Quantum logical operations realized with single photons
Scientists from all over the world are working on concepts for future quantum computers and their experimental realization. Commonly, a typical quantum computer is considered to be based on a network of quantum particles that serve for storing, encoding and processing quantum information. In analogy to the case of a classical computer a quantum logic gate that assigns output signals to input signals in a deterministic way would be an essential building block. A team around Dr. Stephan Dürr from the Quantum Dynamics Division of Prof. Gerhard Rempe at the Max Planck Institute of Quantum Optics has now demonstrated in an experiment how an important gate operation – the exchange of the binary bit values 0 and 1 – can be realized with single photons. A first light pulse containing one photon only is stored as an excitation in an ultracold cloud of about 100 000 rubidium atoms. This gives rise to the effect that a second light pulse that passes through the cloud exhibits a phase shift of 180 degrees.
"Photons are ideal carriers of quantum information because they hardly interact with their environment and can easily be transmitted over long distances," explains Dr. Stephan Dürr, leader of the project. "Therefore we are very interested in the development of a photon-photon-quantum gate where a single light pulse can modify an incoming photonic qubit in a deterministic way."
Modern data processing is based on the principle that information can be encoded in a binary system. In this context, logic gates fulfil the task of implementing truth tables which uniquely assign a specific output pattern to a given input signal. For instance an input value of 0 can be transformed into an output value of 1 or vice versa. In a photon-photon-quantum gate, this corresponds to the process of a single photon manipulating the state of a second single photon in a deterministic way. This interaction has to be mediated by matter. Up to now no physical system could be found to provide a sufficiently strong interaction.
In this experiment a cloud of about 100 000 rubidium atoms is cooled down to a temperature of 0.5 microkelvin and caught in a dipole trap composed of several light fields. Next, a rapid sequence of three light pulses impinges onto the cloud: the first so-called control pulse determines whether the second target pulse is significantly modified when it passes through the cloud, i.e. whether the gate operation is switched on or off. A third pulse is used to retrieve an excitation that has potentially been stored.
The light pulses consist of two components: on the one hand, they contain red signal light so weak that a light pulse carries only one photon on average. With a wavelength of 780 nm it is near-resonant with a certain atomic transition. Without further treatment the light pulse would pass through the atomic cloud and acquire a certain phase shift. However, by adding blue coupling light of high intensity with a wavelength of 480 nm the photon in the signal pulse can be stored in a controlled and reversible way. During this process, one atom in the cloud is transferred into a highly excited Rydberg state where one electron is located at a large distance from the nucleus.
In the next step, the atoms are irradiated with the target pulse which is also composed of both signal and coupling light. As the Rydberg atom exhibits a long-range van der Waals interaction with the other atoms in the cloud, atomic energy levels inside a certain region around the Rydberg atom are shifted. This results in a larger detuning of the target pulse from the atomic levels compared to the case without a previously stored control pulse.
Because of this detuning the target pulse picks up a phase shift that differs by 180 degrees from the phase shift obtained when no control excitation is stored. "It is this additional phase shift, caused by the van der Waals interaction, that really matters," says Dr. Dürr. "This makes it possible to generate quantum states that are orthogonal to each other, which corresponds to a bit flip from 0 to 1." In the last step, a coupling light pulse retrieves the signal photon that is stored in the cloud.
In a series of measurements, using wave plates and a polarizing beam splitter the scientists determined the polarization of both red signal photons after passing through the atomic cloud. Thereby they were able to show that the light pulse had picked up an additional phase shift of 180 degrees whenever the signal laser was switched on during the control pulse. The whole cycle – the storage of the control pulse, the propagation of the target pulse and the retrieval of the control excitation – takes only a few microseconds.
"The experiment demonstrates that we can rotate the polarization plane of the photonic qubit in the target pulse with just one control photon," resumes Dr. Dürr. "This is an important prerequisite for the realization of a quantum gate. However, a quantum gate also has to provide the possibility to generate an entangled final state from two separate initial states. To achieve this goal we are planning to do further experiments."


Researchers have demonstrated the ability of an optical chip to simulate the motion of atoms within molecules at the quantum level, which could open superior means for developing chemicals that are used as pharmaceuticals.
In an optical chip, light is used to process information, in the place of electricity, where the chip functions as a quantum computing circuit when it uses single particles of light, called photons. Data collected from the chip can be used to carry out a frame-by-frame reconstruction of atomic motions to produce a virtual movie of the quantum vibrations of a molecule, which is the core concept of the study reported in the Nature journal on May 30th, 2018.
These outcomes are the fruit of an association between scientists from the University of Bristol, MIT, IUPUI, Nokia Bell Labs, and NTT. In addition to opening the door for highly efficient pharmaceutical developments, the study could induce innovative techniques of molecular modeling for industrial chemists.
In the 1960s, when lasers were invented, experimental chemists conceptualized their use in the disintegration of molecules. However, the vibrations inside molecules instantaneously redistribute the laser energy before disintegration of the targeted molecular bond. If the behavior of molecules has to be controlled, it is necessary to gain insights into the way they vibrate at the quantum level. However, massive computational power is required to model these dynamics, even more than what is anticipated from future generations of supercomputers.
The Quantum Engineering and Technology Labs at Bristol have pioneered the application of optical chips, in which single photons of light are controlled, as the fundamental circuitry for quantum computers. It is anticipated that quantum computers will become exponentially faster when compared to traditional supercomputers in solving specific problems. However, developing a quantum computer is a highly difficult long-term goal.
As described in Nature, the researchers illustrated an innovative course to achieve molecular modeling that could turn out an early application of photonic quantum technologies. The new techniques harness an analogy between the vibrations of atoms in molecules and photons of light in optical chips.
According to Bristol physicist Dr Anthony Laing, who headed the study, “We can think of the atoms in molecules as being connected by springs. Across the whole molecule, the connected atoms will collectively vibrate, like a complicated dance routine. At a quantum level, the energy of the dance goes up or down in well-defined levels, as if the beat of the music has moved up or down a notch. Each notch represents a quantum of vibration.
Light also comes in quantised packets called photons. Mathematically, a quantum of light is like a quantum of molecular vibration. Using integrated chips, we can control the behaviour of photons very precisely. We can program a photonic chip to mimic the vibrations of a molecule.
Dr Anthony Laing
“We program the chip, mapping its components to the structure of a particular molecule, say ammonia, then simulate how a particular vibrational pattern evolves over some time interval. By taking many time intervals, we essentially build up a movie of the molecular dynamics.” Dr Laing added.
Talking about the versatility of the simulator, first author Dr Chris Sparrow, who was a student on the project, stated that, “The chip can be reprogrammed in a few seconds to simulate different molecules. In these experiments we simulated the dynamics of ammonia and a type of formaldehyde, and other more exotic molecules. We simulated a water molecule reaching thermal equilibrium with its environment, and energy transport in a protein fragment.
In this type of simulation, because time is a controllable parameter, we can immediately jump to the most interesting points of the movie. Or play the simulation in slow motion. We can even rewind the simulation to understand the origins of a particular vibrational pattern.
Dr Chris Sparrow
Joint first author, Dr Enrique Martín-Lopéz, who is at present a Senior Researcher with Nokia Bell Labs, added, “We were also able to show how a machine learning algorithm can identify the type of vibration that best breaks apart an ammonia molecule. A key feature of the photonic simulator that enables this is its tracking of energy moving through the molecule, from one localised vibration to another. Developing these quantum simulation techniques further has clear industrial relevance.”
Japanese Telecoms company NTT fabricated the photonic chip used in the experiments.
Dr Laing described the main directions for the future of the study, “Scaling up the simulators to a size where they can provide an advantage over conventional computing methods will likely require error correction or error mitigation techniques. And we want to further develop the sophistication of molecular model that we use as the program for the simulator. Part of this study was to demonstrate techniques that go beyond the standard harmonic approximation of molecular dynamics. We need to push these methods to increase the real-world accuracy of our models.
This approach to quantum simulation uses analogies between photonics and molecular vibrations as a starting point. This gives us a head start in being able to implement interesting simulations. Building on this, we hope that we can realise quantum simulation and modelling tools that provide a practical advantage in the coming years.
Dr Anthony Laing
The researchers acknowledge support from the European Research Council (ERC). A.N. is thankful for support from the Wilkinson Foundation. J.C. is supported by EU H2020 Marie Sklodowska-Curie grant number 751016. Y.N.J. was supported by NSF grant number DMR-1054020. J.L.O’B. acknowledges a Royal Society Wolfson Merit Award and a Royal Academy of Engineering Chair in Emerging Technologies. A.L acknowledges a fellowship support from EPSRC.
Credit: University of Bristol


January 20, 2006 feature
Quantum Computing Steps Forward
With the University of Michigan’s latest production of a quantum chip, it’s another step forward for quantum computers that will someday dwarf the abilities of today’s machines.
Working with individual ions or atoms – much smaller than the transistors of even the most advanced microchips - quantum computers may be both more powerful and more compact than existing computers by various orders of magnitude.
Common computers today are thousands of times more powerful and more compact than the first 30 ton behemoths, but they use virtually the same logic. The fundamental design has gone unchanged for 50 years.
