Complex 3D nanoscale architectures based on DNA self-assembly can conduct electricity without resistance and may provide a platform for fabricating quantum computing and sensing devices
Three-dimensional (3-D) nanostructured materials — those with complex shapes at a size scale of billionths of a meter — that can conduct electricity without resistance could be used in a range of quantum devices. For example, such 3-D superconducting nanostructures could find application in signal amplifiers to enhance the speed and accuracy of quantum computers and ultrasensitive magnetic field sensors for medical imaging and subsurface geology mapping. However, traditional fabrication tools such as lithography have been limited to 1-D and 2-D nanostructures like superconducting wires and thin films.
Now, scientists from the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Columbia University, and Bar-Ilan University in Israel have developed a platform for making 3-D superconducting nano-architectures with a prescribed organization. As reported in the November 10, 2020, issue of Nature Communications, this platform is based on the self-assembly of DNA into desired 3-D shapes at the nanoscale. In DNA self-assembly, a single long strand of DNA is folded by shorter complementary “staple” strands at specific locations — similar to origami, the Japanese art of paper folding.
“Because of its structural programmability, DNA can provide an assembly platform for building designed nanostructures,” said co-corresponding author Oleg Gang, leader of the Soft and Bio Nanomaterials Group at Brookhaven Lab’s Center for Functional Nanomaterials (CFN) and a professor of chemical engineering and of applied physics and materials science at Columbia Engineering. “However, the fragility of DNA makes it seem unsuitable for functional device fabrication and nanomanufacturing that requires inorganic materials. In this study, we showed how DNA can serve as a scaffold for building 3-D nanoscale architectures that can be fully “converted” into inorganic materials like superconductors.”
To make the scaffold, the Brookhaven and Columbia Engineering scientists first designed octahedral-shaped DNA origami “frames.” Aaron Michelson, Gang’s graduate student, applied a DNA-programmable strategy so that these frames would assemble into desired lattices. Then, he used a chemistry technique to coat the DNA lattices with silicon dioxide (silica), solidifying the originally soft constructions, which required a liquid environment to preserve their structure. The team tailored the fabrication process so the structures were true to their design, as confirmed by imaging at the CFN Electron Microscopy Facility and small-angle x-ray scattering at the Complex Materials Scattering beamline of Brookhaven’s National Synchrotron Light Source II (NSLS-II). These experiments demonstrated that the structural integrity was preserved after they coated the DNA lattices.
“In its original form, DNA is completely unusable for processing with conventional nanotechnology methods,” said Gang. “But once we coat the DNA with silica, we have a mechanically robust 3-D architecture that we can deposit inorganic materials on using these methods. This is analogous to traditional nanomanufacturing, in which valuable materials are deposited onto flat substrates, typically silicon, to add functionality.”
The team shipped the silica-coated DNA lattices from the CFN to Bar-Ilan’s Institute of Superconductivity, which is headed by Yosi Yeshurun. Gang and Yeshurun became acquainted a couple years ago, when Gang delivered a seminar on his DNA assembly research. Yeshurun — who over the past decade has been studying the properties of superconductivity at the nanoscale — thought that Gang’s DNA-based approach could provide a solution to a problem he was trying to solve: How can we fabricate superconducting nanoscale structures in three dimensions?
“Previously, making 3-D nanosuperconductors involved a very elaborate and difficult process using conventional fabrication techniques,” said Yeshurun, co-corresponding author. “Here, we found a relatively simple way using Oleg’s DNA structures.”
At the Institute of Superconductivity, Yeshurun’s graduate student Lior Shani evaporated a low-temperature superconductor (niobium) onto a silicon chip containing a small sample of the lattices. The evaporation rate and silicon substrate temperature had to be carefully controlled so that niobium coated the sample but did not penetrate all the way through. If that happened, a short could occur between the electrodes used for the electronic transport measurements.
“We cut a special channel in the substrate to ensure that the current would only go through the sample itself,” explained Yeshurun.
The measurements revealed a 3-D array of Josephson junctions, or thin nonsuperconducting barriers through which superconducting current tunnels. Arrays of Josephson junctions are key to leveraging quantum phenomena in practical technologies, such as superconducting quantum interference devices for magnetic field sensing. In 3-D, more junctions can be packed into a small volume, increasing device power.
“DNA origami has been producing beautiful and ornate 3-D nanoscale structures for almost 15 years, but DNA itself is not necessarily a useful functional material,” said Evan Runnerstrom, program manager for materials design at the U.S. Army Combat Capabilities Development Command Army Research Laboratory of the U.S. Army Research Office, which funded the work in part. “What Prof. Gang has shown here is that you can leverage DNA origami as a template to create useful 3-D nanostructures of functional materials, like superconducting niobium. This ability to arbitrarily design and fabricate complex 3-D-structured functional materials from the bottom-up will accelerate the Army’s modernization efforts in areas like sensing, optics, and quantum computing.”
“We demonstrated a pathway for how complex DNA organizations can be used to create highly nanostructured 3-D superconducting materials,” said Gang. “This material conversion pathway gives us an ability to make a variety of systems with interesting properties — not only superconductivity but also other electronic, mechanical, optical, and catalytic properties. We can envision it as a “molecular lithography,” where the power of DNA programmability is transferred to 3-D inorganic nanofabrication.”
Reference: “DNA-assembled superconducting 3D nanoscale architectures” by Lior Shani, Aaron N. Michelson, Brian Minevich, Yafit Fleger, Michael Stern, Avner Shaulov, Yosef Yeshurun and Oleg Gang, 10 November 2020, Nature Communications.
This research was supported by the U.S. Department of Defense, Army Research Office; DOE Office of Science; Israeli Ministry of Science and Technology; and Israel Science Foundation. Both CFN and NSLS-II are DOE Office of Science User Facilities. Some imaging studies were carried out at the Imaging Facility of the City University of New York Advanced Science Research Center.


The roots of encryption go deep into human history. Encryption has been used for centuries to encode messages, usually to keep government secrets, but also to protect business or trade secrets such as the formula to make silk or pottery. Early encryption was fairly simplistic, largely relying on paper and pencil techniques like steganography, transposition and substitution. In the last century, encryption methods have advanced at a rapid clip, first by leveraging automation and the use of machinery and then by employing advanced mathematics and powerful computers.
