Physicists at MIT and elsewhere have observed evidence of Majorana fermions — particles that are theorized to also be their own antiparticle — on the surface of a common metal: gold. This is the first sighting of Majorana fermions on a platform that can potentially be scaled up. The results, published in the Proceedings of the National Academy of Sciences, are a major step toward isolating the particles as stable, error-proof qubits for quantum computing.
In particle physics, fermions are a class of elementary particles that includes electrons, protons, neutrons, and quarks, all of which make up the building blocks of matter. For the most part, these particles are considered Dirac fermions, after the English physicist Paul Dirac, who first predicted that all fermionic fundamental particles should have a counterpart, somewhere in the universe, in the form of an antiparticle — essentially, an identical twin of opposite charge.
In 1937, the Italian theoretical physicist Ettore Majorana extended Dirac’s theory, predicting that among fermions, there should be some particles, since named Majorana fermions, that are indistinguishable from their antiparticles. Mysteriously, the physicist disappeared during a ferry trip off the Italian coast just a year after making his prediction. Scientists have been looking for Majorana’s enigmatic particle ever since. It has been suggested, but not proven, that the neutrino may be a Majorana particle. On the other hand, theorists have predicted that Majorana fermions may also exist in solids under special conditions.
Now the MIT-led team has observed evidence of Majorana fermions in a material system they designed and fabricated, which consists of nanowires of gold grown atop a superconducting material, vanadium, and dotted with small, ferromagnetic “islands” of europium sulfide. When the researchers scanned the surface near the islands, they saw signature signal spikes near zero energy on the very top surface of gold that, according to theory, should only be generated by pairs of Majorana fermions.
“Majorana ferminons are these exotic things, that have long been a dream to see, and we now see them in a very simple material — gold,” says Jagadeesh Moodera, a senior research scientist in MIT’s Department of Physics, and a member of MIT’s Plasma Science and Fusion Center. “We’ve shown they are there, and stable, and easily scalable.”
“The next push will be to take these objects and make them into qubits, which would be huge progress toward practical quantum computing,” adds co-author Patrick Lee, the William and Emma Rogers Professor of Physics at MIT.
Lee and Moodera’s coauthors include former MIT postdoc and first author Sujit Manna (currently on the faculty at the Indian Institute of Technology at Delhi), and former MIT postdoc Peng Wei of University of California at Riverside, along with Yingming Xie and Kam Tuen Law of the Hong Kong University of Science and Technology.
If they could be harnessed, Majorana fermions would be ideal as qubits, or individual computational units for quantum computers. The idea is that a qubit would be made of combinations of pairs of Majorana fermions, each of which would be separated from its partner. If noise errors affect one member of the pair, the other should remain unaffected, thereby preserving the integrity of the qubit and enabling it to correctly carry out a computation.
Scientists have looked for Majorana fermions in semiconductors, the materials used in conventional, transistor-based computing. In their experiments, researchers have combined semiconductors with superconductors — materials through which electrons can travel without resistance. This combination imparts superconductive properties to conventional semiconductors, which physicists believe should induce particles in the semiconductor to split , forming the pair of Majorana fermions.
“There are several material platforms where people believe they’ve seen Majorana particles,” Lee says. “The evidence is stronger and stronger, but it’s still not 100 percent proven.”
What’s more, the semiconductor-based setups to date have been difficult to scale up to produce the thousands or millions of qubits needed for a practical quantum computer, because they require growing very precise crystals of semiconducting material and it is very challenging to turn these into high-quality superconductors.
About a decade ago, Lee, working with his graduate student Andrew Potter, had an idea: Perhaps physicists might be able to observe Majorana fermions in metal, a material that readily becomes superconductive in proximity with a superconductor. Scientists routinely make metals, including gold, into superconductors. Lee’s idea was to see if gold’s surface state — its very top layer of atoms — could be made to be superconductive. If this could be achieved, then gold could serve as a clean, atomically precise system in which researchers could observe Majorana fermions.
Lee proposed, based on Moodera’s prior work with ferromagnetic insulators, that if it were placed atop a superconductive surface state of gold, then researchers should have a good chance of clearly seeing signatures of Majorana fermions.
“When we first proposed this, I couldn’t convince a lot of experimentalists to try it, because the technology was daunting,” says Lee who eventually partnered with Moodera’s experimental group to to secure crucial funding from the Templeton Foundation to realize the design. “Jagadeesh and Peng really had to reinvent the wheel. It was extremely courageous to jump into this, because it’s really a high-risk, but we think a high-payoff, thing.”
Over the last few years, the researchers have characterized gold’s surface state and proved that it could work as a platform for observing Majorana fermions, after which the group began fabricating the setup that Lee envisioned years ago.
They first grew a sheet of superconducting vanadium, on top of which they overlaid nanowires of gold layer, measuring about 4 nanometers thick. They tested the conductivity of gold’s very top layer, and found that it did, in fact, become superconductive in proximity with the vanadium. They then deposited over the gold nanowires “islands” of europium sulfide, a ferromagnetic material that is able to provide the needed internal magnetic fields to create the Majorana fermions.
The team then applied a tiny voltage and used scanning tunneling microscopy, a specialized technique that enabled the researchers to scan the energy spectrum around each island on gold’s surface.
Moodera and his colleagues then looked for a very specific energy signature that only Majorana fermions should produce, if they exist. In any superconducting material, electrons travel through at certain energy ranges. There is however a desert, or “energy gap” where there should be no electrons. If there is a spike inside this gap, it is very likely a signature of Majorana fermions.
Looking through their data, the researchers observed spikes inside this energy gap on opposite ends of several islands along the the direction of the magnetic field, that were clear signatures of pairs of Majorana fermions.
“We only see this spike on opposite sides of the island, as theory predicted,” Moodera says. “Anywhere else, you don’t see it.”
“In my talks, I like to say that we are finding Majorana, on an island in a sea of gold,” Lee adds.
Moodera says the team’s setup, requiring just three layers — gold sandwiched between a ferromagnet and a superconductor — is an “easily achievable, stable system” that should also be economically scalable compared to conventional, semiconductor-based approaches to generate qubits.
“Seeing a pair of Majorana fermions is an important step toward making a qubit,” Wei says. “The next step is to make a qubit from these particles, and we now have some ideas for how to go about doing this.”
This research was funded, in part, by the John Templeton Foundation, the U.S. Office of Naval Research, the National Science Foundation, and the U.S. Department of Energy.


Classical computing is built on the power of the bit, which is, in essence, a micro transistor on a chip that can be either on or off, representing a 1 or a 0 in binary code. The quantum computing equivalent is the qubit. Unlike bits, qubits can exist in more than one “state” at a time, enabling quantum computers to perform computational functions exponentially faster than can classical computers.
To date, most efforts to build quantum computers have relied on qubits created in superconducting wires chilled to near absolute zero or on trapped ions held in place by lasers. But those approaches face certain challenges, most notably that the qubits are highly sensitive to environmental factors. As the number of qubits increases, those factors are more likely to compound and interrupt the entanglement of qubits required for a quantum computer to work.
Another approach, developed more recently, is to use a photon as an optical qubit to encode quantum information and to integrate the components necessary for that process into a photonic integrated circuit (PIC). Galan Moody, an assistant professor in the UC Santa Barbara College of Engineering’s Department of Electrical and Computer Engineering (ECE), has received a Defense University Research Instrumentation Program (DURIP) Award from the U.S. Department of Defense and the Air Force Office of Scientific Research to build a quantum photonic computing testbed. He will conduct his research in a lab set aside for such activity in recently completed Henley Hall, the new home of the College of Engineering’s Institute for Energy Efficiency (IEE).
The grant supports the development or acquisition of new instrumentation to be used in fundamental and applied research across all areas of science and engineering. “My field is quantum photonics, so we’re working to develop new types of quantum light sources and ways to manipulate and detect quantum states of light for use in such applications as quantum photonic computing and quantum communications,” Moody said.
“At a high level,” he explained, the concept of quantum photonic computing is “exactly the same as what Google is doing with superconducting qubits or what other companies are doing with trapped ions. There are a lot of different platforms for computing, and one of them is to use photonic integrated circuits to generate entangled photons, entanglement being the foundation for many different quantum applications.”
To place an entire quantum photonics system onto a chip measuring about one square centimeter would be a tremendous achievement. Fortunately, the well-developed photonics infrastructure — including AIM Photonics, which has a center at UCSB led by ECE professor and photonics pioneer John Bowers, also director of the IEE — lends itself to that pursuit and to scaling up whatever quantum photonics platform is most promising. Photonics for classical applications is a mature technology industry that, Moody said, “has basically mastered large-scale and wafer-scale fabrication of devices.”
It is reliable, so whatever Moody and his team design, they can fabricate themselves or even order from foundries, knowing they will get exactly what they want.
The Photonic Edge
The process of creating photonic qubits begins with generating high-quality single photons or pairs of entangled photons. A qubit can then be defined in several different ways, most often in the photon’s polarization (the orientation of the optical wave) or in the path that the photons travel. Moody and his team can create PICs that control these aspects of the photons, which become the carriers of quantum information and can be manipulated to perform logic operations.
