Five years ago, teams of physicists at Harvard University caused quite a sensation when they demonstrated that light pulses could be drastically slowed down and even brought to a standstill, then reactivated at will and sent on their merry way. Commentators were quick to predict stunning new applications in communications and in optical and quantum computing.
The enthusiasm quickly evaporated, however, when it sank in that the experiments at Harvard had required enormously complex laser apparatus that could fill a room.
Now, though, separate groups in the United States and Europe say that they have built and successfully tested more compact, rugged, and efficient means of delaying the pulses. Their work seems to clear the way for the kinds of applications foreseen by the Harvard pioneers, including not just those in optical switching and quantum communications but also others in network synchronization, radar, and even computer memory.
Of course, you can slow a light beam by directing it through glass or any other material with a relatively high index of refraction. And a dark piece of paper will stop a beam quite dependably. But by absorbing the photons, the paper destroys the beam irretrievably. What the Harvard researchers had found was a way of slowing or stopping light pulses without destroying their constituent photons and then re-creating the pulses utterly unchanged.
Lene Vestergaard Hau, a Danish physicist at Harvard, was the first to stop light. What she had done, in effect, was imprint the information carried by photons into spin patterns in clouds of atomic gases—”parking” the pulses in a gaseous medium, as she put it—and then reconstitute the pulses as desired, in a technique somewhat reminiscent of holography. Any information carried by the beam would remain perfectly intact.
Hau’s close competitor at Harvard, Mikhail Lukin, anticipated using this stop-light technology as a means of transporting quantum states from one part of a computer to another, an essential process in any large computer based on quantum principles.
There are nearer-term possibilities, too: a buffer for a router, for example, in which an optical delay line might keep one train of light pulses briefly on hold, allowing another train to pass through the router. Phased-array radars, commonly used in the military, could also benefit. In a phased-array radar, many small antennas transmit pulses that are delayed electronically in a systematic way to create a narrow beam that can be steered by changing the delays to the individual antennas.
But producing and controlling these delays electronically is costly. It might be cheaper to devise a system in which electronic input is converted to optical signals, delayed in a tunable system, and then reconverted into electronic signals that are fed to microwave signal amplifiers and individual antennas in the correct phase.
In the new work, the European and U.S. groups are slowing light pulses in optical fibers rather than in atomic gases, by up to several nanoseconds. They’re taking advantage of a phenomenon known as stimulated Brillouin scattering, which involves using sound waves to change the refractive index in a material. When incoming light waves encounter the changed refractive index, they scatter and slow down as some of the light is reflected back into the fiber and interferes with the incoming beam.
Both groups—a team led by Luc Thévenaz at the Swiss Federal Institute of Technology, in Lausanne, and the other led by Alexander Gaeta at Cornell University, in Ithaca, N.Y.—were able to send data pulses with wavelengths of roughly 1550 nanometers through one end of spooled optical fibers. The fibers ranged in length from several hundred meters to a few kilometers, simulating real-world conditions.
Using a pump beam with a slightly different frequency from the data beam, the teams generated sound waves in the fiber. The sound wave scatters the control beam, lowering its frequency to that of the data beam. Both beams interfere constructively, slowing the pulse down.
The team led by Gaeta reported delaying 15-nanosecond-long pulses by more than 25 ns. The Lausanne team reported similar results, delaying pulses by up to 30 ns [see photo, " Taking Pulse”]. To be sure, those delay times of barely more than a pulse length are still too short for data to actually be represented. ”To be useful, this effect should be capable of delaying the pulse by at least a few pulse lengths,” comments Harvard’s Lukin.
Another limit, especially for broadband applications, is the maximum frequency of the delayed pulses achieved in the experiments, which was only 35 megahertz. But that problem seems solvable: both groups recently reported success in increasing the bandwidth by modulating the control beam, giving it a bandwidth of several hundred megahertz. That additional bandwidth increased the bandwidth of the slowed pulses, too. ”There is no real limit for the extension of the bandwidth—we can extend it up to many tenths of a gigahertz,” says Thévenaz.
The first real-world applications may not be that distant, says Daniel Gauthier of Duke University, in Durham, N.C., who participated in the Gaeta group’s research. One application he sees right away is a pulse regenerator. Its use would restore pulse trains that have been distorted by traveling over long distances through optical fibers and are out of sync with the system clock, which enables the system to determine where meaningful data strings start. ”You need to resynchronize the data pulse stream with the system clock, and for that you need one-pulse-width adjustment,” says Gauthier.


Despite the difficulties, however, there has been progress in several areas of quantum computing. As the state of a qubit is, in effect, outside of the physical universe, the quantum computer can move away from classical computer designs using transistors connected by microscopic wires.
Moore's Law has so far delivered massive growth in computer processing power as transistors and the connections between then become smaller with each passing year. However, things are starting to change, and solid-state quantum computers look set to bridge the gap between traditional transistor based computers and their quantum cousins.
In a quantum computer, the computations are carried out by an exchange of information between individual qubits. This exchange of information is achieved by teleportation. This doesn't mean that a qubit, such as an atom or photon, is 'dematerialised' à la Star Trek, but that the properties of one qubit are transferred to another. This has been achieved at the University of Vienna and the Austrian Academy of Science.
An optical fibre was used to connect lab buildings that were situated apart from each other across the river Danube. The lab was able to teleport qubits of information using a technique called polarisation.
They succeeded in exploiting the entanglement phenomenon, which meant that two particles were tied together when in fact they're physically separate – the spooky distance that Einstein talked about. The particles existed in a parallel universe where they were able to change their state.
As a result, they could exchange information, which is just what they would need to do in order to make meaningful calculations. So how far away are we from building working quantum computers?
Actually, we have already constructed some of these near-mythical machines, even though they've employed relatively few working qubits. The earliest example was built in 1998 by scientists working at MIT and the University of Waterloo. It only had three qubits, but it showed the world that quantum computers were not just a fairy tale that physicists told their children.
Two years later, a seven-qubit quantum computer that used nuclear magnetic resonance to manipulate atomic nuclei was built by Los Alamos National Labs. 2000 was also the year that IBM proved it too could build a quantum computer. Dr Isaac Chuang led the team that built a five-qubit quantum computer which enabled five fluorine atoms to interact together.
The following year saw IBM once again demonstrate a working quantum computer. This time the firm was able to use Shor's algorithm. IBM used a seven-qubit quantum computer to find the factors of the number 15.
A more complex quantum computer was also built in 2006 by MIT and Waterloo, and in 2007 a company called D-Wave burst onto the market with what it claimed was the world's first 16-qubit quantum machine.
RIDE D-WAVE: D-Wave Systems' 16-qubit quantum computer is the subject of much debate
D-Wave has yet to prove that its system is a true quantum computer, but this year also saw a team at Yale build the first solid-state quantum processor. The two-qubit superconducting chip was able to perform some basic calculations.
The significance of this development by Yale's scientists is that it shows that a quantum computer can be built using electronics not that dissimilar to the components found in your desktop PC.
Yale's system used artificial atoms that could be placed in the superpositional state quantum computers require. Until this development, scientists could not get a qubit to last longer than a nanosecond.In comparison, the Yale qubit lasted microseconds. This is long enough to perform meaningful calculations.
Scientists working at the Universities of Manchester and Edinburgh have combined tiny magnets with molecular machines to create what could end up being the building blocks for future quantum computers. Professor David Leigh of the University of Edinburgh's School of Chemistry said:
"This development brings super-fast, non-silicon-based computing a step closer. The magnetic molecules involved have potential to be used as qubits, and combining them with molecular machines enables them to move, which could be useful for building quantum computers. The major challenges we face now are to bring many of these qubits together to build a device that could perform calculations, and to discover how to communicate between them."
Looking forward to that goal, one of the most promising developments in the field is quantum dots. These are nano-constructions made of semiconductor material. As such, we can use many of the techniques that we now use to build traditional computers to harness quantum dot technology.
It may be possible to manufacture quantum dots in much the same way as we currently manufacture microprocessors. If the technology were successful, we could build quantum computers with as many qubits as we need. As things stand it's still too early to make complete logic gates from quantum dots, but the technology looks very promising indeed.
The supercomputers we have today look like abacuses when compared to the processing power that quantum computers promise. With so many different avenues being explored by scientists, the final working structure of the quantum computer has yet to be realised.
What recent work does show is that it's a realistic ambition to build a commercial quantum computer over the next few years. When that power arrives, we'll see a truly quantum shift in how we all manipulate information.
First published in PC Plus Issue 289
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Scientists have gotten one step closer to a quantum internet by creating the world’s first multinode quantum network.
Researchers at the QuTech research centre in Netherlands created the system, which is formed from three quantum nodes entangled by the spooky laws of quantum physics that govern subatomic particles. it’s the first time that more than two quantum bits, or “qubits,” that do the calculations in quantum computing are linked together as “nodes,” or network endpoints.
Researchers expect the first quantum networks to unlock a wealth of computing applications that can’t be performed by existing classical devices — like faster computation and improved cryptography.
“It will allow us to attach quantum computers for more computing power, create unhackable networks and connect atomic clocks and telescopes along side unprecedented levels of coordination,” Matteo Pompili, a member of the QuTech research team that created the network at Delft University of Technology in Netherlands, told Live Science. “There also are a lot of applications that we can’t really foresee. One might be to create an algorithm which will run elections in secure way, as an example .”