While encryption today involves powerful computers, it wasn't always so complicated or ubiquitous.
Early Encryption Methods
It is said that in 700 B.C., the Spartan military used scytales to send secret messages during battle. The sender and the recipient each possessed a wooden rod of the same diameter and length. The sender would tightly wind a piece of parchment or leather around the stick and write a message. The unwound document would be sent to the recipient, who would wind it around his stick to decode the message. In its unwound state, the message was gibberish.
Julius Caesar created one of the simplest and most recognized encryption techniques: the Caesar cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter some fixed number of positions down the alphabet. For example, with a left shift of 3, D would be replaced by A, E would become B, and so on. He used this method in his private correspondence at a time when many of his enemies could not read and other may have assumed the message was written in a foreign language. It is therefore assumed to have been reasonably secure in the first century B.C., but today a single-alphabet substitution cipher is easily broken and offers essentially zero security.
In the 15th century, Italy’s Leon Battista Alberti was the quintessential Renaissance man. Mostly known for being an artist, he also is credited as an author, architect, priest, poet, linguist, philosopher and cryptographer. In 1467, Alberti invented the first polyalphabetic substitution cipher. The Alberti Cipher consisted of two metal discs on the same axle, one inside the other, and involved mixed alphabets and variable rotations. It changed the course of encryption: unlike previous ciphers, the Alberti Cipher was impossible to break without knowledge of the method. This was because the frequency distribution of the letters was masked, and frequency analysis – the only known technique for attacking ciphers at that time – was no help.
During his tenure as George Washington’s Secretary of State, Thomas Jefferson invented the Jefferson disk, or wheel cipher. The system used a set of wheels or disks, and the letters of the alphabet were inscribed on each wheel in random order. Turning them would scramble and unscramble words. Each disk is marked with a unique number, and the hole in the center of the disk allowed them to be stacked on an axle in any order desired. To encrypt the message, both sender and receiver had to arrange the disks in the same predefined order. By using 36 disks, Jefferson’s disk was considered unbreakable at the time.
Encryption and War
Jefferson’s disk was independently reinvented in the late 19th century by Commandant Etienne Bazeries, and named Bazeries cylinder. It was used as a U.S. Army field cipher after World War I. But perhaps the most famous war time encryption machine is Engima. Invented by Arthur Scherbius, Enigma was Germany's main cryptographic technology during World War II. The Enigma machine consisted of a basic keyboard, a display that would reveal the cipher text letter and a scrambling mechanism. Each plain text letter entered via the keyboard was transcribed to its corresponding cipher text letter. Enigma was eventually broken due in large part to the work of Marian Rejewski, a Polish statistician, mathematician and code breaker. Before Germany invaded Poland, Rejewski transferred all his research to the English and the French. The team at Bletchley Park, including Alan Turing, used Rejewski's work to build bombes, electromechanical machines that were designed specifically to break Enigma. This work is credited with being a crucial step to ending World War II.
Encryption in Today’s Computing World
Advances in computing led to even greater advances in encryption. In 1979, the National Bureau of Standards invented Data Encryption Standard (DES) using what was then state-of-the-art 56-bit encryption – even supercomputers of the day could not crack it. In general, the longer the key is, the more difficult it is to crack the code. This holds true because deciphering an encrypted message by brute force would require the attacker to try every possible key. DES was the standard for encryption for more than 20 years, until 1998, when the Electronic Frontier Foundation broke the DES key. It took 56 hours in 1998, and only 22 hours to accomplish the same feat in 1999.
As we can see, as technology advances, so does the quality of encryption. Once the internet began to see increased commercial transaction use, DES was finally replaced by the Advanced Encryption Standard, or AES, which was found through a competition open to the public and approved by NIST. This method is still in use today.
But perhaps one of the most notable advances in the study of cryptography since World War II is the introduction of the asymmetric key ciphers (also known as public key encryption). Whitfield Diffie and Martin Hellman were pioneers in the field of asymmetric cryptographic techniques. These are algorithms that use a pair of mathematically related keys, each of which decrypts the encryption performed using the other. By designating one key of the pair as private, and the other as public (often widely available), no secure channel is needed for key exchange. You can reuse the same key pair indefinitely – as long as the private key stays secret. Most importantly, in an asymmetric key system, the encryption and decryption keys are not identical, which means that, for the first time in history, two people could secure communications without any prior interaction – ideal for internet transactions.
Ronald L. Rivest, Adi Shamir and Leonard M. Adleman were inspired by Diffie and Hellman to create a practical public key system. The result was RSA, which was based on the difficulty of factoring large numbers, and is a common cryptograhic technique on the internet today.
Now that we have widespread use of encryption, what challenges do we face? To break encryption, the most basic method of attack is brute force. This is why keys are getting longer and longer – to create more possible solutions and increase the resources required to perform such large computations. There are more than a few informed experts who believe that quantum computing may bring forth the ability to break codes in the foreseeable future. Some of the industry’s brightest minds are working on quantum-resistant encryption so that we can continue to exchange sensitive information privately.
There are also concerns about cost and downtime when deploying encryption schemes. For enterprise-class encryption, you used to need to account and plan for downtime while tens of thousands of files or a large database was getting encrypted. But now you have the option of enterprise encryption without downtime, with Vormetric Live Data Transformation. In fact, a database of any size or any number of files can be used while undergoing encryption. We call it zero-downtime encryption, and it’s an industry game-changer.
And now as we have more and more services moving to the cloud, encrypting and securing data is even more critical. More sensitive data is residing in the cloud, and ensuring that data is secure can be a challenging task. However, there are new strategies for cloud data protection such as transparent and application-level encryption. Additional methods of encryption can involve tokenization and dynamic data masking. I would be remiss if I didn’t add key management to the mix, as well. Compliance mandates, data-residency requirements, government regulations and best practices require that enterprises protect and maintain encryption keys in accordance with specific frameworks and laws. Allowing organizations to “bring your own key,” also known as BYOK, enables maximum control and trust between the data owner and cloud provider, and is considered a best practice for internal and external compliance controls.
