Newswise — Using AI and computer automation, Technion researchers have developed a “conjecture generator” that creates mathematical conjectures, which are considered to be the starting point for developing mathematical theorems. They have already used it to generate a number of previously unknown formulas. The study, which was published in Nature, was carried out by undergraduates from different faculties under the tutelage of Assistant Professor Ido Kaminer of the Andrew and Erna Viterbi Faculty of Electrical Engineering at the Technion.
The project deals with one of the most fundamental elements of mathematics – mathematical constants. A mathematical constant is a number with a fixed value that emerges naturally from different mathematical calculations and mathematical structures in different fields. Many mathematical constants are of great importance in mathematics, but also in disciplines that are external to mathematics, including biology, physics, and ecology. The golden ratio and Euler’s number are examples of such fundamental constants. Perhaps the most famous constant is pi, which was studied in ancient times in the context of the circumference of a circle. Today, pi appears in numerous formulas in all branches of science, with many math aficionados competing over who can recall more digits after the decimal point:
The Technion researchers proposed and examined a new idea: The use of computer algorithms to automatically generate mathematical conjectures that appear in the form of formulas for mathematical constants.
A conjecture is a mathematical conclusion or proposition that has not been proved; once the conjecture is proved, it becomes a theorem. Discovery of a mathematical conjecture on fundamental constants is relatively rare, and its source often lies in mathematical genius and exceptional human intuition. Newton, Riemann, Goldbach, Gauss, Euler, and Ramanujan are examples of such genius, and the new approach presented in the paper is named after Srinivasa Ramanujan.
Ramanujan, an Indian mathematician born in 1887, grew up in a poor family, yet managed to arrive in Cambridge at the age of 26 at the initiative of British mathematicians Godfrey Hardy and John Littlewood. Within a few years he fell ill and returned to India, where he died at the age of 32. During his brief life he accomplished great achievements in the world of mathematics. One of Ramanujan’s rare capabilities was the intuitive formulation of unproven mathematical formulas. The Technion research team therefore decided to name their algorithm “the Ramanujan Machine,” as it generates conjectures without proving them, by “imitating” intuition using AI and considerable computer automation.
According to Prof. Kaminer, “Our results are impressive because the computer doesn’t care if proving the formula is easy or difficult, and doesn’t base the new results on any prior mathematical knowledge, but only on the numbers in mathematical constants. To a large degree, our algorithms work in the same way as Ramanujan himself, who presented results without proof. It’s important to point out that the algorithm itself is incapable of proving the conjectures it found – at this point, the task is left to be resolved by human mathematicians.”
The conjectures generated by the Technion’s Ramanujan Machine have delivered new formulas for well-known mathematical constants such as pi, Euler’s number (e), Apéry’s constant (which is related to the Riemann zeta function), and the Catalan constant. Surprisingly, the algorithms developed by the Technion researchers succeeded not only in creating known formulas for these famous constants, but in discovering several conjectures that were heretofore unknown. The researchers estimate this algorithm will be able to significantly expedite the generation of mathematical conjectures on fundamental constants and help to identify new relationships between these constants.
As mentioned, until now, these conjectures were based on rare genius. This is why in hundreds of years of research, only a few dozens of formulas were found. It took the Technion’s Ramanujan Machine just a few hours to discover all the formulas for pi discovered by Gauss, the “Prince of Mathematics,” during a lifetime of work, along with dozens of new formulas that were unknown to Gauss.
According to the researchers, “Similar ideas can in the future lead to the development of mathematical conjectures in all areas of mathematics, and in this way provide a meaningful tool for mathematical research.”
The research team has launched a website, RamanujanMachine.com, which is intended to inspire the public to be more involved in the advancement of mathematical research by providing algorithmic tools that will be available to mathematicians and the public at large. Even before the article was published, hundreds of students, experts, and amateur mathematicians had signed up to the website.
The research study started out as an undergraduate project in the Rothschild Scholars Technion Program for Excellence with the participation of Gal Raayoni and George Pisha, and continued as part of the research projects conducted in the Andrew and Erna Viterbi Faculty of Electrical Engineering with the participation of Shahar Gottlieb, Yoav Harris, and Doron Haviv. This is also where the most significant breakthrough was made – by an algorithm developed by Shahar Gottlieb – which led to the article’s publication in Nature. Prof. Kaminer adds that the most interesting mathematical discovery made by the Ramanujan Machine’s algorithms to date relates to a new algebraic structure concealed within a Catalan constant. The structure was discovered by high school student Yahel Manor, who participated in the project as part of the Alpha Program for science-oriented youth. Prof. Kaminer added that, “Industry colleagues Uri Mendlovic and Yaron Hadad also participated in the study, and contributed greatly to the mathematical and algorithmic concepts that form the foundation for the Ramanujan Machine. It is important to emphasize that the entire project was executed on a voluntary basis, received no funding, and participants joined the team out of pure scientific curiosity.”
Prof. Ido Kaminer is the head of the Robert and Ruth Magid Electron Beam Quantum Dynamics Laboratory. He is a faculty member in the Andrew and Erna Viterbi Faculty of Electrical Engineering and the Solid State Institute, and affiliated with the Helen Diller Quantum Center and the Russell Berrie Nanotechology Institute.
For more than a century, the Technion – Israel Institute of Technology has pioneered in science and technology education and delivered world-changing impact. Proudly a global university, the Technion has long leveraged boundary-crossing collaborations to advance breakthrough research and technologies. Now with a presence in three countries, the Technion will prepare the next generation of global innovators. Technion people, ideas and inventions make immeasurable contributions to the world, innovating in fields from cancer research and sustainable energy to quantum computing and computer science to do good around the world.
The American Technion Society supports visionary education and world-changing impact through the Technion – Israel Institute of Technology. Based in New York City, we represent thousands of US donors, alumni and stakeholders who invest in the Technion’s growth and innovation to advance critical research and technologies that serve the State of Israel and the global good. Over more than 75 years, our nationwide supporter network has funded new Technion scholarships, research, labs, and facilities that have helped deliver world-changing contributions and extend Technion education to campuses in three countries.


“Quantum computers promise to solve problems that conventional computers cannot, because qubits can exist in multiple states at the same time. Using this quantum physics phenomenon, qubits can perform large amounts of calculations simultaneously, which can greatly speed up the speed of solving complex problems.
Quantum computers promise to solve problems that conventional computers cannot, because qubits can exist in multiple states at the same time. Using this quantum physics phenomenon, qubits can perform large amounts of calculations simultaneously, which can greatly speed up the speed of solving complex problems.
Traditional computer vs quantum computer
One of the differences between quantum computers and traditional computers is computing power. The former can solve a large number of operations that are difficult for traditional computers to handle.
For example, given the same complex data task, a quantum computer can do it in a matter of minutes, while today’s best-performing conventional computers would take thousands of years.
The key to this is the “qubits” that are the core of a quantum computer. From the perspective of quantum physics, qubits can exist in multiple states at the same time, and can perform a large number of operations several times higher than traditional computers, greatly speeding up the speed of solving complex problems.
For most quantum computers, qubits must be kept operating at extremely cold temperatures close to stopping atoms from moving. As a result, qubits are often placed in a special refrigerator, also known as a “quantum refrigerator,” while other devices are placed around the quantum refrigerator.
But controlling a quantum processor requires hundreds of wires to go in and out of the refrigerator, a wire design that would greatly constrain the ability of a quantum system to scale to the hundreds or thousands of qubits needed to demonstrate quantum utility, while also preventing Making qubits send and receive information is very difficult. This has also become a problem that scientists must solve in the process of advancing the development of quantum computing.
However, as companies managed to increase the number of qubits in a chip, and thus the computing power of the chip, they started to run into a problem.
The ultimate goal is to minimize the number of wires going into the chiller. Intel recognizes that quantum control is an integral part of the puzzle it needs to solve to develop large-scale commercial quantum systems.
Intel unveils cryogenic chips
At the IEEE International Electron Devices Conference in San Francisco this week, Intel Corp. unveiled a cryogenic chip designed to speed up a quantum computer they have developed in collaboration with the QuTech research group at Delft University.
The chip, called Horse Ridge, one of the coldest areas in Oregon, uses specially designed transistors to provide microwave control signals for Intel’s quantum computing chips.
The chip is designed to operate at 4 Kelvin, slightly above the temperature of the qubit chip itself. The company made the chip using its 22-nanometer FinFET process, although the transistors that make up the control circuitry required extensive redesign.
The chip can control multiple qubits in a quantum computer, and Intel sees the development of the chip as a major milestone on the road toward a truly viable quantum computer.
Intel claims that Horse Ridge lays the foundation for future controllers that will be able to control thousands or even millions of qubits, enabling the realization of quantum computers. Miniaturization is key, they claim, and it’s worth noting that miniaturization is one of Intel’s strong suits.
Dealing with the Difficulties of Quantum Computers
While most quantum chips and computers need to be placed at absolute zero to function properly, the Horse Ridge chip operates at about 4 degrees Kelvin, which is slightly warmer than absolute zero.
Because each of these particles is individually controlled, the wiring’s ability to scale quantum computing systems to hundreds or thousands of qubits achieves remarkable performance levels.
Horse Ridge SoCs use sophisticated signal processing techniques to convert instructions into microwave pulses that can manipulate the state of qubits.