The approach has several advantages over other methods of creating qubits. For instance, the aforementioned environmental effects that can cause qubits to lose their coherence do not affect coherence in photons, which, Moody says, “can maintain that entanglement for a very long time. The challenge is not coherence but, rather, getting the photons to become entangled in the first place.”
“That,” Moody notes, “is because photons don’t naturally interact; rather, they pass right through each other and go their separate ways. But they have to interact in some way to create an entangled state. We’re working on how to create PIC-based quantum light sources that produce high-quality photons as efficiently as possible and then how to get all the photons to interact in a way that allows us to build a scalable quantum processor or new devices for long-distance quantum communications.”
Quantum computers are super efficient, and the photonics approach to quantum technologies is even more so. When Google “demonstrated quantum supremacy” in fall 2019 using the quantum computer built in its Goleta laboratory under the leadership of UCSB physics professor John Martinis, the company claimed that its machine, named Sycamore, could do a series of test calculations in 200 seconds that a super-computer would need closer to 10,000 years to complete. Recently, a Chinese team using a laboratory-scale table-top experiment claimed that, with a photon-based quantum processor, “You could do in two hundred seconds what would take a super-computer 2.5 billion years to accomplish,” Moody said.
Another advantage is that photonics is naturally scalable to thousands and, eventually, millions of components, which can be done by leveraging the wafer-scale fabrication technologies developed for classical photonics. Today, the most advanced PICs comprise nearly five thousand components and could be expanded by a factor of two or four with existing fabrication technologies, a stage of development comparable to that of digital electronics in the 1960s and 1970s. “Even a few hundred components are enough to perform important quantum computing operations with light, at least on a small scale between a few qubits,” said Moody. With further development, quantum photonic chips can be scaled to tens or hundreds of qubits using the existing photonics infrastructure.
Moody’s team is developing a new materials platform, based on gallium arsenide and silicon dioxide, to generate single and entangled photons, and it promises to be much more efficient than comparable systems. In fact, they have a forthcoming paper showing that their new quantum light source is nearly a thousand times more efficient than any other on-chip light source.
In terms of the process, Moody says, “At the macro level, we work on making better light sources and integrating many of them onto a chip. Then, we combine these with on-chip programmable processors, analogous to electronic transistors used for classical logic operations, and with arrays of single-photon detectors to try to implement quantum logic operations with photons as efficiently as possible.”
For more accessible applications, like communications, no computing need occur. “It involves taking a great light source and manipulating a property of the photon states (such as polarization), then sending those off to some other chip that’s up in a satellite or in some other part of the world, which can measure the photons and send a signal back that you can collect,” Moody said.
One catch, for now, is that the single-photon detectors, which are used to signal whether the logic operations were performed, work with very high efficiency when they are on the chip; however, some of them work only if the chip is cooled to cryogenic temperatures.
“If we want to integrate everything on chip and put detectors on chip as well, then we’re going to need to cool the whole thing down,” Moody said. “We’re going to build a setup to be able to do that and test the various quantum photonic components designed and fabricated for this. The DURIP award enables exactly this: developing the instrumentation to be able to test large-scale quantum photonic chips from cryogenic temperatures all the way up to room temperature.”
There are also challenges associated with cooling the chip to cryogenic temperatures. Said Moody, “It’s getting this whole platform up and running, interfacing the instrumentation, and making all the custom parts we need to be able to look at large-scale photonic chips for quantum applications at cryogenic temperatures.”


A quantum computer is based on phenomena such as superposition and entanglement and uses such phenomena to perform operations on data. While binary digital electronic computers are based on transistors, quantum-mechanical phenomena form the basis of quantum computers.
Theoretically, large-scale quantum computers would be far more efficient and faster at solving certain problems than any classical computers. For example, Shor’s algorithm, a quantum algorithm for integer factorization running on a quantum computer would beat hands down the corresponding problem running on a classical computer. Another example where quantum computers would reign supreme is the simulation of quantum many-body systems. Quantum algorithms, such as Simon’s algorithm, run much faster compared to any possible probabilistic classical algorithm. It’s important to note that, in principle, a classical computer could simulate a quantum algorithm. This is because quantum computation does not violate the Church–Turing thesis. To perform this, however, a classical computer would require an inordinate amount of resources. Quantum computers, on the contrary, might efficiently solve problems that are too complex to be practically solved by classical computers.
A quantum computer uses quantum states to represent bits simultaneously to achieve an exponential increase in speed and power. Enormous, complex problems usually requiring massive amount of resources and time can be solved in a reasonable amount of time. This is highly beneficial for IoT data that requires a lot of computation power and other complex optimization functions. In drug discovery, for example, trillions of combinations of amino acids are examined to find a single elusive protein.
On the quantum level, you’re able to program the atoms to represent all possible input combinations simultaneously. That means when you run an algorithm, all possible input combinations are tested at once. With a regular computer, you’d have to serially cycle through every possible input combination to arrive at your solution. Interestingly, solving the most complex problems this way would take longer than the age of the universe.
For certain types of problems, quantum computers can provide an exponential speed boost. Quantum database search is the most well-known example of this.
Besides factorization and discrete logarithms, quantum algorithms offer more than polynomial speedup over the best-known classical algorithms. Simulation of quantum physical processes in solid state physics as well as chemistry, approximation of Jones polynomials, and solving Pell’s equation are some well-known examples.
A composite system is always expressible as a sum or superposition of products of states of local constituents.
Binary Bits vs. Quantum Qubits
With quantum computers, information is not held in individual units but rather in the system as a whole. The system can exist in two states at the same time. This is courtesy of the superposition principle of quantum mechanics. This “qubit” can store a “0” and “1” simultaneously. If you build a system comprising two qubits, it can hold four values at once — 00, 01, 10, and 11.
Quantum computers differ from digital computers, which use binary system and are based on transistors. Digital computing requires data encoded into bits, where a bit can be only in one exclusive state (0 or 1). Quantum computation, on the other hand, uses quantum bits (qubits), which need not be in an exclusive state; rather, the qubits can be in superpositions of states. A quantum Turing machine is a theoretical model of such a computer, also known as the universal quantum computer. The major groundwork in the field of quantum computing was done by Paul Benioff and Yuri Manin in 1980, Richard Feynman in 1982, and David Deutsch in 1985. In 1968, a quantum computer with spins as qubits was also formulated for use as a quantum space–time.
A classical computer has bits for memory, where each bit represents either a 0 or 1. A quantum computer, instead, has a sequence of qubits. A single qubit can represent 0, 1, or any quantum superposition of those two qubit states. Similarly, a pair of qubits can be in any quantum superposition of four states, and three qubits in any superposition of eight states. Generalizing, a quantum computer with n qubits can be in an arbitrary superposition of up to 2n different states simultaneously. A classical computer, in contrast, can exclusively be in just one of these 2n states at a time.
A quantum computer operates on qubits. The qubits are set in a perfect drift, representing the specific problem at hand. Subsequently, a precise sequence of quantum logic gates is used to manipulate these qubits. The quantum algorithm is the sequence of gates to be applied to solve the problem. When a measurement is done, the qubit system collapses into a classical state, where each qubit is 0 or 1. Thus, the outcome can at most be n classical bits of information. An important aspect of quantum algorithms is their probabilistic nature. They associate a certain known probability with a correct solution.
A particle having spin states “up” and “down” (usually written | ↓ ⟩ and | ↑ ⟩ , or | 0 ⟩ and | 1 ⟩) can be considered an example of an implementation of qubits in a quantum computer. But in fact, any system possessing an observable quantity A, which is conserved under time evolution such that A has at least two discrete and sufficiently spaced consecutive eigenvalues, is a suitable candidate for implementing a qubit. This is true because any such system can be mapped onto an effective spin-1/2 system.
Qubits are not just the particles themselves. In addition to the controlled particles, qubits also have the means of control, such as the devices that trap particles and switch them between different states.
Research on Quantum Computers
As of 2017, the development of actual quantum computers is rapidly gaining pace. Advances are being made in both practical and theoretical research. National governments and military agencies are taking deep interest and funding in quantum computing research. Once developed, quantum computers can be employed in a variety of fields such as civilian, business, trade, environmental and national security purposes.
Learn more: https://amyxinternetofthings.com/


In quantum teleportation, the properties of quantum entanglement are used to send a spin state (qubit) between observers without physically moving the involved particle. The particles themselves are not really teleported, but the state of one particle is destroyed on one side and extracted on the other side, so the information that the state encodes is communicated. The process is not instantaneous, because information must be communicated classically between observers as part of the process. The usefulness of quantum teleportation lies in its ability to send quantum information arbitrarily far distances without exposing quantum states to thermal decoherence from the environment or other adverse effects.
Although quantum teleportation can in principle be used to actually teleport macroscopic objects (in the sense that two objects in exactly the same quantum state are identical), the number of entangled states necessary to accomplish this is well outside anything physically achievable, since maintaining such a massive number of entangled states without decohering is a difficult problem. Quantum teleportation, is, however, vital to the operation of quantum computers, in which manipulation of quantum information is of paramount importance. Quantum teleportation may eventually assist in the development of a "quantum internet" that would function by transporting information between local quantum computers using quantum teleportation .