In much same way that the normal computer bit is that the basic unit of digital information, the qubit is that the basic unit of quantum information. Just like the bit, the qubit are often either a 1 or a 0, which represent 2 possible positions during a two-state system.
But that’s almost where the similarities end. Because of the bizarre laws of the quantum world, the qubit can exist during a superposition of both the 1 and 0 states until the instant it’s measured, when it’ll randomly collapse into either a 1 or a 0. This strange behavior is that the key to the power of quantum computing, because it allows a qubit to perform multiple calculations simultaneously.
The biggest challenge in linking those qubits together into a quantum network is in establishing and maintaining a process called entanglement, or what Einstein dubbed “spooky action at a distance.” this is often when two qubits become coupled, linking their properties in order that any change in one particle will cause a change in other, even they’re separated by vast distances.
You can entangle quantum nodes during a lot of the way , but one common method works by first entangling the stationary qubits (which form the network’s nodes) with photons, or light particles, before firing the photons at one another . once they meet, the 2 photons also become entangled, thereby entangling the qubits. This binds the 2 stationary nodes that are separated by a distance. Any change made to at least one is reflected by an instant change to other.
“Spooky action at a distance” lets scientists change the state of a particle by altering the state of its distant entangled partner, effectively teleporting information across big gaps. But maintaining a state of entanglement may be a tough task, especially because the entangled system is usually in danger of interacting with the outside world and being destroyed by a process called decoherence.
This means, first, that the quantum nodes need to be kept at extremely cold temperatures inside devices called cryostats to minimize the probabilities that the qubits will interfere with something outside the system. Second, the photons utilized in the entanglement can’t travel very long distances before they’re absorbed or scattered, — destroying the signal being sent between two nodes.
“The problem is, unlike classical networks, you can’t amplify quantum signals. If you are trying to copy the qubit, you destroy the first copy,” Pompili said, referring to physics’ “no-cloning theorem,” which states that it’s impossible to make a identical copy of an unknown quantum state. “This really limits the distances we can send quantum signals to the tens of hundreds kilometers. If you would like to line up quantum communication with someone on the opposite side of the world, you’ll need relay nodes in between.”
To solve the matter, the team created a network with three nodes, during which photons essentially “pass” the entanglement from a qubit at one among the outer nodes to at least one at the middle node. The middle node has two qubits — one to acquire an entangled state and one to store it. Once the entanglement between one outer node and therefore the middle node is stored, the middle node entangles the opposite outer node with its spare qubit. With all of this done, the middle node entangles its two qubits, causing the qubits of the outer nodes to become entangled.
But designing this weird quantum mechanical spin on the classic “river crossing puzzle” was the smallest amount of the researchers troubles — weird, for sure, but not too tricky a idea. To form the entangled photons and beam them to the nodes in right way, the researchers had to use a complex system of mirrors and laser light. The really tough part was the technological challenge of reducing pesky noise in system, also as ensuring all of the lasers used to produce the photons were perfectly synchronized.
“We’re talking about having 3-4 lasers for each node, so you begin to possess 10 lasers and three cryostats that each one have to work on same time, along side all of the electronics and synchronization,” Pompili said.
The three-node system is especially useful because the memory qubit allows researchers to establish entanglement across the network node by node, instead of the more demanding requirement of doing it all at once. As soon as this is often done, information are often beamed across the network.
Some of the researchers next steps with their new network are going to attempt this information beaming, along with improving essential components of the network’s computing abilities in order that they will work like regular computer networks do. All of those things will set the size that the new quantum network could reach.
They also want to see if their system will allow them to establish entanglement b/w Delft and therefore the Hague, two Dutch cities that are roughly 6 miles (10 kilometers) apart.
“Right now, all of our nodes are within 10-20 meters [32- 66 feet] of each-other,” Pompili said. “If you would like something useful, you would like to travel to kilometers. This is often getting to be the first time that we’re getting to make a link between long distances.”
The researchers published their findings April 16 in the journal Science.


You’ve heard that the first computer was the size of a small house, right? And how amazing it is that we all carry computers around in our pockets now? Well, some computers still are the size of houses—or even apartment buildings. These huge computers are so big because they’re super fast. And they’re capable of some amazing things.
Exascale supercomputers are the next frontier in computing. They can quickly analyze massive volumes of data and realistically simulate many of the extremely complex processes and relationships behind the fundamental forces of the universe—in a way that’s never been done before. Many industries and systems could be affected, including precision medicine, climate science, and nuclear physics. Here’s a little more about how exascale computing works and how it stands to change the world.
How is computer speed measured?
One way scientists measure computer performance speed is in floating-point operations per second (FLOPS). These operations are simple arithmetic, like addition or multiplication, involving a number containing a decimal, like 3.5. A person can typically solve an operation such as addition with a pencil and paper in one second—that’s 1 FLOPS. Computers can solve these operations much faster. They are so fast that scientists use prefixes to talk about the speed.
A typical laptop is capable of a few teraFLOPS, or a trillion operations per second.
What is a supercomputer?
The first supercomputer was developed in 1964, running 3,000,000 FLOPS, or 3 megaFLOPS.
Since then, research teams have been in a constant race to build a faster computer. In 1996, computers hit the terascale milestone—that’s 12 zeros—when the US Department of Energy’s Intel ASCI Red supercomputer was measured at 1.06 teraFLOPS. The Roadrunner supercomputer was the first to pass the petascale milestone (15 zeros) when it was recorded running 1.026 petaFLOPS in 2008.
Exascale computing is more than a million times faster than ASCI Red’s peak performance. “Exa” means 18 zeros. That means an exascale computer can perform more than 1,000,000,000,000,000,000 FLOPS, or 1 exaFLOPS. To contextualize how powerful an exascale computer is, an individual would have to perform one sum every second for 31,688,765,000 years to equal what an exascale computer can do in one single second.
In May 2022, the Frontier supercomputer at the Oak Ridge National Laboratory in Tennessee clocked in at 1.1 exaFLOPS, becoming the first exascale computer on record and the current fastest supercomputer in the world. Over the coming years, Frontier could reach a theoretical peak of two exaFLOPS.
Which industries could be affected by exascale computing?
Exascale computing could allow scientists to solve problems that have until now been impossible. With exascale, exponential increases in memory, storage, and compute power may drive breakthroughs in several industries: energy production, storage, transmission, materials science, heavy industry, chemical design, AI and machine learning, cancer research and treatment, earthquake risk assessment, and many more. Here are some of the areas where exascale computing might be used:
- Clean energy. Exascale computing could help develop resilient clean-energy systems. New materials developed with exascale computing can perform in extreme environments or adapt to changes in the water cycle, for example.
- Medical research. Exascale computing can support the analysis of massive data volumes and complex environmental genomes. It can also support cancer research in analyzing patient genetics, tumor genomes, molecular simulations, and more.
- Manufacturing. Using exascale computing could accelerate the adoption of additive manufacturing by allowing faster and more accurate modeling and simulation of manufacturing components.
How is exascale computing different from quantum computing?
Exascale computers are digital computers, like today’s laptops and phones, but with much more powerful hardware. On the other hand, quantum computers are a totally new approach to building a computer. Quantum computers won’t replace today’s computers. But using the principles of quantum physics, quantum computing will be able to solve very complex statistical problems that are difficult for today’s computers. Quantum computing has so much potential and momentum that McKinsey has identified it as one of the next big trends in tech.
Put simply, exascale computing—and all classical computing—is built on bits. A bit is a unit of information that can store either a zero or a one. By contrast, quantum computing is built on qubits, which can store any combination of zero and one at the same time. When classical computers solve a problem with multiple variables, they have to conduct new calculations every time a variable changes. Each calculation is a single path to a single result. On the other hand, quantum computers have a larger working space, which means they can explore a massive number of paths simultaneously. This possibility means that quantum computers can be much, much faster than classical computers.
For a more in-depth exploration of these topics, see McKinsey’s insights on digital. Learn more about our Digital Practice—and check out digital-related job opportunities if you’re interested in working at McKinsey.
“Quantum computing just might save the planet,” May 19, 2022. Peter Cooper, Philipp Ernst, Dieter Kiewell, and Dickon Pinner.
“Quantum computing use cases are getting real—what you need to know,” December 14, 2021. Matteo Biondi, Anna Heid, Nicolaus Henke, Niko Mohr, Lorenzo Pautasso, Ivan Ostojic, Linde Wester, and Rodney Zemmel.
“Top trends in tech,” June 11, 2021, Jacomo Corbo, Nicolaus Henke, and Ivan Ostojic.
“A game plan for quantum computing,” February 6, 2020, Alexandre Ménard, Ivan Ostojic, Mark Patel, and Daniel Volz.


What is quantum physics?
Let’s start with the fundamental information representation in a regular computer - the bit. It can be 1 or 0 which is convenient for representing information via a switch controlling the flow of electricity; 1 and 0 map to on and off.
In a quantum computer the fundamental representation of information is a qubit. A qubit can represent not only a 0 or 1, but a combination of both at the same time. Well, what do we mean really by “both”? This is a tricky question, because this is where our everyday experience doesn’t help, and the laws of quantum mechanics take over. Quantum mechanics tells us the state of qubit can be any complex “superposition” of a 0 and 1.
