Rev. X 6, 031033 (2016).
- L. Rayleigh, “XXXI. Investigations in Optics, with Special Reference to the Spectroscope,” Philos. Mag. 8, 261 (1879).
- C. W. Helstrom, “Resolution of Point Sources of Light as Analyzed by Quantum Detection Theory,” IEEE Trans. Inf. Theory 19, 389 (1973).
- Private Communication.
- B. Mandelbrot, “On the Derivation of Statistical Thermodynamics from Purely Phenomenological Principles,” J. Math. Phys. 5, 164 (1964).
- T. Z. Sheng, K. Durak, and A. Ling, “Fault-Tolerant and Finite-Error Localization for Point Emitters Within the Diffraction Limit,” arXiv:1605.07297.
- F. Yang, A. Taschilina, E. S. Moiseev, C. Simon, and A. I. Lvovsky, “Far-Field Linear Optical Superresolution via Heterodyne Detection in a Higher-Order Local Oscillator Mode,” arXiv:1606.02662.
- W. K. Tham, H. Ferretti, and A. M. Steinberg, “Beating Rayleigh’s Curse by Imaging Using Phase Information,” arXiv:1606.02666.
- M. Paur, B. Stoklasa, Z. Hradil, L. L. Sanchez-Soto, and J. Rehacek, “Achieving Quantum-Limited Optical Resolution,” arXiv:1606.08332.
- C. Lupo and S. Pirandola, “Ultimate Precision Bound of Quantum and Sub-Wavelength Imaging,” arXiv:1604.07367.


1: The Strangest Force
Begin your exploration of gravity with Isaac Newton and the famous story of the apple. Why was it such a breakthrough to connect a falling apple with the faraway moon? Review the essential characteristics of gravity and learn why small asteroids and large planets have such different shapes.
2: Free Fall and Inertia
Review three great discoveries by the "grandfather" of gravity research, Galileo Galilei. His most famous experiment may never have happened, but his principle of inertia, law of free fall, and principle of relativity are the basis for everything that comes later in the science of gravity-including key breakthroughs by Einstein.
3: Revolution in the Heavens
Drawing on ideas and observations of Nicolaus Copernicus and Tycho Brahe, Johannes Kepler achieved a great insight about gravity by discovering three laws of planetary motion, relating to the mathematics of orbits. The cause of planetary motion, he determined, must lie in the sun.
4: Universal Gravitation
See how Newton was able to finish Kepler's revolution by formulating the law of universal gravitation, which says that every object exerts an attractive force on every other object. Also explore Newton's related discovery of the three laws of motion, which underlie the science of mechanics.
5: The Art of Experiment
Learn how distances in the solar system were first determined. Then chart Henry Cavendish's historic experiment that found the value of Newton's gravitational constant. Cavendish's work allows almost everything in the universe to be weighed. Then see a confirmation of the equivalence principle, which says that gravitational and inertial mass are identical.
6: Escape Velocity, Energy, and Rotation
Begin the first of several lectures that dig deeper into Newton's laws than Newton himself was able to go. In this lecture, apply the key concepts of energy and angular momentum to study how gravity affects motion. As an example, use simple algebra to calculate the escape velocity from Earth.
7: Stars in Their Courses-Orbital Mechanics
Newton was the first to realize that objects could, in theory, be sent into orbit around Earth. Explore how this works in practice, using the ideas of energy and angular momentum to study how satellites, moons, planets, and stars move through space.
8: What Are Tides? Earth and Beyond
Trace the origin of tides to the simple fact that gravity varies from point to point in space. This leads not just to the rise and fall of the ocean, but to the gradual slowing of Earth's rotation, Saturn's spectacular ring system, volcanoes on Jupiter's moon Io, and many other phenomena.
9: Nudge-Perturbations of Orbits
For the next three lectures, study the effects of gravity on the motions of more than two bodies. Here, see how even very small orbital changes-small perturbations-are significant. Such effects have revealed the presence of unknown planets, both in our own solar system and around other stars.
10: Resonance-Surprises in the Intricate Dance
Resonance happens whenever a small periodic force produces a large effect on a periodic motion-for example, when you push a child on a swing. Learn how resonance due to gravitational interactions between three bodies can lead to amazing phenomena with planets, asteroids, and rings of planets.