Quantum computing is whole new ball game. The secret lies in the almost magical property of quantum matter to adopt two states simultaneously. Normal integrated circuits store data using transistors which have just two states – on and off. Each quantum circuit, or qubit, can represent at least three states: on, off or both by an effect called quantum superposition. This means much more data can be stored on each individual circuit.
Actually, qubits can potentially contain many states. Dr Andrew White, Senior Lecturer in Physics at University of Queensland describes a qubit like this: “A quantum computer takes that on or off state and adds many different possible states. The first thing, if you think of the globe, let the South Pole be on, the North Pole off – that’s not a very good description of the globe. A quantum computer let’s you describe information by saying, look, you can take an arrow from Earth’s center and point it at the North Pole, South Pole or Los Angeles or London, and that’s richer description. You can fit much more information on a single qubit.”
Based on Dr. White’s description, a single qubit could replace a whole bank of conventional memory. Normal memory holds a large array of binary numbers expressed as on or off transistors – ones or zeros. Many transistors are needed to express anything more than just a simple number – hence today’s computers need for large memories. For example: you need 8 bits plus one bit for error correction to store the binary number for 256 which is expressed as 11111111. Going back to our globe example, our arrow could point to Amsterdam which could represent 256 – or any other number. A single qubit could store more information than thousands of transistors.
This compact storage leads to another advantage: speed. Without the need to access many memory locations to read data, retrieval is almost instantaneous.
Quantum computers will represent a huge leap in processing power as well – they could execute instructions exponentially faster because there would be almost no limit to the size of the instruction. Currently, most computers use 32 or 64 bit instructions.
There is another exciting benefit to working with quantum reactions: Entanglement. It describes the ability of quantum matter to “link” two particles. Change one particle and the other changes – instantaneously, even though there is no physical connection! And distance may be irrelevant! This property – not fully understood – would enable computers to talk to each other with no time lag over long distances.
Anton Zeilinger at the Institute of Experimental Physics in Vienna, Austria, preformed an experiment to demonstrate entanglement: their group strung an optical-fiber cable in a sewer tunnel under the Danube River with an "entangled" photon at each end. They measured of the state of polarization in one photon (horizontal, vertical, etc…) establishing that the other proton immediately had an identical polarization.
What will be the difference to normal computer users? Try instant access to any type of data – whether it is in your computer or on the other side of the planet. As for processing power, few users rarely exceed the abilities of today’s computers. Much computer hardware is used to generate the fancy graphical interface we call Windows – with plenty left over in reserve.
Those not familiar with computer science are often surprised to learn there are still a few applications that cannot run easily on today’s computers. They lack of sufficient processing power to do climate modeling, artificial intelligence or break strong encryption.
The NSA (National Security Agency) would love to be able to break many a foreign power’s encrypted communications, but has been stymied by the lack of a sufficiently fast computer for the job. Experts estimate it would take more than the lifetime of the Universe using all the computers in the world to break a 1024 bit encryption key – the current standard for serious encryption applications. It’s worth noting that most commercial encryption only uses a 40 bit key. A quantum computer has the potential to break any encryption in a few days.
Scientists who study global warming and climate would like to have finer-grained models to be able to predict the weather more effectively and determine the real impact man’s activities have over the planet. Current computers, although fast, still take hours or days to produce weather simulations that lack detail.
Artificial intelligence is another field that could use the extra processing power. Current algorithms simply can’t be processed fast enough and, admittedly, may need more refining. However, a quantum computer could theoretically contain more processing power than the human brain in a smaller space – making true AI possible.
In fact, more powerful computers often come along well before a use is found for them. In the future, more uses will be found for quantum machines as their tremendous processing power becomes available.
But having the machine is not enough. All of today’s software is based on the silicon technology it runs on. New software is already being written to take advantage of quantum computation.
One of the most important steps is to write software for error checking. All computers use some type of system to make sure a bit hasn’t accidentally “flopped” from a one to a zero. Quantum computer components, because of their atomic size, will be very susceptible to errors. In fact, one of the biggest problems faced by the scientists working on quantum computing is the problem associated with checking the state of an object so small. How does one check the value of a qubit without changing it? Error checking will be of critical importance and computer scientists have already developed some ideas to insure accuracy in quantum systems.
They have also already developed algorithms and equipment for super strong quantum encryption designed to allow hacker-proof security for communications. The National Security Agency and Federal Reserve banks can now buy a quantum cryptographic system from several companies. Anyone who intercepts and tries to read the stream of photons used will disturb the photons in a way that is detectable to both sender and receiver.
Quantum encryption represents the first major commercial implementation for what has become known as quantum information science - a blending of quantum mechanics and information theory.
As for the software you use in day-to-day computing, no changes will be necessary. Just as software emulators permit Apple users to run Windows and Windows software on the Mac’s Power PC processor – albeit sacrificing some speed – an emulator could quite easily run any programs today at speeds that make the today’s fastest processors look frozen. So you won’t need to run out and buy Microsoft Office 2030 for Quantum Computers – although Bill Gates, if he’s still alive, might like that.
It may also change the way we do computing. Like times past when computers were very expensive, we may share a large, centralized quantum computer – one that has the capacity to handle quadrillions of transactions. Connections would be via fiber optic connections and personal data – a whole lifetimes worth – could be stored on a quantum USB-type memory the size of a credit card. This would eliminate the need to have millions of PCs that require upgrading every few years.
Don’t expect any of this to happen tomorrow. Scientists are still struggling with some tough problems. Which is the best material from which to make quantum systems? How to check qubit values and not lose the information at the same time? What mechanisms are involved in entanglement? Some experts predict it will be 20 years before we see the first fully functional computers that use quantum materials.
No mater how long it takes, money will continue to flow into research efforts. Silicon-based processors are beginning to near the physical limit of smallness and speed. Intel’s best processors currently fabricated using .15 micron process and run 3GHZ.
One day we may have more processing power than we know what to do with. It will be up to our imaginations – something no computer may ever accurately match - to think of new problems for these enormously powerful machines to solve.
by Philip Dunn, Copyright 2005 PhysOrg.com


Physicists at LMU, together with colleagues at Saarland University, have successfully demonstrated the transport of an entangled state between an atom and a photon via an optic fiber over a distance of up to 20 km – thus setting a new record.
‘Entanglement’ describes a very particular type of quantum state which is not attributed to a single particle alone, but which is shared between two different particles. It irrevocably links their subsequent fates together – no matter how far apart they are – which famously led Albert Einstein to call the phenomenon as “spooky action at a distance”. Entanglement has become a cornerstone of new technologies based on effects at the quantum level and is distribution over long distances a central goal in quantum communication. Now LMU researchers led by physicist Harald Weinfurter, in collaboration with a team at the University of the Saarland in Saarbrücken, have shown that the entangled state of an atom and a photon can be transmitted via an optic fiber (like those used in telecommunications networks) over a distance of up to 20 km. The previous record was 700 meters. “The experiment represents a milestone, insofar as the distance covered confirms that quantum information can be distributed on a large scale with little loss,” says Weinfurter. “Our work therefore constitutes a crucial step toward the future realization of quantum networks.”
Quantum networks essentially consist of quantum memories (made up of one or more atoms, for example) that act as nodes, and communication channels in which photons (light quanta) can propagate to link the nodes together. In their experiment, the researchers entangled a rubidium atom with a photon, and were able to detect the entangled state – which now shares the quantum properties of both particles – after its passage through a 20-km coil of optic fiber.
The biggest problem the experimenters faced start with the properties of the rubidium atom. Following targeted excitation, these atoms emit photons with a wavelength of 780 nanometers, in the near-infrared region of the spectrum. “In an optic fiber made of glass, light at this wavelength is rapidly absorbed,” Weinfurter explains. Conventional telecommunications networks therefore make use of wavelengths around 1550 nanometers, which markedly reduces losses in transit.
Obviously, this wavelength would also improve the experimenters’ chances of success. So Matthias Bock, a member of the group in Saarbrücken, built what is called a quantum frequency converter that was specifically designed to increase the wavelength of the emitted photons from 780 to 1520 nanometers. This task itself posed a number of extremely demanding technical challenges. For it was imperative to ensure that conversion from only a single photon to only one other photon happens and that none of the other properties of the entangled state, especially the polarization of the photon, were altered during the conversion process. Otherwise, the entangled state would be lost. “Thanks to the use of this highly efficient converter, we were able to maintain the entangled state over a much longer range at telecommunications wavelengths, and therefore to transport the quantum information that it carries over long distances,” says Weinfurter.
In the next step, the researchers plan to frequency convert the light emitted by a second atom, which should enable them to generate entanglement between the two atoms over long telecommunications fibers. The properties of glass-fiber cables vary depending on factors such as the temperature and strain to which they are exposed. For this reason, the team intends to first carry out this experiment under controlled conditions in the laboratory. In the event of success, field experiments will be undertaken also adding new nodes to a growing network. After all, even long journeys can be successfully completely by taking one step at a time.
Physical Review Letters, 2020
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
- IBM adds 10 historically Black colleges and universities to quantum computing centeron February 22, 2021 at 5:02 am
The IBM-HBCU Quantum Center announced on Monday that it is adding 10 historically Black colleges and universities to the center's 13 founding institutions. The center was launched last fall with the ...