Fortunately we can visualize these superposition states of the qubit as points on the surface of a sphere. Now, instead of a switch with one of two values, we can represent the state of a qubit mathematically as a point on the surface of a sphere. Different points represent different qubit states - different combinations of 0 and 1.
Many of the logic operations used in regular computers can be mapped to rotations of the qubit state on the sphere. For instance, a NOT gate which flips 0<->1 has an analog quantum bit flip which rotates a qubit state along a meridian of the bloch sphere. This can rotate 0-->1, 1-->0, and does the same to any superposition as well.
What’s a superposition? That’s a state in which a qubit cannot be purely described as being 1 or 0, but rather some complex combination. In our graphical representation, a state on the equator of the Bloch sphere is actually an equal superposition of 0 and 1. Move towards the N pole and it’s a bit more heavily weighted to 0. Move the other way and it’s more heavily weighted to 1. Move around the equator and something different changes - the phase of the qubit. At different points on the sphere this leads the superposition to change between |0>+|1> and |0>-|1>. Changing this is a bit like moving along a wave from peak to trough and back - it’s the same wave, just different phases.
Now here’s something interesting - when you measure a qubit in superposition, you get either a 0 or 1. That’s it. You can never determine if a qubit was in a superposition with one measurement; instead you have to perform many measurements. Even if the exact same state is prepared every time, the outcome of each measurement will always be random. The likelihood of measuring 0 or 1 is determined by how much 0 or 1 appears in the superposition - where you are on the sphere. This idea - that measurement collapses quantum superpositions - has huge impacts on how quantum computers actually function!
We build real qubits using all kinds of different hardware - tiny loops of superconducting circuits or individual atoms in traps. We can use two different physical states to form a qubit and then perform logical operations by blasting the atoms with light - either microwaves or laser light. Tiny pulses timed just right can flip the qubit from one state to another [illustrate laser impinging atom, atom flipping from one orbital to the other]
There’s one more element we use in quantum computers - entanglement. This is a special link between quantum systems that can only be described using quantum physics. In a sense when two objects - like qubits - become entangled, they can’t really be described as two objects any longer. They’re now one shared object - a condition that can again be induced by applying the right pulse of laser or microwave radiation. There are various ways to represent this visually as we do in Q-CTRL products, but it has huge impacts on how adding qubits to a quantum computer increases the overall performance of the system.
Now we can get to the heart of why quantum computing is really hard: Noise. We know that when you hear that word you probably think about loud sounds like the noise coming from traffic that makes it hard to concentrate. We mean something a bit different here; noise describes all of the things that cause interference in a quantum computer.
Just like a mobile phone call can suffer interference leading it to break up, a quantum computer is susceptible to interference from all sorts of sources, like electromagnetic signals coming from WiFi or disturbances in the Earth’s magnetic field. When qubits in a quantum computer are exposed to this kind of noise, the information in them gets degraded just the way sound quality is degraded by interference on a call. This is known as decoherence.
When a qubit is sitting idle - not even being used in a computation - its state can be affected by interference. But when we’re performing a quantum logic operation, like a bit flip, we can also suffer from errors that cause us to rotate by the wrong amount. In either case the quantum state doesn’t end up where you expect, and over time can be randomized or even totally erased - clearly not a good thing when that quantum state was actually representing information.
Compared with standard computers, quantum computers are extremely sensitive to this kind of noise. A typical transistor in a microprocessor can run for about a billion years at a billion operations per second, without ever suffering a hardware fault. By contrast, typical quantum bits become randomized in about one one-thousandth of a second. That’s a huge difference.
Quantum algorithms need to execute many operations across a large number of qubits. Decoherence causes the information in our qubits to become randomized - and this leads to errors in the algorithm. The greater the influence of noise, the shorter the algorithm that can be run. Right now, instead of trillions of operations, we can typically only perform dozens before noise causes a fatal error.
So what do we do about this? To start, for the past two decades teams have been working to make their hardware more passively stable - shielding it from the noise that causes decoherence.
At the same time theorists have designed a clever algorithm called Quantum Error Correction that can identify and fix errors in the hardware. Sounds amazing! But the downside is that to make it work you have to spread the information in one qubit over lots of qubits. In many estimates it may take 1000 or more physical qubits to realize just one error-corrected qubit. And the worse your noise is, the more you need. Today’s machines are nowhere near capable of getting benefits from this kind of Quantum Error Correction.
This is where Q-CTRL comes in. We add something extra - quantum firmware - which can stabilize the qubits against noise and decoherence without the need for extra resources.
Learn more about Q-CTRL’s quantum firmware here.
Learn how Quantum Error Correction can enable the quantum computing revolution
Learn how quantum firmware accelerates the performance of quantum computers.
Learn how about the current "noisy" era of quantum computing and what it stands to deliver.
Learn the basics of how to build quantum algorithms for quantum computing.
Discover the fundamentals of quantum physics for quantum computing
Learn how the fragility of quantum hardware lets us detect the undetectable
Discover the technology that will power a new information age
Learn how quantum control accelerates the path to useful quantum technologies.
Take the next step on your journey with short articles to help you understand how quantum computing and sensing will transform the world


China’s moon landing: a giant leap for space science
The People’s Republic of China successfully landed the first spacecraft, called the Chang’e-4, on the far side of the moon on Jan. 2. The probe was sent to search for rare earth metals and helium-3, used to make safer, more productive energy. Scientists hope to find answers about the evolution of our solar system and the origins of the universe. This is a stunning achievement.
The landing of Chang’e-4 is just part of China’s overall space program that is centered on improving conditions for the country’s centrally planned society. China is developing a lunar space station and plans on sending a crew to the moon as soon as 2022.
In contrast to China, the U.S. space program is driven by capitalist competition and the development of military weapons. Trump called on the Department of Defense and the Pentagon to develop a “Space Force,” a sixth branch of the military for the purpose of protecting U.S. assets in space and attacking its enemies during wars.
The National Aeronautics and Space Administration’s International Space Station will soon be decommissioned. There are no plans for a replacement. NASA’s budget has been stalled.
Contributions of China’s space program
China now produces 90 percent of the world’s rare earth metals. The Chang’e-4 landing is searching for new sources of valuable materials like copper, aluminum, iron and rare earth metals essential for emerging technologies like cell phones, computers and other electronics and medical equipment.
Nuclear fusion, the next generation of nuclear power, will someday replace nuclear fission, which is now used to fuel nuclear power plants. Fusion can generate four times as much energy as fission without hazardous environmental problems like radioactive waste.
Helium-3, an ideal fuel for nuclear fusion, is an isotope of the element helium. There are an estimated 1 to 5 million tons of it on the moon, compared to only 15 tons on Earth. Once nuclear fusion technology matures, it will take 100 tons of helium-3 each year to meet global energy demands.
Ouyang Ziyang, a prominent Chinese space scientist, predicted 13 years ago: “Each year three space shuttle missions could bring enough fuel for all human beings across the world.” (China Daily, July 26, 2006) For now it is too expensive to haul helium-3 back to Earth, but it may be useful as fuel for future spacecraft to explore deeper into space.
Because the moon rotates just once each time it circles the Earth, only one side of its surface is visible from Earth. The far side of the moon is shielded from noise caused by radio waves, cell phones, power lines, GPS satellites and Wi-Fi.
Scientists stationed on the moon’s far side will be able to look more deeply into space. In so doing, more will be learned about the evolution of the universe, the birth of the first stars and the formation of our solar system.
Photographs from a Soviet spacecraft in 1959 showed that the far side of the moon has a thicker, older crust with deeper and more numerous craters. Scientists don’t know with certainty why the crust is thicker there. Change’e-4 is designed to help answer that question.
Craters created by ancient asteroid hits on the thicker crust have not been filled in with lava flows since they were formed. Because of this, they may hold information about the early history of the moon’s formation and the development of our solar system.
Chang’e-4 landed inside the oldest, deepest crater, called the Von Kármán Crater, on the far side of the moon. This basin offers scientists more information on the moon’s composition, structure and evolution and may be rich in rare earth metals and iron.
Since the moon blocks transmissions from the Chang’e-4 probe, China launched a relay satellite called “Queqiao,” or “Magpie Bridge,” which bounces information and images from the probe back to China’s receiving stations.
The Chang’e-4 lander carried the first mini-greenhouse to the moon. A mini biosphere is being set up with six live species: cotton, rapeseed, potato, fruit fly, yeast and arabidopsis, a flowering plant in the mustard family. This is a crucial step in establishing a longer visit by astronauts and developing a lunar space station.
Cooperation and education, not competition
Deng Xiaoping, China’s leader from 1978 until his retirement in 1989, told the world in 1978 that China was not taking part in the space race. He explained that the goal of China’s space program was to improve the standard of living for the Chinese people. It would focus on communications, remote sensing and meteorology.
Scientists from Sweden and Germany collaborated with China on designs for some of the eight scientific instruments used in the Chang’e-4 mission. The Swedish Institute of Space Physics developed an instrument that will investigate how solar wind interacts with the lunar surface.
Instead of working with China, President Donald Trump argued that the Chinese and Russian space programs are a threat to his Space Force. This is U.S. imperialist saber rattling.
China provides four times as many college degrees in science, technology, engineering and mathematics (STEM) than the United States. Federal funding for education has decreased in the U.S., where a college degree is very expensive — and does not ensure better jobs for graduates. The U.S. is falling behind in space exploration and in other areas of scientific development.