11: The Million-Body Problem
Consider the problem of gravitational interactions between millions of bodies, such as the countless stars in a galaxy. Amazingly, mathematics can reveal useful information even in these complicated cases. Discover how the analysis of the motions of galaxies led to the prediction of dark matter.
12: The Billion-Year Battle
Explore the physics of stars, which are balls of gas in a billion-year battle between the inward pull of gravity and the outward pressure produced by nuclear fusion. Follow this story to its ultimate finish-the triumph of gravity in massive stars that end their lives as black holes.
13: From Forces to Fields
For the rest of the course, focus on the revolutionary view of gravitation launched by Albert Einstein. Review new ideas about fields that allowed physics to extend beyond Newtonian mechanics. Then see how Einstein modified Newton's laws and created the special theory of relativity.
14: The Falling Laboratory
Einstein focused on gravity in his general theory of relativity. Hear about his "happiest thought"-the realization that a man in free fall perceives gravity as zero. This simple insight resolved a mystery going all the way back to Newton and led Einstein to the startling discovery that gravity affects light and time.
15: Spacetime in Zero Gravity
In an influential interpretation of relativity, Einstein's former mathematics professor Hermann Minkowski reformulated the theory in terms of four-dimensional geometry, which he called spacetime. Learn how to plot events in this coordinate system in cases where gravity is zero.
16: Spacetime Tells Matter How to Move
See how gravity affects Minkowski's spacetime geometry, discovering that motion in a gravitational field follows the straightest path in curved spacetime. The curvature in spacetime is not caused by gravity; it is gravity. This startling idea is the essence of Einstein's general theory of relativity.
18: Light in Curved Spacetime
See how Einstein's general theory of relativity predicts the bending of light in a gravitational field, famously confirmed in 1919 by the British scientist Arthur Eddington. Learn how this phenomenon creates natural gravitational lenses-and how the bending of light reveals invisible matter in deep space.
19: Gravitomagnetism and Gravitational Waves
The general theory of relativity predicts new phenomena of gravity analogous to those of electromagnetism. Discover how ultra-sensitive experiments have detected the gravitomagnetism of the Earth, and follow the search for elusive gravitational waves that travel through space.
20: Gravity's Horizon-Anatomy of a Black Hole
Plunge into the subject of black holes, which are massive objects that have collapsed completely under their own gravity. Learn how black holes distort spacetime and explore the supermassive black holes that lie at the hearts of galaxies. Then ask: Are there such things as micro-black holes?
21: Which Universe Is Ours?
Investigate what Einstein called his "greatest mistake"-his rejection of his own theory's prediction that spacetime should be dynamic and evolving. Chart the work of a group of scientists, including Alexander Friedman, Georges Lemaître, and Edwin Hubble, who advanced the realization that our universe is expanding from an apparent big bang.
22: Cosmic Antigravity-Inflation and Dark Energy
Using everything you've learned about gravity, investigate cosmic antigravity, starting with cosmic inflation, a phenomenon that exponentially increased the size of the universe during the big bang. Then, learn why dark matter cannot be made of ordinary protons and neutrons, and explore the recent discovery that the expansion of the universe is accelerating, powered by a mysterious dark energy inh...
23: The Force of Creation
Use a black hole to test the laws of thermodynamics, taking a deeper look at the capacity of gravity to pull matter together and increase entropy at the same time. Probe Stephen Hawking's most surprising discovery, and then learn that the same force that pulls the apple down and steers the stars in their courses is also nature's ultimate source of order and complexity.
24: The Next Revolution
Survey the greatest unsolved problem in theoretical physics: the search for a quantum theory of gravity. Examine string theory, loop quantum gravity, and also entropic gravity, which suggests a revolutionary link with thermodynamics. Close the course with a deepened appreciation for the connection between everyday features of gravity and the most exciting questions in contemporary physics and cosm...
Gravity is about both phenomena near at hand at the human scale, everyday and intuitive, and phenomena far off at an astronomical scale.