Later this month, Thales will release the results of our annual Global Encryption Study. While I won’t give away the findings, I can share that keeping pace with cloud adoption and escalating threats is a major pain point for organizations and business leaders. It is our focus and vision to make protecting your data as transparent and operationally “invisible” as possible. It is a tough mission, but a worthy one. I hope you’ll download that report when it becomes available, as I think you’ll find the results eye-opening.


Causality is one of the oldest and most important concepts of Physics. Even recently, at the beginning of the XX century, with the invention of Special Relativity, this concept was in some sense rediscovered. As in a relativistic framework the events can change their temporal order a great effort was made in order to preserve causality in the theory.
There is a general consensus in the scientific community about this concept: For all scientific theories, even for all the theories that will come in the future, causality should be preserved. If causal relations are broken an important number of paradoxes and counter-intuitive results arise. You could even go back in time and kill your grandgrandfather!
In quantum mechanics the discovery of entangled states, that are states with correlations than can act immediately even in they are separated by a distance of millions of light years, challenged this concept. The solution for preserving causality was to accept that quantum systems are intrinsically random and no theory can give a complete description of them.
Very recently, in Reference 1, a paper published in Nature Communications by Ognyan Oreshkov and coworkers, from the University of Vienna, the concept of causality itself is discussed. Just by assuming that quantum mechanics is valid only locally, they show that it is difficult to talk about ‘causal order’. As it has been made before in order to analyze the effects of quantum mechanics the authors decided to illustrate their result with a thought experiment.
The rules of this experiment are:
- There are two parties, Alice and Bob. They are in labs that are far away from each other.
- They both receive one random bit, either 0 or 1.
- They can send information out between their labs.
- They have to guess the bit of each other. This decision should be made at the same time they send their information out.
Obviously, the experiment should be repeated several times, and the goal is to guess the bit of the other party as much times as possible. The ‘figure of merit’ that measures how well we are performing the game is the probability of guessing for both Alice and Bob together, that is a number between 0 and 1.
Let see what can we do in a classical, and causal, framework. It is clear that the success probability will depend in this case on the time order of the events. If Alice sends her information first, she can use it in order to communicate Bob what her bit was. Indeed, Bob will succeed all the time. The problem now is that Alice has no clue about Bob’s bit, so the best she can do is just say something at random. The same problem arises if it is Bob the first in sending the information. So, in the best possible scenario, the probability of success is 1 for one of them, the one that acts second, and ½ for the other one, the one that acts first. That means that the best possible probability in a classical causal framework is ¾.
So, is there any difference in a quantum mechanics framework? Not really, quantum mechanics is also a theory with a definite causal background and has to fulfill the same constrains. But, what happens if we slightly modify quantum mechanics in order to remove the space-time background, making it only valid locally, but not globally? That is the problem analyzed in Ref. 1 by Oreshkov et al. There, the authors performed a similar experiment, where it is assumed that Alice and Bob can make any kind of quantum operation in their labs. In these labs quantum mechanics holds, but there is not any assumption of a preexisting background time, or global causal structure. In this scenario, that differs from normal quantum mechanics, they show that the limit of the probability of success can be enhanced beyond the causal limit.
The rules for the non-causal quantum game are:
- Each laboratory is isolated.
- Quantum mechanics can be applied locally in the labs, but there is no assumption of what happens globally.
- There is also no assumptions about the spatio-temporal location of the experiments. That means that it is not define who makes the measurement before.
- They don’t need to communicate in this case. This is a necessary assumption in this case, because in this case there is not a definite spatio-temporal order, so it is not defined who acts first and can communicate and who is second and can not.
Based on these assumptions the authors create a new framework based on local quantum mechanics for analyzing the possible options of Alice and Bob. The results are surprising, they find a possibility of reaching a success probability of 0,853, that is higher than the ¾ probability of the best causal scenario. Even, without communication between them.
And what does it mean? Is causality broken in this new theory and we can communicate now with our dead grandgrandfather? That could be very interesting for science fiction writers, but it is not like that. The authors claim in their paper that, as quantum mechanics can be applied locally to Alice and Bob’s labs, causality should be preserved. This is due to the noise in the evolution ‘backward in time’ and it is compatible with the Novikov principle.
So, if causality itself is not broken, why is this result interesting? First, the analysis of new possible frameworks is always useful. In general relativity, for instance, when one imposes only local constrains new and interesting features arise, as exotic causal structures. It looks like that something similar happens in the quantum regime. Also, this results imply that if quantum mechanics only works locally new kind of correlations appear, stronger than the ones that are usual in normal quantum mechanics, like entanglement. Even, if these correlations can not break the causal order, as is expectable, the potential implications are huge. We should not forget that entanglement leads to interesting applications as quantum computing, quantum teleportation or cryptography. We can not know which applications these new correlations may have.
Finally, there is a more important question: Are these correlations something real or just a mathematical trick? About this question, the authors mention in the discussion of their paper that maybe these correlations can be found in regimes where the actual theories are untested, such as, for example, those in which quantum mechanics and general relativity become relevant.
So, in my opinion, for the moment this result is purely theoretical, but very interesting in any case. This kind of studies, even if they are just theory, usually open a door to new ways of thinking. Also new theories and potential applications can be realized from it. Only time can show how useful it will be.


The most pressing questions facing researchers today require deep and broad knowledge, often spanning multiple disciplines. To tackle these problems, Syracuse University is establishing groups, or “clusters,” of scholars from diverse backgrounds dedicated to working on common projects. The clusters were chosen as areas where the University has potential to find breakthrough or breakout solutions to society’s greatest challenges. One field recently added to the research clusters is quantum information science.
Why do we need quantum information science?
To study the smallest particles that exist, scientists from all over the world came together to construct the largest machine every built. The Large Hadron Collider (LHC) is a 17-mile-long track, built deep underground, on the border of France and Switzerland. It uses almost 10,000 magnets, kept at a temperature of about -450 degrees Fahrenheit, and has cost over $4 billion. It has collected and archived 100 petabytes of data—the equivalent amount of HD-quality video would take more than 800 years to watch. Ideally, the results of particle collision experiments at the LHC would be used to confirm results predicted by theories. However, the events at the LHC are so complex that we cannot yet solve the equations governing them precisely, either by hand or on today’s computers. Until we can do that, we must simply wait and continue collecting data.