Speaking about students in China, U.S. astronomer and professor Chris Impey said, “They have very young engineers in their space program — very keen, very well trained, very ambitious.” He said China’s space program, like its economy, is growing explosively, at roughly 10 percent a year for the past decade. (NPR, May 11, 2015)
China’s impressive space program
In other areas of science and technology, like artificial intelligence and quantum computing, China is developing more quickly than the U.S. China recently launched a quantum satellite into space that physicists say can lead to a super-secure, super-fast quantum-internet system for China.
The first Chinese satellite launch, which happened in 1970, focused on commercial applications. Since 2003, China has launched two space labs and sent six crews, including 12 taikonauts (Chinese astronauts), into low orbit.
In 2016, China completed the world’s largest telescope built to detect radio signals, potential signs of life, from distant planets. That year, the country launched the Tiangong 2 space lab, which has been orbiting Earth since then.
Last year China sent 38 launches into space, more than any other country. Many of them carried GPS-type systems that already cover China and much of Asia. China is currently working on developing a space lab to be stationed on the moon, after which the country will be able to send crews of scientists to continue exploration there.
The Chang’e-4 is the first moon landing by any country since 2013.
China annually spends about $2 billion on its space budget, compared to NASA’s $18 billion budget — and its space program is growing 10 times faster! How can China, a still-developing country, make these profound advances with less money?
Deirdre Griswold, editor of Workers World newspaper, answered this question in a WW Commentary on Dec. 20 — “because the basic infrastructure is publicly owned, not in the hands of a profit-seeking, parasitic ruling class.”


Researchers at EPFL in Switzerland have used superconducting technology to make two types of optomechanical lattice, including a honeycomb-shaped lattice that can mimic some of the physics of graphene. [Image: Andrea Bancora / EPFL]
Scientists in Switzerland have shown how a superconducting circuit can be used to investigate the physics of topological lattices such as strained graphene. They say their work—an implementation of what is known as cavity optomechanics—could potentially be used to create highly entangled mechanical states, a valuable resource for quantum computing and communication based on mechanical oscillators (Nature, doi: 10.1038/s41586-022-05367-9).
Optically probing mechanical systems
Optomechanical systems use electromagnetic fields to control the vibrations of mechanical objects, taking advantage of the fact that light carries momentum and can therefore exert pressure. At visible wavelengths, such systems involve a laser propagating in an optical cavity whose end mirror is free to vibrate. Microwave devices instead couple longer-wavelength radiation to an LC circuit featuring a vibrating capacitor.
In recent years, physicists have also started to use optomechanics to probe the quantum behavior of macroscopic mechanical systems. This has involved manipulating such systems in a number of ways, such as cooling them to their quantum ground states or entangling mechanical oscillators located some distance from one another.
Such systems, however, tend to comprise only one or two optomechanical modes. Researchers would like to develop 2D lattices of optomechanical oscillators, as these could shed light on more complex phenomena such as the topology of light and sound or the quantum many-body dynamics of macroscopic systems.
Precise control via improved fabrication
Building optomechanical lattices where each building block consists of mechanical and optical modes requires very precise control of the properties of individual lattice sites. Tobias Kippenberg, Amir Youssefi and colleagues at the Swiss Federal Institute of Technology Lausanne (EPFL) have now shown how this is possible by constructing an optomechanical system from a superconducting circuit.
Key to the work is a parallel-plate vacuum capacitor, which features a suspended top plate that can vibrate. The conventional method for fabricating such capacitors makes it hard to control the size of the gap between the device’s two plates, and with that the resonant mechanical and microwave frequencies as well as the coupling strength between those.
Kippenberg and colleagues got round this problem by devising a new fabrication process. First, they etched a trench in a silicon substrate; then, they placed a thin slice of aluminum on the bottom of the trench to serve as the capacitor’s lower plate, before covering that with a layer of silicon dioxide. After that, they leveled off the surface of the silicon dioxide using chemical mechanical polishing and rested a second aluminum plate on top of the leveled surface. By finally removing the silicon dioxide layer, they were able to suspend the upper aluminum plate precisely above the lower one.
The fact that the superconducting circuit had to be cooled to cryogenic temperatures induced a tensile stress in the upper plate. This kept the plate flat and the gap size dependent on the depth of the trench, thereby restricting fluctuations in the resonant frequencies of the microwaves and mechanical oscillations to 0.5% and 1%, respectively.
The researchers fabricated multiple instances of these capacitors, each one linked to a spiral-shaped inductor to produce a distinct LC resonator. Each resonator was in turn magnetically coupled to its neighbors, with the coupling magnitude determined by the physical distance between resonators.
Edge states and a honeycomb lattice
The EPFL team implemented two types of circuit. In one, the researchers lined up ten resonators in a chain to mimic what are known as topologically protected edge states. In the other they arranged 24 resonators in the shape of a honeycomb lattice, with the couplings along one of the three lattice axes set so that alternate couplings had high and low values. This allowed them to reproduce the physics of one-carbon-atom-thick sheets of graphene under strain—as can occur, for example, if the material is adsorbed onto substrates like silicon dioxide.
By studying the optomechanical interactions between the resonators, the researchers were able to directly measure the resonators’ collective behavior and work out the full Hamiltonian, a function expressing the system’s combined kinetic and potential energy. They say that previously it had only been possible to measure the behavior of such superconducting circuits indirectly, using (for example) near-field scanning probes or laser scanning microscopy.
The researchers reckon that such optomechanical lattices could in future shed light on “the rich physics in multimode optomechanics.” Using degenerate mechanical oscillators, the explain, should make it possible to “create collective long-range interactions and observe strong cooperative effects on mechanical motion.” Also, the system might enable highly entangled mechanical states to be created, according to the researchers—something that could benefit future quantum information technology based on mechanical oscillators.


Australian scientists put the quantum world on a microchip
This article is an installment of Future Explored, a weekly guide to world-changing technology. You can get stories like this one straight to your inbox every Thursday morning by subscribing here.
An Australian startup just modeled a molecule on a microchip, placing atoms in silicon with sub-nanometer precision.
This ability to simulate molecules on the atomic scale — where matter is ruled by quantum mechanics — could improve our understanding of the quantum world and lead to the creation of incredible new materials, such as high-temperature superconductors or super efficient solar cells.
“We could start to mimic how nature behaves and then we can start to make new kinds of materials and devices that the world has never seen before,” said Michelle Simmons, founder of Silicon Quantum Computing, the startup responsible for the microchip.
A couple of million years after making our first stone tools, humans discovered that when we zoom in on matter, looking at the atoms and subatomic particles that comprise it, they adhere to a different set of rules than the ones that govern objects on a larger scale.
These rules (“quantum mechanics”) can have their own useful applications — MRI scanners, solar cells, and atomic clocks all take advantage of quantum phenomena.
“We can start to make new kinds of materials and devices that the world has never seen before.”Michelle Simmons
But while it’s easy to heft a rock and extrapolate that it might be good for bashing things, it’s not so easy to see or understand how matter behaves on the quantum scale — especially since observation itself affects quantum systems.
We can use computer programs to simulate how some small molecules behave on the atomic or subatomic level, but that isn’t a viable option for larger molecules: there’s too many possible interactions between their particles.
“If we can start to understand materials at [the quantum] level, we can design things that have never been made before,” Simmons told ScienceAlert. “The question is: how do you actually control nature at that level?”
The quantum simulator
The answer, it seems, is by modeling molecules on silicon chips.
For a recent study, the SQC team successfully manufactured a microchip at the atomic scale, creating 10 uniformly sized artificial atoms — also known as “quantum dots” — and then using a scanning tunneling microscope to precisely position the dots in silicon.
The team modeled their chip after the structure of polyacetylene, a molecule made from carbon and hydrogen atoms connected by alternating single and double carbon bonds.
Once it was built, they could apply an electric charge to one part of the chip (the “source”) and study how it moved along the chain of atoms to exit at another part (the “drain”).
“We’re literally building it from the bottom up, where we are mimicking the polyacetylene molecule by putting atoms in silicon with the exact distances that represent the single and double carbon-carbon bonds,” said Simmons.
Based on theoretical predictions, polyacetylene is supposed to behave differently depending on whether the chain of molecules begins and ends with double carbon bonds or single carbon bonds.
To check if their modeling technique was accurate, the researchers created one chip based on each version — and saw that the number electrical peaks did change as the current ran through each version.
“This confirms long-standing theoretical predictions and demonstrates our ability to precisely simulate the polyacetylene molecule,” according to SQC.
The team also observed an electron existing in two places simultaneously, an example of the quantum phenomenon superposition.
“What [this model is] showing is that you can literally mimic what actually happens in the real molecule, and that’s why it’s exciting because the signatures of the two chains are very different,” said Simmons.
The team chose a 10-dot chain of the polyacetylene molecule to demonstrate its tech because that’s something we can simulate with classical computers. Now they’re looking to scale up.
“We’re near the limit of what classical computers can do, so it’s like stepping off the edge into the unknown,” said Simmons. “And this is the thing that’s exciting — we can now make bigger devices that are beyond what a classical computer can model.”
These future quantum models could be for materials that lead to new batteries, pharmaceuticals, and more, predicts Simmons.
“It won’t be long before we can start to realize new materials that have never existed before,” she said.
We’d love to hear from you! If you have a comment about this article or if you have a tip for a future Freethink story, please email us at [email protected].