The solution is to put as much control and readout electronics as possible into the refrigerator, possibly even integrating them on a qubit chip.
Horse Ridge integrates the control electronics onto a chip used to operate inside the refrigerator using the qubit chip. It is programmed with instructions corresponding to basic qubit operations, which are converted into microwave pulses that can control the state of the qubits.
Milestones of Horse Ridge
For a long time, in the race to realize the functions of quantum computers and unleash their potential, researchers have paid more attention to the manufacture of qubits, building test chips to demonstrate the powerful capabilities of a few qubits in superposition states.
But early quantum hardware development at Intel, including the design, testing and characterization of silicon spin qubit and superconducting qubit systems, identified the main bottlenecks preventing quantum computing from scaling commercially: interconnect and control.
Intel sees Horse Ridge as opening an “elegant solution” that allows multiple qubits to be controlled, and sets a clear path for building systems that can control more qubits in the future, an important milestone toward quantum utility .
Through Horse Ridge, Intel is able to scale quantum systems to the hundreds or thousands of qubits needed to demonstrate quantum utility, and thus the millions of qubits needed to achieve commercially viable quantum solutions.
Manufacturing the control chip, which is done in-house at Intel, will greatly improve the company’s ability to design, test and optimize commercially viable quantum computers.
These devices are often custom-designed to control individual qubits, requiring hundreds of connecting wires to go in and out of the refrigerator. But this extensive control cable for each qubit hinders the ability to scale quantum systems to the hundreds or thousands of qubits needed to demonstrate quantum utility, let alone commercially viable quantum solutions. Millions of qubits.
With Horse Ridge, Intel can radically simplify the control electronics needed to operate quantum systems. Replacing these bulky instruments with highly integrated SoCs will simplify system design and allow the use of sophisticated signal processing techniques to speed up setup times, improve qubit performance, and enable systems to efficiently scale to larger qubit counts.
Microsoft and Amazon join the fray
Quantum computing is a hot research field. Although we have not yet seen what a quantum computer is, IBM, Google, and Amazon are already vying for the quantum market.
Quantum computing will enable most consumption to be realized through the cloud. If it can be successful, quantum computers will have a very amazing increase in computing power. Judging from the prototype pictures disclosed by various companies, they are all huge and controlled by hundreds of wires. A behemoth with dense lines.
Of course, other quantum computing companies with massive qubit numbers are working on the same problem. Earlier this year, Google described some ideas for a cryogenic control circuit for its machines. In short, Intel’s breakthroughs are very helpful for them to launch higher-integrated quantum chips.
・Microsoft announced a cloud computing service called Azure Quantum at its Ignite conference in November; it integrates Microsoft’s previously released quantum programming tools with cloud services, allowing coders to work on simulated quantum hardware or on real quantum computers Run quantum code.
・Amazon unveiled a preview of Amazon Braket at AWS re:Invent in December; it also said that the creation of the “AWS Quantum Computing Center,” a physics lab near Caltech, is underway Bringing together the world’s leading quantum computing researchers and engineers to accelerate the development of quantum computing hardware and software.
Quantum computing currently faces many challenges, one of which is that superconducting qubits only really work at temperatures close to absolute zero.
Both Google and IBM required bulky control and cooling systems to develop quantum computing, some tubes larger than a human being, and hundreds of wires to connect to external microwave transmitters.
Despite the great emphasis placed on the qubits themselves, the ability to control multiple qubits simultaneously has been an industry challenge. Intel recognizes that quantum control is one of the most pressing challenges for us to develop large-scale commercial quantum computing systems. That’s why we’re investing in quantum error correction and control.
Competitors to Intel, Google and IBM, are primarily focused on superconducting qubits, the quantum computing systems driven by them that need to operate in the millikelvin range, just a tad above absolute zero.
But Intel believes that silicon spin qubits have the potential to work at higher temperatures, about 1 Kelvin, in hopes of enabling differentiated competition.
Given that Intel once tried to recreate the leadership of computer chips in the field of mobile chips, poured years of effort and huge sums of money, but it still ended in failure, now calling Horse Ridge a disruptive achievement and a “killer” that surpassed Google and IBM. It’s too early.


NIST's Atomic Clocks
All clocks must have a regular, constant or repetitive process or action to mark off equal increments of time. Examples include the daily movement of the sun across the sky, a swinging pendulum or vibrating crystal. In the case of atomic clocks, the beat is kept by a transition between two energy levels in an atom.
NIST-F1 and NIST-F2 are microwave clocks, based on a particular vibration in cesium atoms of about 9 billion cycles per second. Optical atomic clocks are based on ions or atoms vibrating at optical frequencies (visible, ultraviolet or infrared light), which are about 100,000 times higher than microwave frequencies. Because optical clocks divide time into smaller units—like a ruler with finer tick marks—they ultimately could be perhaps 100 times more accurate and stable than microwave clocks. Higher frequency is one of the features enabling improved accuracy and stability. One key advance making optical atomic clocks possible was the development of frequency combs at JILA, NIST and elsewhere. Frequency combs link optical frequencies to lower frequencies that can be correlated with microwave standards and counted.
NIST's first all-optical atomic clock, and the best in the world for several years, was based on a single mercury ion. Its performance was then surpassed by NIST's quantum logic clock, based on a single aluminum ion. This clock got its nickname because it borrows techniques from experimental quantum computing. Aluminum is insensitive to changes in magnetic and electric fields and temperature, making it a great ion for atomic clocks, but it wasn't practical until NIST developed new quantum computing technologies.
NIST and JILA are leaders in the development of so-called optical lattice clocks. These clocks trap thousands of heavy metal atoms in an "optical lattice" formed by intersecting laser beams. Research clocks at NIST use ytterbium atoms and JILA research clocks use strontium atoms. Thanks to the presence of so many atoms, these clocks offer the advantages of strong signals and parallel processing. In addition, the atoms are held virtually still in the lattice, reducing errors from atomic motion and collisions that otherwise would need to be corrected.
Optical lattice clocks are rapidly improving, and continue to set new performance records so often that it is difficult to keep track of the latest records. Both the JILA strontium and NIST ytterbium optical lattice clocks are rapidly advancing in stability. And now, for the first time in decades, a single type of atomic clock, an optical lattice clock, simultaneously holds the records for both precision and stability – and it is likely optical lattice clock performance will continue to significantly improve.
This rapid improvement in optical lattice clocks at JILA and NIST results from key scientific breakthroughs. One has been the development of extremely stable lasers, including the world's most stable laser at JILA. Another key breakthrough has been development of new theories about how atoms trapped in the optical lattices interact, and application of these theories to significantly reduce the uncertainties in optical lattice clocks. And much of the improvement results from the hard and creative work of many scientists, students and postdoctoral fellows to continually find new ways to make a series of many small improvements in clock performance.
NIST also has demonstrated a calcium atomic clock that is extremely stable for short time periods. This clock has the potential to be made portable, making it attractive for commercial applications.
Evaluating Atomic Clock Performance
Accuracy refers to a clock's capability to measure the accepted value of the frequency at which the clock atoms vibrate, or resonate. Accuracy is crucial for time measurements that must be traced to primary standards such as NIST-F1 and NIST-F2. Technical terms for accuracy include "systematic uncertainty" or "fractional frequency uncertainty"—that is, how well scientists can define shifts from the true frequency of an atom with confidence.
Cesium standards like NIST-F1 and NIST-F2 are the ultimate "rulers" for time because the definition of the SI second is based on the cesium atom. More specifically, the SI unit of frequency, the Hertz, is defined internationally by the oscillations of a cesium atom. Officially, no atomic clock can be more accurate than the best cesium clock by definition. That is, only a direct measurement of the particular cesium transition can be considered the ultimate measurement of accuracy, and all other (non-cesium) clocks can only be compared to the accuracy of a cesium clock. This is partly a semantic issue. If after further development and testing the definition of the second (or Hertz) were changed to be based on the strontium atom transition, for example, the NIST/JILA strontium atom lattice clock would become the most accurate clock in the world.
To get around this measurement hurdle, NIST scientists evaluate optical atomic clocks by comparing them to each other (to obtain a ratio, or relative frequency, for which there is no official unit), and by measuring all deviations from the true resonant frequency of the atom involved, carefully accounting for all possible perturbations such as magnetic fields in the environment. The optical clock performance is also directly compared to the NIST-F1 standard. For several years both NIST ion clocks have had measured relative uncertainties much smaller than NIST-F1's.
(In general literature, NIST sometimes uses the term "precise" to describe the performance of optical clocks, because it less technical and has a more positive connotation than uncertainty. Precision implies that repeated measurements fall within a particular error spread around a given value. In everyday definitions of precision, this value is not necessarily the "correct" one—you can be precise without necessarily being accurate. However, in the context of optical clocks, NIST uses precision specifically to mean the spread around the true or accepted value for the atom's resonant frequency.)
Stability is another important metric for evaluating atomic clocks. NIST defines stability as how precisely the duration of each clock tick matches every other tick. Because the ticks of any atomic clock must be averaged for some period to provide the best results, a key benefit of high stability is that optimal results can be achieved very quickly. Stability is not traceable to a time standard, but in many applications stability is more important than absolute accuracy. For example, most communications and GPS positioning applications depend on synchronization of different clocks, requiring stability but not necessarily the greatest accuracy. (Other common terms for stability include precision.)