Below is a sketch of an algorithm for teleporting quantum information. Suppose Alice has state C, which she wants to send to Bob. To achieve this, Alice and Bob should follow the sequence of steps:
1) Generate an entangled pair of electrons with spin states A and B, in a particular Bell state:
Separate the entangled electrons, sending A to Alice and B to Bob.
2) Alice measures the "Bell state" (described below) of A and C, entangling A and C.
3) Alice sends the result of her measurement to Bob via some classical method of communication.
4) Bob measures the spin of state B along an axis determined by Alice's measurement
Since step 3 involves communicating via some classical method, the information in the entangled state must respect causality. Relativity is not violated because the information cannot be communicated faster than the classical communication in step 3 can be performed, which is sub-lightspeed.
The idea of quantum teleportation, which can be seen in the mathematics below, is that Alice's measurement disentangles A and B and entangles A and C. Depending on what particular entangled state Alice sees, Bob will know exactly how B was disentangled, and can manipulate B to take the state that C had originally. Thus the state C was "teleported" from Alice to Bob, who now has a state that looks identical to how C originally looked. It is important to note that state C is not preserved in the processes: the no-cloning and no-deletion theorems of quantum mechanics prevent quantum information from being perfectly replicated or destroyed. Bob receives a state that looks like C did originally, but Alice no longer has the original state C in the end, since it is now in an entangled state with A.
Which of the following is true of quantum teleportation?
1) Quantum information is transferred between states
2) The teleported particle is physically transferred between locations
3) A quantum state is cloned between observers
4) Quantum information is permanently removed from the system
As a review, recall the Pauli matrices:
The spin operators along each axis are defined as times each of for the axes respectively.
These Pauli matrices are used to construct Bell states, an orthonormal basis of entangled states for the tensor product space of spin- particles:
Measurements that project tensor products of spin states onto the Bell basis are called Bell measurements.
Now, follow the algorithm sketched in the previous section. Suppose Alice starts with state C, which she wants to send Bob. State C can be written in the most general form:
with and normalized complex constants.
1) Generate an entangled pair of electrons A and B in the Bell state:
The state of the full system of three particles is therefore . This is a product state between entangled pair AB and non-entangled C.
2) Alice measures the Bell state of AC, entangling A and C while disentangling B. The process of measuring the Bell state projects a non-entangled state into an entangled state, since all four Bell states are entangled.
Expanding Alice's full original state, she starts with:
Multiplying out the states and changing to the Bell basis of A and C, this state can be rewritten:
When Alice measures the Bell state of A and C, she will find one of , each with probability . Whichever she measures, the state of particle B will be after measurement.
3) To send Bob the state of particle C, therefore, Alice does not need to send Bob the possibly infinite amount of information contained in the coefficients and which may be real numbers out to arbitrary precision. She needs only to send the integer of the Bell state of A and C, which is a maximum of two bits of information. Alice can send this information to Bob in whatever classical way she likes.
4) Bob receives the integer from Alice that labels the Bell state that she measured. After Alice's measurement, the overall state of the system is:
Bob therefore applies to the disentangled state on his end, by measuring the spin along axis . Since for all , Bob is left with the overall state:
Bob has therefore changed the spin state of particle B to:
which is identical to the original state of particle C that Alice wanted to send. The information in state C has been "teleported" to Bob's state: the final spin state of B looks like C's original state. Note, however, that the particles involved never change between observers: Alice always has A and C, and Bob always has B.
- Pirandola, S., & Braunstein, S. Physics: Unite to build a quantum Internet. Retrieved from http://www.nature.com/news/physics-unite-to-build-a-quantum-internet-1.19716
- Debenben, . quantum teleportation diagram. Retrieved from https://commons.wikimedia.org/w/index.php?curid=34503176


Learn about parallel computing, the rise of heterogeneous processing (also known as hybrid processing), and the prospect of quantum engineering as a field of study!
Parallel computing used to be a way of sharing tasks between processor cores.
When processor clock rates stopped increasing, the response of the microprocessor companies was to increase the number of cores on a chip to increase throughput.
But now, the increased use of specialized processing elements has become more popular.
A GPU is a good example of this. A GPU is very different from an x86 or ARM processor and is tuned for a different type of processing.
GPUs are very good at matrix math and vector math. Originally, they were designed to process pixels. They use a lot of floating point math because the math behind how a pixel value is computed is very complex.
A GPU is very useful if you have a number of identical operations you have to calculate at the same time.
GPUs used to be external daughter cards, but in the last year or two the GPU manufacturers are starting to release low power parts suitable for embedded applications. They include several traditional cores and a GPU.
So, now you can build embedded systems that take advantage of machine learning algorithms that would have traditionally required too much processing power and too much thermal power.
This is an example of a heterogeneous processor (AMD) or hybrid processor. A heterogeneous processor contains cores of different types, and a software architect figures out which types of workloads are processed by which type of core.
Andrew Chen (professor) has predicted that this will increase in popularity because it’s become difficult to take advantage of shrinking the semiconductor feature size.
This year or next year, we will start to see heterogeneous processors (MOOR) with multiple types of cores.
Traditional processors are tuned for algorithms on integer and floating point operations where there isn’t an advantage to doing more than one thing at a time. The dependency chain is very linear.
A GPU is good at doing multiple computations at the same time so it can be useful when there aren’t tight dependency chains.
Neither processor is very good at doing real-time processing. If you have real time constraints – the latency between an ADC and the “answer” returned by the system must be short – there is a lot of computing required right now. So, a new type of digital hardware is required. Right now, ASICs and FPGAs tend to fill that gap, as we’ve discussed in the All about ASICs podcast.
Quantum cores (like we discussed in the what is quantum computing podcast) are something that we could see on processor boards at some point. Dedicated quantum computers that can exceed the performance of traditional computers will be introduced within the next 50 years, and as soon as the next 10 or 15 years.
To be a consumer product, a quantum computer would have to be a solid state device, but their existence is purely speculative at this point in time.
Quantum computing is reinventing how processing happens. And, quantum computers are going to tackle very different types of problems than conventional computers.
There is a catalog on the web of problems and algorithms that would be substantially better on a quantum on a computer than a traditional computer.
People are creating algorithms for computers that don’t even exist yet.
The Economist estimated that the total spend on quantum computing research is over 1 Billion dollars per year globally. A huge portion of that is generated by the promise of these algorithms and papers. The interest is driven by this.
Quantum computers will not completely replace typical processors.
Lee’s opinion is that the quantum computing industry is still very speculative, but the upsides are so great that neither the incumbent large computing companies nor the industrialized countries want to be left behind if it does take off.
The promise of quantum computing is beyond just the commercial industry, it’s international and inter-industry. You can find long whitepapers from all sorts of different governments laying out a quantum computing research strategy. There’s also a lot of venture capitalists investing in quantum computing.
Is this research and development public, or is there a lot of proprietary information out there? It’s a mixture, many of the startups and companies have software components that they are open sourcing and claim to have “bits of physics” working (quantum bits or qbits), but they are definitely keeping trade secrets.
19:50 Quantum communication means space lasers.
Engineering with quantum effects has promise as an industry. One can send photons with entangled states. The Chinese government has a satellite that can generate these photons and send them to base stations. If anyone reads them they can tell because the wave function collapsed too soon.
Quantum sensing promises to develop accelerometers and gyroscopes that are orders of magnitude more sensitive than what’s commercially available today.
Quantum engineering could become a new field. Much like electrical engineering was born 140 years ago, electronics was born roughly 70 years ago, computer science was born out of math and electrical engineering. It’s possible that the birth of quantum engineering will be considered to be some point in the next 5 years or last 5 years.
Lee’s favorite quantum state is the Bell state. It’s the equal probability state between 1 and 0, among other interesting properties. The Bell state encapsulates a lot of the quantum weirdness in one snippet of math.


Reliable quantum computing would make it possible to solve certain types of extremely complex technological problems millions of times faster than today’s most powerful supercomputers. Other types of problems that quantum computing could tackle would not even be feasible with today’s fastest machines. The key word is “reliable.” If the enormous potential of quantum computing is to be fully realized, scientists must learn to create “fault-tolerant” quantum computers. A small but important step toward this goal has been achieved by an international collaboration of researchers from China’s Tsinghua University and the U.S. Department of Energy (DOE)’s Lawrence Berkeley National Laboratory (Berkeley Lab) working at the Advanced Light Source (ALS).
Using premier beams of ultraviolet light at the ALS, a DOE national user facility for synchrotron radiation, the collaboration has reported the first demonstration of high-temperature superconductivity in the surface of a topological insulator – a unique class of advanced materials that are electrically insulating on the inside but conducting on the surface. Inducing high-temperature superconductivity on the surface of a topological insulator opens the door to the creation of a pre-requisite for fault-tolerant quantum computing, a mysterious quasiparticle known as the “Majorana zero mode.”
“We have shown that by interfacing a topological insulator, bismuth selenide, with a high temperature superconductor, BSCCO (bismuth strontium calcium copper oxide), it is possible to induce superconductivity in the topological surface state,” says Alexei Fedorov, a staff scientist for ALS beamline 12.0.1, where the induced high temperature superconductivity of the topological insulator heterostructure was confirmed. “This is the first reported demonstration of induced high temperature superconductivity in a topological surface state.”