In the last post of this series, we discussed how supercharging quantum computing with Quantum Mechanics’ principles allows high computational power. To come to terms with this, we must first delve into the math behind quantum memory.
A computer needs memory. It stores input and output data as a transitional place to operate on data. We care about this functionality because data encodes states.
In the classical sense, a state refers to the particular arrangement that something is in at a specific moment. Examples of a classical state are the position of a door: either open or closed; the color of a marker: red, blue, yellow, etc.; the value of a bit: 0 or 1 / false or true; and so on.
As you know from daily life, these states are discrete; that is, there is only one particular arrangement a system can be in. However, a quantum state is continuous. Given a set of basis states, a quantum state may be a combination of those basis states. Translated to concrete terms, a quantum state of the colored marker could be 50% red, 25% blue, and 25% yellow. Another well-known example is Schrodinger’s Cat thought experiment; there is a 50-50 chance that the cat is either dead or alive.
Intuitively, this does not make sense for how we perceive things to behave in the real world. A bit, by definition, is either 0 or 1; it cannot be in a 0.5 position or some other state because you know what state it is in by seeing the state. The critical point in our perception is seeing, or rather more generally, measuring. Measurement is what differentiates them. When you measure a classical state, you expect the same state to always be the same; a 1 will always be a 1. On the other hand, a measurement of a quantum state need not always be the same. While it is true that a state must be found in some defined arrangement, a quantum state allows you to find a different defined arrangement each time.
By nature, this measurement process on a quantum state is random, and the specific quantum state defines its probability distribution.
This state hand-wavy explanation becomes challenging to track fast, so to understand, we use math to describe this. To represent a state we use the notation . For example, the state of the color red and the logical state 0 can be represented as and , respectively. This is known as the bra-ket notation or Dirac notation in quantum mechanics.
To say that the system is in one of these states, we write or . For now, this syntax works perfectly for defining the classical state of a system. However, to expand the usefulness of this nomenclature for our needs, we must think of as a vector in some space, with each basis state being an axis. For a mixed state, we could write something like for a bit.
Considering that a state or would not make much sense because a scaled classical state should not change being in that state. Therefore, we can restrict ourselves to having as a unit vector. This forces our state to be a point on the unit circle, in which we know . This is interesting because the probability is always in the range, which applies to both and , and the sum of the probabilities of all possible states should equal 1, another rule these two follow. Then, we can argue that the square of the state coefficient is the probability of being in that state. Thus, we call these coefficients probability amplitudes.
We can verify this argument with the example of a quantum state being 50% in and . Mathematically, we could write that if and refer in some sense to the probability or proportion, then we would want in this case. For this to be true, and for the vector to be normalized, then we must have which means that or 50-50 chance for and .
What about negative numbers, though, for probability amplitudes? It turns out that Quantum Mechanics allows not only negative numbers but also complex numbers. The theory must work!
We have expanded the two possible states of a bit from this quantum mechanical model of states to a whole space of possible linear combinations. We call this quantum mechanical bit a qubit. The state of a qubit is represented in the form where and are complex numbers. The space with all the possibilities of its state is shown geometrically by the Bloch Sphere.
The Block Sphere is not a geometric representation of the vector form of the state (as that would take four dimensions: two real and two imaginary axes), but rather it is a mapping of every possible state of a single qubit. The basis states are for the positive z-axis and for the negative z-axis. Any state with real probability amplitudes lies along the plane. For example, the equal probability state: is the rightmost point of the circle: . Any point with a component will have a complex probability amplitude.
In general, any state can be mapped on the Bloch Sphere using spherical coordinates:
From the behavior implanted in this mathematical foundation, two key properties arise from quantum mechanics: superposition and entanglement.
We discussed before the ability of a quantum state to combine multiple basis states simultaneously. This is called superposition. When the state of a system is a mixture of basis states, we say that it is a superposed state. The remarkable aspect of superposed states is that measurement is probabilistic by nature. While classical computation finds this property problematic, we will see later that it is this property that provides the quantum computer with its incredible parallel power.
For a group of interacting quantum states, like in a quantum computer, another helpful property arises entanglement. It connects multiple quantum states such that the measurement of one quantum state also obtains information about the other states. For example, if I prepared two qubits with specific states independent of each other and measured their states, I would obtain results only dependent on the initial state I set them in. However, if I made the qubits interact in some predictable way such that I flipped the value of one quantum state depending on the value of the other state, then from the measurement of either qubit, I could know the state of the other qubit without having measured it.
Schrodinger’s Cat also gives us an analogy: knowing the state of the cat or the detonator gives you information about the other. An alive cat means an untriggered detector and vice versa.
As we will find, entanglement enables us to exploit the parallel power from superposition.
With this crash course on qubits, mathematical state representation, measurement, and some quantum properties, we can now tackle how a quantum computer works, which we will discuss in the next post.
Nielsen, M., & Chuang, I. (2010). Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511976667
A Modern Approach to Quantum Mechanics, John S. Townsend
Why are complex numbers needed in quantum mechanics? Some answers for the introductory level, American Journal of Physics 88, 39 (2020); https://doi.org/10.1119/10.0000258
Dirac, P. (1939). A new notation for quantum mechanics. Mathematical Proceedings of the Cambridge Philosophical Society, 35(3), 416-418. doi:10.1017/S0305004100021162
Superposed State Image. Andrew Daley. Quantum Optics and Quantum Many-Body Systems. https://qoqms.phys.strath.ac.uk/research_qc.html
Bloch Sphere Image. By Smite-Meister – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=5829358
Schrodinger’s Cat Image. Mother Jones. https://www.motherjones.com/kevin-drum/2018/09/schrodingers-cat-is-alive-one-twelfth-of-the-time/
ArrayFire Quantum Simulator. https://github.com/arrayfire/afQuantumSim


Quantum computers promise huge speedups on some computational problems because they harness a strange physical property called entanglement, in which the physical state of one tiny particle depends on measurements made of another. In quantum computers, entanglement is a computational resource, roughly like a chip’s clock cycles — kilohertz, megahertz, gigahertz — and memory in a conventional computer.
In a recent paper in the journal Proceedings of the National Academy of Sciences, researchers at MIT and IBM’s Thomas J. Watson Research Center show that simple systems of quantum particles exhibit exponentially more entanglement than was previously believed. That means that quantum computers — or other quantum information devices — powerful enough to be of practical use could be closer than we thought.
Where ordinary computers deal in bits of information, quantum computers deal in quantum bits, or qubits. Previously, researchers believed that in a certain class of simple quantum systems, the degree of entanglement was, at best, proportional to the logarithm of the number of qubits.
“For models that satisfy certain physical-reasonability criteria — i.e., they’re not too contrived; they’re something that you could in principle realize in the lab — people thought that a factor of the log of the system size was the best you can do,” says Ramis Movassagh, a researcher at Watson and one of the paper’s two co-authors. “What we proved is that the entanglement scales as the square root of the system size. Which is really exponentially more.”
That means that a 10,000-qubit quantum computer could exhibit about 10 times as much entanglement as previously thought. And that difference increases exponentially as more qubits are added.
Logical or physical?
This matters because of the distinction, in quantum computing, between logical qubits and physical qubits. A logical qubit is an abstraction used to formulate quantum algorithms; a physical qubit is a tiny bit of matter whose quantum states are both controllable and entangled with those of other physical qubits.
A computation involving, say, 100 logical qubits would already be beyond the capacity of all the conventional computers in the world. But with most of today’s theoretical designs for general-purpose quantum computers, realizing a single logical qubit requires somewhere around 100 physical qubits. Most of the physical qubits are used for quantum error correction and to encode operations between logical qubits.
Since preserving entanglement across large groups of qubits is the biggest obstacle to developing working quantum devices, extracting more entanglement from smaller clusters of qubits could make quantum computing devices more practical.
Qubits are analogous to bits in a conventional computer, but where a conventional bit can take on the values 0 or 1, a qubit can be in “superposition,” meaning that it takes on both values at once. If qubits are entangled, they can take on all their possible states simultaneously. One qubit can take on two states, two qubits four, three qubits eight, four qubits 16, and so on. It’s the ability to, in some sense, evaluate computational alternatives simultaneously that gives quantum computers their extraordinary power.
In the new paper, Peter Shor, the Morss Professor of Applied Mathematics at MIT, and Movassagh, who completed his PhD with Shor at MIT, analyze systems of qubits called spin chains. In quantum physics, “spin” describes the way a bit of matter — it could be an electron, or an atom, or a molecule — orients itself in a magnetic field. Shor and Movassagh consider bits of matter with five possible spin states: two up states, two corresponding down states, and a zero, or flat, state.
Previously, theorists had demonstrated strong entanglement in spin chains whose elements had 21 spin states and interacted with each other in complex ways. But such systems would be extremely difficult to build in the lab.
Chain, chain, chain
A spin chain can be envisioned as a sequence of particles lined up next to each other. Interactions between the spins of adjacent particles determine the total energy of the system.
Shor and Movassagh first considered the set of all possible orientations of their spin chain whose net energy was zero. That means that if somewhere there was a spin up, of either of the two types, somewhere there had to be a corresponding spin down.
Then they considered the superposition of all those possible states of the spin chain. But the major breakthrough of the paper was to convert that superposition into the lowest-energy state of a Hamiltonian.