The optical lattice clocks at NIST and JILA are much more stable than NIST-F1. NIST-F1 must be averaged for about 400,000 seconds (about five days) to achieve its best performance of about 1 second in 100 million years. In contrast, the ytterbium and strontium lattice clocks reach that level of performance in a few seconds of averaging, and after a few hours of averaging are about 100 times more stable than NIST-F1.
NIST scientists are also working to improve the portability of next-generation atomic clocks for applications outside the laboratory.


In the early days of research on black holes, before they even had that name, physicists did not yet know if these bizarre objects existed in the real world. They might have been a quirk of the complicated math used in the then still young general theory of relativity, which describes gravity. Over the years, though, evidence has accumulated that black holes are very real and even exist right here in our galaxy.
Today another strange prediction from general relativity—wormholes, those fantastical sounding tunnels to the other side of the universe—hang in the same sort of balance. Are they real? And if they are out there in our cosmos, could humans hope to use them for getting around? After their prediction in 1935, research seemed to point toward no—wormholes appeared unlikely to be an element of reality. But new work offers hints of how they could arise, and the process may be easier than physicists have long thought.
The original idea of a wormhole came from physicists Albert Einstein and Nathan Rosen. They studied the strange equations that we now know describe that unescapable pocket of space we call a black hole and asked what they really represented. Einstein and Rosen discovered that, theoretically at least, a black hole’s surface might work as a bridge that connected to a second patch of space. The journey might be as if you went down the drain of your bathtub, and instead of getting stuck in the pipes, you came out into another tub just like the first.
Subsequent work expanded this idea but turned up two persistent challenges that prevent the formation of easily spotted, humanly usable wormholes: fragility and tininess. First, it turns out that in general relativity, the gravitational attraction of any normal matter passing through a wormhole acts to pull the tunnel shut. Making a stable wormhole requires some kind of extra, atypical ingredient that acts to keep the hole open, which researchers call “exotic” matter.
Second, the kinds of wormhole-creating processes that scientists had studied rely on effects that could prevent a macroscopic traveler from entering. The challenge is that the process that creates the wormhole and the exotic matter that stabilizes it cannot stray too far from familiar physics. “Exotic” does not mean physicists can dream up any sort of stuff that gets the job done on paper. But so far, familiar physics has delivered only microscopic wormholes. A bigger wormhole seems to require a process or type of matter that is both unusual and believable. “That’s the delicacy,” says Brianna Grado-White, a physicist and wormhole researcher at Brandeis University.
A breakthrough occurred in late 2017, when physicists Ping Gao and Daniel Jafferis, both then at Harvard University, and Aron Wall, then at the Institute for Advanced Study in Princeton, N.J., discovered a way to prop open wormholes with quantum entanglement—a kind of long-distance connection between quantum entities. The peculiar nature of entanglement allows it to provide the exotic ingredient needed for wormhole stability. And because entanglement is a standard feature of quantum physics, it is relatively easy to create. “It’s really a beautiful theoretical idea,” says Nabil Iqbal, a physicist at Durham University in England, who was not involved in the research. Though the method helps to stabilize wormholes, it can still deliver only microscopic ones. But this new approach has inspired a stream of work that uses the entanglement trick with different sorts of matter in the hopes of bigger, longer-lasting holes.
One easy-to-picture idea comes from a preprint study by Iqbal and his Durham University colleague Simon Ross. The two tried to see if they could make the Gao-Jafferis-Wall method produce a large wormhole. “We thought it would be interesting, from a sci-fi point of view, to push the limits and see whether this thing could exist,” Iqbal says. Their work showed how special disturbances within the magnetic fields surrounding a black hole could, in theory, generate stable wormholes. Unfortunately, the effect still only forms microscopic wormholes, and Iqbal says it is highly unlikely the situation would occur in reality.
Iqbal and Ross’s work highlights the delicate part of wormhole construction: finding a realistic process that does not require something added from way beyond the bounds of familiar physics. Physicist Juan Maldacena of the Institute for Advanced Study, who had suggested connections between wormholes and entanglement back in 2013, and his collaborator Alexey Milekhin of Princeton University have found a method that could produce large holes. The catch in their approach is that the mysterious dark matter that fills our universe must behave in a particular way, and we may not live in a universe anything like this. “We have a limited toolbox,” Grado-White says. “To get something to look the way we need it, there’s only so many things we can do with that toolbox.”
The boom in wormhole research continues. So far, nothing like a made-to-order human-sized wormhole machine looks likely, but the results do show progress. “We’re learning that we can, in fact, build wormholes that stay open using simple quantum effects,” Grado-White says. “For a very long time, we didn’t think these things were possible to build—it turns out that we can.”


Whether it’s an app, a software feature, or an interface element, programmers possess the magical ability to create something new out of virtually nothing. Just give them the hardware and a coding language, and they can spin up a program.
But what if there was no other software to learn from, and computer hardware didn’t yet exist?
Welcome to the world of Ada Lovelace, the 19th-century English writer and mathematician most famous for being popularly described as the world’s first computer programmer — and all approximately one full century before the creation of the first programmable, electronic, general-purpose digital computers.
Lovelace only lived to the age of 36, but did enough during her short life to more than cement her legacy in the history of computing. (In Steve Jobs biographer Walter Isaacson’s book The Innovators, she is the title of chapter one: ground zero of the tech revolution.)
Working with the English polymath Charles Babbage on his proposed mechanical general-purpose computer, the Analytical Engine, Lovelace recognized its potential for more than just calculation. This conceptual leap, seeing the manipulation of digits as being not simply the key to faster math, underpins most of what has followed in the world of computation.
“To Babbage, the Engine was little more than a big calculating machine,” Christopher Hollings, Departmental Lecturer in Mathematics and its History at the Mathematical Institute of the University of Oxford, and co-author of Ada Lovelace: The Making of a Computer Scientist, told Digital Trends. “But Lovelace seems to have recognized that its programmable nature might mean that it would be capable of much more, that it might be able to do creative mathematics, or even compose original music. The fact that she was speculating about the capabilities of a machine that never existed, in combination with the fact that her comments tally with what we now know of computing, is what has given her writings modern interest.”
Hollings said that there is a popular myth that Ada Lovelace was pushed into studying math by her mother to divert her from any “dangerous poetical tendencies” that she might have inherited from her absentee father, the Romantic poet Lord Byron. (Who, like his daughter, tragically died at the age of 36.) However, he noted, the truth is likely to be “much more prosaic — and interesting” than that.
“Lady Byron had, unusually for a woman at that time, been educated in mathematics in her youth, had enjoyed it, and wanted to pass that on to her own daughter,” Hollings explained. “And I think the desire to study mathematics is the strongest influence on what Lovelace did in computing. From the mid-1830s, she was determined to learn higher mathematics and she put in years of work in order to do so, and this led directly into her collaboration with Babbage.”
Lovelace’s insights into computing included hypothesizing about the concept of a computer able to be programmed and reprogrammed to perform limitless activities; seeing the potential for storing, manipulating, processing, and acting upon anything — from words to music — that could be expressed in symbols; describing one of the first step-by-step computer algorithms, and — finally — posing the question of whether a machine can ever truly think (she believed not). As such, while her work concerned hardware that never appeared during her lifetime, she nonetheless laid crucial foundational steps.
Lovelace served as a first in another important way: One of the first tragic stories in the history of computing. Beyond the “notes” (some 19,136 words in total) she wrote in connection to Babbage’s Analytical Engine, she never published another scientific paper. As noted, she also died young, of uterine cancer, after several turbulent years, including a toxic relationship and problems with opiates. These have shaped several of the previous popular tellings of her story — although this is now changing.
“Much of the interest in the past has been more to do with who her father was, and the romantic idea of an unconventional aristocrat,” Hollings said. “Lurid tales of adultery, gambling, and drug addiction have also been thrown into the mix, probably in a way that they would not have been — certainly not with the same emphasis — if the discussion were about a man.”
Nonetheless, today Lovelace is widely viewed as both a feminist icon and a computing pioneer. She is frequently referenced in history books, has multiple biographies dedicated to exploring her life, and is namechecked in various places — whether that’s the naming of Ada, a programming language developed by the U.S. Defense Department, or of internal cryptocurrency used by the Cardano public blockchain. In all, she’s one of the most famous names in her field and, while her untimely death means there will continue to be debate around what she did or didn’t contribute, Ada Lovelace has more than cemented her place in history.
And with people continuing to probe questions like whether or not a machine could ever achieve sentience, don’t expect that to change any time soon.
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One of the hottest topics of conversation among the C-suite has been quantum computing. Unlike traditional computers, businesses use quantum computers to complete complex tasks faster and more efficiently. This is mainly due to their extraordinary ability to compute and analyze large volumes of data. Indeed, Google recently made news by claiming quantum supremacy, saying that its computers can execute tasks that a classical computer cannot. Many other giant firms are boasting about their lightning-fast supercomputers and opting for AI/ML consulting firms. However, we are curious as to what quantum computing is and what applications it has in the actual world. We’ll go over all of this in this post, as well as some of the most practical quantum computing applications.
What Is Quantum Computing?