The results of this research are presented in the journal Nature Physics in a paper titled “Fully gapped topological surface states in Bi2Se3 induced by a d-wave high temperature superconductor.” The corresponding authors are Shuyun Zhou and Xi Chen of Tsinghua University in Beijing, China. The lead authors are Eryin Wang and Hao Ding, also with Tsinghua University. Wang is currently an ALS Doctoral fellow in residence.
For all of its boundless potential, quantum computing faces a serious flaw. The quantum data bit or “qubit” used to process and store information is fragile and easily perturbed by electrons and other elements in its surrounding environment. Utilizing topological insulators is considered one promising approach for solving this “decoherence” problem because qubits in a topological quantum computer would be made from Majorana zero modes, which are naturally immune from decoherence. Information processed and stored in such topological qubits would always be preserved. While the ALS collaboration has not yet identified a Majorana zero mode in their bismuth selenide/BSCCO heterostructures, they believe their material is fertile ground for doing so.
“Our studies reveal a large superconducting pairing gap on the topological surface states of thin films of the bismuth selenide topological insulator when grown on BSCCO,” Fedorov says. “This suggests that Majorana zero modes are likely to exist, bound to magnetic vortices in this material, but we will have to do other types of measurements to find it.”
The high quality bismuth selenide/BSCCO topological thin film heterostructure was made at Tsinghua University in the laboratory of Xi Chen and Qi-Kun Xue using molecular beam epitaxy.
“Our study was made possible by the high quality topological insulator film heterostructure that the Chen and Xue groups managed to grow,” says Zhou, who did much of her research at the ALS before returning to China. “Bismuth selenide and the BSSCO ceramic have very different crystal structures and symmetries, which made the growth of such a heterostructure particularly challenging.”
Says Chen, “By controlling the growth kinetics carefully using molecular beam epitaxy, we managed to grow a topological insulator film with controlled thickness on a freshly cleaved BSCCO surface. This provided a cleaner and better-controlled interface, and also opened up opportunities for surface sensitive measurements.”
The bismuth selenide/BSCCO material was brought to the ALS to study the electronic states on its surface using a technique known as ARPES, for angle-resolved photoemission spectroscopy. In ARPES, a beam of X-ray photons striking the material’s surface causes the photoemission of electrons. The kinetic energy of these photoelectrons and the angles at which they are ejected are then measured to obtain an electronic spectrum.
“Previous work on topological insulators revealed superconductivity at only a few Kelvin with a gap of about one milli-electron volt,” Fedorov says. “Such a small energy scale and ultra-low temperature makes it particularly challenging to realize Majorana zero modes experimentally, and to distinguish these modes from other states. Using ARPES, we show evidence of a superconducting gap persisting in the surfaces of our material up to the transition temperature of BSCCO. As the gap and transition temperature in our heterostructure reflect almost an order of magnitude increase over previous work, we believe ours is a better system to search for Majorana zero modes.”
This research was primarily supported by the National Natural Science Foundation of China.
Filed Under: M2M (machine to machine)


Quantum simulators permit the study of quantum system in a programmable fashion. In this instance, simulators are special purpose devices designed to provide insight about specific physics problems. Quantum simulators may be contrasted with generally programmable "digital" quantum computers, which would be capable of solving a wider class of quantum problems.
A universal quantum simulator is a quantum computer proposed by Yuri Manin in 1980 and Richard Feynman in 1982. Feynman showed that a classical Turing machine would not be able to simulate a quantum effect, while his hypothetical universal quantum computer would be able to mimic needed quantum effect.
A quantum system of many particles could be simulated by a quantum computer using a number of quantum bits similar to the number of particles in the original system. This has been extended to much larger classes of quantum systems.
Quantum simulators have been realized on a number of experimental platforms, including systems of ultracold quantum gases, polar molecules, trapped ions, photonic systems, quantum dots, and superconducting circuits.
Many important problems in physics, especially low-temperature physics and many-body physics, remain poorly understood because the underlying quantum mechanics is vastly complex. Conventional computers, including supercomputers, are inadequate for simulating quantum systems with as few as 30 particles. Better computational tools are needed to understand and rationally design materials whose properties are believed to depend on the collective quantum behavior of hundreds of particles. Quantum simulators provide an alternative route to understanding the properties of these systems. These simulators create clean realizations of specific systems of interest, which allows precise realizations of their properties. Precise control over and broad tunability of parameters of the system allows the influence of various parameters to be cleanly disentangled.
Quantum simulators can solve problems which are difficult to simulate on classical computers because they directly exploit quantum properties of real particles. In particular, they exploit a property of quantum mechanics called superposition, wherein a quantum particle is made to be in two distinct states at the same time, for example, aligned and anti-aligned with an external magnetic field. Crucially, simulators also take advantage of a second quantum property called entanglement, allowing the behavior of even physically well separated particles to be correlated.
Ion trap based system forms an ideal setting for simulating interactions in quantum spin models. A trapped-ion simulator, built by a team that included the NIST can engineer and control interactions among hundreds of quantum bits (qubits). Previous endeavors were unable to go beyond 30 quantum bits. The capability of this simulator is 10 times more than previous devices. It has passed a series of important benchmarking tests that indicate a capability to solve problems in material science that are impossible to model on conventional computers.
The trapped-ion simulator consists of a tiny, single-plane crystal of hundreds of beryllium ions, less than 1 millimeter in diameter, hovering inside a device called a Penning trap. The outermost electron of each ion acts as a tiny quantum magnet and is used as a qubit, the quantum equivalent of a “1” or a “0” in a conventional computer. In the benchmarking experiment, physicists used laser beams to cool the ions to near absolute zero. Carefully timed microwave and laser pulses then caused the qubits to interact, mimicking the quantum behavior of materials otherwise very difficult to study in the laboratory. Although the two systems may outwardly appear dissimilar, their behavior is engineered to be mathematically identical. In this way, simulators allow researchers to vary parameters that couldn’t be changed in natural solids, such as atomic lattice spacing and geometry.
Friedenauer et al., adiabatically manipulated 2 spins, showing their separation into ferromagnetic and antiferromagnetic states. Kim et al., extended the trapped ion quantum simulator to 3 spins, with global antiferromagnetic Ising interactions featuring frustration and showing the link between frustration and entanglement and Islam et al., used adiabatic quantum simulation to demonstrate the sharpening of a phase transition between paramagnetic and ferromagnetic ordering as the number of spins increased from 2 to 9. Barreiro et al. created a digital quantum simulator of interacting spins with up to 5 trapped ions by coupling to an open reservoir and Lanyon et al. demonstrated digital quantum simulation with up to 6 ions. Islam, et al., demonstrated adiabatic quantum simulation of the transverse Ising model with variable (long) range interactions with up to 18 trapped ion spins, showing control of the level of spin frustration by adjusting the antiferromagnetic interaction range. Britton, et al. from NIST has experimentally benchmarked Ising interactions in a system of hundreds of qubits for studies of quantum magnetism. Pagano, et al., reported a new cryogenic ion trapping system designed for long time storage of large ion chains demonstrating coherent one and two-qubit operations for chains of up to 44 ions.
Many ultracold atom experiments are examples of quantum simulators. These include experiments studying bosons or fermions in optical lattices, the unitary Fermi gas, Rydberg atom arrays in optical tweezers. A common thread for these experiments is the capability of realizing generic Hamiltonians, such as the Hubbard or transverse-field Ising Hamiltonian. Major aims of these experiments include identifying low-temperature phases or tracking out-of-equilibrium dynamics for various models, problems which are theoretically and numerically intractable. Other experiments have realized condensed matter models in regimes which are difficult or impossible to realize with conventional materials, such as the Haldane model and the Harper-Hofstadter model.
Quantum simulators using superconducting qubits fall into two main categories. First, so called quantum annealers determine ground states of certain Hamiltonians after an adiabatic ramp. This approach is sometimes called adiabatic quantum computing. Second, many systems emulate specific Hamiltonians and study their ground state properties, quantum phase transitions, or time dynamics. Several important recent results include the realization of a Mott insulator in a driven-dissipative Bose-Hubbard system and studies of phase transitions in lattices of superconducting resonators coupled to qubits.


Think back a second. When was it that you got your first smartphone? What about the first time that you streamed a show online?
Those things were available to us around 12-15 years ago, depending on how tech-savvy you were at that time. Now, though, smartphones and fast computers are ubiquitous. Not only that, but they’re affordable.
The cutting-edge technology just keeps slicing deeper and deeper to the point that we’re used to advanced progress. We expect to be amazed, then we get bored of our amazement and look for the next thing.
That said, is computer processor speed just going to keep getting better?
We’re going to look at this question today, giving you some insights into the world of technology and where it’s headed. Let’s get started.
How Do Computer Processors Work?
To start this discussion, we have to know a few things about how computer processors work. A few basic insights into CPUs allow us to have a better grasp of what the future might hold.
A central processing unit (CPU) is considered the brain of the computer. It’s where all of the complex tasks take place, and it manages everything you do while you use a device. The CPU reaches into the random access memory and hard drive storage to get information in a matter of milliseconds.
It also interacts with your graphics processing unit to generate all of the beautiful images and 3D renderings you engage with on-screen.
The processor consists of 10s of millions of transistors made of semiconductor materials. Simply put, a semiconductor allows or blocks electrical signals to flow, depending on the situation.