A Hamiltonian is a matrix — a big grid of numbers — that figures in the standard equation for describing the evolution of a quantum system. For any given state of the particles in the system, the Hamiltonian provides the system’s total energy.
In the previous 30 years, Movassagh says, no one had found an example of a Hamiltonian whose lowest-energy state corresponded to a system with as much entanglement as his and Shor’s exhibits. And even for Shor and Movassagh, finding that Hamiltonian required a little bit of luck.
“Originally, we wanted to prove a different problem,” Movassagh says. “We tried to come up with a model that proved some other theorem on generic aspects of entanglement, and we kept failing. But by failing, our models became more and more interesting. At some point, these models started violating this log factor, and they took on a life of their own.”
Pros and cons
“It’s a beautiful result, a beautiful paper,” says Israel Klich, an associate professor of physics at the University of Virginia. “It certainly made for a lot of interest in some parts of the physics community. The result is in fact very, very succinct and simple. It’s a relatively simple Hamiltonian whose ground state one can understand by simple combinatorial means.”
“Inspired by this work, we recently introduced a new variation on this model that is even more entangled, which has, actually, linear scaling of entanglement,” Klich adds. “The reason this was possible is that if you look at the ground state wave function, it’s so easy to understand how entanglement builds up there, and that gave us the idea of how to string it on to be even more entangled.”
But John Cardy, an emeritus professor of physics at Oxford University and a visiting professor at the University of California at Berkeley, doesn’t find the MIT researchers’ Hamiltonian so simple. “If you read the description of the Hamiltonian, it takes a lot of description,” he says. “When we have physically reasonable Hamiltonians, we can just write them down in one expression. They do have an equation that tells you what the Hamiltonian is. But to explain what all those ingredients are requires this whole formalism that is deliberately designed, as far as I can tell, to get the result that they want.”
“But I don’t want to sound unduly negative, because this is the way that science proceeds,” he adds. “You find one counterexample, then you might find others that are more reasonable.”


A concept of computer engineering, Neuromorphic Computing refers to the designing of computers that are based on the systems found in the human brain and the nervous system.
Driven by the vast potential and ability of the human brain, neuromorphic computing devises computers that can work as efficiently as the human brain without acquiring large room for the placement of software.
Inspired by the human brain and the functioning of the nervous system, Neuromorphic Computing was a concept introduced in the 1980s. Yet this concept has taken the front seat in recent times as Artificial Intelligence has led scientists to advance Neuromorphic Computing to excel in the field of technology.
One of the technological advancements that has rekindled the interest of scientists in neuromorphic computing is the development of the Artificial Neural Network model (ANN).
Since traditional computers, backed by CPUs (Computer Processing Units) do not have the ability to support neuromorphic computing, modern computers are now being built with adequate hardware to support such technology.
Backed by the advanced technology of neuromorphic computing, computers can now act and work like the human brain. With the help of algorithms and data, neuromorphic computing enables computers to work rapidly and on low energy too.
While the definition of this concept can be a bit too complicated to understand, the working of neuromorphic computing can make you understand the essence of it more easily. Let's begin with the working of neuromorphic computing.
The working of neuromorphic computing-enabled devices begins with the placement of Artificial Neural Networks (ANN) that comprise millions of artificial neurons. These neurons are similar to the human brain neurons.
Enabling a machine (computer) to act and work like the human brain, layers of these artificial neurons pass signals to one another. These electric signals or electric spikes convert input into an output that results in the working of neuromorphic computing machines.
The passing on of electric spikes or signals functions on the basis of Spiking Neural Networks (SNN). This spiking neural network architecture further enables an artificial machine to work like the human brain does and perform functions that humans can do on a daily basis.
This can involve visual recognition, interpretation of data, and a lot more such tasks. Since these artificial neurons only consume power when the electric spikes are passed through them, neuromorphic computing machines are low-power-consuming computers as compared to traditional computers.
By imitating the neuro-biological networks present in the human brain, neuromorphic computing machines work like a human brain and perform tasks efficiently and effectively.
Bringing on the ability to work like the human brain, neuromorphic computing has advanced the developments in the field of technology. The engineering of computers in the earlier times led to the generation of traditional computers that consumed a lot of space for functioning.
However, computers working on the basis of neuromorphic computing consume much less space with an in-built capability to work faster and better.
(Must check: AI with Neuroscience)
Neuromorphic computers are specifically known for their rapid response system because their processing is highly rapid. As compared to traditional computers, neuromorphic computers are built to work like a human brain and so their rapid response system is a major highlight.
Owing to the concept of Spiking Neural Networks (SNN), neuromorphic machines work when electric spikes or signals are passed through the artificial neurons. These artificial neurons work only when electric spikes are passed through them thus consuming low energy.
Modern computers have a knack for adaptability and so do neuromorphic computers. With higher adaptability, neuromorphic computers work well according to the evolving demands of technology. With changing times, neuromorphic computers adapt themselves and change from time to time resulting in efficient working.
Machines working on the principle of neuromorphic computing are highly fast-paced when it comes to learning. Establishing algorithms based on interpretation of data and formulating algorithms as and when new data is fed into such computers, neuromorphic computing enables machines to learn rapidly.
One of the most striking features of neuromorphic computing is its mobile architecture. Unlike traditional computers that used to consume vast space for working, neuromorphic computers are mobile and handy. They do not require much space and are highly efficient in terms of space occupancy.
(Most related: Top Deep Learning Algorithms)
An essential realm of AI, neuromorphic AI computing is significant because of its advanced technology. Leading to the functioning of artificial computers like the human brain, neuromorphic computing has opened the doors to better technology and rapid growth in computer engineering.
Not only does it lead to rapid growth but neuromorphic computing chips have also revolutionized the way computers work. From the analysis of data to machine learning algorithms, computers can do almost anything today.
While neuromorphic computing was a concept introduced in the 1980s, it has only been brought into the limelight in recent times. With numerous neuromorphic computing applications in physics, data analytics, and numerical algorithms, the significance of this concept is unmatched.
Even though the concept has many challenges to face, it still is leading the revolution of making computers work along the lines of the human brain.
"We’ve seen a lot of progress in scaling and industrialization of neuromorphic architectures. Still, building and deploying complete neuromorphic solutions will require overcoming some additional challenges." (From)
Artificial Intelligence technology intends to impart human abilities in computers to make them work like humans. On the other hand, neuromorphic computing attempts to engineer computers that work like the human brain does. Comprising millions of artificial neurons that pass on electric signals to one another, neuromorphic computing has been a revolutionary concept in the realm of Artificial Intelligence.
By inducing the technology of information processing, neuromorphic computers have become the leaders of AI that, as many say, have resulted in the 3rd wave. The third generation of AI has led scientists to draw parallels with the human brain and its abilities like the interpretation of data and adaptation.
With the help of one of the techniques of AI, (machine learning), neuromorphic computing has advanced the process of information processing and enabled computers to work with better and bigger technology.
Thanks to AI, neuromorphic computing has reinvented its place in the field of technology and is pushing the limits of AI further. Intertwined with each other, neuromorphic computing and AI have a long way to go as both attempt to mimic human abilities and imitate them in computer software.
"Intel Labs is driving computer-science research that contributes to this third generation of AI. Key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing, which creates algorithmic approaches to dealing with the uncertainty, ambiguity, and contradiction in the natural world." Intel- Neuromorphic Computing
In simple terms, Artificial Intelligence future is Neuromorphic Computing. Setting forth the third wave or era of AI, neuromorphic computing will take over the technological advancements of the field and become the driving force of artificial intelligence future scope.
While the current wave of AI is faced with a number of challenges like heavy processing hardware and software storage capacity, the third wave of neuromorphic computing in AI will most likely put a stop to these challenges and enable the human-like activities performed by computers.
Neuromorphic chips, being manufactured by big tech giants like IBM, will be the key factor in making computers function like the human nervous system.
"The neuromorphic computing market is poised to grow rapidly over the next decade to reach approximately $1.78 billion (around Rs11,570 crore) by 2025, according to a 10 April report by US-based Research and Markets. The reason is simple—the growing interest of companies in Artificial Intelligence, or AI, which can always do with more and more computing power." Neuromorphic computing the future of AI
To conclude, neuromorphic computing will bring forth the untouched capabilities of AI and will set a revolutionary example in the coming years.
The objective of neuromorphic computing is to make computers behave like a human brain and work along the lines of the human nervous system, and neuromorphic computing posits the engineering of computers in a way that comprises millions of artificial silicon neurons enabled to transfer electric spikes from one another.
(Recommended blog: How quantum computing improves ML?)
In the long run, this concept will gain more relevance and regard as it is all set to bring about the 3rd wave of Artificial Intelligence.
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US Takes an Important Step Toward Quantum Internet
(Inside Science) -- While researchers continue to make quantum computers increasingly capable, regular computers still hold a massive advantage: Their data, represented in sequences of zeros and ones, can ride the information superhighway. Quantum computers, which instead run on quantum superpositions of zeros and ones, can’t use the internet to communicate with each other.
Multiple projects across the world are working to create a “quantum internet,” a network where quantum computers can share and exchange information. One such project, a collaboration between Brookhaven National Lab and Stony Brook University in New York, recently hit a major milestone: demonstrating that quantum bits, or qubits, from two distant quantum computers can be entangled in a third location. This is a critical step in creating a quantum internet, and significantly, the researchers did it over standard internet cables.