Quantum computing is a branch of computing that focuses on developing computer technology based on quantum theory’s concepts. Quantum computers utilize quantum physics’ distinctive properties, such as superposition, entanglement, and quantum interference, in computing. This method introduces new concepts to traditional programming methods. Although quantum computing has obvious scalability and incoherence issues, it allows for several simultaneous operations and eliminates the tunnel effect.
How Does Quantum Computing Work?
The qubit rather than the conventional bit serves as the fundamental unit of information in quantum computing. Basically, a quantum computer comprises three major components: an area that houses the qubits, a means for sending signals to the qubits, and a classical computer that runs a program and sends instructions. The key feature of this advanced system is that it allows for the coherent superposition of ones and zeros. Classical computers can only encode data in bits with values of 1 or 0, severely limiting their possibilities.
In contrast, Quantum computing makes use of quantum bits, also known as qubits. It takes advantage of subatomic particles’ unusual capacity to exist in many states, such as using 1 and 0 at the same time. As a result, instead of 1s or 0s, quantum computers execute computations based on the probability of an object’s condition. This means that they can process exponentially more data than traditional computers. To maximize coherence and minimize interference, the unit that holds qubits is kept at a temperature just above absolute zero. This is applicable in some techniques of qubit storage. Other forms of qubit housing use a vacuum chamber to help minimize vibrations and stabilize the qubits.
Quantum Computer Uses And Application Areas
Although a quantum computer cannot perform all tasks quicker than a classical computer, there are a few areas where quantum computers have the potential to have a significant effect. Here are some of its best applications.
Quantum simulators are machines that exploit quantum effects to actively answer queries about model systems and, through them, real systems. Because quantum computers utilize quantum phenomena in their computing, they are particularly good at mimicking other quantum systems. Quantum simulation can be approached from both a theoretical and an experimental standpoint, paving the path for new discoveries. Understanding the precise quantum dynamics of chemical reactions, for example, can have enormous environmental benefits. We could develop technologies that are faster and more energy-efficient.
Due to the increasing amount of cyber-attacks that occur on a daily basis around the world, the online security environment has become rather vulnerable. Despite the fact that organizations are instituting the necessary security standards, traditional digital systems find the process challenging and unfeasible. As a result, cybersecurity has remained a major worry all over the world. We are becoming even more vulnerable to these risks as our reliance on technology grows. Quantum computing, along with machine learning, can aid in the development of various strategies to combat these cyber threats. The intractability of problems like integer factorization is used in conventional cryptography, which is commonly used to safeguard data transfer. Many of these problems could be solved more quickly with quantum computers. Additionally, Quantum computing can also aid in the development of encryption systems, commonly known as quantum cryptography.
Quantum computers have unique properties that make them potentially more effective at addressing complicated optimization issues. This is accomplished by using the quantum property of superposition to represent all possible answers and identifying economic & impactful solutions. For these issues, traditional approaches have either exponentially increasing compute times or sub-optimal performance. Quantum optimization methods, such as quantum approximate optimization algorithms, promise to provide answers that improve on sub-optimal solutions without requiring exponentially larger computation durations. As a result, we can identify solutions that were previously unthinkable by using quantum-inspired optimization methods.
Web Pages Ranking
A quantum algorithm discovered in 1996 dramatically speeds up the solution to unstructured data searches by running the search in fewer steps than any other method. It’s thought that a quantum computer could rank the most important Web pages faster than traditional computers and that this quantum speedup would improve as the number of pages to rank grew. Furthermore, according to many researchers, top AI development companies, and AI/ML consulting firms, a quantum computer will be able to spit out a yes-or-no answer 10 times faster than a traditional computer when evaluating whether the Web’s page rankings should be changed.
Data security, optimization, and searches will all be altered by quantum computers. Despite the fact that quantum computers will be able to crack many of today’s encryption techniques, it is likely that they will develop hack-proof alternatives. Quantum computing differs from regular computing in how it operates and what are its uses. The race is obviously on, even though a genuine quantum computer is still a long way off. Quantum computers aren’t meant to be a replacement for traditional computers; rather, they’re supposed to be an additional tool for tackling specific challenges. As a result, quantum computing has significantly increased in power and can now be utilized for large-scale data processing and simulations. If you have any concerns regarding how quantum computing can affect your business or how to get started, please contact us at Ksolves, the top AI development company.


The materials could open up possibilities for a new kind of devices based on spintronics, which makes use of a characteristic of electrons called spin, instead of using their electrical charge the way electronic devices do. It could also allow for much faster control of existing technologies such as magnetic data storage.
Topological insulators are materials that possess paradoxical properties. The three-dimensional bulk of the material behaves just like a conventional insulator (such as quartz or glass), which blocks the movement of electric currents. Yet the material’s outer surface behaves as an extremely good conductor, allowing electricity to flow freely.
The key to understanding the properties of any solid material is to analyze the behavior of electrons within the material — in particular determining what combinations of energy, momentum and spin are possible for these electrons, explains MIT assistant professor of physics Nuh Gedik, senior author of two recent papers describing the new findings. This set of combinations is what determines a material’s key properties — such as whether it is a metal or not, or whether it is transparent or opaque. “It’s very important, but it’s very challenging to measure,” Gedik says.
The traditional way of measuring this is to shine a light on a chunk of the solid material: The light knocks electrons out of the solid, and their energy, momentum and spin can be measured once they are ejected. The challenge, Gedik says, is that such measurements just give you data for one particular point. In order to fill in additional points on this landscape, the traditional approach is to rotate the material slightly, take another reading, then rotate it again, and so on — a very slow process.
Gedik and his team, including graduate students Yihua Wang and James McIver, and MIT Pappalardo postdoctoral fellow David Hsieh, instead devised a method that can provide a detailed three-dimensional mapping of the electron energy, momentum and spin states all at once. They did this by using short, intense pulses of circularly polarized laser light whose time of travel can be precisely measured.
By using this new technique, the MIT researchers were able to image how the spin and motion are related, for electrons travelling in all different directions and with
different momenta, all in a fraction of the time it would take using alternative methods, Wang says. This method was described in a paper by Gedik and his team that appeared Nov. 11 in the journal Physical Review Letters.
In addition to demonstrating this novel method and showing its effectiveness, Gedik says, “we learned something that was not expected.” They found that instead of the spin being precisely aligned perpendicular to the direction of the electrons’ motion, when the electrons moved with higher energies there was an unexpected tilt, a sort of warping of the expected alignment. Understanding that distortion “will be important when these materials are used in new technologies,” Gedik says.
The team’s high-speed method of measuring electron motion and spin is not limited to studying topological insulators, but could also have applications for studying materials such as magnets and superconductors, the researchers say.
One unusual characteristic of the way electrons flow across the surface of these materials is that unlike in ordinary metal conductors, impurities in the material have very little effect on the overall electrical conductivity. In most metals, impurities quickly degrade the conductivity and thus hinder the flow of electricity. This relative imperviousness to impurities could make topological insulators an important new material for some electronic applications, though the materials are so new that the most important applications may not yet be foreseen. One possibility is that they could be used for transmission of electrical current in situations where ordinary metals would heat up too much (because of the blocking effect of impurities), damaging the materials.
In a second paper, appearing today in the journal Nature Nanotechnology, Gedik and his team show that a method similar to the one they used to map the electron states can also be used to control the flow of electrons across the surface of these materials. That works because the electrons always spin in a direction nearly perpendicular to their direction of travel, but only electrons spinning in a particular direction are affected by a given circularly polarized laser beam. Thus, that beam can be used to push aside all of the electrons flowing in one direction, leaving a usable electric current flowing the other way.
“This has very immediate device possibilities,” Gedik says, because it allows the flow of current to be controlled completely by a laser beam, with no direct electronic interaction. One possible application would be in a new kind of electromagnetic storage, such as that used in computer hard drives, which now use an electric current to “flip” each storage bit from a 0 to a 1 or vice versa. Being able to control the bits with light could offer a much quicker response time, the team says.
This harnessing of electron behavior could also be a key enabling technology that could lead to the creation of spintronic circuits, using the spin of the electrons to carry information instead of their electric charge. Among other things, such devices could be an important part of creating new quantum computing systems, which many researchers think could have significant advantages over ordinary computers for solving certain kinds of highly complex problems.
Professor of physics Zhi-Xun Shen of Stanford University, who was not involved in this work, says the MIT team has confirmed the theorized structure of the topological surface by using their novel experimental method. In addition to this confirmation, he says, their second paper “is to date one of the most direct experimental evidences for optical coupling” between the laser and the surface currents, and thus “has interesting potential for opto-spintronics.”


You probably don’t realize it, but you very likely already employ quantum technology on a regular basis. Get in your car, switch on Waze or Google Maps, and you are already harnessing quantum effects. A GPS receiver works by measuring the tiny time delays in signals from multiple satellites separated in space. Doing this requires very stable and very accurate time measurement: enter the atomic clock. Such clocks, which reside inside every GPS satellite, often use quantum superposition. They employ atoms of Cesium or Rubidium to achieve an extremely stable “tick,” one accessible only within the atoms themselves. The primary standard for time, operated using this kind of physics, is so stable that it will lose just one second in 100 million years. That kind of stability powers not just GPS but other systems as well, including the synchronization protocols that govern Internet operations.
A clock that loses just a second in 100 million years (or more) may sound like more than we need, but this early application of quantum technology represents the start of something much bigger - building a new generation of quantum-enhanced sensors.