- Encrypted Quantum Computing: When Ignorance Is Wantedon February 21, 2021 at 3:58 pm
Quantum technologies for computers open up new concepts of preserving the privacy of input and output data of a computation. Scientists from the University of Vienna, the Singapore University of Techn ...
- IBM Reveals Five Year Quantum Development Roadmapon February 18, 2021 at 7:47 pm
Every year we get closer to mainstream use of quantum computers. IBM's Quantum roadmap shows how the company plans to make quantum accessible to more developers.
- Quantum network is step towards ultrasecure interneton February 17, 2021 at 8:29 pm
Physicists have taken a major step towards a future quantum version of the Internet by linking three quantum devices in a network. A quantum internet would enable ultrasecure communications and unlock ...
- BP Joins IBM Quantum Networkon February 17, 2021 at 3:58 am
BP (NYSE: BP) has announced that it has joined the IBM Quantum Network to advance the use of quantum computing in the energy industry. ...
Go deeper with Google Headlines on:
Go deeper with Bing News on:
- China launches cloud-based quantum computing operating system software to challenge U.S. in technologyon February 22, 2021 at 11:31 pm
Quantum computers achieve their immense power by replacing traditional bits with qubits, which can function as both a '1' and a '0' at the same time.
- Researchers create 'beautiful marriage' of quantum enemieson February 22, 2021 at 1:45 pm
Cornell University scientists have identified a new contender when it comes to quantum materials for computing and low-temperature electronics.
- Planet Earth Report –“The Quantum Century to Events That Could Have Ended Humanity”on February 22, 2021 at 6:57 am
Planet Earth Report” provides descriptive links to headline news by leading science journalists about the extraordinary discoveries, technology, people, and events changing our knowledge of Planet ...
- B’luru institute takes big leap in quantum communicationon February 22, 2021 at 4:38 am
Researchers team headed by Urbasi Sinha at the Raman Research Institute (RRI) successfully demonstrated free space quantum distribution key between two ...
- Scientists create ‘beautiful marriage’ of quantum enemieson February 22, 2021 at 3:17 am
Cornell scientists have identified a new contender when it comes to quantum materials for computing and low-temperature electronics. Using nitride-based materials, the researchers created a material ...


Revising Moore's Law
Ever noticed that computers become outdated remarkably quickly? It's nice to have increasingly powerful computers available, and very profitable for the computer industry to have a new model available every couple of years. So how do they manage to make it happen?
Every new generation of computer hardware has roughly twice the processing power of the version two years before it. It's a phenomenon known as Moore's Law and it's held true for nearly 50 years.
But could Moore's Law be coming to an end? Could we be reaching the limit of how fast computer processors can actually be? And if so, what then?
Moore's Law states that the number of transistors that fit on a certain area on a computer chip doubles every two years.
In the past few years, it's become clear that we're reaching the limit of just how small, and just how powerful, we can make processors. As a result, developers are now looking towards radical design changes, using exotic materials, and applying plenty of creative thinking in the quest for solutions.
One of the fields attracting a lot of attention is the study of quantum behaviour of electrons and how this applies to computing.
Existing (or "classical") computer hardware works by storing data in a binary format within transistors. The smallest piece of information – a "bit" – can have one of two states: "off" or "on", "0" or "1".
Quantum computing, on the other hand, allows us to use many physical systems (such as electrons, photons, or tiny magnets) as quantum bits, or "qubits".
These qubits can be engineered to contain the same binary information as classical bits – i.e. "0" or "1" – but, that's not all. Unlike any existing computer, one made of qubits can also encode an exponentially-larger amount of information than a simple binary state.
Let's put this into perspective.
Fourteen bits in your computer's central processing unit (CPU) can contain, well, 14 bits of binary information – 14 pieces of information which are either "0" or "1".
Conversely, 14 qubits in a quantum computer can contain the equivalent of 214 bits of information. That's 16,384 bits, far more than the 14 pieces of binary information possible in a classical system.
Let's take it one step further and use 300 qubits as an example. Three hundred qubits is the equivalent of 2300 classical bits which is approximately the same as the number of particles in the entire universe.
So how can quantum bits store so much more information than classical bits? Well, it's all down to a phenomenon known as quantum entanglement.
A quantum particle is said to be "entangled" with another when its properties are only defined in relation to the other. Two entangled quantum particles could be physically separated, but if you observe them individually you will find correlations between them that cannot be accounted for by assuming they act independently of each other.
It may appear as if acting on one particle influences the other one instantly, even faster than the speed of light.
In reality, the entanglement makes the particles acquire "non-local" properties. No "action at a distance" is required, and the principles of relativity (i.e. no information can be transported faster than the speed of light) are respected.
Odd as this may sound, entangled particles create a distinguishable and legitimate state that can be used as a code to carry additional information without using additional bits.
The availability of these entangled states is the reason quantum bits can encode exponentially more information that classical ones.
While qubits can store an exponentially-greater amount of information than classical bits, quantum computing is still in its infancy.
In fact, at the moment, there are only a few examples where quantum computers can be used to complete tasks more effectively than classical hardware. These include:
- The ability to decipher encrypted information much faster than is currently possible
- The ability to search an unsorted database quickly and effectively.
The most advanced calculation done with quantum bits so far is the factoring of 15 = 3 × 5.
This may seem unimpressive, but it proves that quantum computing can be used in this capacity. With more research and more time, we'll be able to factorise extremely large numbers – ones that are thousands of digits long – in a matter of minutes, rather than the millions of years it would take now.
Given these limitations, it's not true to say that quantum computers will be able to replace existing computers. For one thing, the expected clock speed of a quantum computer is not likely to be any faster than that of a classical one.
Therefore, if we run the the same algorithm on a quantum and on a classical computer, the classical one will usually win. Quantum computers will only be better if an algorithm exists where the presence of entangled quantum states can be exploited to reduce the number of steps required in a calculation.
At this stage we don't know of any quantum algorithm to reduce the complexity of, say, web browsing or text editing, but the search is on.
Regardless of how powerful and widespread quantum computers will be in decades to come, the basic research being undertaken to construct these machines is already very useful in the construction of classical systems.
One of the most promising uses for quantum computing today involves the use of single atoms coupled to silicon transistors. That is, the exact same components used in classical computers but scaled to single atoms.
In this way, many of the things we learn in the pursuit of a quantum computer can be reused for the purpose of pushing classical ones yet a step further in their miniaturisation.
Quantum computing won't provide us with a replacement to classical computers if and when Moore's Law grinds to a halt.
But it will help solve some interesting and challenging problems in computing.
Andrea Morella is a senior lecturer in Quantum Nanosystems at University of New South Wales. This article first appeared in The Conversation on June 2. Republished with permission.


Welcome to Introduction to Quantum Computing. I am your guide, Associate Professor Chris Ferrie, a researcher in the UTS Centre for Quantum Software and Information. This is Lecture 6. It would probably be a good idea to have read the previous Lectures before continuing.
What did you learn last week?
In the last few weeks you completed your basic training. You now know about quantum information, how to write it, how to read it, and how to dial it up to 11 with entanglement. Entangled states were those that could not be written as product states (in any basis!). With multiqubit states and gates, you have all the tools you need to start creating quantum algorithms.What will you learn this week?
This week you will be introduced to the first two canonical quantum protocols: superdense coding and teleportation. These demonstrate that entanglement can be used as a resource for some tasks. You’ll have your first taste as designing protocols as well as analysing them.What will you be able to do at the end of this week 5?
At the end of this module, you should be able to answer these questions:
What is superdense coding?
What is quantum teleportation?
What is entanglement swapping?
How is entanglement a useful resource?
Superdense coding by design
Recall the Holevo theorem: a qubit cannot convey more than one bit of information. But… what if it could? Superdense coding is a communication protocol which allows two bits of information to be sent using a single qubit. How? Entanglement, of course!
Simple communication protocols are usually phrased as a game with players named Alice and Bob. The players can have agreed upon strategies beforehand and are often constrained by what information they can pass to each other. There is always some goal, or win condition, that the players are working toward. In the case of superdense coding, Alice can only send a single qubit to Bob, but must convey two bits of information. They can meet beforehand and agree on some strategy, which they obviously must do since Holevo’s theorem proves that only a single bit of information can be conveyed with the qubit Alice sends.
So, first of all, we know that two qubits are required. But Alice can only send one, and so Bob must possess the other. If the state of the two qubits is a product state, Bob’s qubit contains no information about the bits Alice needs to convey. So, the state must be entangled. We know how to do that with the Hadamard and CNOT gate. The first step in the protocol is for Alice and Bob to create a pre-arranged entangled pair of qubits.
Once they are separated, Alice can only send back her qubit to Bob. Alice needs to perform some unitary on her qubit to encode the bits she wants to send. Whatever she ends up doing to her qubit, call it unitary U, we can see that Bob still needs to perform some action to decode the information contained in the pair. Why? Notice that, after Bob possesses both qubits, the state of the pair is U|0⟩⊗|0⟩ + U|1⟩⊗|1⟩, which is still entangled. To get a definitive answer, Bob has to disentangle the state to reduce it to one of the basis states, which depends on both bits. In other words, he must end up with the state |b₁⟩⊗|b₂⟩. Let’s assume he does this by inverting the original entangling operation.