The Importance of Transistors
As a semiconductor, a transistor manages electrical signals in either a positive or negative fashion. When it’s positive to the current, it allows it to continue or directs it in the right way. When negative, that signal is stopped.
It’s like a little traffic cop that stops and starts traffic to keep things flowing smoothly. This little device is the absolute building block for all computers and pieces of modern technology.
It might not seem like that’s very complex or that it could power something as influential as the iPhone. That said, these devices are all just the result of small electrical signals getting directed to produce specific, mechanical actions.
When you press a single key on your keyboard, there’s a simple and elegant process that takes place. The button sends a signal to the CPU, which then sends a signal to the screen, and the letter pops up in an instant. That process is reflective of almost any process you do on the computer.
It’s simple, but the complexity compounds each time you press another button. In the case of the transistor, that little traffic cop gets multiplied by orders of magnitude and placed in a microchip.
The microchip is an essential worker for the CPU. A chip the size of your fingernail holds billions (yes, billions) of transistors.
Moore’s Law and The Future of Technology
At some point, the only devices available had ten or twenty transistors in them. That was some time back in the sixties or seventies when computer technology took hold.
The more transistors you include in a device, though, the better it is. When they’re placed on a microchip, they’re said to be included in an “integrated circuit.” When you increase the ability of an integrated circuit to house transistors, you improve the quality of the device in question.
One of the founders of Intel computers, Gordon Moore, proposed an idea. He said that, so long as the price stays consistent, the integrated circuit will be able to house double the number of components every 18 to 24 months.
As a result, the performance of technology will be twice as good as it was a year and a half prior. His law held up for the first twenty years of the computer.
Since then, it has had years when advancement fell behind his estimate and years when it surpassed his estimate. That said, the slope of Moore’s law and the slope of microprocessor ability are eerily close to one another.
If nothing else, we can look to Moore’s law to estimate roughly how good technology will be in the near and distant future, barring any big changes to the situation.
It will keep doubling and improving ad infinitum in that case, though. Can we be sure that that will happen?
How Can Things Improve?
The thing about Moore’s law is that it was created when one couldn’t foresee the technology we have now. Technology breeds paradigm shifts, and that’s what we can expect in the next decades if Moore’s law is correct until then.
We’ll hypothetically reach a point when we no longer need transistors and microchips at all. People are already producing transistors that are the size of a handful of atoms pushed together.
That’s approaching the size of the fundamental building blocks of the universe as far as we know. What lies beyond that advancement is difficult to say, but things are accelerated by the fact that computers are actually doing the thinking for us in some instances.
There are more neurons in the human mind than microchips in the smartest computer, but that doesn’t mean that computers aren’t better at thinking logically and recalling information than we are. Artificial intelligence thinks critically in real-time, and it might be able to produce better computers than we can.
Is Quantum Computing Just Science Fiction?
Quantum computers are already in existence, although they’re not as powerful as classical computers with microchips yet. Yet is the keyword, though.
The science hasn’t gotten narrowed down into perfection as of yet, but the idea is that artificial intelligence will keep chipping away at the stone until David emerges.
Quantum computing plays on the random nature of quantum states like entanglement, superposition, and more. Without getting too deep into the terminology, it might help to understand, basically, what those things are.
Quantum mechanics state that particles and waves exist to different degrees at different times and their existence is relative to the observer at a particular time. Ent anglement is an instance when the particle and wave occupy the same space in such a way that the observer can’t say that either one doesn’t exist.
Superimposition suggests that both particle and wave are atop one another in an instance that produces a third, equally viable state. Those things are heady enough as it is, but introduce computing into the mix and you’ve got a real brain-melter.
The result is that computers will work trillions of times faster than ours do. The implications of that are hard to imagine, especially for our consumer technology.
What To Expect From Computer Processor Speed
Whether or not Moore’s law is correct, we can be sure that things will improve. Provided that there’s no extreme climate disaster or global collapse, technology will improve.
Phones, computers, and other devices are essential to the lifestyles of billions of people on earth. There’s a lot of money waiting for the individuals or companies that think up new ways to improve our lives through technology.
There are also a lot of issues on planet earth that something like quantum computing could fix. Supply chain management, hunger, poverty, and numerous other essential problems might get solved by a more intelligent computer.
So, there are more than enough carrots dangling in front of humanity to push the technology cart forward. Whether that will keep happening in a way that doubles every couple of years, only time will tell.
That said, quantum computing advancements will be a paradigm shift for the entire idea of technology. The speed of our computers today was almost unimaginable 30 years ago. Things are incredibly fast and easy to use now.
You can get the scoop on modern computers and start enjoying them if you’re not already.
Where Will It End?
If things scale up at an exponential rate as they have, it’s impossible to imagine what the state of technology could be. Just like people 100 years ago would faint if they saw a smartphone, we might do the same if we saw what was possible 20 years from now.
The difference for us is that things change at an exponential rate. What would have taken 100 years might take only ten now. Ten years from now, it’ll only take one year to do what took us ten, and so on and so forth.
If things keep multiplying upon themselves like that, the only question is “where does it all end?” Will the singularity come and take us over? Will we merge with technology in some way?
Science fiction has to take the reins from that point on.
Want to Learn More About Computer Chips?
Hopefully, our look at computer processor speed was interesting to you. There’s a lot more to learn and keep track of as things move forward, though.
We’re here to keep you filled in. Explore our site for more ideas on technology, central processing unit insights, processor cores, and much more.


computers go digital
Technology Research News
Some of the same properties that would
make quantum computers phenomenally powerful are also properties that
make it difficult to actually build them.
Problems that would take the fastest possible classical computer longer
than the lifetime of the universe to solve would be hours-long exercises
for large-scale quantum computers. Such machines would be able to rapidly
search huge databases and would render today's encryption methods useless.
The key to quantum computers' potential is that quantum bits, the basic
building blocks of quantum computing logic circuits, can represent a mix
of 1 and 0 at the same time, allowing a string of qubits to represent
every possible answer to a problem at the same time. This means a quantum
computer could check every possible answer using a single set of operations.
Classical computers, in contrast, check each answer one at a time.
But today's qubits are difficult to work with and prone to errors, and
the faster they go the more errors they produce. One of the challenges
of building a quantum computer is reducing errors. Researchers from the
University of Wisconsin at Madison have eased the problem with a method
that reduces error rates by two orders of magnitude.
Today's computers are digital, meaning they use signals that are either
on or off to represent two states -- a 1 or a 0 -- and all computations
are done using combinations of these binary numbers. One advantage of
using just two states is the signals that represent those states don't
have to be exact, they simply have to be clearly closer to 1 than 0 or
Qubits are analog devices, meaning they produce variable, continuous signals
rather than discrete on and off states. For example, a particle can be
in one of two orientations, spin up and spin down, but also some mix of
the two. The 1s and 0s of digital information are mapped to the spin up
and spin down states, but quantum computations have to be precise to ensure
that the given particle is actually in one of those two states. "Classical
bits have only two states... quantum bits can be in between," said Robert
Joynt, a physics professor at the University of Wisconsin at Madison.
A qubit continually rotates between 0 and 1, which makes it prone to errors,
said Joynt. "A rotation of a qubit can, for example, fall a little bit
short with only a very minor error in the input signal," he said.
The researchers' method makes quantum computing a pseudo-digital operation.
"In our set-up, a definite rotation rate for the qubits is associated
with a range of input signals. [This way] the input does not have to be
exceedingly precise," said Joynt.
Easing the requirements for precision could go a long way toward making
quantum computers viable. "The driving force [for the idea] was objections
from experienced electrical engineers, particularly at IBM, who believed
that quantum computing would not work... the because the specs for the
driving electronics would be much too [demanding]," said Joynt.
The researchers are applying the pseudo-digital qubits to their ongoing
efforts to build a solid-state quantum computer. Their design calls for
thousands of individually-controlled electrons in a silicon chip. The
chip would allow for careful control of the interactions between neighboring
electrons so that the states of the electrons could be used to carry out
computations. Some of the fundamental logic operations in quantum computers
are carried out through the interactions of pairs of qubits.
The researchers added the pseudo-digital qubits concept to their design
by having pairs of electrons slide past each other rather than crash into
each other, said Joynt. When the electrons are well separated the interaction
is off, representing a 0, and when they are within close range the interaction
is on, representing a 1.
When the researchers simulated the technique, they found that it reduced
operational error rates by more than two orders of magnitude, according
to Joynt. The researchers' pseudo-digital qubits could be implemented
in other types of quantum computers, he added.
The pseudo-digital approach is a good one, said Bruce Kane, a visiting
associate research scientist at the University of Maryland. "My guess
is that future quantum computers will use the pseudo-digital approach,"
he said. It remains to be seen whether the devices the researchers are
building will work well, however, he said.
Quantum computing naturally has many similarities to analog rather than
digital computing, said Kane. Because digital computers operate using
just two states -- 1 and 0 -- inputs can always be rounded. This type
of rounding, however, is impossible in quantum computing, he said. "It
[is usually] necessary to control parameters very precisely to keep the
computation on track," he said.