Quantum sensors turn the inherent weakness of quantum technology - its instability against the environment - into a strength. It takes a huge amount of work to isolate a quantum system in a way that allows it to be used faithfully as a clock. In general these devices are REALLY sensitive to everything around them; the most sensitive experiments to date have shown that such clocks can measure the effect of lifting the clock by a bit more than one foot (gravity changes as you move away from the center of the Earth). But quantum sensors deliver more than just sensitivity - quantum sensors also give the benefit of stability over long times. Conventional sensor instruments slowly change over time, meaning that averaging longer to reduce measurement noise becomes impossible. But because quantum sensors use immutable quantities - like the structure of atoms - their measurements tend to be very stable over long times.
Let’s explore one exciting kind of quantum sensor based on the same core technology as used in atomic clocks - cold trapped atoms. Cold atoms can be exploited for ultra-sensitive interferometric measurements using the wavelike nature of matter. Instead of building interferometers with light reflected off of (matter-based) mirrors (as widely used in telecom optical modulators), one can build atom interferometers using matter “reflected”off of pulses of light. Such atom interferometers have the benefit that the atoms themselves have mass, making them sensitive to both gravity and general acceleration. Accordingly, there is an emerging area of work on quantum-enabled “PNT” or positioning, navigation, and timing. Here, atomic accelerometers may enable dead reckoning navigation in environments such as space or GPS-denied battlefields.
More broadly, leveraging these capabilities and advantages, atomic devices are routinely used for both magnetometry and gravimetry. They could thus be deployed by military personnel to detect underground, hardened structures, submarines, or hidden weapons systems. Imagine a detector which can measure via changes in gravity whether a mountain is being hollowed out in a hostile nation with a furtive weapons program. In civilian applications these devices form the basis of new ways to monitor the climate - from underground aquifer levels through to ice-sheet thickness. Totally new forms of Earth observation for the space sector are now emerging, enabled by new small-form quantum sensors. Those capabilities flow into new data streams for long-term weather forecasting and insurance against weather events in agriculture. And of course the mining industry has long relied on advanced instrumentation for improved aerial survey and productivity enhancement.
Of course trapped atoms aren’t the only technology relevant to quantum sensing. There’s been a huge amount of research showing how solid-state devices like imperfections in diamond can be used as sensitive magnetometers. These have the advantage that they can be used in biological environments - even in vivo. They may not be as sensitive as atomic devices, but by virtue of their tiny size they can access new applications that are not possible with trapped atoms.
Overall, quantum sensing provides a route to gain massive technological advantages well before quantum computing comes online. And if you aren’t sure how much impact quantum sensors may have, just take a step back and think about how atomic clocks and GPS have already shaped your daily life.
Q-CTRL’s work in quantum sensing
Q-CTRL is active across all applications of quantum technology, producing the fundamental enabling capabilities in quantum control to help our customers realize the true potential of quantum tech. But with quantum sensors we go one step further, taking a “software-first” approach to building and designing our own hardware powered by quantum control. Placing the advantages of quantum control front and center enables huge reductions in system size and improvements in noise rejection, ultimately unlocking totally new applications.
We’re excited to be building a new generation of atomic navigation systems for space exploration and terrestrial applications. And we’re thrilled to have assembled one of the most impressive teams of quantum sensing experts in the world.
Partially adapted from The Leap into Quantum Technology: A Primer for National Security Professionals


Posts showcasing the wonder, beauty, and potential of cutting-edge materials research—freely contributed by physicists from across the country. (Funsize Physics is not responsible for any minds that are blown.)
You may have heard that there are three main phases of matter: solids, liquids, and gases (plus plasma if you want to get fancy). Liquids can take virtually any shape and deform instantly. Solid materials possess interesting electronic and magnetic properties essential to our daily life. But how about designing rigid liquids with magnetic properties? Impossible? Not anymore. Click to learn more!
Instead of pencil, paper, and eraser, we can use combinations of lasers and magnetic materials to write, read, and and erase information by varying the temperature and magnetic field. Here we apply our laser "pencil" to magnetic "paper" to write the letter “N” (Go Huskers!!). This technique allows us write, erase, and rewrite tiny magnetic memories like those found in your computer hard drive and other devices. Click to learn how it works!
It’s a hot summer day. You desperately want something cold to drink, but unfortunately, your bottle of root beer has been sitting in a hot car all day. You put it into a bucket full of ice to cool it down. But it’s taking forever! How, you wonder, could you speed the process up? The same question is important for understanding how electronic devices work, and how we can make them work better by controlling the temperature of the electrons that power them. Read on to find out what a bottle of root beer in a cooler full of ice and a nanowire in a vat of liquid helium have in common!
Diodes, also known as rectifiers, are a basic component of modern electronics. As we work to create smaller, more powerful and more energy-efficient electronic devices, reducing the size of diodes is a major objective. Recently, a research team from the University of Georgia developed the world's smallest diode using a single DNA molecule. This diode is so small that it cannot be seen by conventional microscopes.
We think we're pretty familiar with how ordinary liquids behave, but it turns out that some of the basic things we know are no longer true when we look at these liquids on short enough length scales and fast enough time scales. The liquids start to behave more like solids, pushing back when you push on them, and slipping across solid surfaces instead of being dragged along. Click to ride the tiny-but-mighty new wave of nanofluidics!
Scientists are working to develop electronic devices that store and process information by manipulating a property of electrons called spin—a research area aptly known as spintronics. The semiconductors we are developing will not only be faster and cheaper than those used in conventional devices, but will also have more functionality.
Materials that are absolutely perfect—in other words, materials that contain no defect of any kind—are usually not very interesting. Imagine being married to a saint: you would quickly be bored out of your mind! Defects and impurities can considerably change many properties of materials in ways that allow a wide range of applications.
Semiconductors are materials with properties intermediate between metals and non-conducting insulators, defined by the amount of energy needed to make an electron conductive in the material. The non-conducting electrons occupy a continuum of energy states, but two of these states (the “heavy hole” and “light hole”) are nearly identical in energy. The heavy hole is easy to observe and study, but the light hole eludes most observers.
Solids are generally divided into metals, which conduct electricity, and insulators, which do not. Some oxides straddle this boundary, however: a material's structure and properties suggest it should be a metal, but it sometimes behaves as an insulator. Researchers at the University of California, Santa Barbara are digging into the mechanisms of this transformation and are aiming to harness it for use in novel electronic devices.
You may know that the media used in magnetic recording technologies, such as computer hard drives, are made of millions of tiny nanomagnets. Each nanomagnet can be switched up or down to record bits of information as ones and zeros. These media are constantly subjected to magnetic fields in order to write, read, and erase information. If you have ever placed a magnet too close to your laptop or cell phone, you know that exposure to an external magnetic field can disrupt information stored this way. Did you know that it is possible for the nanomagnets to "remember" their previous state, if carefully manipulated under specific magnetic field and temperature conditions? Using a kind of memory called topological magnetic memory, scientists have found out how to imprint memory into magnetic thin films by cooling the material under the right conditions.
Inside solids, the properties of photons can be altered in ways that create a kind of "artificial gravity" that affects light. Researchers at the University of Pittsburgh tracked photons with a streak camera and found that whey they enter a solid-state structure, they act just like a ball being thrown in the air: they slow down as they move up, come to a momentary stop, and fall back the other way. Studying this "slow reflection" will allow us to manipulate light's behavior, including its speed and direction, with potential applications in telecommunications and quantum computing technologies.
In a unique state of matter called a superfluid, tiny "tornadoes" form that may play an important role in nanotechnology, superconductivity, and other applications. Just as tornadoes are invisible air currents that become visible when they suck debris into their cores, the quantum vortices in superfluids attract atoms that make the vortices visible. Quantum vortices are so small they can only be imaged using very short-wavelength x-rays, however.
Would you rather have data storage that is compact or reliable? Both, of course! Digital electronic devices like hard drives rely on magnetic memory to store data, encoding information as “0”s and “1”s that correspond to the direction of the magnetic moment, or spin, of atoms in individual bits of material. For magnetic memory to work, the magnetization should not change until the data is erased or rewritten. Unfortunately, some magnetic materials that are promising for high density storage have low data stability, which can be improved by squeezing or stretching the crystal structures of magnetic memory materials, enhancing a material property called magnetic anisotropy.
Neutron radiation detection is an important issue for the space program, satellite communications, and national defense. But since neutrons have no electric charge, they can pass through many kinds of solid objects without stopping. This makes it difficult to build devices to detect them, so we need special materials that can absorb neutrons and leave a measurable signature when they do. Researchers at the University of Nebraska-Lincoln are studying the effects of solar neutron radiation on two types of materials on the International Space Station (ISS), using detectors made of very stable compounds that contain boron-10 and lithium-6.
To increase our use of solar energy, we need to create more efficient, stable, and cost-effective solar cells. What if we could use an inkjet printer to fabricate them? A new type of solar cell uses a class of materials called perovskites, which have a special crystal structure that interacts with light in a way that produces an electric voltage. We've developed a method to produce perovskite thin films using an inket printer, which in the future could pave the way to manufacture solar cells that are surprisingly simple and cheap.
Fool's gold is a beautiful mineral often mistaken for gold, but recent research shows that its scientific value may be great indeed. Using a liquid similar to Gatorade, it can be turned into a magnet at the flick of a switch! Read on to learn more!