About Benjamin Schumacher
Dr. Benjamin Schumacher is Professor of Physics at Kenyon College, where he has taught for 20 years. He received his Ph.D. in Theoretical Physics from The University of Texas at Austin in 1990. Professor Schumacher is the author of numerous scientific papers and two books, including Physics in Spacetime: An Introduction to Special Relativity. As one of the founders of quantum information theory, he introduced the term qubit, invented quantum data compression (also known as Schumacher compression), and established several fundamental results about the information capacity of quantum systems. For his contributions, he won the 2002 Quantum Communication Award, the premier international prize in the field, and was named a Fellow of the American Physical Society. Besides working on quantum information theory, he has done physics research on black holes, thermodynamics, and statistical mechanics. Professor Schumacher has spent sabbaticals working at Los Alamos National Laboratory and as a Moore Distinguished Scholar at the Institute for Quantum Information at California Institute of Technology. He has also done research at the Isaac Newton Institute of Cambridge University, the Santa Fe Institute, the Perimeter Institute, the University of New Mexico, the University of Montreal, the University of Innsbruck, and the University of Queensland.


Modern computers needed just weeks to correctly solve models that took human theoretical physicists six years to figure out.
OKINAWA, Japan — Machine learning, or the ability of AI systems and computers to learn and improve from experiences, has made some incredible leaps and bounds in recent years and is already starting to make its way into various industries, often times completely reinventing what would have been considered impossible a few decades ago. One such example would be the growing popularity and prevalence of self-driving cars. AI systems have also recently made headlines for besting the top-ranked human chess players in the world, or solving a Rubik’s cube in an absurdly insignificant amount of time.
Now, an international study conducted at the Okinawa Institute of Science and Technology Graduate University finds that modern computers can also solve complex scientific problems just as accurately as human theoretical physicists — only much, much faster.
It took such physicists six years to identify unusual magnetic phases within what’s known as a pyrochlore model. But, with the help of a machine, scientists were able to accomplish the same feat in a matter of weeks!
“This feels like a really significant step,” says Professor Nic Shannon, leader of the Theory of Quantum Matter (TQM) Unit at OIST, in a release. “Computers are now able to carry out science in a very meaningful way and tackle problems that have long frustrated scientists.”
Every single atom within a magnet is associated with a tiny magnetic moment, usually called a “spin.” In typical magnets, such as the ones that are in all likelihood stuck on your fridge right now, these spins are ordered so that they all point in a singular direction. It’s this corresponding pattern that results in a strong magnetic field. This same phenomenon applies to solid materials as well, all the atoms in a particular object are ordered in one direction.
However, just like how matter can exist as a solid, a liquid, or a gas, so too can magnetic substances. Researchers in the quantum matter unit focus on especially unusual magnetic phases called “spin liquids.” Within these spin liquids, there are often competing, or “frustrated” interactions between individual spins, in which they constantly fluctuate in direction. This is similar to the disorder seen in liquid phases of matter.
In the future, these phases may prove incredibly useful in quantum computing.
The research team wanted to discover which of these spin liquids were capable of existing in frustrated pyrochlore magnets. To start, they built a diagram illustrating how different phases could occur as spins interacted in various ways when temperatures fluctuated. That diagram was completed in 2017, but actually reading and using the illustration to identify some semblance of rules governing these interactions between spins proved an incredibly difficult, and long, task.
“These magnets are quite literally frustrating,” quips Professor Shannon. “Even the simplest model on a pyrochlore lattice took our team years to solve.”
So, the research team decided to see if computers could help them out.
“To be honest, I was fairly sure that the machine would fail,” Professor Shannon says. “This is the first time I’ve been shocked by a result – I’ve been surprised, I’ve been happy, but never shocked.”
Researchers collaborated with machine learning experts from the University of Munich who had already developed a way to represent spin configurations in a computer. This innovation was then combined with a machine capable of categorizing complex data into different groups.
“This is the first time I’ve been shocked by a result – I’ve been surprised, I’ve been happy, but never shocked.” -Professor Nic Shannon
“The advantage of this type of machine is that unlike other support vector machines, it doesn’t require any prior training and it isn’t a black box – the results can be interpreted. The data are not only classified into groups; you can also interrogate the machine to see how it made its final decision and learn about the distinct properties of each group,” says Dr. Ludovic Jaubert, a CNRS researcher at the University of Bordeaux.
The machine was provided with 250,000 spin configuration variations. Remarkably, without being given any information on which phases were present, the machine successfully created an identical replication of the phase diagram.