Elsewhere, drug companies are also studying particle interactions, but with the goal of designing molecules for use in medicines. To test the behavior of these molecules, researchers often use computer simulations. But even the most powerful computers cannot simulate the complexity of large molecules, making it necessary to use approximations, or to abandon the simulations altogether and turn to trial-and-error laboratory tests. In any case, finding the optimal molecule for a given task means testing each possible solution, one at a time.
Many of today’s problems rely on computers to store and process vast amounts of data to find a single, optimal result. From finding the fastest route between two locations or predicting whether it will rain next week, to understanding the fabric of our universe or discovering life-saving medicines, even the most powerful computers do not have the ability to predict solutions exactly, and researchers must run intensive experiments or make approximations to run simulations.
Although the speed of computers increases each year, so does our demand. Instead of trying to make our current computers do more of the same, we need a different type of machine altogether. The idea for such a computer has been around since the mid-20th century but gathered momentum in the 1980s and 1990s. It is called a quantum computer, and the study of how it works is called quantum information science. Quantum computers offer the new approach we need to store and process data, and they happen to be particularly poised to solve optimization and particle physics problems.
What is quantum information?
Imagine we both close our eyes and I flip a coin. The coin lands, but we keep our eyes closed. We don’t know the state of the coin—whether it has heads or tails facing up—but we know that the coin is in one of two possible states. Each of us has a fifty-percent chance of guessing the state correctly, but the coin is and always will be heads-up or tails-up.
Now imagine that, instead of a coin, I use an object that has different rules. If we open our eyes and look at the object, it will be in one of two states, just like the coin. But if we keep our eyes closed, the object will not be sitting there in that same state, just waiting for us to look. Instead, while our eyes are closed, the object is in a different, third kind of state.
How do we know that the object is in this third state without being able to see it? Moreover, how do we know that the everyday coin isn’t in this third kind of state before we open our eyes? These are the kinds of questions that quantum mechanics elicits and then answers, proving over and over that this different kind of state really does exist, and that it really is different than its classical counterpart.
In a classical computer, like the one on your desk or in your phone or in your car, information is stored in the physical states of objects in the computer. The nature of these objects is that they can, like coins, be in one of two states (heads or tails, 1 or 0, on or off), and nothing in between. In a computer with a hard drive, these objects are little magnets that can each point only north or south, while in a computer with a solid-state drive, these objects are transistors that are either charged or not charged. These objects are called bits . Just as we build a word that carries meaning by putting specific letters in a specific order, a computer constructs a piece of information from specific values of bits in a specific order.
A quantum computer also stores information in the physical states of objects. These objects, called qubits , will also, when measured, each be found to be in either a 1 or 0 state. Unlike a bit, however, the state of a qubit before we measure it is different. It’s a third kind of state. Amazingly, this third state is related to the other two, and we know exactly how. The state of the qubit before being measured is a combination of its likelihoods of being found to be a 1 or a 0.
The qubit is a new object with which to do computation. While the information contained in a bit is classical (either 1 or 0) a qubit contains quantum information . And because this is an object with new properties, it can potentially solve new problems, or old problems in new ways, using quantum computation techniques that were not accessible before . The new rules a qubit offers for doing computation are strange (Einstein called one of them “spooky”), but if we can understand them and harness them, the power of computers could grow tremendously.
What can quantum information do?
Just the fact that the state of a qubit can be something other than 1 or 0 means that a single qubit can hold an amount of information that a classical computer would need whole sequences of bits to construct. It’s true that if we measured the state of the qubit, we would lose all that special information, but it is actually possible to use the qubit for computation without measuring it, while it’s still in that third kind of state. This means that problems that require checking and comparing data, like cracking passwords or finding the best route between two places on a map, will become, literally, exponentially easier.
In addition to being able to do old computations in new ways, qubits offer the potential to solve problems that are intractable with classical computers. For example, simulating quantum-mechanical systems becomes much more straightforward using objects that are quantum-mechanical themselves. Particle collisions, like those at the LHC, or molecular interactions for potential pharmaceuticals, are therefore natural candidates for quantum computation.
“We are at a critical juncture in the field of quantum information,” says Britton Plourde, a professor of physics in the College of Arts and Sciences . “Applications of quantum computing are already being pursued intensively by many corporate research labs and new startup companies.” Quantum computation’s potential applications have also garnered attention from governments around the world. “The Chinese government has invested $11 billion recently to establish a national lab in this area, and in 2019 the U.S. enacted the National Quantum Initiative, which commits $1.2 billion to quantum technology efforts,” Plourde says.
For young researchers, the field offers an opportunity to contribute to cutting-edge applications of experimental and theoretical physics, chemistry, engineering and computer science. “Undergraduates involved in this type of research will have many research options if they choose to attend graduate school and career opportunities if they decide to work in industry,” says Plourde. The Quantum Information Science Cluster at Syracuse University will provide undergraduate and graduate students with a program to explore this field, both through classes and research helmed by a diverse group of scholars.


In a key step toward creating a working quantum computer, Princeton researchers have developed a method that may allow the quick and reliable transfer of quantum information throughout a computing device.
The finding, by a team led by Princeton physicist Jason Petta, could eventually allow engineers to build quantum computers consisting of millions of quantum bits, or qubits. So far, quantum researchers have only been able to manipulate small numbers of qubits, not enough for a practical machine.
"The whole game at this point in quantum computing is trying to build a larger system," said Andrew Houck, an assistant professor of electrical engineering who is part of the research team.
To make the transfer, Petta's team used a stream of microwave photons to analyze a pair of electrons trapped in a tiny cage called a quantum dot. The "spin state" of the electrons – information about how they are spinning - serves as the qubit, a basic unit of information. The microwave stream allows the scientists to read that information.