“Part of the challenge of building a quantum internet is, to what extent can I even get quantum information through the kinds of fiber networks that we use for normal communications?” said Joseph Lykken, deputy director of research at Fermi National Accelerator Laboratory and head of the Fermilab Quantum Institute. “That’s really important, and they’re doing this at a longer distance at Brookhaven-Stony Brook than I think almost anybody else.”
A new kind of computing needs a new kind of internet
Quantum computers aren’t superpowerful versions of classical computers. Instead, they approach computing in a whole new way. They can theoretically take advantage of quantum mechanical concepts such as superposition and entanglement to solve certain types of problems -- for example, ones that show up when encrypting data or simulating chemical reactions -- much faster than traditional approaches. Quantum computing technology is still in the early stages of development, and many of the most promising applications remain unrealized. Other applications may have yet to be discovered.
Similarly, the “quantum internet” will not be a superfast and secure version of today’s internet. Instead, it will likely have particular applications transferring quantum information between computers. To do this, the computers’ qubits are entangled, meaning they are put in a superposition in which their separate possible quantum states become dependent on each other and the qubits then become a single quantum system. Measuring the state of one of these qubits breaks the superposition, immediately influencing the state of the others -- and this measurement/entanglement process is how quantum information can be transmitted.
Entanglement between two quantum computers has been experimentally possible for several years, but the team at Brookhaven and Stony Brook has gone one step further: They have created the longest quantum network in the United States by showing that two quantum computers can be entangled using a third node. This is the first step in building a network where many computers can “talk” to each other through a central node.
To do the experiment, the researchers faced a challenge unique to quantum systems: In order to entangle quantum particles, which make up qubits, the particles must arrive at the node completely indistinguishable from one another even though they took different paths to get there. The more different the path, the more difficult this is -- and the network between Brookhaven and Stony Brook runs over traditional fiber-optic cables that are miles long, going under the neighborhoods and highways of Long Island.
“It’s not really feasible to lay new cables everywhere, so being able to use what’s in the ground was important,” said Kerstin Kleese Van Dam, the director of Brookhaven’s Computational Science Initiative.
Any unexpected interaction between one of the transmitted quantum particles and its environment might have made it distinguishable from the other. But despite all the potential sources of interference, the experiment was able to prove that the particles could travel over 70 kilometers (almost 45 miles) over traditional infrastructure and still arrive indistinguishable.
“Our results demonstrate that these photons can be entangled, that the measurement will work,” said Eden Figueroa, a quantum physicist at Stony Brook University and lead scientist of the project.
The recent experiment was one-way: The quantum computers sent their qubits to the node, but the node simply determined whether they could be entangled and didn’t send anything back. The next step, Figueroa said, is to entangle the computers’ quantum memories, which would be analogous to linking two traditional computers’ hard drives.
“Down the line we hope that instead of just memories, we will be entangling computers -- not just connecting the hard drives but also the processing units,” Figueroa said. “Of course, that’s not easy.”
How far away is the quantum internet?
The remaining obstacles to a quantum internet are a blend of research questions and infrastructure concerns. One issue is that manipulating qubits between quantum computers requires synchronization and supervision in a way that the management of traditional bits doesn’t. This means that while quantum computers can’t directly exchange quantum information over the internet, they still need conventional computers that do use the internet to communicate.
“You cannot build a quantum network and be successful without a classical network,” said Inder Monga, the director of the Energy Sciences Network, which provides networking services to all U.S. national labs. “You have to control, manage and synchronize the quantum devices over the classical network to really transmit information between the two ends of a quantum network.”
This reliance on traditional internet means that the endeavor to build a quantum internet is very interdisciplinary, Monga and Figueroa said. It requires expertise in basic quantum computing research as well as communication infrastructure engineering.
“There are as many research problems as are engineering problems,” Monga said, “and to really get to the vision of the quantum internet, it will require a strong collaboration between people and funding to solve not just the basic physics research problems but also the really grand engineering challenges as well.”
A central obstacle to the quantum internet is what Figueroa calls “the holy grail of quantum communication”: a quantum repeater. A quantum repeater works like an amplifier, in that it receives a signal of quantum information and passes it on so that entanglement between computers can happen at a greater distance. This is necessary to make a quantum internet that spreads beyond Long Island. But there’s a catch: Any interaction with a qubit breaks its superposition -- and for information to be transmitted, that can’t happen until the qubit reaches its destination. A true quantum repeater would be able to amplify a qubit without interacting with it, a seemingly paradoxical task.
The recent experiment is essentially half of a quantum repeater. Kleese Van Dam and Figueroa see a completed quantum repeater in the near future: possibly as soon as 2022, Figueroa said. They plan to transmit entanglement to a third lab in Brooklyn but need a quantum repeater to do so.
“We hope that in a few years, we might actually have a working system with repeaters,” Figueroa said. “The minute we can demonstrate that quantum repeater connection, you just need to reproduce the same architecture, again and again, to connect places that are more and more distant from each other.” He sees a network across New York state in 10-15 years.
The last obstacle is far more distant, in a future where the New York quantum network is connected to the one being built by Argonne National Laboratory and the University of Chicago, or the one being built in Europe. Those networks are built using fundamentally different quantum computers -- while the New York network uses computers whose qubits are embedded in single trapped atoms, the other networks use what are called solid-state systems to make and manipulate qubits. The two kinds of quantum computers perform computation with completely different architecture.
“You can imagine that the actual quantum internet is going to be a collection of solid-state-based quantum computers like the ones in Chicago and atomic-based quantum computers like the ones we have here, and we have to find a way to connect all of them to really come out with a first prototype of the quantum internet,” Figueroa said. “That would be very cool. That would be like science fiction.”
In July 2020, the U.S. Department of Energy released a “blueprint” of their strategy to create a national quantum internet. This effort includes the Brookhaven-Stony Brook project and the Argonne-University of Chicago project, which are in turn both supported by research at other national labs such as Fermi National Accelerator Laboratory, and Lawrence Berkeley, Oak Ridge, and Los Alamos National Laboratories.
“While quantum computing has gotten a lot of press and funding, the wave is going toward quantum networking,” Figueroa said, “because unless you connect quantum computers into this quantum internet, their applications will be limited. So, it is a good time to be doing these kinds of experiments.”


The current Cybersecurity architecture is based on cryptographic algorithms implemented all over the world. Authentication, digital signatures, encryption, electronic transactions, time-stamping, and secure network communication are few examples of everyday security implementations that are based on cryptography. The success of these cryptographic measures is dependent on the computational complexity of the Cryptographic protocols. For instance, the Advanced Encryption Standard (AES) used in symmetric cryptography has a key length of 128 (AES-128), 192 (AES-192), and 256 (AES-256) bits. If an attacker launches a brute-force attack against AES-128, it requires 2127 combination of keys which may take thousands of years with current computation powers. However, with the evolution and advancement in quantum computing, the complexity of these cryptographic algorithms may reduce significantly. Although it’s a great achievement in IT development sector, it can also be a great security threat to the current Cyberspace architecture. In this article, we will discover the idea of quantum computing, the current advancements in the field, and security challenges that may arise as the quantum computing flourishes with time.
What is Quantum Computing
Quantum computing is the scientific study and advancement in computer technology that is based on quantum physics. Quantum physics is the study of energy and matter at the most fundamental (i-e atomic and sub-atomic) level. To understand quantum computing, we need to understand few quantum physics terminologies including quantum, qubit, entanglement, and superposition.
Quantum: Quantum in physics is the smallest possible unit of a physical property. In quantum computing, the quantum is the mechanics used by the systems to calculate the outputs.
Superposition: The concept of superposition can be understood with the help of a coin example. Let’s assume flipping of a coin. In classical computing, we can only predict one of two positions of the coin i-e heads or tails. However, if we can see both heads and tails at the same time and all the states in between, the coin is said to be in superposition.
Qubit: The basic unit of information in quantum computing is called quantum bit or simply the qubit. The role of qubit in quantum computing is like the role of binary bits (0,1) in classical computing. However, the qubit can hold a superposition of all possible states.
Entanglement: Entanglement is the property to correlate the results of one qubit with other qubits. This property allows adding more qubits to the system and solving exponentially complex problems.
Quantum interference: Quantum interference is the native behavior of the qubits while attaining the superposition. Reducing interference is one of the biggest challenges of quantum computers technology.
To conclude the above, quantum computing can be thought of a computation that benefits from the collective properties of quantum states. Quantum computing relies on quantum computers with three major requirements: a space to hold the qubits, mechanism to transfer signals to the qubits, and classical computers to run commands and instructions.
Applications of Quantum Computing
Although Quantum computing is at evolutionary stage, experts have predicted the future use of Quantum computing in different domains. Following is a brief summary of some of the most popular fields where Quantum computing is expected to play a major role in future.
Artificial Intelligence (AI) is a fast-growing field with widespread applications, such as, Objects recognition, Speech recognition, Fraud detection, Spam detection, Malware detection, etc. Although traditional computers are handling majority of AI tasks efficiently, they do have limitations while solving complex computational problems. On the other hand, Quantum computer can manage these problems with better accuracy. For instance, AI is used in diagnostics of various ailments by discovering patterns. Quantum computing can make the discovery process faster with more accuracy.