Most importantly, when the research team looked into how the machine was able to classify all the different types of spin liquid, they discovered that it had calculated the exact mathematical equations representing each phase. A remarkable achievement that would have taken a team of humans years to accomplish, was completed by the machine within a matter of weeks.
“Most of this time was human time, so further speed ups are still possible,” said Prof. Pollet. “Based on what we now know, the machine could solve the problem in a day.”
“We are thrilled by the success of the machine, which could have huge implications for theoretical physics,” added Prof. Shannon. “The next step will be to give the machine an even more difficult problem, that humans haven’t managed to solve yet, and see whether the machine can do better.”
The study is published in Physical Review B.


What is Quantum Computing
Quantum Computing Introduction
Quantum computing is based on the principles of quantum physics. In physics, a quantum is the minimum amount of any physical entity involved in an interaction – Source Wikipedia.
Quantum computing has intrigued and fascinated us since decades and still remains elusive.While technology has advanced by leaps and bounds, quantum computing is still in its infancy. However, with the rate we are advancing scientifically, quantum computers necessarily gonna be commercially viable and will certainly replace classical computers of today, though it will take time.
Quantum computers make use of the principles of quantum mechanics (superposition and quantum entanglement – more on this later) to achieve the speed and efficiency which they are well known for. While classical computers use transistors as their building blocks, quantum computers are based on qubits which is a fundamental digital storage unit in quantum computers.
Quantum Computers versus classical computers
The classical computer equivalent of qubit is a bit which can either be 0 or 1 at a time unlike quantum computers wherein the qubit can be 0,1 or both at a time giving it additional processing capability and hence speed.
In classical computers a bit represents any of the 2 possible discrete voltage levels (HIGH = 1 or LOW = 0) flowing through a digital circuit which is called a flip flop which is a binary storage device made of transistors. It’s the flip flop which stores binary data in classical computers. Below diagram represents these voltage levels.
In quantum computers, qubit represents the atomic unit of data which stores info and exists in superposition which can be 0 or 1 or both at the same time. A qubit can be electron, photon, ion or an atom and their respective control devices which work in tandem act as registers called qubit registers and quantum processors. Since the qubits can be in both states simultaneously, a quantum computer is light years ahead in terms of speed when compared to their traditional classical counterparts. It has faster processing capability than the fastest supercomputer we have today.
In a quantum computer, electron spin is used to represent the state of a qubit and the control devices like quantum dots, ion traps, optical traps etc. use electric fields to change the electron spin and hence control qubit states. These control devices are made of semiconducting materials. New advanced techniques of creating qubits using super conducting materials are being discussed and researched by Scientists and Engineers.
More on Superposition and quantum entanglement
Quantum superposition says that much like waves two or more quantum states (for eg. electron spin) can be superposed which will result in another valid state which can be a mix of two states i.e. 01 (in simple binary terms). Thus a qubit can have 3 values like 0, 1, 01. More the number of qubits, the permutations will drastically increase in terms of number of states and hence more data can be stored and processed simultaneously. However, the superposed state remains valid only until we observe them using some observation technique. The moment we see the quantum particles, their wave function collapses and we will always find them to be either in 1 or 0 state only. This has been experimentally verified using Double slit experiment.
Entanglement in simple terms is a phenomenon in which quantum states of 2 or more objects or particles have to be described with respect to each other irrespective of the spatial difference between them. These states are intertwined for eg. if 2 particles are entangled and one spins down, the other should spin up and vice versa. Due to this principle, the measurement of state of one particle decides the state of another. This signifies an instantaneous information sharing mechanism which someone might perceive as faster than light transfer of information between quantum particles, however this is not the case. Quantum entanglement has applications in the emerging technologies of quantum computing and quantum cryptography, and has been used to realize quantum teleportation experimentally as shown by Chinese scientists Last year.
Limitations of Today’s Quantum Computers
1) While quantum computing is advancing , we still have not been able to build a full fledged general purpose quantum computer.The quantum computers today are more geared towards solving complex computing problems only like an ASIC chip does and they are 100 times slower on some other computing operations like gaming video streaming etc.
2) They are expensive and produce a lot of noise in the qubits which makes them more error prone by destroying the data stored in qubits.