By working backwards through this computation, we can figure out what Alice needs to do for the whole protocol to work out as desired. That is, we start with |b₁⟩⊗|b₂⟩ and apply the inverse of each operation preceding it. In this case, both the Hadamard and CNOT are self-inverse, so we apply them to get.
Now we need to find some operations of the form (U⊗I), where U depends on b₁b₂, to get back to our initial entangled state |0⟩⊗|0⟩ + |1⟩⊗|1⟩. We’ve done half the work in noticing that a Z gate can apply the phase. But at this point you might be thinking we are stuck since it’s Bob’s qubit that depends on b₂. However, a quick check shows that the symmetry of this entangled state saves us: |0⟩⊗|b₂⟩ + |1⟩⊗|¬b₂⟩ =|b₂⟩⊗|0⟩ + |¬b₂⟩⊗|1⟩. We can then see that the bit flipping X gate will get us back to the correct state.
Putting it all together, the protocol looks like this.
As an exercise, step through each gate of the algorithm to prove the the entire circuits acts as |0⟩⊗|0⟩ ↦|b₁⟩⊗|b₂⟩.
It’s worth pausing here and asking, you know, why? Presumably sending two bits is much easier than sending a qubit to someone. But recalling Holevo’s theorem again, imagine that an eavesdropper intercepted the qubit. Could they decode the two-bit message? No! The most they could learn is one bit. Quantum entanglement enables secure quantum communication.
Superdense coding allowed Alice to communicate two bits with one qubit. Quantum teleportation is in some sense the opposite — it allows Alice to instead communicate one qubit with two bits.This should be surprising — how could Alice communicate a qubit over the telephone with just two bits? Again, the answer is entanglement.
Since it is so similar to superdense coding, I’ll just show you the circuit.
Let’s approach this one from the perspective of verifying the circuit. We need to prove that the state in the first register |𝜓⟩ = 𝛼|0⟩ + 𝛽|1⟩ is the same as the same as the final register at the output. To do so, we step through applying one gate at a time.
Not so bad, right? Now, I know you may be wondering what this has to do with teleportation, as seen on TV. Well, I have nothing to say that this webcomic doesn’t already say about that.
Something cool about the teleportation protocol is that it preserves entanglement. That is, if the qubit Alice wants to send to Bob is entangled with another qubit held by, say, Charlie, the qubit Bob ends up with at the end of the protocol will be entangled with Charlie. In circuit form, it looks like this.
But it gets even better! Imagine now Alice and Bob share an entangled pair, Alice and Charlie share an entangled pair, and Charlie and Diane share an entangled pair. So, we have 6 qubits in total and they are paired off. Alice and Bob perform the teleportation protocol, as do Charlie and Diane. Alice and Charlie measure — and hence collapse — each of the qubits they had initially entangled. Those qubits are individually teleported to Bob and Diane, respectively. But, here’s the kicker — Bob and Diane now share entanglement! This protocol is called entanglement swapping and I’m sure you can imagine how it might be useful in a quantum networking scenario.
IBM’s Qiskit Textbook contains an introductory discussion of both Quantum Teleportation and Superdense Coding in Chapter 3.


Quantum physics—the laws that govern the behavior of smallest components of our universe, such as fundamental particles, atoms and molecules—is admittedly a tough subject, a complicated path of intricate mathematics and scientific theory. Those outside the field who brave the journey often find themselves in a confusing place where the classical principles they learned in school no longer apply and the new rules seem…well…a bit unbelievable. In the quantum world, things can be in two places at once? Better yet, they can be two things at once? What???
If this has been your experience, don’t worry—you’re in very good company. Respected scientists, including Albert Einstein, felt the same way, and made many attempts to prove that these strange new theories couldn’t be correct. Each attempt, however, failed, and instead reinforced the reality of quantum physics in contrast to our conventional intuition. But this is good news—the properties buried in quantum theory hold great promise for exciting, real-world applications.
So how do we make sense of these bizarre new rules? What really makes quantum physics so different, so strange, and so promising? To start, let’s take a look back to 1900 and the work of physicist Max Planck, who first drew back the curtain on the mysterious quantum world.
That year, Planck was embroiled in a nagging physics problem—how to explain the radiation of light emanating from hot objects. At the time, there were two conflicting laws, neither of which was quite right. Sandwiching visible light on the electromagnetic spectrum are infrared waves, which have longer wavelengths and a lower frequency, and ultraviolet waves, which have shorter wavelengths and a higher frequency. One law—Wien’s law—could accurately predict the experimental results of ultraviolet waves, but fell apart when it came to infrared waves. Conversely, the Rayleigh-Jeans law covered infrared waves, but didn’t work for ultraviolet. What Planck needed, then, was one law that would correctly apply to both ends of the spectrum.
For the birth of quantum physics, the details of Planck’s solution to this problem were far less important than the trick he used to arrive at it. This trick, which Planck later on called “happy guesswork,” was simple but unsettling: the radiation energy had to be chopped up into tiny packages, or particles of light. Based on everything physicists knew at the time, this claim was outrageous: light was understood as a wave, which left little space for particles of light, nowadays known as photons. So now light could be…both? While it was not his intent, Planck’s trick was the first step in a chain reaction that turned the physics world upside-down.
We now understand that it’s not just light, but all of the fundamental components of our universe that embrace this dual nature and the other properties of the quantum world. To explain, let’s take another step back, this time to our early science education, and picture electrons—the negatively charged fundamental particles that, together with the positively charged protons and neutral neutrons, make up atoms. Are you picturing them as miniature billiard balls? What about a light wave? Do you imagine it as a tiny version of what comes crashing against the shoreline?
These are convenient pictures, because they are easy to imagine. But what is your evidence that these mental pictures really describe the nature of an electron, and the nature of light? With your sensory perception, you cannot see a single electron, nor observe a light wave oscillate. And, as it turns out, neither light, nor electrons, nor atoms, nor even molecules are simply waves, or just particles.
When it comes to strange quantum properties, this dual wave-particle nature is just the tip of the iceberg. One of the most striking concepts is that of quantum entanglement. It can be illustrated like this: imagine being the proud parent of two children, Susy and Sam, who have just hit the age of disagreeing with each other all the time. They both like mac & cheese as well as pizza. Sadly, this is no longer sufficient to guarantee a drama-free dinner. As a counter strategy, you and your partner team up and question Sam and Susy simultaneously in different rooms. This way, they cannot coordinate their dissent, and you have a 50 percent chance of random agreement on the dinner choice.
Believe it or not, in the quantum world you would be doomed. In an experiment, the two parties could be photons, and the dinner question could be a measurement of their polarization. Polarization corresponds to the direction of oscillation—moving up and down or from side to side—when light behaves as a wave. Even if you separate the two parties, eliminating all communication, quantum physics allows for an invisible link between them known as entanglement. Quantum-Susy might change her answer from day to day (even pizza gets boring after a while), but every single time there is perfect anti-correlation with quantum-Sam’s answer: if one wants pizza, the other opts for mac & cheese—all the time!
This is just one example of the many bizarre properties we know to be true based on careful calculation and experimentation. But if we’re so sure, why do we witness so little of the quantum world?
Much of quantum physics happens at length scales so small that they remain hidden to us, even when using the most powerful microscopes. In addition, witnessing quantum physics at work turns out to be radically different from what you might call an “observation.” Seeing that an object is the color red is a fairly straightforward, unobtrusive process. Probing a quantum object like an electron or photon is an entirely different matter. True quantum behavior tends to be fragile, and attempting to measure it often constitutes a major but unavoidable disruption that usually prevents quantum weirdness from becoming directly visible.
However, just because we cannot see quantum physics in action doesn’t mean that is hasn’t affected our lives in a tangible, positive way. The impact of quantum physics has been enormous: not only is it the prime common factor in nearly all physics Nobel Prizes awarded in the past one-hundred years, but it has also been a crucial driving force in technological advances ranging from lasers and superconductors to medical imaging like MRIs. Indeed, imagining a world in which quantum physics had never been discovered would amount to eliminating a lot of the technology we take for granted each and every day.
The grandest vision, perhaps, is that of harnessing the power of quantum physics for a completely new kind of supercomputer. Such a quantum computer could solve tasks in a heartbeat that would currently require centuries of computation time on the fastest computers available today. Sounds intriguing? Many physicists around the world working on the hardware of such a machine would agree. (To learn more about what would make a quantum computer so powerful, check out the slideshow above.)
They would also explain, however, how daunting the challenges are in this endeavor. Overcoming the fragile nature of quantum behavior is not an easy task—one that rivals the quantum leap of faith taken by Planck and his colleagues to bring us into this new and exciting world.


What is Spintronics?
Spintronics, also known as spin electronics, is an emerging solid-state device technology that exploits the intrinsic spin properties of an electron and its associated magnetic moment, in addition to the electron charge. Conventional electronic and semiconductor devices rely on the transport of electron charge carriers. Whereas, spintronics deal with spin-charge coupling in metallic systems with implications in the efficiency of data storage and transfer. Spintronic systems are of particular interest in the field of quantum computing and neuromorphic computing.