Think of the hard disk in your computer. Information is stored there in the form of magnetic "bits." But do you know how small a magnet can be? Some molecules make magnetic magic, and these special molecules may give rise to the ultrafast, high precision, low power devices of the future.
For the past two decades, giant bubble enthusiasts have been creating soap film bubbles of ever-increasing volumes. As of 2020, the world record for a free-floating soap bubble stands at 96.27 cubic meters, a volume equal to about 25,000 U.S. gallons! For a spherical bubble, this corresponds to a diameter of more than 18 feet and a surface area of over 1,000 square feet. How are such large films created and how do they remain stable? What is the secret to giant bubble juice? Click to find out more!
How can you fabricate a huge number of nanostructures in a split second? Self-assembly is a fast technique for the mass production of materials and complex structures. But before self-assembly is ready for prime time, scientists need to establish ways to control this process, so that desired nanostructures emerge from the unstructured soup of basic building blocks that are fast-moving atoms and molecules.
Superconductors are materials that permit electrical current to flow without energy loss. Their amazing properties form the basis for MRI (magnetic resonance imaging) devices and high-speed maglev trains, as well as emerging technologies such as quantum computers. At the heart of all superconductors is the bunching of electrons into pairs. Click the image to learn more about the "dancing" behavior of these electron pairs!


Google's quantum beyond-classical experiment used 53 noisy qubits to demonstrate it could perform a calculation in 200 seconds on a quantum computer that would take 10,000 years on the largest classical computer using existing algorithms. This marks the beginning of the Noisy Intermediate-Scale Quantum (NISQ) computing era. In the coming years, quantum devices with tens-to-hundreds of noisy qubits are expected to become a reality.
Quantum computing relies on properties of quantum mechanics to compute problems that would be out of reach for classical computers. A quantum computer uses qubits. Qubits are like regular bits in a computer, but with the added ability to be put into a superposition and share entanglement with one another.
Classical computers perform deterministic classical operations or can emulate probabilistic processes using sampling methods. By harnessing superposition and entanglement, quantum computers can perform quantum operations that are difficult to emulate at scale with classical computers. Ideas for leveraging NISQ quantum computing include optimization, quantum simulation, cryptography, and machine learning.
Quantum machine learning
Quantum machine learning (QML) is built on two concepts: quantum data and hybrid quantum-classical models.
Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer, like the samples gathered from the Sycamore processor for Google’s demonstration of quantum supremacy. Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store. The quantum supremacy experiment showed it is possible to sample from an extremely complex joint probability distribution of 2^53 Hilbert space.
The quantum data generated by NISQ processors are noisy and typically entangled just before the measurement occurs. Heuristic machine learning techniques can create models that maximize extraction of useful classical information from noisy entangled data. The TensorFlow Quantum (TFQ) library provides primitives to develop models that disentangle and generalize correlations in quantum data—opening up opportunities to improve existing quantum algorithms or discover new quantum algorithms.
The following are examples of quantum data that can be generated or simulated on a quantum device:
- Chemical simulation —Extract information about chemical structures and dynamics with potential applications to material science, computational chemistry, computational biology, and drug discovery.
- Quantum matter simulation —Model and design high temperature superconductivity or other exotic states of matter which exhibits many-body quantum effects.
- Quantum control —Hybrid quantum-classical models can be variationally trained to perform optimal open or closed-loop control, calibration, and error mitigation. This includes error detection and correction strategies for quantum devices and quantum processors.
- Quantum communication networks —Use machine learning to discriminate among non-orthogonal quantum states, with application to design and construction of structured quantum repeaters, quantum receivers, and purification units.
- Quantum metrology —Quantum-enhanced high precision measurements such as quantum sensing and quantum imaging are inherently done on probes that are small-scale quantum devices and could be designed or improved by variational quantum models.
Hybrid quantum-classical models
A quantum model can represent and generalize data with a quantum mechanical origin. Because near-term quantum processors are still fairly small and noisy, quantum models cannot generalize quantum data using quantum processors alone. NISQ processors must work in concert with classical co-processors to become effective. Since TensorFlow already supports heterogeneous computing across CPUs, GPUs, and TPUs, it is used as the base platform to experiment with hybrid quantum-classical algorithms.
A quantum neural network (QNN) is used to describe a parameterized quantum computational model that is best executed on a quantum computer. This term is often interchangeable with parameterized quantum circuit (PQC).
A goal of TensorFlow Quantum is to help discover algorithms for the NISQ-era, with particular interest in:
- Use classical machine learning to enhance NISQ algorithms. The hope is that techniques from classical machine learning can enhance our understanding of quantum computing. In meta-learning for quantum neural networks via classical recurrent neural networks, a recurrent neural network (RNN) is used to discover that optimization of the control parameters for algorithms like the QAOA and VQE are more efficient than simple off the shelf optimizers. And machine learning for quantum control uses reinforcement learning to help mitigate errors and produce higher quality quantum gates.
- Model quantum data with quantum circuits. Classically modeling quantum data is possible if you have an exact description of the datasource—but sometimes this isn’t possible. To solve this problem, you can try modeling on the quantum computer itself and measure/observe the important statistics. Quantum convolutional neural networks shows a quantum circuit designed with a structure analogous to a convolutional neural network (CNN) to detect different topological phases of matter. The quantum computer holds the data and the model. The classical processor sees only measurement samples from the model output and never the data itself. In Robust entanglement renormalization on a noisy quantum computer, the authors learn to compress information about quantum many-body systems using a DMERA model.
Other areas of interest in quantum machine learning include:
- Modeling purely classical data on quantum computers.
- Quantum-inspired classical algorithms.
- Supervised learning with quantum classifiers.
- Adaptive layer-wise learning for quantum neural network.
- Quantum dynamics learning.
- Generative modeling of mixed quantum states .
- Classification with quantum neural networks on near term processors.


Quantum computing is the area of study focused on developing computer technology based on the principles of quantum theory. The quantum computer, following the laws of quantum physics, would gain enormous processing power through the ability to be in multiple states, and to perform tasks using all possible permutations simultaneously.
A Comparison of Classical and Quantum Computing
Classical computing relies, at its ultimate level, on principles expressed by Boolean algebra. Data must be processed in an exclusive binary state at any point in time or bits. While the time that each transistor or capacitor need be either in 0 or 1 before switching states is now measurable in billionths of a second, there is still a limit as to how quickly these devices can be made to switch state. As we progress to smaller and faster circuits, we begin to reach the physical limits of materials and the threshold for classical laws of physics to apply. Beyond this, the quantum world takes over.
In a quantum computer, a number of elemental particles such as electrons or photons can be used with either their charge or polarization acting as a representation of 0 and/or 1. Each of these particles is known as a quantum bit, or qubit, the nature and behavior of these particles form the basis of quantum computing.
Quantum Superposition and Entanglement
The two most relevant aspects of quantum physics are the principles of superposition and entanglement.
- Superposition: Think of a qubit as an electron in a magnetic field. The electron’s spin may be either in alignment with the field, which is known as a spin-up state, or opposite to the field, which is known as a spin-down state. According to quantum law, the particle enters a superposition of states, in which it behaves as if it were in both states simultaneously. Each qubit utilized could take a superposition of both 0 and 1.
- Entanglement: Particles that have interacted at some point retain a type of connection and can be entangled with each other in pairs, in a process known as correlation. Knowing the spin state of one entangled particle – up or down – allows one to know that the spin of its mate is in the opposite direction. Quantum entanglement allows qubits that are separated by incredible distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated.
Taken together, quantum superposition and entanglement create an enormously enhanced computing power. Where a 2-bit register in an ordinary computer can store only one of four binary configurations (00, 01, 10, or 11) at any given time, a 2-qubit register in a quantum computer can store all four numbers simultaneously, because each qubit represents two values. If more qubits are added, the increased capacity is expanded exponentially.
Difficulties with Quantum Computers
- Interference – During the computation phase of a quantum calculation, the slightest disturbance in a quantum system (say a stray photon or wave of EM radiation) causes the quantum computation to collapse, a process known as de-coherence. A quantum computer must be totally isolated from all external interference during the computation phase.
- Error correction – Given the nature of quantum computing, error correction is ultra critical – even a single error in a calculation can cause the validity of the entire computation to collapse.
- Output observance – Closely related to the above two, retrieving output data after a quantum calculation is complete risks corrupting the data.
The Future of Quantum Computing
The biggest and most important one is the ability to factorize a very large number into two prime numbers. That’s really important because that’s what almost all encryption of internet applications use and can be de-encrypted. A quantum computer should be able to do that relatively quickly. Calculating the positions of individual atoms in very large molecules like polymers and in viruses. The way that the particles interact with each other – if you have a quantum computer you could use it to develop drugs and understand how molecules work a bit better.
Even though there are many problems to overcome, the breakthroughs in the last 15 years, and especially in the last 3, have made some form of practical quantum computing possible. However, the potential that this technology offers is attracting tremendous interest from both the government and the private sector. It is this potential that is rapidly breaking down the barriers to this technology, but whether all barriers can be broken, and when, is very much an open question.
This text is also available in Ahmed Banafa’s LinkedIn profile
Ahmed Banafa, Author the Books:


pic 1. Quantum entanglement is sharing a deep connection – in a quantum scale of things.