3) They are not energy efficient and thus consume a lot of power and also are every expensive.
4) They are to be maintained under sub zero temperatures which again calls for huge maintenance costs.
Threats posed by quantum computers to cyber security and cryptography
If a quantum computer having sufficient number of qubits can run without succumbing to noise, it can use Shor’s Algorithm to easily break public key encryption algorithms like RSA, DSS etc. This calls for more advanced cryptography techniques and algorithms. Thus, if quantum computers go mainstream, computer science fields like cryptography will need a drastic shift and advancement to be crack resistant to quantum computing.
IBM Q Experience
IBM has exposed a quantum computer on cloud here. It’s a good platform for quantum computing enthusiasts to create and run programs that execute on quantum computer like the Shor’s Algorithm which runs in Polynomial time on quantum computer but takes exponential time on classical computers.


Understanding magnetism at its most fundamental level is vital for developing more powerful electronics, but materials with more complex magnetic structures require more sophisticated tools to study them — powerful tools called simply “neutrons”.
The two most powerful sources in the world neutron scattering The Oak Ridge National Laboratory (ORNL) of the US Department of Energy (DOE) is undergoing modernization. Adding an advanced feature called spherical neutron polarimetry will allow researchers using the ORNL (HFIR) and Spallation Neutron Source (SNS) isotope reactors to measure materials with exotic magnetic structures and quantum states that were previously unavailable in the United States.
“Neutrons are ideal for studying magnetic phenomena,” said ORNL researcher Nicholas Silva. “They are electrically neutral or chargeless and have magnetic moments that make them look like tiny magnets.”
When neutrons pass through a material and scatter the magnetic fields created by the atoms of the material, they draw an atomic portrait or even a three-dimensional model of the atomic arrangement of the material and show how the atoms behave in the system.
Neutrons have a “rotation” or orientation, similar to the north and south poles of fridge magnets. In a typical neutron beam, the neutrons within the beam have spins arranged randomly. The measurement of some highly dynamic or complex magnetic systems, however, requires more uniformity, which is ensured by a polarized neutron beam, in which each neutron spin is aligned in parallel and with the same orientation.
“Neutron polarization filters allow us to see what we don’t want to see, which can spoil the signal of interest to us,” said a scientist from Barry Wynn. “Just like polarized lenses allow anglers to see fish floating below that would otherwise be blocked by water reflection.”
Neutrons will change their spins in a predictable way when they scatter. The use of a polarized beam allows researchers to better understand what is happening in the material by setting the neutron spin to and measuring the neutron spin after the beam hits the sample. For example, a neutron spin can be flipped in the opposite direction during scattering.
“In the USA, most of the measurements we have done with polarized neutrons so far have been based on whether the neutron scattered from the material or its a magnetic fieldrotates 180 degrees or maintains its orientation. We call it a coup, not a coup, ”said Wynn.
“But there is a problem with that. If we get any scatter in the sample other than a coup without a coup or a coup with a spin — or something other than 0 and 180 degrees — the strategy will explode in our face. ”
This strategy works well for conventional magnetic materials, such as ferromagnets and antiferromagnets, in which all magnetic atoms are directed either in the same direction or in alternative directions, but remain parallel to their neighbors. However, the strategy does not work for more complex magnetic structures.
For example, the technique is limited when it comes to the study of exotic particles, such as skyrmions, quasiparticles that exhibit chiral motion, or entangled vortices, or whirlpools of asymmetric field lines of force. Such particles provide exciting potential for materials used in modern data storage and quantum computing applications.
To solve this problem, polarization scientist Peter Jiang leads the ORNL team, which includes Wynne and Silva, as part of a research project aimed at developing spherical neutron polarimetry for several ORNL ray lines. The technology will allow neutron measurements of materials that do not correspond to traditional areas with spin flip and without flip, or, in other words, allow researchers to see the dynamic magnetic behavior that exists between them.
“Traditional methods are not complicated enough to study some complex magnetic systems,” Jiang said. “Now we are no longer limited to coups. This allows us to look at magnetic devices that we could not understand before. ”
Spherical neutron polarimetry was used in Europe, and now Jiang and the ORNL team are adapting the technology to SNS and HFIR instruments. They create technology based on ongoing research by Tianhao Wang, first a graduate student at Indiana University, Bloomington, and then a postdoctoral research on the ORNL team.