Every electron can exist in one of the two spin states: spin-up and spin-down. In other words, electrons can rotate either clockwise or counterclockwise with constant frequency around its axis. They can represent 0 or 1 in logic operations. In ordinary materials, the spin-up magnetic moments cancel with spin-down magnetic moments and therefore are of no use for spintronics. Ferromagnetic materials are needed for spintronics which can provide a surplus accumulation of different spins in a tiny region called domains. These majority-up and majority-down domains are randomly scattered and with an externally applied magnetic field will line up the domains in the direction of the electric field.
Spintronics is the driving technology behind next-generation nano-electronic devices to increase their memory and processing capabilities while reducing power consumption. In these devices, the spin polarization is controlled either by magnetic layers or via spin-orbit coupling. Spin waves, also known as magnons, can be used to carry spin current without causing heat. Spintronics is also used in the semiconductor industry to manufacture different types of transistors, lasers and integrated magnetic sensors.
The miniaturization of microelectronic components is a basic necessity for semiconductor devices. However, over the years of miniaturization, the physical size of semiconductor electronics will soon approach a fundamental barrier. Therefore, device engineers and physicists feel that quantum mechanics can help in future miniaturization. After all, electronic spin is a quantum phenomenon. Spintronics combined with nanotechnology can be a perfect solution for the future miniaturized devices.
Types of Spintronics
- Metal-based spintronics: Giant-magneto resistance (GMR) in magnetic (metal) multilayers was discovered in 1988 and led to the birth of spintronics. GMR based metal spintronics became the standard technology for read-heads of hard disk drives. Later, a large tunnel-magnetoresistance (TMR) between two magnetic metals separated by a thin insulator was demonstrated at room temperature in 1994. Magnetic tunnel junction (MTJ) is currently the preferred choice for manufacturing of magnetic random-access memory (MRAM) devices.
- Semiconductor based spintronics: Despite rapid advancement in metal-based spintronics, a major focus was to find novel ways to generate and utilize spin-polarization currents in semiconductors. Doped semiconductor materials display dilute ferromagnetism, and strong ferromagnetism is essential for achieving spintronics. The selection of materials for semiconductor spintronics depends on the ability of the material to provide ferromagnetism at room temperature. Majority of the work is focussed on the use of GaAs (Gallium Arsenide) and InAs (Indium Arsenide) at semiconductor-semiconductor or semiconductor-metal interfaces. Spins in semiconductors can be easily manipulated and controlled. Spintronics based devices can easily integrate with existing semiconductor technology. Semiconductor spintronics combined with photonics and magnetics can provide multi-functional devices such as spin-transistors, spin-LEDs, memory devices, optical switches operating at terahertz frequencies and few other devices.
There are different ways to create spin polarisation and harness spin degree of freedom in metals and semiconductors. Few important ways are listed below.
- Spin-injection from a ferromagnetic material.
- A stray field (magnetic or electric) can induce population difference of spin polarised electrons in ferromagnetic materials.
- Electromagnetic waves such as circularly polarized light and microwave excite spin polarised electrons in semiconductors depending on optical selection rule. A spin-polarised electron current can be extended further to spin generation by electromagnetic waves, including spin pumping and high-frequency spin induction.
- A thermal gradient can also produce spin polarised carrier flow using the spin Seebeck and Nernst effect, and this can be useful in energy harvesting.
There are many spintronic based devices in the market ranging from transistors, oscillators, memory units to quantum computing. A few prominent devices are listed below.
- Spin transistor: The basic idea of a spin transistor is to control the spin orientation by applying a gate voltage. A spin field-effect transistor (FETs) consists of ferromagnetic source and drain electrodes, a semiconductor channel that contains a layer of electrons, and a gate electrode attached to the semiconductor. The spin-polarised electrons are injected from the source electrode. The spin precision angle controls the flow of current. The gate electrode controls the rotation of the electron spin after entering the semiconductor channel. The success of these transistors depends on efficient injection of spin currents from a ferromagnetic metal to a semiconductor.
- Quantum dots or Spin-based computers: In quantum dots, electron motion is quantized in all directions and conducting electrons are confined within the nano-meter distances. The charge and spin of electrons can be controlled in these dots. The spin of an electron confined to quantum dots can be used as quantum bits, and these arrays of quantum dots can serve to build quantum computers. Already, quantum dots are useful in electronic and optic devices such as quantum-dot lasers, memory chips, and also in quantum cryptography.
- Hard disk drive (HDD) read head: GMR based HDD was introduced by IBM in 1997. Later, TMR based HDD was introduced by Seagate in 2005. A new head assisted magnetic recording (HAMR) drive was demonstrated by Seagate in 2012.
- Magnetic Sensors: Magnetic sensors are used to detect position, angle, rotation and magnetic fields. These sensors are built mainly based on Hall, GMR and AMR effects. A highly sensitive magnetic sensor is used in magnetoencephalography to map the brain.
Spintronics is at the verge of becoming a major technology for microelectronics. Many devices have started entering the market with a recent launch of magnetic memory production at a large scale. However, there is a need for further improvements in spintronic device applications, and few are noted below.
- Development of low power devices
- Unconventional computing such as stochastic computing using spintronic devices
- Energy harvesting using spin-diodes or spin-caloritronics
- Need for the development of artificial neurons and synapses based on spintronic devices.


Google’s recent announcement that its quantum computer had achieved “quantum supremacy” has garnered significant global attention. And for good reason. Sycamore, Google’s 53-bit quantum computer reportedly performed a 200-second calculation in the time it would have taken the world’s fastest supercomputer, the IBM Summit, 10,000 years. Beyond conventional silicon computers, quantum computers represent a new era in the evolution of computational technology. Nonetheless, the challenges confronting the field suggest that there is a very long way to go.
Born out of the thinking of Max Planck, Niels Bohr, and Albert Einstein, quantum theory offers new and unexplored potential for driving the evolution of computer science. Quantum computers operate on completely different principles compared to their conventional counterparts. Where classical computers are fast and efficient, they are simply not very good at problems that involve exponential complexity. Quantum researchers utilize the properties of electrons as an engine for performing exponentially fast calculations.
Quantum computers are expected to transform cryptography, pattern matching, drug discovery and ultimately boost artificial intelligence (AI) training. However, the current generation of quantum computers are extremely sensitive to perturbations, noise, and other environmental effects that can cause their “quantum state” to waver and disappear— an effect referred to as decoherence.
Contemporary quantum computers require exacting demands of stability and temperature for maintaining quantum states. In fact, researchers have only been able to maintain a quantum state for a tiny fraction of a second— not long enough to carry out a useful algorithm. This instability remains the biggest challenge facing quantum computing.
Designing a quantum computer with qubits
Research on quantum computing remains at a very early stage. Much like the traditional computers introduced in the 1950s, quantum computers remain big, clunky machines. The most common design of quantum computers rely on multiple layers of superconducting circuits sequestered in a controlled environment and cooled step-wise to temperatures colder than deep space.
Where a conventional computer uses transistors as a substrate for information processing, quantum computers can use anything that demonstrates quantum behavior. This can include an atom, a molecule, or more commonly, an electron. Due to “superposition”, quantum computers can perform multiple calculations at once, giving them the potential to be exponentially more powerful than conventional computers.
Superposition is best understood as the capacity for electrons to be at different positions at the same time. Quantum computers leverage the superposition of quantum states to manage calculations on orders of magnitude faster than silicon processors. As demonstrated by the famous double-slit experiment involving a single photon of light, photons may produce a wavelike interference pattern or superposition of all available paths.
The most common quantum computers today leverage electrons to move beyond the binary logic of silicon computing. In conventional computing, information is stored as bits and exist as either ones or zeros. Unlike a conventional bit, the quantum bit or qubit can store and manipulate much more information than just ones and zeros. For example, A 10-qubit quantum computer can process 1,024 possible inputs at once (instead of analyzing them one at a time).
The magic of qubits is that they can exist in superposition, or in multiple states at once. Using the example of Schrödinger’s cat, any given qubit can hold a 0 and a 1 at the same time. Thus, a single qubit can represent far more information than a binary bit. As an example, a four-qubit computer register can hold 16 different numbers simultaneously.
Using code to manipulate electrons, many engineers are hoping to develop quantum algorithms to exploit the vast computational potential of quantum computers. Generally, the goal is to encode parts of a problem into a complex quantum state using qubits. Then, manipulating that state in order to drive it towards something that will eventually represent the solution. Solutions can be measured by collapsing the superpositions into deterministic sequences of zeros and ones.
The race for high-performance quantum computers
Quantum computers hold the promise of virtually limitless supercomputing power, pushing the envelope on supercomputing or high-performance computing (HPC). However, the current state of noisy quantum computers have a coherence time of a mere 100 microseconds. This is the maximum length of time in which an experiment can be run on a quantum processor before errors take over.
The most common quantum computer designs today consist of superconductor computers and spin computers. Superconductors are the most well-established method for maintaining a quantum state: Metallic superconductors are used at near-zero temperatures in order to conduct electrons. Electrons must be free from all radiation or light particles and kept at a freezing temperature. Google’s quantum computer, for example, is cooled to an astonishing 460 degrees below zero.