"We create a cavity with mirrors on both ends – but they don't reflect visible light, they reflect microwave radiation," Petta said. "Then we send microwaves in one end, and we look at the microwaves as they come out the other end. The microwaves are affected by the spin states of the electrons in the cavity, and we can read that change."
In an ordinary sense, the distances involved are very small; the entire apparatus operates over a little more than a centimeter. But on the subatomic scale, they are vast. It is like coordinating the motion of a top spinning on the moon with another on the surface of the earth.
"It's the most amazing thing," said Jake Taylor, a physicist at the National Institute of Standards and Technology and the Joint Quantum Institute at the University of Maryland, who worked on the project with the Princeton team. "You have a single electron almost completely changing the properties of an inch-long electrical system."
For years, teams of scientists have pursued the idea of using quantum mechanics to build a new machine that would revolutionize computing. The goal is not build a faster or more powerful computer, but to build one that approaches problems in a completely different fashion.
Standard computers store information as classical "bits", which can take on a value of either 0 or 1. These bits allow programmers to create the complex instructions that are the basis for modern computing power. Since Alan Turing took the first steps toward creating a computer at Princeton in 1936, engineers have created vastly more powerful and complex machines, but this basic binary system has remained unchanged.
The power of a quantum computer comes from the strange rules of quantum mechanics, which describe the universe of subatomic particles. Quantum mechanics says that an electron can spin in one direction, representing a 1, or in another direction, a 0. But it can also be in something called "superposition" representing all states between 1 and 0. If scientists and engineers can build a working machine that takes advantage of this, they would open up entirely new fields of computing.
"The point of a quantum computer is not that they can do what a normal computer can do but faster; that's not what they are," said Houck. "The quantum computer would allow us to approach problems differently. It would allow us to solve problems that cannot be solved with a normal computer."
Mathematicians are still working on possible uses for a quantum system, but the machines could allow them to accomplish tasks such as factoring currently unfactorable numbers, breaking codes or predicting the behavior of molecules.
One challenge facing scientists is that the spins of electrons, or any other quantum particles, are incredibly delicate. Any outside influences, whether a wisp of magnetism or glimpse of light, destabilizes the electrons' spins and introduces errors.
Over the years, scientists have developed techniques to observe spin states without disturbing them. (This year's Nobel Prize in physics honored two scientists who first demonstrated the direct observation of quantum particles.) But analyzing small numbers of spins is not enough; millions will be required to make a real quantum processor.
To approach the problem, Petta's team combined techniques from two branches of science: from materials science, they used a structure called a quantum dot to hold and analyze electrons' spins; and from optics, they adopted a microwave channel to transfer the spin information from the dot.
To make the quantum dots, the team isolated a pair of electrons on a small section of material called a "semiconductor nanowire." Basically, that means a wire that is so thin that it can hold electrons like soda bubbles in a straw. They then created small "cages" along the wire. The cages are set up so that electrons will settle into a particular cage depending on their energy level.
This is how the team reads the spin state: electrons of similar spin will repel, while those of different spins will attract. So the team manipulates the electrons to a certain energy level and then reads their position. If they are in the same cage, they are spinning differently; if they are in different cages, the spins are the same.
The second step is to place this quantum dot inside the microwave channel. This allows the team to transfer the information about the pair's spin state – the qubit.
Petta said the next step is to increase the reliability of the setup for a single electron pair. After that, the team plans to add more quantum dots to create more qubits. Team members are cautiously optimistic. There appear to be no insurmountable problems at this point but, as with any system, increasing complexity could lead to unforeseen difficulties.
"The methods we are using here are scalable, and we would like to use them in a larger system," Petta said. "But to make use of the scaling, it needs to work a little better. The first step is to make better mirrors for the microwave cavity."
The research was reported in the journal Nature on Oct. 18. In addition to Petta, Houck and Taylor, the research team includes associate research scholar Karl Petersson, undergraduate student Louis McFaul, post-doctoral researcher Minkyung Jung and graduate student Michael Schroer of the Princeton physics department.
Support for the research was provided by the National Science Foundation, the Alfred P. Sloan Foundation, the Packard Foundation, the Army Research Office, and the Defense Advanced Research Projects Agency Quantum Entanglement Science and Technology Program.


In a previous article, I introduced the recent open-sourcing of quantum computing software by DWave. DWave is the maker of a quantum computer being used and studied by a number of groups, including NASA and Google, and there are other quantum computers in the works too. Although the field is still young, recent progress has been making headlines.
If we can make practical quantum computers, they will be very powerful—but to see why requires understanding what makes them different. In this article, I’ll explain the underlying physics that makes quantum computing possible.
Quantum computers aren’t just a new, faster model of the computer in front of you. They’re based on a completely different method of storing information and decision-making. It’s like comparing a jet turbine to a propeller: they achieve the same purpose, but the complexity and power are vastly disproportionate.
A Bit About Traditional Computers
Let’s begin by reminding ourselves how digital computers work.
The basic ingredient is the binary digit, or bit, which may take only the values 0 or 1. In modern computers, bits take the form of tiny electrical switches called transistors. Transistors are in one of two states. When they are switched on, they conduct electrical current. This is the “1” state. When switched off, they are not conducting current. This is the “0” state.
In a physical computer chip, we might find a series of transistors in the following states: on, on, off, on. In binary, the mathematical language of computation, the series becomes 1101.
This might appear to be an inadequately crude method of communicating information—how could we possibly convey the rich tapestry of the world using only this black-and-white mold? The first step is recognizing that bits can represent numbers in our traditional counting system. For example, 1101 represents the number 13 and 0110 represents the number 6.
In fact, these are the only ways we can represent 13 and 6 using bits, creating a unique translation dictionary between strings of bits and normal numbers. In this way, we can assemble arbitrarily large numbers by stringing together bits. The MacBook Pro uses a 64-bit processor to express every number up to 18,446,744,073,709,551,615.
(Check out this video to learn more about how binary works.)
But if computers could merely store numbers, we would not find them very useful. The reason computers have become ubiquitous is we can use these numbers to further represent many other things.
Take shades of gray: simply interpolate between pure black (0) and pure white (255, by convention). Colors can be decomposed into red, green, and blue components, each having their value interpolated up to 255. Logic operations, musical notes, letters in the alphabet, internet pages, online dating profiles and many other types of information may be expressed in the same way.