When the physical and statistical properties of a particle are fundamentally dependent on the properties of one or several other particles, these particles are said to be entangled. Without any physical interaction these particles can remain deeply connected to each other, even when they are vast distances apart.
Entanglement is a theoretical prediction that comes from the equations of quantum mechanics. Two particles can become entangled if they share the same state in a way that makes it possible to consider them not individually, but as a system. A laser beam fired through a certain type of crystal can cause individual photons to be split into pairs of entangled photons. Remarkably, quantum mechanics says that even if you separate those particles and send them to opposite directions they can remain entangled and inextricably connected. At least in theory. Conservation of the entanglement is very much susceptible to noise and “quantumness” disrupting decoherence (read more in Superposition text) if we are dealing with anything else but a perfectly isolated laboratory environment.
To understand the profound meaning of entanglement you can consider the quality that an electron possesses called the “spin”. Generally, just as it is common for other quantum qualities, the spin of an electron remains uncertain and fuzzy until it is measured, as explained by Superposition. With two entangled particles whenever one of them is measured with spin up the other one must, no matter how far away it is from its entangled pair, be spin down. In other words whenever we inflict a measurement on one entangled particle, we automatically change its counterpart to correlate no matter the distance and with nothing, no physical force, attaching these two particles to one another.
Fig 1. Changing one of the entangled particles spin will immediately do so with the other one, seemingly faster than light. This is what Einstein called “The spooky action at a distance”.
The physicists Niels Bohr and Werner Heisenberg argued in 1934 among other quantum theory questions that an object’s state only truly existed once it became associated with a measurement, which meant somebody needed to observe it experimentally. Until then, its nature was merely a possibility. Upon measurement the system’s spin is fixed either up or down.
To other physicists, such as Albert Einstein and Erwin Schrödinger, this was as preposterous as saying a cat inside a box is neither alive nor dead until you look. A paradox in other words. No action taken on the first particle could instantaneously affect the other, since this would involve information being transmitted faster than light, which is forbidden by the theory of relativity. Theory of relativity states for example that if anything were to travel faster than light it would violate the laws of causality and is a theory many times tested to not fall short. From Einstein’s work with Podolsky and Rosen an idea to solve this “spooky action at a distance” was argued to be solved with the thought of a more deterministic theory still unknown to science and hidden local variables that were coded in the particles and that could not be later influenced.
In 1964 John Stewart Bell made a theoretical article that argued quantum physics to be incompatible with the local hidden variables theories, and that was later proven correct. Decades later, Bohr’s ideas still stand strong, and the strange nature of quantum entanglement is a solid part of modern physics. An interesting theory tested in the laboratory (using entanglement) is one that aims to quantize general relativity and unify the foundations of modern physics by leaving out time altogether. The results of testing this in 2013 with a toy Universe model suggested that time itself is an emergent phenomenon that comes about because of the nature of entanglement and that. While not working as the unifying theory for modern physics it paves a way for more research regarding entanglement. Entanglement continues to boggle the minds and remains as a part of the strange world of subatomic physics that we call quantum.
More to read and links to text:
Malin, Shimon, World Scientific 2012: Nature loves to hide: Quantum Physics and reality, a western perspective
Science alert website, What is Quantum Entanglement? https://www.sciencealert.com/entanglement
Fobes 11 August 2015, Chad Orzel: How Quantum Randomness Saves Relativity https://www.forbes.com/sites/chadorzel/2015/08/11/how-quantum-randomness-saves-relativity/
Space.com website 31 July 2019, Yasemin Saplakoglu: ‘Spooky’ Quantum Entanglement Finally Captured in Stunning Photo https://www.space.com/quantum-entanglement-photo.html
Medium webpage 23 October 2013, The Physics ArXiv Blog: Quantum Experiment Shows How Time ‘Emerges’ from Entanglement https://medium.com/the-physics-arxiv-blog/quantum-experiment-shows-how-time-emerges-from-entanglement-d5d3dc850933
Ekaterina Moreva, Giorgio Brida, Marco Gramegna et al. 17 October 2013, Time from quantum entanglement: an experimental illustration, https://arxiv.org/pdf/1310.4691
Noora Heiskanen with thanks to Silvia Cotroneo and Jani-Petri Martikainen


Superconductivity is a fascinating phenomenon in which, below a so-called critical temperature, a material loses all its resistance to electrical currents. In certain materials, at low temperatures, all electrons are entangled in a single, macroscopic quantum state, meaning that they no longer behave as individual particles but as a collective – resulting in superconductivity. The general theory for this collective electron behaviour has been known for a long time, but one family of materials, the cuprates, refuses to conform to the paradigm. It was long thought that for these materials the mechanism that ‘glues together’ the electrons must be special, but recently the attention has shifted and now physicists investigate the non-superconducting states of cuprates, hoping to find out their differences with normal superconductors.
Most superconductors, when heated to exceed their critical temperature, change into ‘ordinary’ metals. The quantum entanglement that causes the collective behaviour of the electrons fades away, and the electrons start to behave like an ordinary ‘gas’ of charged particles.
Cuprates are special, first of all because their critical temperature is considerably higher than that of other superconductors. On top of that, they have very special measurable properties even in their ‘metal phase’. In 2009, physicist Nigel Hussey observed experimentally that the electrons in these materials form a new type of structure, different from that in ordinary metals, and the term ‘strange metal’ was born.
At nearly the same time, originating in Stanford in the United States, physicists started applying the theoretical machinery of string theory – a theory for a very different phenomenon, the behavior of gravity at the quantum level – to the description of electrons in metals. Completely unexpectedly, this machinery turned out to be able to predict certain phenomena that experimentally were known to occur in cuprates and other strange metals. Theoretical physicists Jan Zaanen and Koenraad Schalm (Leiden University) were involved in the early stages of these developments and made important contributions. In 2017, the pioneering work was transformed into a national research programme funded by NWO: Strange Metals. The programme is a special collaboration that involves both experimental and theoretical groups.
Special behaviour at low temperatures
The higher the temperature of a material, the more ‘noise’ measurements will show. To make the special properties of the strange metal state clearly visible, one would like to study the material at a temperature that is as low as possible, at most 1 degree above the absolute temperature minimum of -273°C. The obstacle for this is superconductivity itself: most strange metals already turn into superconductors when cooled to temperatures around -200°C. For this reason, in the Strange Metals programme, the choice was made to focus exclusively on a material with the chemical name Bi2Sr2CuO6, also known as ‘Bi2201’. This material becomes superconducting at about 35 degrees above the absolute minimum temperature. That is still too ‘hot’ for good measurements, but now the researchers can use a trick: superconductivity can be suppressed by a magnetic field.
The general rule of thumb is: the larger the critical temperature of a material, the stronger the magnetic field required to suppress superconductivity. Since for Bi2201 the critical temperature is already quite low, the required magnetic field comes just within reach of the biggest magnets available in the Netherlands. This allowed PhD students Jake Ayres and Maarten Berben working within the groups of Hussey (HFML-FELIX, Bristol) and Van Heumen to eventually study the strange metal state of Bi2201 at various low temperatures and various magnetic field strengths.
In this domain, the differences between strange metals and ordinary metals become strikingly visible. For ordinary metals, for example, one expects the electrical resistance to increase quadratically with temperature: increase the temperature by a factor of two, and the resistance will grow by a factor of four. The same holds if it is not the temperature but the magnetic field that is increased. The Dutch/UK team has now shown that these golden rules do not hold for cuprates. In these materials a new phase exists where the resistance depends linearly on the temperature and field strength: if one of these increases by a factor of two, so does the resistance. Contrary to what was observed before, the group discovered that this behaviour persists for a large range of the parameters.
At the moment, there are two widely accepted theories that could explain the linear behaviour of the resistance. The first theory assumes that the linear behaviour only occurs near very specific values of the temperature and magnetic field strength. With the new measurements, this theory has now come under considerable pressure. The second theory is the theory of extreme quantum entanglement that comes from the string theoretic approach. Within this theory it is possible to observe the linear behavior for a large range of parameters. Surprisingly, therefore, it seems that to describe strange metals, one truly needs a theory that can also be used to describe quantum gravity!
Quantum gravity in the lab
The link between strange metals and quantum gravity has special observable effects. In an extensive analysis, the team shows that within the conventional models of electrical transport, it is absolutely impossible to properly explain the data. Their analysis shows that there exists a previously unobserved mechanism that makes the electrons lose energy. This loss occurs at extremely short time scales related to a fundamental constant of nature in quantum mechanics: Planck’s constant. According to general theory, this is the shortest time scale at which a quantum system can lose energy – something which moreover is only possible when the system is maximally entangled. This fingerprint of quantum gravity behaviour in the data excites many supporters of the link with string theory: it would be a first clue of physics far beyond the usual model of metals.
To shed further light on the tension between ‘normal’ and ‘strange’ behaviour of metals, further experiments are needed. In that respect, promising developments still lie ahead within the Strange Metals program. Using a technique called ‘optical spectroscopy’, Van Heumen expects to be able to provide new details soon, and the groups of Mark Golden (Amsterdam) and Milan Allan (Leiden) are also working on results that could cause new surprises when it comes to the mysterious relation between quantum gravity and strange metals.