The researchers' method is an attempt to find systems that "pretty much
automatically have only two interaction strengths," said Kane. No system
can have exactly this behavior, so the method doesn't eliminate the problem
of errors creeping into a quantum computation, but it can reduce the severity
of the errors, he said.
The researchers have shown how to minimize the adverse effects of turning
interactions on and off in quantum computing, said Seth Lloyd, a professor
of mechanical engineering at the Massachusetts Institute of Technology.
"Although I doubt that this exact architecture will prove to be the one
that is used to construct large-scale quantum computers, it is exactly
this sort of imaginative quantum-mechanical engineering that is required
to solve the problems of large-scale quantum computation," he said.
One of the challenges in implementing the scheme in a real quantum computer
is fabricating the tiny qubits precisely, said Joynt. "The real issue
is fabrication of quite complicated nanostructures," he said.
The researchers are working on qubits made from two basic pieces -- a
semiconductor sandwich structure "which is really a monster club sandwich,"
said Joynt; and a gate structure, which controls the state of a qubit
so that it can represent a one or a zero.
The researchers have made progress on the semiconductor sandwich structure
and are gearing up now to produce the gate structure, "which is quite
complex," Joynt said.
The researchers are also working on a readout apparatus that will fit
on the chip. Reading the quantum states of particles is tricky because
quantum states are easily disturbed.
It will take a decade to develop simple demonstration models, and probably
20 years before the devices can be used in practical quantum computers,
Joynt's research colleagues were Mark Friesen and M. A. Eriksson. They
published the research in the December 9, 2002 issue of Applied Physics
Letters. The research was funded by the National Science Foundation (NSF)
and the Army research office (ARO).
Timeline: 10-20 years
TRN Categories: Physics; Quantum Computing and Communications
Story Type: News
Related Elements: Technical paper, "Pseudo-Digital Quantum
Bits," Applied Physics Letters, December 9, 2002.
29/February 5, 2003
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switch flips atoms
Technology Research News
Atoms and subatomic particles are like
microscopic tops that can spin in one of two directions, up or down. Spintronics
and quantum computing use these spin directions to represent the ones
and zeros of digital information. Today's electronics, in contrast, use
the presence or absence of electric charge to represent binary numbers.
A team of researchers from the Max Planck Institute and the Technical
University of Munich in Germany has used an electronic switch to transfer
the spin of a group of electrons to the nuclei of atoms in a semiconductor.
Information transfer between electrons and atoms is a key component of
spintronics and quantum computing. Atoms in semiconductor crystals are
better suited to preserving spin and thereby storing information than
electrons because they are fixed in position and they are better insulated
from the environment than electrons. Electrons, however, can flow in currents,
which makes them better suited to transmitting information.
Computers based on spintronics would be faster, use less electrical power
and store data more densely than electronic computers. Data would also
remain in memory after the power was turned off, allowing spintronics
computers to start instantly.
Quantum computers can use the interactions of individual particles to
solve certain problems, like cracking secret codes and searching large
databases, that are beyond the abilities of the fastest classical computer
The researchers' experiment proved that it is possible to transfer spin
between atoms and electrons, but a lot of work remains before the capability
can be put to practical use, said Jurgen Smet, a scientist at the Max
Planck Institute. The experiment "brings us one step closer, but we have
a large number of giant leaps to go to make something useful and practical,"
said Smet. "We have succeeded... in a very crude manner for a large ensemble
of nuclei, however under extreme conditions, like nearly absolute zero
temperature and... a large, stationery magnetic field."
Ordinarily, the spins of electrons and atoms in a semiconductor are isolated
from each other. The energy associated with electron spin is considerably
greater than the energy associated with atomic spin, and this energy mismatch
usually keeps the electrons from changing the atomic spin. But by using
a gate, or electronic switch, to control the density of electrons in the
semiconductor, the researchers found that at certain densities the interactions
between electrons affect the spins of the semiconductor's atoms.
Atomic spins can also be flipped using magnetic fields, which is how hard
disk drives in today's computers work. But disk drives are larger, slower
and require more energy than the integrated circuits on computer chips.
"One would like all-electronic nuclear solid-state devices so that one
can marry the benefits of the technology used in present-day electronics
with those of quantum computation or spintronics," said Smet.
The researchers' experiment shows that electronic control of atomic spin
in semiconductors is possible. However, their technique is unlikely to
lead directly to practical technology, said Smet. "The physics we exploit
to flip the nuclear spins actually also requires these low temperatures,
so there is at least no straightforward rule on how to scale this up,"
Still, the research shows that spintronics could be a viable successor
to today's electronics. "Atoms... are the smallest unit of which a semiconductor
crystal is composed. If you were to extrapolate Moore's Law... you'll
find that in the next decade or so we end up with a dimension on the order
of the atom," said Smet. Moore's Law, which has held true for the past
couple of decades, states that computer speeds double every 18 months
as manufacturers shrink computer circuits. "Clearly a paradigm shift has
to occur. That is one reason why long-term researchers fervently think
about ways to explore the spin degree of freedom of the nucleus of atoms,"
Controlling atomic spin could also be used in quantum computing. But to
do so, however, the researchers' technique would need to be applied to
individual atoms. "This kind of control is not something we will manage
to achieve within the next two decades," said Smet.
The researchers device serves as a miniature laboratory for probing the
fundamental interactions between electrons and nuclei and exploring the
basis for exchanging information between the two spin systems, said David
Awschalom, a professor of physics at the University of California at Santa
Barbara. "This is a beautiful experiment," he said. "Many people envision
that future quantum computing will use nuclear spins for information storage,
and thus it is important to explore these basic interactions."
Smet's research colleagues were Rainer Deutschmann, Frank Ertland and
Gerhard Abstreiter of the Technical University of Munich, Werner Wegscheider
of the Technical University of Munich and the University of Regensburg,
and Klaus von Klitzing of the Max Planck Institute. They published their
research in the January 17, 2002 issue of the journal Nature. The research
was funded by the German Ministry of Science and Education (BMBF) and
the German National Science Foundation (DFG).
Timeline: >20 years
TRN Categories: Materials Science and Engineering; Quantum
Story Type: News
Related Elements: Technical paper, "Gate-voltage control
of spin interactions between electrons and nuclei in a semiconductor,"
Nature, January 17, 2002
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Many people credit Professor Richard Feynman, a Nobel Prize-winning physicist, for conceiving the notion of a quantum computer. Physicist Joseph John Fernandez notes, “In a  lecture titled Simulating Physics with Computers, Professor Feynman talked about why physicists need computers, and what they require of these devices. … Feynman asked the following question: can a classical, universal computer simulate any physical system? And in particular, what about quantum systems?” Feynman’s question is a good one since weird things happen at the quantum level. Fernandez notes trying to model quantum systems with a classical system isn’t possible. He explains, “For classical computers, the memory requirements for these calculations are too much. The true simulation of physical systems becomes intractable. This is where the interest in quantum computers started to grow.” James Norman explains, “Quantum computers can be game changers because they can solve important problems no existing computer can. While conventional computing scales linearly, QC scales exponentially when adding new bits. Exponential scaling always wins, and it’s never close.” Fernandez concludes, “Professor Feynman was definitely onto something!” A quantum computer’s exponential scaling properties theoretically allow it to solve problems much faster than classical computers. Proving that quantum computers actually work faster is where an argument between Google and IBM began.
Google’s claim to quantum supremacy
Sarah Kaplan (@sarahkaplan48) reports, “For the first time, a machine that runs on the mind-boggling physics of quantum mechanics has reportedly solved a problem that would stump the world’s top supercomputers — a breakthrough known as ‘quantum supremacy.’” Amy Thomson (@athomson6) adds, “Alphabet Inc.’s Google said it’s built a computer that’s reached ‘quantum supremacy,’ performing a computation in 200 seconds that would take the fastest supercomputers about 10,000 years. … Google’s tests … were conducted using a quantum chip it developed in-house.”
In a blog post, Google engineering director Hartmut Neven stated, “This achievement is the result of years of research and the dedication of many people. It’s also the beginning of a new journey: figuring out how to put this technology to work. We’re working with the research community and have open-sourced tools to enable others to work alongside us to identify new applications.” The announcement, published in the journal Nature, inspired Dr. Michael Wall (@MichaelDWall) to declare, “We have just entered the age of quantum supremacy.” He quotes study co-author Brooks Foxen, a graduate student researcher in physics at Google AI Quantum in Mountain View and the University of California, Santa Barbara, who states, “It is likely that the classical simulation time, currently estimated at 10,000 years, will be reduced by improved classical hardware and algorithms, but, since we are currently 1.5 trillion times faster, we feel comfortable laying claim to this achievement.”
Google CEO Sundar Pichai (@sundarpichai) writes, “While we’re excited for what’s ahead, we are also very humbled by the journey it took to get here. And we’re mindful of the wisdom left to us by the great Nobel Laureate Richard Feynman: ‘If you think you understand quantum mechanics, you don’t understand quantum mechanics.’ In many ways, the exercise of building a quantum computer is one long lesson in everything we don’t yet understand about the world around us. While the universe operates fundamentally at a quantum level, human beings don’t experience it that way. In fact many principles of quantum mechanics directly contradict our surface level observations about nature. Yet the properties of quantum mechanics hold enormous potential for computing. … For those of us working in science and technology, it’s the ‘hello world’ moment we’ve been waiting for — the most meaningful milestone to date in the quest to make quantum computing a reality.”