The more recent spin method of quantum computing uses a single electron within silicon to create qubits. Only a few nanometers in size, these electrons are called quantum dots and can operate at higher temperatures. In fact, a new silicon chip capable of manipulating the spin of a single electron could ultimately allow future quantum computers to be built using conventional electronic technology.
Thanks largely to research by IBM, Google, Microsoft and others, the United States remains the leader in patents related to quantum computers. In the future, quantum computers are expected to become very good at highly specific problem-solving. Quantum computing performs best in probabilistic situations such as weather prediction, market forecasting, and breaking encryption.
In the U.S., IBM and Google are racing to create the first truly useful quantum computer. In July 2016, Google engineers used a quantum device to simulate a hydrogen molecule. IBM is also working on developing quantum computing technologies and recently introduced the IBM Quantum Experience, a quantum computing platform delivered via the Cloud. Since 2016, IBM has provided researchers with a five-qubit cloud-based quantum computer and made its 20-qubit system available online at the end of 2017.
In addition to IBM and Google, D-Wave, a Canadian company based in Vancouver has also been a leader in developing an early-stage quantum computer. D-Wave utilizes a method known as quantum annealing. Running adiabatic quantum computing algorithms, D-Wave’s machine finds a “good enough” or “local minima” solution. Volkswagen has leveraged D-Wave’s quantum annealing technology, using it to carry out research on traffic flow optimization in Beijing with 2,000 qubits.
One very promising application of quantum technology is quantum communications. Researchers are working towards creating ultra-secure communication networks that could form the basis of a quantum internet. Where sensitive data is currently encrypted and transmitted using digital “keys” (1 and 0s), quantum communications has already demonstrated the capacity to secure encrypted information using qubits. Quantum key distribution (QKD), for example, combines digitally encrypted data with keys that are encoded and transmitted using quantum state using qubits.
China has become a global leader in the drive to develop quantum communication technologies. Pouring vast sums of money into quantum research, China filed almost twice as many patents as the United States in the field of quantum technology in 2017 alone. That same year, the country launched a dedicated quantum communications satellite called Micius, staging the world’s first quantum key distribution-secured video conference between Beijing and Vienna.
An arcane field only a decade ago, quantum computing has matured at an astonishing pace. As countries around the world continue to move the needle on supercomputing, we will likely see revolutionary applications in the field of quantum technology. Nonetheless, the mainstream application of quantum computing remains decades away. Quantum computing represents a revolution in computational technologies; that goes without saying. But there remains significant work ahead.


A team of researchers at The University of Texas at Austin and the University of California, Riverside have found a way to produce a long-hypothesized phenomenon—the transfer of energy between silicon and organic, carbon-based molecules—in a breakthrough that has implications for information storage in quantum computing, solar energy conversion, and medical imaging. The research is described in a paper out today in the journal Nature Chemistry.
Silicon is one of the planet’s most abundant materials and a critical component in everything from the semiconductors that power our computers to the cells used in nearly all solar energy panels. For all of its abilities, however, silicon has some problems when it comes to converting light into electricity. Different colors of light are comprised of photons, particles that carry light’s energy. Silicon can efficiently convert red photons into electricity, but with blue photons, which carry twice the energy of red photons, silicon loses most of the energy as heat.
The new discovery provides scientists with a way to boost silicon’s efficiency by pairing it with a carbon-based material that converts blue photons into pairs of red photons that can be more efficiently used by silicon. This hybrid material can also be tweaked to operate in reverse, taking in red light and converting it into blue light, which has implications for medical treatments and quantum computing.
“The organic molecule we’ve paired silicon with is a type of carbon ash called anthracene. It’s basically soot,” said Sean Roberts, a UT Austin assistant professor of chemistry. The paper describes a method for chemically connecting silicon to anthracene, creating a molecular power line that allows energy to transfer between the silicon and ash-like substance. “We now can finely tune this material to react to different wavelengths of light. Imagine, for quantum computing, being able to tweak and optimize a material to turn one blue photon into two red photons or two red photons into one blue. It’s perfect for information storage.”
For four decades, scientists have hypothesized that pairing silicon with a type of organic material that better absorbs blue and green light efficiently could be the key to improving silicon’s ability to convert light into electricity. But simply layering the two materials never brought about the anticipated “spin-triplet exciton transfer,” a particular type of energy transfer from the carbon-based material to silicon, needed to realize this goal. Roberts and materials scientists at UC Riverside describe how they broke through the impasse with tiny chemical wires that connect silicon nanocrystals to anthracene, producing the predicted energy transfer between them for the first-time.
“The challenge has been getting pairs of excited electrons out of these organic materials and into silicon. It can’t be done just by depositing one on top of the other,” Roberts said. “It takes building a new type of chemical interface between the silicon and this material to allow them to electronically communicate.”
Roberts and his graduate student Emily Raulerson measured the effect in a specially designed molecule that attaches to a silicon nanocrystal, the innovation of collaborators Ming Lee Tang, Lorenzo Mangolini and Pan Xia of UC Riverside. Using an ultrafast laser, Roberts and Raulerson found that the new molecular wire between the two materials was not only fast, resilient and efficient, it could effectively transfer about 90% of the energy from the nanocrystal to the molecule.
“We can use this chemistry to create materials that absorb and emit any color of light,” said Raulerson, who says that, with further fine-tuning, similar silicon nanocrystals tethered to a molecule could generate a variety of applications, from battery-less night-vision goggles to new miniature electronics.
Other highly efficient processes of this sort, called photon up-conversion, previously relied on toxic materials. As the new approach uses exclusively non-toxic materials, it opens the door for applications in human medicine, bioimaging and environmentally sustainable technologies, something that Roberts and fellow UT Austin chemist Michael Rose are working towards.
At UC Riverside, Tang’s lab pioneered how to attach the organic molecules to the silicon nanoparticles, and Mangolini’s group engineered the silicon nanocrystals.
“The novelty is really how to get the two parts of this structure—the organic molecules and the quantum confined silicon nanocrystals—to work together,” said Mangolini, an associate professor of mechanical engineering. “We are the first group to really put the two together.”
The paper’s other authors include Devin Coleman and Carter Gerke of UC Riverside.
Reference: “Achieving spin-triplet exciton transfer between silicon and molecular acceptors for photon upconversion” by Pan Xia, Emily K. Raulerson, Devin Coleman, Carter S. Gerke, Lorenzo Mangolini, Ming Lee Tang and Sean T. Roberts, 2 December 2019, Nature Chemistry.
Funding for the research was provided by the National Science Foundation, the Robert A. Welch Foundation, the Research Corporation for Science Advancement, the Air Force Office of Scientific Research and the Department of Energy. Additionally, Raulerson holds the Leon O. Morgan Graduate Fellowship at UT Austin.


As engineers and researchers work on developing and perfecting their machine learning and AI algorithms, the end goal is ultimately to recreate the human brain. The most perfect AI imaginable would be able to process the world around us through typical sensory input but leverage the storage and computing strengths of supercomputers.
With that end goal in mind, it's not hard to understand the ways that AI is evolving as it continues to be developed. Deep learning AI is able to interpret patterns and derive conclusions. In essence, it's learning how to mimic the way that humans process the world around us.
That said, from the onset, AIs generally need typical computer input, like coded data. Developing AIs that can process the world through audio and visual input, sensory input, is a much harder task.
In order to understand artificial intelligence in the context of a perception-based interface, we need to understand what the end goal is. We need to understand how the brain is modeled and works.
Our brain from a computer's perspective
Our brains are essentially the world's most powerful supercomputers, except for the fact that they're made out of organic material, rather than silicon and other materials.
Our right brain is largely perception-based, it's focused on the interpretation of environmental inputs like taste, feel, sound, sight, etc. Our left brain, on the other hand, is focused on rational thought. Our senses provide patterns to our right brain, and to our left brain, those senses provide the rationale for decision making. In a sense, we have two AIs in our head that work together to create a logical, yet also emotionally swayed machine.
Human intelligence and our definition of what an intelligent thing is all drawback to how we ourselves process the world. In order for artificial intelligence to truly succeed, that is to be the best version of itself that it can be, then it needs to be intelligent from a human perspective.
All this draws back to modern AI in a simple way, AI is programmed in how to make a decision. Machine learning algorithms allow code to be pseudo-organically generated so that algorithms can "learn" in a sense. All of this programming is based on reasoning, on "if, then, do this."
Arguably, our brain's decision-making process is just as much based on emotions and feeling as it is reason. Emotional intelligence is a significant portion of what makes intelligence. It's the ability to read a situation, to understand other human's emotions and reactions. In order for AIs to evolve and be the best possible algorithm, they need to be able to process sensory input and emotion.
Integrating emotional & human intelligence into modern AI
Most artificial intelligence systems are primarily created on the foundation of deep learning algorithms. This is the means of exposing a computer program to thousands of examples and AI learning how to solve problems through this process. Deep learning can be boiled down to teaching a computer how to be smart.
After any given deep learning phase for an AI, the system can perceive the inputs that it was trained on and make decisions therein. The decision-making tree that the AI forms from traditional deep learning mimics the way the right side of our brain works. It is based on the perception of inputs, of pseudo-senses.
Deep learning is a way of getting computers to reason, not just with if-then statements, but through the understanding of the situation. That said, the current situations AI are being trained on aren't as complex as interpreting a conversation with Becky to see if she's into you. Rather it's more along the lines of is this a dark cat, a black bag, or the night sky. Primitive, but still sensory perception...