Finance industry uses different simulations and algorithms to find out the best investment opportunities. Traditional computers are used to find out the best indicators by comparing past data. Technically, the traditional computers are not good enough to make the right decisions as compared to the Quantum computers. Quantum computers, being fast and more intelligent, can make right choices of investments in a very short span of time.
Quantum computing can also facilitate the weather forecasting sector. Traditional computing technology often makes wrong weather predictions since climate indicators change dramatically, making it difficult for traditional computers to timely and precisely predict the changes. These issues can easily be resolved with the introduction of Quantum computing in Weather forecasting field.
Quantum computing is expected to play a major role in solving optimization problems in industries using different quantum approaches (models). There are two main models in Quantum computing that are used for optimization problems. These are: (1) Universal Gate Model and (2) Quantum Annealers. Energy grids, large autonomous fleets, financial portfolio, logistic routes, and manufacturing designs are few example sectors where Quantum computing can play the optimization role.
With classical computers and technology, pharmaceutical industries spend billions of dollars and years to launch and market the product. Quantum computing can reduce this cost and time through correct drug designing, efficient testing, and faster target (patients) identification. Products designed and tested using Quantum computing shall be less prone to errors and trials as compared to pharmaceutical products prepared using classical R&D techniques.
Quantum Computing Security Challenges
Although Quantum computing is going to be a miraculous addition to the global technology, it can create undesired problems for the Cybersecurity industry at the same time. In Cyberspace, data is considered as the most valuable asset for any organization. Global industries spend billions of dollars to secure data from being hacked. The main technique used for data security is the application of encryption algorithms. Majority of the existing encryption techniques used by the enterprises are based on the idea of combinatorics, a mathematical field that deals with selection, arrangement, and mathematical operations within a discrete or finite systems. Quantum computing can solve the combinatorics quite easily to break almost all such encryption algorithms. This capability of Quantum computers is a big threat to the current encryption standards including AES and RSA public key cryptography.
There is a great deal of benefits for industries to adopt Quantum computing in the future. Data storage methods, processing techniques, and complex calculations in no time are the key attributes that differentiate Quantum computing from classical computing. Threat to current encryption algorithms and immense utilization of energy are the main drawbacks of Quantum technology. Many organizations have started working on Quantum-ready algorithms to counter the future Quantum threats to Cybersecurity.


Every particle reaction happens because of interaction with one of the fundamental forces. Baryons and mesons (Hadrons, not Leptons) are affected by the strong force. The weak force affects most particles. Particle decay occurs because of the 3 forces. Electrons don’t decay, they just turn themselves into other things, some with an electric charge.
The law of conservation of parity of particle (not true for the beta decay of nuclei) states that, if an isolated ensemble of particles has a definite parity, then the parity remains invariable in the process of ensemble evolution. Parity is a property that is important in the quantum-mechanical description of a physical system. In most cases, it relates to the symmetry of the wave function representing a system of fundamental particles. A parity transformation replaces such a system with a type of mirror image.
A strange particle is an elementary particle with a strangeness quantum number different from zero. The classification of particles, as mesons and baryons, follows the quark/anti-quark and three quark content respectively.
Particles of matter transfer discrete amounts of energy by exchanging bosons with each other. Each fundamental force has its own corresponding boson – the strong force is carried by the “gluon”, the electromagnetic force is carried by the “photon”, and the “W and Z bosons” are responsible for the weak force.
Bosons are force–carrying particles. This means that they are made up of tiny bundles of energy. Photon – Light is made up of a type of boson called a photon.
Mesons are hadrons that do not decay into protons, such as pinons and kaons. Pinons and kaons can be positive, neutral, and negative. Baryons and mesons aren’t fundamental particles and so can be split into smaller particles known as quarks. Leptons − Leptons are particles that interact using the weak nuclear force.
Hadrons are the heaviest particles. This group is then split- up into baryons and mesons. Baryons are the heaviest particles of all, followed by mesons. Leptons are the lightest particles. These 3 families carry a force from place to place. All 3 families have anti-particles.
Symmetry is the casual structure built into the creation module. The creation module has a two-way arrow of time that is built into it. All current information is always passed back into the versatile storage unit. These informational totals can’t be changed or deleted.
The closed sub-atomic quantum system is a duplicate of the macro quantum system. The two systems interact on a binary basis.
Photons mediate electromagnetic interactions between particles in quantum electrodynamics. An isolated electron at a constant velocity cannot emit or absorb a real photon; doing so would violate conservation of energy and momentum. Instead, virtual photons can transfer momentum between two charged particles.
Everything quantum has a wave-particle property duality. Also, quantum indeterminacy can be quantitatively characterized by a probability distribution on the set of outcomes of measurements of an observable. The distribution is uniquely determined by the system state, and moreover, quantum mechanics provides a recipe for calculating this probability distribution. Indeterminacy in measurement was not an innovation of quantum mechanics, since it had been established early on by experimentalists that errors in measurement may lead to indeterminate outcomes.
Retro causality, or backward causation, is a concept of cause and effect in which an effect precedes its cause in time and so a later event affects an earlier one. In quantum physics, the distinction between cause and effect is not made at the most fundamental level and so time-symmetric systems can be viewed as causal or retrocausal. Quantum entanglement does seem to violate causality in the context of Bell’s theorem but in reality, it doesn’t do so. In the subatomic realm, where the laws of quantum physics make seemingly impossible feats routine, the one thing that we always considered beyond the pale might just be true. This idea that the future can influence the present, and that the present can influence the past, is known as retrocausality. Entanglement of two particles does not violate causality when the particles are first entangled.
Key dates Dalton s Plum Pudding model Thomson s electron discovery Rutherford s scattering of α particles with Geiger & Marsden, Manchester Bohr theory of the atom Rutherford – the discovery of proton Dirac predicts positron Pauli predicts neutrino Fermi names predicted neutrino Anderson observes positron Chadwick – the discovery of neutron Segre & Chamberlain – observed anti-proton Neutrino observed Salam & Weinberg predicted W & Z bosons Perl – discovery of Tau 1784MeV/c² CERN W & Z bosons observed. Higgs Boson (not quite yet). Forces: 1. strong (hadronic) nuclear interaction (carried by exchange bosons called gluons). 2. electromagnetic interaction (carried by exchange particle photon). 3. weak nuclear interaction (carried by W & Z exchange bosons). 4. gravitational interaction (carried by predicted graviton exchange boson). atom. Hadrons. Strong, hadronic interactions. (made of quarks up, down & strange (flavors) top, bottom & charm (newer).) Leptons. Spin ½ s …so fermions. Weak interactions. Fundamental particles. No size. Mostly light. Each has an anti-particle. Baryons. Spin ½ s …so fermions. Heavy hadrons. Triplets of quarks. Mesons. Spin 0,1,2,..so bosons .. Intermediate mass. Quark & anti-quark. Muon νμ Neutrino. Electron e. Muon μ. Tau τ. (heavy) Nucleons. Others. Electron neutrino νe. Tau neutrino ντ. Neutron. Proton. Quarks. Fundamental particles. Up, down, strange, top, bottom, charm. Lambda. Sigma. Xi. Pion up. Kaon. Eta.
COMMON SENSE DOESN’T APPLY TO QUANTUM MECHANICS.
Every symmetry of physics laws leads to a conservation law, and every conservation law arises from a symmetry in the laws of physics.
THE LAW THAT CONTROLS ALL PARTICLE INTERACTIONS IS THIS:
ALL THINGS ARE TRIUNE, WITH BINARY INTERACTIVES. THIS IS THE LINKAGE BETWEEN MATTER AND FORCE CARRYING PARTICLES. FERMIONS AND BOSONS control THE LINKAGE BETWEEN THE PARTICLE ZOO.
THE REALITY OF HOW LIFE FORMS CAME ABOUT ON THIS REMOTE BLUE MARBLE IS THIS: THE EVENT ORIGINATOR WROTE THE CODE, PRODUCED THE BLUEPRINT, AND USED AN EVOLVEMENT PROCESS TO OBTAIN THE REQUISITE RESULT. IT’S ALL JUST A BINARY SOFTWARE PROGRAM.
IT’S ALL ABOUT THE CODE THAT YOU START WITH.
THE DESIGNER/CREATOR’S PROCESS : (recap).
1ST: Write the code for the upcoming big bang that will create another universe. (One universe does not an infinity make.)
2nd: Write the code for the design and descent for all intended results as the event unfolds. ( One event does not an eternity make).
3rd: Set the event in motion. All things are triune, with binary interactives.
4th: Monitor, fine-tune, adjust, and select out on-going.
5th: Use DESIGN AND DECENT as the process. Write a separately coded blueprint for the consciousness of the known thought reposers.
6th: It’s not the people, it’s the event.
7th: Harvesting new crops of known thought reposers was the intended result.
ONE EVENT DOES NOT make AN ETERNITY
EVOLUTION IS ONLY PART OF THE PROCESS.*
ALL THINGS ARE TRIUNE, WITH BINARY INTERACTIVES.
BEYOND GOOD AND EVIL IS ONLY GOOD.
ITS NOT THE PEOPLE, ITS THE EVENT.
GET BACK TO WHERE YOU ONCE BELONGED.
IN THE END, CHOICES ARE NEVER FREE.
WHAT TO DO, IS UP TO YOU. ONE UNIVERSE DOES NOT AN INFINITY MAKE. GETTING BEYOND EVIL IS A START.