The basic technology includes additional optical devices installed both on the incoming beam, which hits the sample – incident beam, and on the output beam, scattering it, which allows measurements of scattered neutrons oriented in any direction. ORNL technology is based on previous prototype designs and will offer several innovations.
In ORNL spherical neutron polarimetry devices, the scattered beam path does not have to coincide with the incident beam, but instead can be angled around the sample.
“This means that if the neutron does not experience a complete flip, we can adjust the field at the other end or move the apparatus to detect neutron scattering in different directions,” Silva explained.
The team also developed two independent cooling systems to allow researchers to learn how magnetic structures change according to temperature. The first system cools two spherical components of the polarization of neutrons located on both sides of the sample to make them superconducting. The second system introduces an additional cryostat with liquid helium gas station an ability that allows researchers to more easily study materials in the temperature range without affecting the temperatures needed for superconductivity in the first system.
Finally, spherical neutron polarimetric devices are made of more efficient materials. While niobium was used for the superconducting sheets in previous designs, yttrium-barium-copper oxide (YBCO) was used in the new design, which was superconducted at a temperature of 93 Kelvin (-292 ° F), which is significantly higher than that of its niobium precursor . In addition, superconducting films are bonded to Mu metal yokes, which combine to shield all other magnetic fields and establish a zero field around the sample to study the spins of the materials in their natural state.
“To achieve superconductivity, a significant amount of cooling power is required. To maintain superconductivity, niobium must be cooled to a temperature below 10 K, therefore, European designs required extensive cooling systems, which often had to be filled with liquid helium manually, ”Jiang said.
“With YBCO high-temperature films, we can use a single-stage closed-cycle refrigerator to cool the film to a temperature well below its critical temperature, so we don’t worry about any loss in superconductivity. And with added liquid helium, an autofill system for the cryostat and a closed-loop cooling system, the device will be easier to use and more efficient. "
Moreover, the system is compact compared to previous systems – high-temperature superconductors, which eliminate the need for a large cooling system, make it mobile.
“In any case, there is evidence of how portable the device is. We moved it to nuclear reactor at the university of missourithen back to HFIR and from HFIR to SNS, ”said Silva. “I assembled it and disassembled it several times, and each time I found simpler ways to connect the parts – only a small quality of the changes in life that we make to increase its usefulness.”
The system was successfully tested, with full polarization measurements using several known materials, including silicon, manganese oxide, and bismuth-iron oxide.
The team plans to implement the system on a three-phase HFIR PTAX spectrometer and a GP-SANS diffractometer, which will be optimized for the stationary neutron beam of the reactor, with full functionality expected by the end of 2020.
Subsequently, the team will develop a similar spherical neutron A polarimetric device exclusively for the HYSPEC SNS device, making it the only device in the world that connects super mirror array and the possibility of a wide angle. The device will also benefit from the unique capabilities provided by the SNS pulse source accelerator.
“At the same time,” said Wynn, “we will have a workhorse at PTAX that will knock out our socks.”
Oak Ridge National Laboratory
ORNL neutrons add enhanced polarization capabilities for measuring magnetic materials (2020, March 16)
restored March 16, 2020
This document is protected by copyright. Other than honest deals for private study or research, no
Part may be reproduced without written permission. Content is provided for informational purposes only.


September 30, 2019 feature
A ten-qubit solid-state spin register with remarkable quantum memory
In years to come, quantum computers and quantum networks might be able to tackle tasks that are inaccessible to traditional computer systems. For instance, they could be used to simulate complex matter or enable fundamentally secure communications.
The elementary building blocks of quantum information systems are known as qubits. For quantum technology to become a tangible reality, researchers will need to identify strategies to control many qubits with very high precision rates.
Spins of individual particles in solids, such as electrons and nuclei have recently shown great promise for the development of quantum networks. While some researchers were able to demonstrate an elementary control of these qubits, so far, no one has reported entangled quantum states containing more than three spins.
In order to reach the computational power necessary to complete complex tasks, quantum registers should be significantly larger than those realized so far. However, controlling individual spins within complex and strongly interacting quantum systems has so far proved to be very challenging.