While deep learning is currently heavily focused on one pathway, meaning AIs are developing specialties, eventually it won't be too far fetched to start training AIs on multiple things at once. Just like a toddler might learn colors and numbers at the same time. Expanding this out, as computer processing power grows, perhaps accelerated by practical quantum computing, there's no question that AIs will evolve to become more human.
Understanding what this all means
Advanced AI will continue to deal with understanding and processing patterns from the world around us. Through this, it will develop more complex models on how to process that information. In a sense, AIs are like toddlers, but soon they're going to be teenagers, and eventually, they may graduate with a doctorate. All figuratively of course... though, an age where an AI graduates a university probably isn't that far off.
When we think about intelligent humans, we usually think of the most rationally minded people. Yet, we miss out on what is so unique about human intelligence – creativity. In a sense, we take for granted our creativity, yet it is the thing that makes us the most intelligent of living beings. Our ability to process situations, not just understand what the sum of two numbers is, is what makes us uniquely intelligent. So uniquely intelligent that we can design and create artificially intelligent beings that will soon be able to match our human intelligence.
While modern AIs are primarily focused on singular strands of intelligence, whether that be finding which picture contains a bicycle or which email is spam, we're already training AIs to be all-around smart, humanly smart.


In a paper slated for the July issue of Nature Physics, researchers at MIT, MIT Lincoln Laboratory, Japan’s Institute of Physical and Chemical Research and NEC describe a new technique that extends the time a qubit can stay in superposition. Perhaps even more important, the same technique can be used to measure the physical characteristics of qubits that knock them out of superposition in the first place, paving the way to better qubit designs.
Researchers have sought to realize qubits in a variety of ways, from test tubes full of molecules sandwiched between powerful magnets to trapped ions manipulated by lasers. The MIT researchers and their colleagues instead used a superconducting circuit, made from several layers of aluminum deposited on a silicon wafer and cooled to just a fraction of a degree above absolute zero. Because of weird quantum effects, current flow through the circuit can be in superposition: The current is, in effect, flowing clockwise and counterclockwise at once.
Before this new paper, the previous published record for keeping a superconducting qubit in superposition was less than 10 microseconds. By repeatedly zapping their qubit with microwave radiation, however, Jonas Bylander and Simon Gustavsson, both postdocs at MIT’s Research Laboratory of Electronics (RLE), and their colleagues were able to keep it alive for 23 microseconds. That may not sound like a very long time, but it’s much closer to the threshold qubits need to cross in order to perform useful computations.
Margin of error
Like a conventional computer program, a quantum computer program would be a long series of simple mathematical operations. What determines the minimum lifetime of a qubit, though, is not the time it takes to perform an operation but the time it takes to ensure that it performed the operation correctly.
“Just as people had done in the ’50s for computer science with conventional classical computation, people have developed what are called error-correcting codes that correct for the errors that occur in these qubits,” says William Oliver, the senior staff member at Lincoln Laboratory and visiting scientist at RLE who led the new study. “To make those error-correction codes feasible, the qubit has to have some minimum lifetime. You could think of the error-correcting code itself as some kind of processing that you need to do to ensure the operation is performed correctly.”
The main threat to the superposition of a qubit is the type of unwanted disturbance that electrical engineers call “noise.” It could be electrical noise from the cables used to program the qubit; it could be heat, or thermal noise (which cooling is intended to prevent); it could even be the electrical properties of impurities in the materials that constitute the qubit itself. By carefully controlling the rate at which they fire microwaves at the qubit, the researchers can filter out noise that occurs outside a narrow frequency band, preventing the qubit from falling out of superposition.
By changing the rate at which they fire the microwaves, however, Bylander, Gustavsson and their colleagues can also measure exactly how much noise the qubit experiences within any given frequency band. Knowing the frequency profile of the noise could help physicists identify its sources and determine how to mitigate it. The technique could also be applied to other types of qubits, not just those that use superconducting circuits.
One key to the system is carefully tailoring the shape of the microwave pulses so that firing them frequently won’t cause computational errors in the qubit. Compounding the problem is that the signal that controls the pulses has to travel to microwave emitters inside the refrigeration tank that helps cool the qubit. “You send some pulse down, [but] it might look different at the sample, because of imperfections in the wires,” Gustavsson says. Gustavsson found a way to “predistort” the signal so it would have the proper shape when it reached the qubit.
Not only do the microwave pulses extend the qubit’s lifetime, but in an actual quantum computer, they would also instruct the qubits in the execution of their error-correcting code. The complexity of that code would vary according to the algorithm that the quantum computer is running, but it would probably have somewhere around 10,000 separate instructions. In the Nature Physics paper, the researchers report hitting their qubit with 250 microwave pulses. They say, however, that since the experiments reported in the paper, they’ve refined their system so that they can get in about 1,000 pulses before their qubit falls out of superposition.
Yet Patrice Bertet, who researches superconducting quantum circuits for the French Atomic Energy Commission, says that using microwaves to extend the lifetime of a superconducting qubit is not the most interesting aspect of Bylander and his colleagues’ new paper. “It is an additional tool that has never been used before, and it’s a nice experiment, it’s a very good experiment,” Bertet says. But he points out that it is simply an extension of a technique that has been used successfully on other types of qubits. More intriguing, he says, is that “they are able to provide a rather detailed spectrum of the noise that the flux qubit sees.” “When the microscopic details [of noise] are a bit clearer, it might help fix it or fight it,” Bertet says. “It’s not yet clear how, but nevertheless, it’s good to know what enemy you fight.”


Using two flat-top diamonds and a lot of pressure, scientists have forced a magnetic crystal into a spin liquid state, which may lead to insights into high-temperature superconductivity and quantum computing.
It sounds like a riddle: What do you get if you take two small diamonds, put a small magnetic crystal between them and squeeze them together very slowly?
The answer is a magnetic liquid, which seems counterintuitive. Liquids become solids under pressure, but not generally the other way around. But this unusual pivotal discovery, unveiled by a team of researchers working at the Advanced Photon Source (APS), a U.S. Department of Energy (DOE) Office of Science User Facility at DOE’s Argonne National Laboratory, may provide scientists with new insight into high-temperature superconductivity and quantum computing.
Though scientists and engineers have been making use of superconducting materials for decades, the exact process by which high-temperature superconductors conduct electricity without resistance remains a quantum mechanical mystery. The telltale signs of a superconductor are a loss of resistance and a loss of magnetism. High-temperature superconductors can operate at temperatures above those of liquid nitrogen (-320 degrees Fahrenheit), making them attractive for lossless transmission lines in power grids and other applications in the energy sector.
But no one really knows how high-temperature superconductors achieve this state. This knowledge is needed to increase these materials’ operating temperature towards ambient temperature, something that would be required for full-scale implementation of superconductors in energy-conserving power grids.
“A quantum spin liquid is a superposition of spin states, fluctuating but entangled. It’s fair to say that this process, should it create a quantum spin liquid with quantum superposition, will have made a qubit, the basic building block of a quantum computer.” — Daniel Haskel, physicist and group leader, XSD
One idea put forth in 1987 by the late theorist Phil Anderson of Princeton University involves putting materials into a quantum spin liquid state, which Anderson proposed could lead to high-temperature superconductivity. The key is the spins of the electrons in each of the material’s atoms, which under certain conditions can be nudged into a state where they become “frustrated” and unable to arrange themselves into an ordered pattern.
To relieve this frustration, electron spin directions fluctuate in time, only aligning with neighboring spins for short periods of time, like a liquid. It is these fluctuations that may aid in the electron pair formation needed for high-temperature superconductivity.
Pressure provides a way to “tune” the separation between electron spins and drive a magnet into a frustrated state where magnetism goes away at a certain pressure and a spin liquid emerges, according to Daniel Haskel, the physicist and group leader in Argonne’s X-ray Science Division (XSD) who led a research team through a series of experiments at the APS to do just that. The team included Argonne assistant physicist Gilberto Fabbris and physicists Jong-Woo Kim and Jung Ho Kim, all of XSD.
Haskel is careful to say that his team’s results, recently published in Physical Review Letters, do not conclusively demonstrate the quantum nature of the spin liquid state, in which the atomic spins would continue to move even at absolute zero temperatures — more experiments would be needed to confirm that.
But they do show that, by applying slow and steady pressure, some magnetic materials can be pushed into a state similar to a liquid, in which the electron spins become disordered and magnetism disappears, while preserving the crystalline arrangement of the atoms hosting the electron spins. Researchers are confident they have created a spin liquid, in which the electron spins are disordered, but are not certain if those spins are entangled, which would be a sign of a quantum spin liquid.
If this is a quantum spin liquid, Haskel said, the ability to create one by this method would have wide implications.
“Some types of quantum spin liquids can enable error-free quantum computing,” Haskel said. “A quantum spin liquid is a superposition of spin states, fluctuating but entangled. It’s fair to say that this process, should it create a quantum spin liquid with quantum superposition, will have made a qubit, the basic building block of a quantum computer.”