Modern computers use billions of transistors and multiple levels of code to produce high-def video and complex apps, but look closely enough, and the digital world reduces to a simple series of bits.
How Quantum Computers Are Different
We need only look in our pocket to see that traditional computers are powerful. But there are some problems they’re ill-suited to solve. This is where quantum computers come in. A quantum computer can solve a special set of problems many magnitudes of order faster than traditional computers.
What makes quantum computers so much faster? They can perform many calculations at once.
“The building blocks of quantum computers are not bits and transistors. They are qubits and physical components so small they operate by the rules of quantum physics.”
This is possible because the building blocks of quantum computers are not bits and transistors. They are qubits and physical components so small they operate by the rules of quantum physics. These components might literally be elementary particles, such as electrons, suspended in magnetic fields.
This is where the weirdness of quantum physics comes into play. The standard shorthand explanation says traditional bits can be either 1 or 0, whereas according to the rules of quantum physics, qubits can be 1, 0, or both at the same time.
This is what truly makes a quantum computer quantum. But let’s dig into what that means a bit more.
Let’s Take a Quantum Hike
To be clear, quantum computers do not offer more discrete states than a traditional computer—the states are still 1 and 0—but there is no longer an exclusive choice between these states required until the very end of a calculation. This may seem paradoxical—how can something be 1 and 0 simultaneously? And even if this is so, why is a choice required at the end?
To better understand how this is possible, imagine hiking with a magnetic compass.
During the day you navigate as you please and the terrain dictates, glancing at your compass and noting that your direction changes. You might begin walking east, then turn north, spin around to go south, before finally nearing northwest. But at the end of each day, you record only whether your encampment is north or south of your departure point that morning.
An example log might read “Day 1: North. Day 2: North. Day 3: South. Day 4: North.”
This two-choice answer belies your more elaborate trajectory containing all the other directions available to the compass. North represents “1” and south represents “0,” but of course, there are many other “intermediate” choices which can be expressed. This is similar to a quantum calculation. During the calculation, a qubit may take any value, but in the final answer there is only a 1 or 0 logged.
So, the qubit’s initial state—the hike’s trailhead—is the problem it’s trying to solve written in binary. The qubit’s final state—the campsite or destination—is its part of the solution, also written in binary. And simplistically, we can think of the qubit’s interim state as a combination of 1 and 0, just as the other directions you moved throughout your hike were combinations of north and south.
The day’s hike around swamps, between hills, and through forests is the quantum calculation—a circuitous route exploring the solution set with a zig northeast, a zag due west, and so on. Eventually, however, each qubit falls into a binary state, and we arrive at our destination.
An Exponential Speed-Up
During a calculation, a qubit pointing in the east direction isn’t simply weighted 50 percent north, 50 percent south—it will specifically remember that it was an eastern direction. This preservation of the direction is called coherence, and it is the most important property for quantum computers.
Coherence is the property of a qubit to experience the full range of values and for qubits to share these values with each other. Four coherent qubits could possess values such as “east, northwest, southeast, west,” whereas incoherent qubits would possess only values “north, north, south, north.” Further, each of their values influences the values of their fellow coherent qubits.
Since qubits sharing mixed states speeds up computation—this is how they perform multiple calculations at once—it is absolutely essential the qubit maintain coherence during the calculation. Otherwise, we are just using a simple, slow digital computer only performing one calculation at a time.
A coherent quantum computer thus considers both 0 and 1 simultaneously, performing a calculation for the north as well as the south, but weighting the answer in a way that preserves the direction of the compass. Mathematically, this can be done using imaginary numbers, meaning we don’t need to consider east as a direction unique from north or south but only as a strange combination of them.
Increasing coherence time has been a major obstacle in making commercially-viable quantum computers. Calculations require at least about 100 nanoseconds, and we have now achieved about 175 nanoseconds. As noted in my last article, this should improve as software improves—the more you can do with a quantum computer, the more resources will pour into the field.
The upshot of all this? Quantum computers offer a massive increase in computing power. A single qubit may concurrently perform two calculations, two qubits may perform four, three qubits eight, and so forth, producing exponentially increasing speed. Just thirty qubits can simultaneously perform more than one billion calculations.
Aimed at the right problems and with the right software, the rise of quantum computers may mark a very significant moment in the history of computation.
Image Credit: Shutterstock


Maybe you’re reading this piece on your laptop. Maybe it’s your smartphone or another mobile device. In either case, your machine works with little pieces of data called bits. Lots of bits. An iPhone X, for instance, has a three-gigabyte processor—about 24 billion bits.
The reason your machine needs so many bits is that they can only be in one of two positions: zero or one. And to solve a complex problem, like simulating processes in the human body, your machine manipulates information in this form. That’s why speed matters, too. You need a machine that can do millions of these computations quickly for it to be useful on a practical, everyday basis.
But what if those bits could be zero and one at the same time, computing multiple possibilities simultaneously? And what if they could influence one another to make more powerful computations possible? A lot fewer bits would yield much greater problem-solving capacity. Exponentially greater, in fact. A machine like that might be able to solve the most complicated problems in the blink of an eye.
Welcome to quantum computing.
“Think of a light switch,” explains physics PhD student Katherine Van Kirk. “It can either be on or off. The bits in a conventional computer are the same way: one or zero. But in a quantum computer, all of your individual bits—we call them qubits—are more like dimmer switches. Qubits can be somewhere between one and zero. And by interacting, the ‘brightness’ of one qubit can come to depend on the ‘brightness’ of another. A machine with these properties might solve complex problems more efficiently than our conventional computers.”
As a PhD student at Harvard’s Graduate School of Arts and Sciences (GSAS), Van Kirk works with George Vasmer Leverett Professor of Physics Mikhail Lukin to develop a powerful new way of computing—one that operates on the atomic level in the realms of probability and uncertainty. As she does, however, she also keeps her eye on issues of social justice and equity, and the human relevance of technological advances in quantum science. Before the field can transform areas like cryptography, medical science, and machine learning, however, researchers need to understand how to isolate the information they want to get out of the new supercomputers. That’s where Van Kirk’s research comes in.