Incoherent transport across the strange metal regime of overdoped cuprates, J. Ayres, M. Berben, M. Čulo, Y.-T. Hsu, E. van Heumen, Y. Huang, J. Zaanen, T. Kondo, T. Takeuchi, J. R. Cooper, C. Putzke, S. Friedemann, A. Carrington and N. E. Hussey. Nature 595 (2021) 661-666.


A new method of relaying information by transferring the state of electrons moves scientists one step closer to creating fully functional quantum computers.
Quantum computing has the potential to revolutionize technology, medicine, and science by providing faster and more efficient processors, sensors, and communication devices. But transferring information and correcting errors within a quantum system remains a challenge to making effective quantum computers.
How do quantum computers work?
A quantum computer operates on the principles of quantum mechanics, a unique set of rules that govern at the extremely small scale of atoms and subatomic particles. When dealing with particles at these scales, many of the rules that govern classical physics no longer apply and quantum effects emerge. A quantum computer can perform complex calculations, factor extremely large numbers, and simulate the behaviors of atoms and particles at levels that classical computers cannot.
Quantum computers have the potential to provide more insight into principles of physics and chemistry by simulating the behavior of matter at unusual conditions at the molecular level. These simulations could be useful in developing new energy sources and studying the conditions of planets and galaxies or comparing compounds that could lead to new drug therapies.
“You and I are quantum systems. The particles in our body obey quantum physics. But, if you try to compute what happens with all of the atoms in our body, you cannot do it on a regular computer,” says John Nichol, an assistant professor of physics at the University of Rochester. “A quantum computer could easily do this.”
Quantum computers could also open doors for faster database searches and cryptography.
“It turns out that almost all of modern cryptography is based on the extreme difficulty for regular computers to factor large numbers,” Nichol says. “Quantum computers can easily factor large numbers and break encryption schemes, so you can imagine why lots of governments are interested in this.”
Ones and zeroes in quantum computers
A regular computer consists of billions of transistors, called bits. Quantum computers, on the other hand, are based on quantum bits, also known as qubits, which can be made from a single electron. Unlike ordinary transistors, which can be either “0” or “1,” qubits can be both “0” and “1” at the same time.
The ability for individual qubits to occupy these “superposition states,” where they are simultaneously in multiple states, underlies the great potential of quantum computers. Just like ordinary computers, however, quantum computers need a way to transfer information between qubits, and this presents a major experimental challenge.
“A quantum computer needs to have many qubits, and they’re really difficult to make and operate,” Nichol says. “The state-of-the art right now is doing something with only a few qubits, so we’re still a long ways away from realizing the full potential of quantum computers.”
All computers, including both regular and quantum computers and devices like smartphones, also have to perform error correction. A regular computer contains copies of bits so if one of the bits goes bad, “the rest are just going to take a majority vote” and fix the error. However, quantum bits cannot be copied, Nichol says, “so you have to be very clever about how you correct for errors. What we’re doing here is one step in that direction.”
Quantum error correction requires that individual qubits interact with many other qubits. This can be difficult because an individual electron is like a bar magnet with a north pole and a south pole that can point either up or down. The direction of the pole—whether the north pole is pointing up or down, for instance—is known as the electron’s magnetic moment or quantum state.
If certain kinds of particles have the same magnetic moment, they can’t be in the same place at the same time. That is, two electrons in the same quantum state cannot sit on top of each other.
“This is one of the main reasons something like a penny, which is made out of metal, doesn’t collapse on itself,” Nichol says. “The electrons are pushing themselves apart because they cannot be in the same place at the same time.”
If two electrons are in opposite states, they can sit on top of each other. A surprising consequence of this is that if the electrons are close enough, their states will swap back and forth in time.
“If you have one electron that’s up and another electron that’s down and you push them together for just the right amount of time, they will swap,” Nichol says. “They did not switch places, but their states switched.”
To force this phenomenon, Nichol and his colleagues cooled down a semiconductor chip to extremely low temperatures. Using quantum dots—nanoscale semiconductors—they trapped four electrons in a row, then moved the electrons so they came in contact and their states switched.
“There’s an easy way to switch the state between two neighboring electrons, but doing it over long distances—in our case, it’s four electrons—requires a lot of control and technical skill,” Nichol says. “Our research shows this is now a viable approach to send information over long distances.”
One step closer
Transmitting the state of an electron back and forth across an array of qubits, without moving the position of electrons, provides a striking example of the possibilities quantum physics could enable for information science.
“This experiment demonstrates that information in quantum states can be transferred without actually transferring the individual electron spins down the chain,” says Michael Manfra, a professor of physics and astronomy at Purdue University. “It is an important step for showing how information can be transmitted quantum-mechanically—in manners quite different than our classical intuition would lead us to believe.”
Nichol likens this to the steps that led from the first computing devices to today’s computers. That said, will we all someday have quantum computers to replace our desktop computers?
“If you had asked that question of IBM in the 1960s, they probably would’ve says no, there’s no way that’s going to happen,” Nichol says. “That’s my reaction now. But, who knows?”
The research appears in Nature.
Source: University of Rochester


Written By Sneha Senthilkumar (Grade 11)
Quantum computing is the new future of information technology. Mighty and possibly revolutionary, these computers will be a force to reckon with within a few decades down the line. Google’s quantum computer, called ‘Sycamore’, was able to solve a specific problem in 200 seconds, while estimating a powerful supercomputer would take a jaw-dropping 10,000 years to perform the same task. Quantum computing is holding a promise of becoming the ‘Belle of the Ball’, taking digital computation and problem-solving capacities to a level we never would have thought possible.
All right. Enough with the majestic introduction. You probably must be thinking of something along the lines of: “Why does she just add ‘quantum’ in front of every word in the dictionary?” or “Does this mean I can finish my history project quicker, with this computer?”. Unfortunately, I don’t really have the best answers for those kinds of queries. But, I can give you an idea as to what these kinds of computers can do, how they work, and the promises they hold for the world in the future.
The computer we use in our day to day lives is a very average computer. It functions based on the binary system, using ‘bits’. Bits can exist as either 0’s or 1’s. There is a lot more certainty in regular bits, as we are very sure of the state of the bit (either 0 or 1).
However quantum computers have a slightly weirder unit of information. They use qubits (quantum bits). Other than the fact that I have yet again added the word ‘quantum’ in front of a regular dictionary word, there is something else that makes it different. The qubits can exist as a 0, and as a 1, at the same time. This phenomena is known as superposition, probably the most important concept in quantum computing too. In quantum physics, a particle, such as an electron, can exist in two different states or places at the same time. However, the catch is that, if a measurement is made on the particle, the wave function will collapse (it will return to a single state/place), and there will be no more superposition.
Because of this fragile nature of superposition, the qubits can’t interact with any other particle (which is technically what I meant by ‘measurement’ in the previous paragraph). If it does get disturbed, then the qubit, that was once existing as both 0 and 1 simultaneously, will return to either a 0 or a 1, just like a classic bit from a classical system. As you can see here, we cannot exactly tell which state (0 or 1) it will become if the wave-function collapses. This is because quantum computing, just like quantum physics, is purely based on probabilities. That is what made physicists skeptical about quantum physics in the first place – the lack of certainty. Yes, the risks of loosing the superposition are there. If the qubit interacts with something, it will collapse to either 0 or 1, making the quantum computer not so ‘quantum’, anymore. But if there is no disturbance, the system will evolve in a deterministic way and maintain its superposition. The ability to remain ‘quantum’ (by maintaining the superposition) over ‘classical’ is known as quantum coherence.
So, now you know how important it is not to make a measurement in superposition, and how it affects the qubit. Let’s discuss a bit about how we ensure that there is no disturbance in the quantum computer. So far, the best developed method of ensuring this is by using superconductors.
A superconductor is basically a special type of material through which a charge can move, without resistance, thereby it does not loose any energy. For example, in electricity cables, there is always some electrical energy lost to the surroundings in the form of thermal energy, as it travels through the cables. This is because of resistance. So, if you have no resistance, then there is no energy lost, hence you get 100% efficiency. But, what does this mean for the qubits? Well qubits are made out of superconductive material, such as aluminium under certain conditions, which makes sure there is no resistance. When the qubit moves without resistance, it means that these qubits won’t interact with anything in its surroundings, which means no ‘measurement’ made! Are you able to make the connections?
So why superconductors? What is so special? Superconductors will prevent the electrons from interacting with each other and other particles in the material.
Superconductors do this by pairing up electrons loosely, far enough that they don’t interact with each other, yet still held together by loose bond, preventing them from interacting with other particles. Hence all the electrons, forming a (what we call) electron superfluid, will move through the system without getting disturbed. So, the quantum coherence is not jeopardised. Mission accomplished.
The quantum computer is stored at temperatures near absolute zero (0 Kelvin/-273℃), which is almost as cold as the vacuum in space. This helps eliminate any possibilities of error in the system too.
Quantum computing mainly comes into play when we are faced with large and more complex problems, which regular computers, even supercomputers, don’t have the power to solve. Although quantum computers are mostly used by military for cryptography, scientists are trying to find ways to bring these computers to the masses. Research has only just begun, as the complexity of the algorithms as well as the built of the computer prove challenging. Currently the only quantum computers are owned by IBM, Google, D-Wave Systems, Toshiba, and a handful of other companies. There is still a very long way to go, but the first step is often the most important leap to innovation.
Featured Image Courtesy – Phys.org