IBM and others dispute the claim
Foxen and other members of her team may feel comfortable declaring they have achieved quantum supremacy, but their claims are being disputed by IBM and some academics. James Sanders (@jas_np) writes, “Quantum computing researchers in academia and firms in competition with Google are dismissing claims of quantum supremacy, though note that this is still a significant milestone toward it.” He explains, “The industry objection to this claim is that the calculation in question is of no practical use outside of research laboratories — even inside labs, the utility of it does not extend meaningfully beyond the synthetic benchmark scenario Google pursued for this paper.” Pichai admits, “We have a long way to go between today’s lab experiments and tomorrow’s practical applications; it will be many years before we can implement a broader set of real-world applications.” He goes on to note, “We can think about today’s news in the context of building the first rocket that successfully left Earth’s gravity to touch the edge of space. At the time, some asked: Why go into space without getting anywhere useful? But it was a big first for science because it allowed humans to envision a totally different realm of travel … to the moon, to Mars, to galaxies beyond our own. It showed us what was possible and nudged the seemingly impossible into frame. That’s what this milestone represents for the world of quantum computing: a moment of possibility.”
IBM researchers Edwin Pednault, John Gunnels, Dmitri Maslov, and Jay Gambetta lay out their objections to Google’s claim for quantum supremacy. They write, “In the paper, it is argued that their device reached ‘quantum supremacy’ and that ‘a state-of-the-art supercomputer would require approximately 10,000 years to perform the equivalent task.’ We argue that an ideal simulation of the same task can be performed on a classical system in 2.5 days and with far greater fidelity. This is in fact a conservative, worst-case estimate, and we expect that with additional refinements the classical cost of the simulation can be further reduced. … Building quantum systems is a feat of science and engineering and benchmarking them is a formidable challenge. Google’s experiment is an excellent demonstration of the progress in superconducting-based quantum computing, showing state-of-the-art gate fidelities on a 53-qubit device, but it should not be viewed as proof that quantum computers are ‘supreme’ over classical computers. … The term ‘quantum supremacy’ is being broadly misinterpreted and causing ever growing amounts of confusion, we urge the community to treat claims that, for the first time, a quantum computer did something that a classical computer cannot with a large dose of skepticism due to the complicated nature of benchmarking an appropriate metric.”
Whether or not Google has achieved quantum supremacy may be in doubt, but everyone seems to agree their achievement is notable and praiseworthy. The IBM researchers conclude, “The concept of quantum computing is inspiring a whole new generation of scientists, including physicists, engineers, and computer scientists, to fundamentally change the landscape of information technology. If you are already pushing the frontiers of quantum computing forward, let’s keep the momentum going.”
 Joseph John Fernandez, “Richard Feynman and the birth of quantum computing,” Medium, 4 January 2018.
 James Norman, “Quantum Computing Will Revolutionize Data Analysis. Maybe Soon,” Seeking Alpha, 14 March 2018.
 Sarah Kaplan, “Google scientists say they’ve achieved ‘quantum supremacy’ breakthrough over classical computers,” The Washington Post, 23 October 2019.
 Amy Thomson, “Google Says Quantum Computer Beat 10,000-Year Task in Minutes,” Data Center Knowledge, 23 October 2019.
 Sundar Pichai, “What our quantum computing milestone means,” Google, 23 October 2019.
 Michael Wall, “‘Supremacy’ Achieved: Quantum Computer Notches Epic Milestone,” Space.com, 23 October 2019.
 James Sanders, “Google’s quantum computing supremacy claim relies on a synthetic benchmark, researchers assert,” TechRepublic, 23 October 2019.
 Edwin Pednault, John Gunnels, Dmitri Maslov, and Jay Gambetta, “On ‘Quantum Supremacy’,” IBM Research Blog, 21 October 2019.


Two major steps toward putting quantum computers into real practice — sending a photon signal on demand from a qubit onto wires and transmitting the signal to a second, distant qubit — have been brought about by a team of scientists at Yale.
Over the past several years, the research team of Professors Robert Schoelkopf in applied physics and Steven Girvin in physics has explored the use of solid-state devices resembling microchips as the basic building blocks in the design of a quantum computer. Now, for the first time, they report that superconducting qubits, or artificial atoms, have been able to communicate information not only to their nearest neighbor, but also to a distant qubit on the chip.
This research now moves quantum computing from “having information” to “communicating information.” In the past information had only been transferred directly from qubit to qubit in a superconducting system. Schoelkopf and Girvin’s team has engineered a superconducting communication ‘bus’ to store and transfer information between distant quantum bits, or qubits, on a chip. This work, according to Schoelkopf, is the first step to making the fundamentals of quantum computing useful.
The first breakthrough reported is the ability to produce on demand — and control — single, discrete microwave photons as the carriers of encoded quantum information. While microwave energy is used in cell phones and ovens, their sources do not produce just one photon. This new system creates a certainty of producing individual photons.
“It is not very difficult to generate signals with one photon on average, but, it is quite difficult to generate exactly one photon each time. To encode quantum information on photons, you want there to be exactly one,” according to postdoctoral associates Andrew Houck and David Schuster who are lead co-authors on the first paper.
“We are reporting the first such source for producing discrete microwave photons, and the first source to generate and guide photons entirely within an electrical circuit,” said Schoelkopf.
In order to successfully perform these experiments, the researchers had to control electrical signals corresponding to one single photon. In comparison, a cell phone emits about 1023 (100,000,000,000,000,000,000,000) photons per second. Further, the extremely low energy of microwave photons mandates the use of highly sensitive detectors and experiment temperatures just above absolute zero.
“In this work we demonstrate only the first half of quantum communication on a chip — quantum information efficiently transferred from a stationary quantum bit to a photon or ‘flying qubit,’” says Schoelkopf. “However, for on-chip quantum communication to become a reality, we need to be able to transfer information from the photon back to a qubit.”
This is exactly what the researchers go on to report in the second breakthrough. Postdoctoral associate Johannes Majer and graduate student Jerry Chow, lead co-authors of the second paper, added a second qubit and used the photon to transfer a quantum state from one qubit to another. This was possible because the microwave photon could be guided on wires — similarly to the way fiber optics can guide visible light — and carried directly to the target qubit. “A novel feature of this experiment is that the photon used is only virtual,” said Majer and Chow, “winking into existence for only the briefest instant before disappearing.”
To allow the crucial communication between the many elements of a conventional computer, engineers wire them all together to form a data “bus,” which is a key element of any computing scheme. Together the new Yale research constitutes the first demonstration of a “quantum bus” for a solid-state electronic system. This approach can in principle be extended to multiple qubits, and to connecting the parts of a future, more complex quantum computer.
However, Schoelkopf likened the current stage of development of quantum computing to conventional computing in the 1950’s, when individual transistors were first being built. Standard computer microprocessors are now made up of a billion transistors, but first it took decades for physicists and engineers to develop integrated circuits with transistors that could be mass produced.
Schoelkopf and Girvin are members of the newly formed Yale Institute for Nanoscience and Quantum Engineering (YINQE), a broad interdisciplinary activity among faculty and students from across the university.
Other Yale authors involved in the research are J.M. Gambetta, J.A. Schreier, J. Koch, B.R. Johnson, L. Frunzio, A. Wallraff, A. Blais and Michel Devoret. Funding for the research was from the National Security Agency under the Army Research Office, the National Science Foundation and Yale University.
Citation: Nature 449, 328-331 (20 September 2007) doi:10.1038/nature06126
& Nature 450, 443-447 (27 September 2007) doi:10.1038/nature06184


Earlier this year, the night-migratory European robin (Erithacus rubecola) made the headlines. Evidence has emerged that it may be using quantum mechanical effects to sense Earth’s magnetic field in order to migrate.
Few expected to find quantum mechanical manipulation in the eye of a bird. Zoologist Eric Warrant, who was not involved in the research, says, that magnetic direction sensing is “the last sense we know, effectually, nothing about.” But this mysterious intelligence appears essential to migration, and hence, to the survival of many birds. So how, exactly, do they do it?
Humans perceive the world around them with five senses — vision, hearing, taste, smell and touch. Many other animals are also able to sense the Earth’s magnetic field. For some time, a collaboration of biologists, chemists and physicists centred at the Universities of Oldenburg (Germany) and Oxford (UK) have been gathering evidence suggesting that the magnetic sense of migratory birds such as European robins is based on a specific light-sensitive protein in the eye. In the current edition of the journal Nature, this team demonstrate that the protein cryptochrome 4, found in birds’ retinas, is sensitive to magnetic fields and could well be the long-sought magnetic sensor…
Hore says “if we can prove that cryptochrome 4 is the magnetic sensor we will have demonstrated a fundamentally quantum mechanism that makes animals sensitive to environmental stimuli a million times weaker than previously thought possible.”University of Oldenburg, “Mechanism of magnetic sensing in birds” at ScienceDaily (June 23, 2021) The paper requires a subscription.
That might explain the precision of migrating birds, returning to a precise spot year after year.