“…a quantum computer might be able to simulate, for instance, some complicated molecule. That's really hard for a classical computer to do. And if you can simulate a complicated molecule, then you can learn about its characteristics, which might be useful in developing pharmaceuticals.”
Capturing the Full Picture
Van Kirk was an undergraduate at Stanford studying engineering physics when she attended her first class in quantum mechanics. She says that she was both flummoxed and smitten by the concepts she encountered.
“When I took quantum mechanics, I fell in love,” she remembers. “I was totally baffled by the fact that an electron can be in two states at once. I wanted to learn everything about it.”
Van Kirk continued to explore the quantum realm at Stanford before enrolling at the University of Cambridge for her master’s degree in applied mathematics. Before she came to Harvard, she worked at IBM, one of the leading developers of quantum computing. She says that one of the challenges with quantum devices is isolating the information you want. Your iPhone may have many times the processing power that the first room-sized computers had, but it works on the same unambiguous principle: zeros and ones. Quantum computers, however, operate in the realm of an exponential number of interdependent possibilities. As part of the Harvard Quantum Initiative (HQI), Van Kirk works to isolate the relevant information.
“A quantum computer doesn’t always just give you the answer to your query,” she says. “Sometimes it gives you a quantum state. And the answer is encoded in it. You might think ‘Easy! To get the answer, I can just take a snapshot of my system.’ But in quantum mechanics, the full picture isn't that easy to capture. You need an exponential number of measurements to get the full picture. And that’s super expensive! Depending on what you’re trying to do, this costliness may force you to lose the advantage of using a quantum computer in the first place.”
At the HQI, Van Kirk is trying not only to come up with a more efficient way to extract information from a quantum system, but also to do it with the tools experimentalists currently have on hand.
“I’m designing a protocol that uses as few measurement snapshots as possible to extract only the information relevant to the problem we’re trying to solve,” she says. “The protocol will be a systematic way to take snapshots from just the right ‘angles.’ And the tools I’m using to develop the protocol are the same ones that the experimentalists have access to in the lab. So, it’s exciting because once it’s finished, it will be immediately useful.”
While Van Kirk cautions that there are still may questions surrounding the application of quantum computing, she says that cryptography—critical for cybersecurity—is an area where it could be transformational. Big numbers are notoriously difficult for even the mightiest supercomputers to factor. The MIT mathematician Peter Shor has developed an algorithm that shows quantum computers could do this work more efficiently than the fastest classical supercomputers. Another area where quantum could have a big impact is medicine.
“Quantum computers might be able to effectively simulate quantum mechanical processes,” Van Kirk says. “What that means is that a quantum computer might be able to simulate, for instance, some complicated molecule. That's really hard for a classical computer to do. And if you can simulate a complicated molecule, then you can learn about its characteristics, which might be useful in developing pharmaceuticals.”
Science and Bias
As excited as Van Kirk can be about quantum computing, she is also concerned about bias and inequality. At the time she took her first class in quantum mechanics at Stanford, Van Kirk was also running an international non-profit dedicated to empowering women in technology. She worries about how the systems we build can reflect our own biases or even historical inequities. She points to ProPublica’s 2016 study of “risk assessment tools” used by the criminal justice system.
“This algorithm gives defendants a ‘risk score,’ which is supposed to capture how likely they are to commit a crime in the future,” she says. “While it doesn’t explicitly consider race as a factor, the algorithm was more likely to rate black defendants as higher risk than white defendants. The tool is used by law enforcement across the country, and in some states, the judges even see the scores during sentencing.”
Biased tools like these are built using machine learning, one of the potential applications of quantum computers. Van Kirk studied how bias might be quantified and mitigated on these machines. She tested one possible metric for measuring bias on a quantum computer using public data from this risk assessment tool and found that it reflected the model’s disparate treatment of Black and white defendants. Along those lines, Van Kirk avidly studies social science research, hoping to draw a concrete link between real-world problems and the work she does on quantum computing. She says that she wants to advance science and equity too.
“I put aside time every week to read about what's being done in fields other than physics,” she says. “Recently I’ve been reading a lot of computer science and molecular biology, but I also enjoy social science articles too. I'm always trying to learn about the problems that exist and ask myself where quantum computers might be able to make an impact.
Van Kirk’s mentor Lukin says that her concern for the social good makes her more than an outstanding student: it makes her a remarkable person.
“The combination of the depth and the breadth of Katherine’s knowledge in subjects ranging from physics to computer science is truly exceptional,” he says. “She cares deeply about the implications of her work for both the broader scientific community and for society as a whole, which I find very inspiring. I look forward to her professional development from a talented student to a mature, exceptional young scientist.”
Now at the close of her first year at GSAS, Van Kirk says she’s not sure where her research will lead her. She is certain about her goal, though. She wants every project she works on to have a positive effect on society.
“I always ask myself if I would have a greater impact than I am having now if I was back growing a nonprofit around some issue that I care about,” she says. “If my answer to that question is ever ‘Yes,’ I will switch gears and try something new.”


Back in 2001, an obscure group of theoretical physicists proved a remarkable result. They showed that it was possible to build a quantum computer out of ordinary optical components, such as mirrors, lenses, lasers, and so on.
That was something of a surprise. Until then, physicists had thought that quantum computing would only be possible using non-linear crystals and other exotic components that are hard to control.
The prospect of using ordinary bits and pieces has an important consequence. It immediately suggests that more powerful devices can be built simply by adding extra components. This problem of scalability has always plagued other attempts to build quantum computers.
The reaction from the theoretical physics community was barely controlled excitement. But in practice, this approach has never lived up to its early promise. That’s because it is hard to build even ordinary optical components into chip-like devices that can be scaled like conventional silicon chips. It is just not possible to manufacture them with the required performance and tolerances.
Today, Benjamin Metcalf at the University of Oxford and a few pals show how they are tackling these problems while aiming for the ultimate goal of scalable quantum computation. These guys have built the first self-contained photonic chip capable of teleportation, one of the fundamental logic operations necessary for quantum computation. The device is a proof-of-principle demonstration that scalable quantum computers of this type are firmly in the crosshairs of experimental physicists. But it also reveals that significant challenges lay ahead.