So what did the team do, and how did they do it? That brings us back to the diamonds, part of a unique experimental setup at the APS. Researchers used two diamond anvils, cut in a similar way to what you’d see in jewelry stores, with a wide base and a narrower, flat edge. They positioned the smaller flat edges together, inserted a sample of magnetic material (in this case a strontium-iridium alloy) between them, and pushed.
“The idea is that as you pressurize it, it brings the atoms closer together,” said Fabbris. “And since we can do that slowly, we can do that continuously, and we can measure the properties of the sample as we go up in pressure.”
When Fabbris says that pressure was applied slowly, he isn’t kidding — each one of these experiments took about a week, he said, using a sample of about 100 microns in diameter, or about the width of a thin sheet of paper. Since researchers didn’t know at what pressure magnetism would disappear, they had to carefully measure with each very slight increase.
And see it disappear they did, at around 20 gigapascals — equivalent to 200,000 atmospheres, or about 200 times more pressure than can be found at the bottom of the Mariana Trench in the Pacific Ocean, the deepest trench on Earth. The spins of the electrons remained correlated over short distances, like a liquid, but remained disordered even at temperatures as low as 1.5 Kelvin (-457 degrees Fahrenheit).
The trick, Haskel said — and the key to creating a spin liquid state — was to preserve the crystalline order and symmetry of the atomic arrangement, since the unwanted effect of random disorder in atomic positions would have led to a different magnetic state, one without the unique properties of the spin liquid state. Haskel likens the electron spins to neighbors on a city block — as they get closer, they all want to make each other happy, changing their spin direction to match their neighbors’. The goal is to get them so close together that they cannot possibly keep all of their neighbors happy, thereby “frustrating” their spin interactions, while still maintaining the structure of the city block.
The research team used the intense X-ray imaging capabilities of the APS to measure the magnetism of the sample, and according to Haskel and Fabbris, the APS is the only facility in the United States where such an experiment could be done. In particular, Fabbris said, the ability to focus in on one type of atom, ignoring all others, was crucial.
“The samples are very small, and if you try to measure magnetism with other techniques in a university lab, you will pick up the magnetic signal from components in the diamond anvil cell,” Fabbris said. “The measurements we did are impossible without a light source like the APS. It is uniquely capable of this.”
Now that the team has achieved a spin liquid state, what’s next? More experimentation is needed to see if a quantum spin liquid has been created. Future experiments will involve probing the nature of spin dynamics and correlations more directly in the spin liquid state. But the recent results, Haskel said, provide a path for realizing these elusive quantum states, one that could lead to new insights into superconductivity and quantum information sciences.
Haskel also pointed forward to the APS Upgrade, a massive project that will see the instrument’s brightness increased up to 1,000 times. This, he said, will allow for much deeper probes into these fascinating states of matter.
“It’s up to anyone’s imagination which surprising quantum mechanical effects are waiting to be discovered,” he said.
Reference: “Possible Quantum Paramagnetism in Compressed Sr2IrO4” by D. Haskel, G. Fabbris, J. H. Kim, L. S. I. Veiga, J. R. L. Mardegan, C. A. Escanhoela, Jr., S. Chikara, V. Struzhkin, T. Senthil, B. J. Kim, G. Cao, and J.-W. Kim, 11 February 2020, Physical Review Letters.


If you are looking for some great science websites for interactive learning, then these eleven plus sites should, at the very least, scratch and itch. Most of these are aimed at younger learners but some will be as, if not more, entertaining for adults.
1. Khan Academy is great for people of all ages
Khan Academy is one of the best resources for STEM learning on the web. And, guess what? It is free. This interactive website is filled to the brim with fantastic content led by professionals and teachers who are experts on the content, with the occasional STEM celebrity appearances.
There is not that much gamification on this website. Most of the learning is done through fun interactive quizzes. The site is perfect if you need to build on the current topics you are learning from at school or are an adult. Khan Academy has courses for every level, from elementary school to college.
2. Curiosity Machine will teach you about AI
Curiosity Machine helps children build, share, and receive feedback from experts. Its main focus is on teaching children, and their parents, about the power of Artificial Intelligence.
Its main focus to bring family members together to learn and build their own AI.
It has a specific “Family Challenge” which is a “free, hands-on AI education program that brings families, schools, communities, and technology know-it-alls together to give everyone the chance to learn, play and create with AI.”
Families will be guided through the basics of AI and are then encouraged to look around their local communities for potential problems to solve using their new skills. Proposals can then be submitted to win the competition.
3. Teachers TryScience is full of online experiments
Teachers TryScience is a website specifically designed to spark any young mind’s interest in science, technology, engineering, and math. At its very core, it aims to bring design-based learning to children at home or at school.
According to the website, it helps children “to solve a problem in environmental science, students might need to employ physics, chemistry, and earth science concepts and skills.”
To this end, it has a large collection of interactive experiments, field trips, and other adventures. It also includes lesson plans, strategies, and tutorials for teachers to better help them deliver awe-inspiring science lessons for their ever-curious students.
4. The Exploratorium is the go-to site for interactive learning
The Exploratorium is the website arm of the San Francisco Exploratorium. This site offers hands-on experiences that will help teach children about basic, and more complex, scientific principles.
It covers subjects from many disciplines of science from biology and earth science to astronomy. The site also has a parent and teacher section that will provide free resources to help you plan and incorporate its interactive material to boost your child’s learning.
5. Science Kids will engage your kid’s mind
Science Kids is another interactive learning website that focuses on teaching children the wonders of science. The site has a great variety of interactive science games covering subjects from living things to physical processes and everything in between.
The great thing about this site’s content is that it not only educates young minds but helps them put that knowledge to practical use to cement it in their memory. One particularly useful game will have your child design and build a virtual electrical circuit.
Each subject comes in modules that are then subdivided into subcategories. Living things, by way of example, is divided into food chains, microbes, and the human body, etc.
6. BrainPOP will do just that
BrainPOP is the place for science learning and it’s very well designed to boot. It is a very active site for young students with a myriad of animations, movies, and short interactive quizzes.
It covers topics like cellular life and genetics, ecology and behavior, forces of nature, our fragile environment, scientific inquiry, and paleontology and anthropology. So young aspiring scientist is bound to find something that will spark their interest.
It also has some interactive coding lessons which are always fantastic ways to learn something they might not normally be exposed to. The site will have them hacking government websites in no time – only joking of course.
7. HHMI Biointeractive – it’s in the name
HHMI‘s website is full of great 3-D interactive, virtual labs, and printable activities for you to use. Its material is both engaging and interesting for science-buffs of all ages.
These guys are famed for their award-winning virtual labs and high-quality informative videos so you know you are in good hands. Their site includes “Click & Learn” activities that include embedded video clips and animations, videos all of which have stop points and assessments to help check you’ve been paying attention.
8. Annenberg Learner Interactives is a great resource for Earth Science students
Annenberg Learner Interactives‘ Earth Science-related topics are full of great and easy-to-understand graphics and other interactive content. It has a good collection of interactive lessons cover the big things like the Earth’s structure to plate tectonics.
The site also covers many other subjects within Earth Sciences, such as the Rock Cycle and Volcanoes, which really makes this subject come alive to any young student. It also has other resources for other scientific subjects with interactive games and other lessons.
9. National Geographic Kids is fun and educational
Being created by National Geographic you know you can trust this site to be top quality. And it doesn’t disappoint.
This site includes a large collection of videos, interactive activities, and fun games that will keep children of all ages engaged for hours on end.
National Geographic Kids‘ site is broken down into helpful subcategories for ease of navigating your child’s learning. Each section contains extensive and informative write-ups on different animals from lions to whales supported with world-class National Geographic footage.
Each section also includes memory games, quizzes, and other different activities to reinforce their learning by applying their new-found knowledge.
10. PhET Interactive Simulations is all about Physics simulations
PhET Interactive Simulations is a real gem of an interactive and fun science-related website. Built and run by the University of Boulder, Colorado it has a vast collection of simulators covering most topics with physics from circuits to waves to quantum mechanics.
Be warned, however, you might find yourself aimlessly playing around with variables without noticing hours of your precious time have passed by. Do not, we repeat do not, try the spring simulation it is too much fun.
It also has some materials covering Earth Science, chemistry, and life sciences but these are far less extensive.
11. Wonderville is great for all ages
Wonderville is another great science-related website that is packed with interactive activities for children.
According to the website Wonderville “makes learning science fun for kids. We help teachers teach and students learn. Used in 170 countries, our awarding-winning STEM content helps create lifelong learners.”
Other than fun and entertaining games it also has a very good blog for the more curious children who want to go deeper into a subject.
Adults love using Brilliant.org. The interactive games on this website do not try to teach you through memorization. The Brilliant team is dedicated to teaching you how to think critically about STEM topics. From Geometry to Quantum Computing, this website is an excellent way to spend your free time, if you are a dedicated life-long learner. Scientific Thinking is one of our favorite courses on Brilliant.Org.
13. The Raspberry Pi Foundation
Raspberry Pi is a powerful but tiny affordable computer that can be used to do everything from creating your own DIY projects at home to learning programming. The mini-computer is great for both kids and adults interested in getting into the science of computing and programming.
The projects pages have a wide range of projects for people of any age. You will have to get your hands on one of the many Rasberry Pi computers to get started. But they are cheap!