Warrant says that one barrier to research on quantum magnetosensing, first proposed thirty years ago, was that the hypothesis was first put forward by a physicist and most biologists weren’t in touch with the physics concepts. However, the team that zeroed in on the magnetosensing mechanism includes members from both disciplines.
The challenge, says study co-author Henrik Mouritsen, was to produce cryptochrome molecules in a beaker because it is impractical to study them inside the eye of a living bird. But they succeeded:
Henrik: Now it’s not a hypothesis that this molecule is magnetically sensitive. We can see that it’s magnetically sensitive.
V/O: The team were also curious how cryptochrome proteins compared between birds that migrate and birds that don’t.
Henrik: We then also made cryptochromes from an extreme non-migratory bird – basically the chicken. And it looks like the cryptochrome-4 from the migratory birds are significantly more magnetically sensitive than the same molecule from a chicken.How quantum mechanics help birds find their way (video, 04:10–4:34 min), Nature, June 23, 2021
The researchers also speculate that, because the processing is done in the bird’s visual field, birds may actually see Earth’s magnetic field — perhaps as a shadow imposed over an aerial view.
How do physicists think it actually works?
Quantum entanglement dictates that if two electrons are created at the same time, the pair will be “entangled” so that whatever happens to one particle affects the other. Otherwise, it would violate fundamental laws of physics.
The two particles remain entangled even when separated by vast distances.
So if one particle is spin-up, the other must be spin-down, but what’s mind-boggling is that neither will have a spin until they’re measured.
That means that not only will you not know what the spin of the electron is until you measure it, but that the actual act of measuring the spin will make it spin-up or spin -own.
As difficult as entanglement is to believe, as well as understand, it is a well established property of quantum mechanics. And some physicists are suggesting that birds and other animals might be using the effect to see and navigate Earth’s magnetic fields.
The process could work via light-triggered interactions on a chemical in bird’s eyes.American Association of Physicists, “Migration via quantum mechanics” at PhysicsCentral
How does the sensing system pick up these magnetic fields?
We already know that spin is significantly affected by magnetic fields. Arrange electrons in the right way around an atom, and collect enough of them together in one place, and the resulting mass of material can be made to move using nothing more than a weak magnetic field like the one that surrounds our planet.Mike McCrae, “Birds Have a Mysterious ‘Quantum Sense’. For The First Time, Scientists Saw It in Action” at ScienceAlert (January 8, 2021)
While this finding is a significant step forward in understanding ways birds might migrate vast distances without getting lost (a “spectacular piece of science,” as Warrant puts it), the researchers have not yet worked with living birds. Thus they cannot definitively say that the cryptochrome 4 protein molecule is the critical ingredient in magnetosensing — only that it is a very promising candidate.
Experimental physicist Rob Sheldon offers Mind Matters News some further thoughts on what, exactly, the researchers did and the larger significance of their find:
Magnetic effects are so small, the molecule needs to be in a very fragile “excited” state to sense the magnetic field. It is thought that the “cytochrome” molecule gets excited by blue light, and in the excited state, magnetic fields preferentially cause it to de-excite in a certain direction.
They tested this in the lab, by coupling the cytochrome to a fluorescing or glowing molecule, shining a dim blue light on the cell, and watch it glow. Then when they passed a magnetic field over the cell, the glow was dimmed, proving that the cytochrome molecule was doing something in response to magnetic field.
Since this sensing is happening at the level of electron spins and excitation, it is an inherently QM [quantum mechanical] effect, hence the title of the article.
This isn’t spooky, and isn’t unusual. Lots of molecules have QM effects. Most of the odor receptors in your nose employ QM effects to identify odorants. Chlorophyll that makes leaves green absorbs light through a QM cascade of electrons. And of course, when the rods & cones in your retina sense photons, it is a QM effect.
What makes the magnetic QM effect so unusual, is that magnetic fields are perhaps 1000 times smaller than the other QM effects I mentioned. So the system has to detect a very weak signal-to-noise-ratio (SNR).
In the lab, we often use difference circuits that are modulated by a frequency and the result is integrated. The difference knocks out the common signal, so its called common mode rejection. The modulation averages over the noise, where real noise always has a zero sum. Then SNR can be boosted by factors of 1000 to 1,000,000, and somehow that is happening in a single cell. That’s the part that is spooky. Packing a $10,000 lock-in amplifier into a 2 micron cell.
Some birds are naturally very intelligent — the New Zealand crow, for example — but in this case, the birds with remarkable perception have access to magnetosensing, a sense we are only beginning to understand.
You may also wish to read:
We knew crows were smart but they turn out to be even smarter. We are only beginning to scratch the surface of the mysteries of animal intelligence. Questions abound: How did crows come to be smart when other birds did not? Most birds would survive better if they were smarter but that doesn’t make it happen.
Do birds really understand what they are saying? Remarkable claims are made for some birds. To understand what they are saying, birds would need to understand abstractions; it’s not clear that they can.


People being people, most of us have gotten used to the idea that the methods we routinely use to protect our information are reliable and safe. This is why you educate your users to check if that little padlock appears in their browser search window before they check their bank balance. It's why we go to the trouble of implementing email encryption as well as secure file transfer systems.
But in the tech industry, change is always on the horizon, which means you need to get used to the idea that what you thought was invulnerable today might easily be threatened tomorrow. One of those changes is quantum computing, and it's a field that's developing quickly. For example, earlier this year, Google announced that it had built the largest quantum computing chip ever: a 72-qubit (a quantum bit) processor.
To put that into context, it's important to explain how a qubit differs from the bit you learned about back in computer science class. Those bits are basic units of information represented by either a 1 or a 0. Qubits, which are represented by the symbol '0> and '1>, can also encompass values of 1 or 0, but can then extend those values to essentially an infinite number of states in between 1 and 0. What happens is that the probability of some number changes as you move between 1 and 0.
We're not going to go into detail about how this works (you can read more about it here), except to say that, by having more potential values between 1 and 0, you can perform some types of computation faster. In some cases, many thousands of times faster than what's possible with today's more advanced desktop CPU architectures, like the Intel i9.
Because of the way quantum computers work, they can be used for jobs that are difficult for these more traditional CPU chipsets. This would include tasks such as multidimensional modeling, simulations, and, yes, codebreaking. It's the codebreaking and encryption cracking that's worrying security experts, and is also freaking out some folks involved with cryptocurrencies as well as those involved with the many other developments being made possible by blockchain technology. Blockchains and cryptocurrencies are, after all, simply very large numbers used to create a unit of whatever currency you're considering. Bitcoin, for example, depends on public key cryptography. Public key cryptography is considered one of the most vulnerable to cracking by a quantum computer, which is part of what's making folks with large Bitcoin investments sweat.
What this means to you is that some types of encryption that you depend on are no longer considered secure. Exactly how that may apply to you is described in more detail in this "Report on Post-Quantum Cryptography" published by the US Department of Commerce's National Institute of Standards and Technology (NIST). What you'll find in this NIST paper is that public key encryption is vulnerable to cracking by using algorithms on a quantum computer. But other means of encryption, including Advanced Encryption Standard (AES), which uses symmetric keys, and Secure Hash Algorithm (SHA-2 and SHA-3), will remain secure with some modifications.
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Table 1 - Impact of Quantum Computing on Common Cryptographic Algorithms - Credit: NIST
The most widely used version of AES, which uses 256-bit keys, is actually relatively secure against quantum computing attacks. AES-256 is commonly used for mundane tasks such as Wi-Fi encryption. However, another commonly used version of encryption, secure sockets layer (SSL), uses public key encryption.
Calming Your Quantum Computing Fears
For now, you don't need to worry, though as an IT professional, you should start to plan. Despite the rapid development of quantum computing, researchers don't appear to have reached the point where they can routinely decrypt routine business communications. While that may come someday, you're still fairly safe for now as long as you remember these key points:
SSL communications are still safe; and because they are ephemeral, your users don't need to worry that there'll be a stored copy of their banking session or credit card purchase to be retrieved and cracked at a later date. However, that may change in the future.
AES-256 will be safe, even against quantum attacks, for some time. Unless your data is valuable enough for a nation-state to spend millions of dollars to crack it, you don't need to worry. However, if your business handles national security data, then maybe you need to find a better way and it'd be a good idea to start staying on top of devleoping cryptographic trends.
Age is important. Unless you need to protect your data for decades against future quantum attacks by using advanced algorithms, then some form of symmetric encryption (including AES) will do.
Be prepared for encryption using longer key lengths because those are much harder to crack. Some keys can be found by using brute force techniques but, if the time to crack them by using the fastest quantum computer exceeds the expected age of the universe, then you're probably safe. Longer key lengths will require more computer power to handle, but probably not enough to bog down your systems when they're needed.
Remember that the quality of encryption is only one part of the security puzzle. Poorly executed encryption, weak or faulty software surrounding the encryption, and poor security practices can still expose your critical data through other vulnerabilities. For example, it doesn't help to encrypt your communications if the bad guys can walk into your office and steal the data out of an unlocked file cabinet or, more often, the trash can.
While some forms of encryption now have a limited lifetime, the fact is, you still have time to determine what data you have that may have vulnerabilities because of encryption, and then evaluate whether or not the risk down the road will affect you immediately. For most day-to-day operations, it won't. But if you deal with sensitive data that has a long lifetime, then you need to start planning for the future now.
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