Recently, a team of researchers at TU Delft and Element Six has successfully demonstrated a fully controllable ten-qubit spin register with a quantum memory up to one minute. Their findings, presented in a paper published in Physical Review X, could pave the way for the development of larger and yet controllable quantum registers, ultimately opening up new exciting possibilities for quantum computing.
"The main objective of our study was to realize a precisely controlled system of a large amount of qubits using the spins of atoms embedded in a diamond," Tim Taminiau, one of the researchers who carried out the study, told Phys.org via email. "These spins are promising quantum bits for quantum computation and quantum networks, but previous results were limited to just a few qubits. The key open challenge is that on the one hand, all the spins in the system must be coupled together to function as a single quantum processor, but on the other hand, this makes it difficult to selectively control them with high precision."
Taminiau and his colleagues successfully developed an entirely new method to control multiple qubits. This technique uses an electron spin qubit to selectively control many individual nuclear spin qubits, while simultaneously decoupling them and thus protecting them from unwanted interactions in the system.
Using their method, the researchers were able to control a considerably larger number of spins compared to previous studies, with remarkably high precision. They applied their technique to a system composed of 10 spins associated to a nitrogen-vacancy (NV) center in diamond. This NV center has an electron spin providing a qubit than can be optically read out (i.e. one can determine its value) and that can be controlled with microwave pulses.
"This electron spin couples to nuclear spins in the environment," Conor Bradley, a Ph.D. student and lead author of the study, explained. "One such nuclear spin is the intrinsic nitrogen nuclear spin of the NV. The additional 8 qubits are carbon-13 nuclear spins surrounding the NV. Naturally about 1.1 percent of the carbon atoms in diamond are carbon-13 and have a spin, i.e. they can be used as a qubit, the other carbon atoms are carbon-12 and carry no spin."
Although the researchers applied their method to a specific 10-qubit system, they believe that it could also be implemented on other systems, including other defect centers in diamond and silicon carbide, quantum dots and donors in silicon. The qubits hosted by these other systems each have their own strengths for completing a variety of complex tasks.
"The main achievement of our study is a 10-spin-qubit quantum system that can store quantum information for very long times up to 75 seconds," Taminiau said. "Although other researchers were able to attain similar results with ions trapped in vacuum, this combination of many qubits, precise control and long-lived quantum memory is unique for chip-based quantum bits."
The system demonstrated by Taminiau and his colleagues could be a key building block for large quantum networks in which multiple NV centers, each providing several qubits, are connected together optically. This particular capability was already outlined and shown by the researchers in a previous study.
"Besides the importance of this study as a demonstration towards larger quantum information systems, this work also provides new insights into the decoherence—the loss of quantum information—for spins in solids," Taminiau said.
The findings gathered by this team of researchers highlight the feasibility of studying how entangled states of multiple spin qubits decohere, as well as how correlations in the noise environment can play a vital role in this process. The method they developed also opens up new possibilities for quantum sensing and atomic-scale imaging of individual spins, where the goal is not to control spins but rather to detect them, in order to gather insight into interesting samples for studies in chemistry, biology and material science.
In their future research, Taminiau and his colleagues plan to demonstrate a technique called quantum error correction. This particular type of error correction could help to overcome all of the inevitable imperfections of existing quantum systems, ultimately enabling the creation of large-scale quantum systems.
"This will require encoding quantum states over many qubits and performing careful measurements to detect and correct errors without disturbing the encoded information," Taminiau added. "This has so far been out of reach for any system, but our results now make it possible to pursue this using spins in diamond."
David D. Awschalom et al. Quantum technologies with optically interfaced solid-state spins, Nature Photonics (2018). DOI: 10.1038/s41566-018-0232-2
J. Cramer et al. Repeated quantum error correction on a continuously encoded qubit by real-time feedback, Nature Communications (2016). DOI: 10.1038/ncomms11526
G. Waldherr et al. Quantum error correction in a solid-state hybrid spin register, Nature (2014). DOI: 10.1038/nature12919
B. Hensen et al. Loophole-free Bell inequality violation using electron spins separated by 1.3 kilometres, Nature (2015). DOI: 10.1038/nature15759
© 2019 Science X Network