Quantum teleportation is a standard procedure in quantum optics laboratories all over the world. It aims to transfer the quantum state of an input qubit, Q1, to a target qubit, Q3. The process begins by creating a pair of entangled photons, Q2, and Q3. These share the same quantum existence so that a measurement on one immediately influences the other.
This measurement is important. Known as a two qubit Bell state measurement, it is carried out on both Q1 and Q2 at the same time. Because Q2 is entangled with Q3, this results in the quantum state of Q1 being transferred to Q3. In other words, the quantum state of Q1 is teleported to Q3, which may be an entirely different part of the universe.
This process is usually carried out using low intensity laser beams and ordinary components such as mirrors and optical fibers. But the new photonic device shrinks all these components onto a single silicon chip.
It has source of photons, beam splitters, silica waveguides to channel the photons through the device as well as components for creating and measuring quantum bits or qubits. One of the key questions these guys set out to answer is how well each of these components work and how their limitations contribute to the overall performance of the chip.
Until now, one problem with this approach is that it is difficult to create high quality single photons in chip-based devices. What’s more, these photons tend to get absorbed by imperfect beam splitters or scattered in the silica waveguides, dramatically reducing the robustness of the process.
The advance that Metcalf and co have achieved is to dramatically improve the quality of their single photon sources while characterizing the losses from other optical components such as beam splitters and waveguides for the first time. In doing so they’ve demonstrated one of the basic logic operations of quantum computing inside a photonic chip for the first time: the teleportation of a qubit.
The new chip is by no means perfect: it performs with around 89 percent fidelity. One source of errors is the photon source, which is far from ideal. “Whilst the success of this experiment relies on our development of high-quality single photon sources with exceptional heralded state purity and heralding efficiency, the absence of a true on-demand single photon source continues to limit the achievable fidelity,” they say.
A more significant source of errors is the non-ideal beam splitters, which by themselves reduce the fidelity of the device to around 90 percent. That’s good enough for secure communication. “But it is still below the fidelity of 99% thought to be required for a fault-tolerant quantum computer,” admit Metcalf and co.
It is inevitable that beam splitters and waveguides made in this way will deviate from their design parameters. The challenge is to ensure that these deviations are kept to a minimum or corrected by other components in real-time.
Finally, future photonic chips will need better materials that reduce the loss of photons due to scattering. That becomes particularly important as chips become larger and more complex.
So the scale of the future challenges are clear. If physicists want to build photonic chips capable of carrying out quantum computation, they will need better photons guns, less lossy materials and active components that can measure correct aberrations in real-time.
That’s a big ask. Large-scale quantum computers are coming but on this evidence, not in the very near future in photonic form.
Ref: arxiv.org/abs/1409.4267 : Quantum Teleportation on a Photonic Chip


Experimental physics is littered with freaky effects, often the product of obscure forces moving and changing objects in ways we don’t expect, but almost always leading to perfectly understandable conclusions. One notable exception – arguably the most notable, in fact – is the double-slit experiment.
Cut two narrow, parallel slits on an opaque sheet and shine light on them. If the conditions are right, you’ll see an interference pattern on the wall behind the sheet – the result, and proof, of the photons’ wavelike behaviour. But if you stick a small detector on each of those slits to track the movement of waves through each one, the interference pattern will be replaced by one or two small pricks of light on the wall – the result, and proof, of the photons behaving like particles and moving only through one slit or the other. Taken together, the experiment demonstrates the wave-particle duality of quantum objects (objects whose behaviour is dictated by quantum forces, as opposed to macroscopic objects that are dominated by classical forces).
In the more than two centuries since the first double-slit experiment, in 1801, many groups of scientists have modified it in different ways to elicit different aspects of the duality, and their implications for the study of the nature of reality. Anil Ananthaswamy’s 2018 book Through Two Doors At Once wends its way through this history, at each step stopping to identify more and more strange discoveries that have only complicated, instead of simplified, the behaviour of particles like photons. One especially weird possibility is contained in a thought-experiment called the Elitzur-Vaidman bomb tester.
Another is contained in a series of famous thought-experiments that American physicist John Wheeler proposed from the late 1970s. Essentially, he asked if each photon could make a ‘decision’ about whether it would travel as a particle or as a wave based on the experimental setup in front of it, if this decision happened in a certain time frame, and if an observer could anticipate this decision-making moment and interfere with it. As bizarre as this sounds, physicists have been able to set up experiments whose results have been consistent with some of Wheeler’s hypotheses.
For example, say you shine a laser at a beam-splitter.
The beam is split in two perpendicular directions; let’s call them A and B. A is made to bounce off a mirror by 90º and moves to a point, which we’ll call P. B is also turned 90º by a mirror in its path and directed to P. If there is a detector at P, physicists have observed a prick of light – indicating both A and B beams were composed of particles. But if there is another beam-splitter at P, then the combined A and B beams are split once again into two beams – and one of them has shown an interference pattern. If A and B were composed of particles until they struck the detector or splitter at P, where did the waves come from? Or, according to Wheeler’s hypothesis, did the photons travelling as part of A and B anticipate that there would be a splitter instead of a detector at P, and decided to become waves? We don’t know. Specifically, there are different interpretations of the experiment’s outcomes that try to make sense of what happened, but we don’t have objective data that supports one exact possibility, in a classical sense.
Wheeler himself concluded that there are no phenomena in the natural universe that are independent of their observations. That is, until you observe something (quantummy) happening, Wheeler figured it wouldn’t have happened (at least not the way you think it did). But more importantly (for this post), both Wheeler’s ideas and the experiments that physicists used to elucidate wave-particle duality kept the focus on the particle, the observer and the test setup. A new study by scientists in the US may complicate this picture even more: they’ve reported evidence that the source of the particles could also influence how the particles behave in an experiment.
Theoretical physicists have anticipated such a finding. For example, one paper published in February 2020 said that when its authors set out to quantify the extent to which a setup would produce an interference pattern or pinpricks of light, they found a simple mathematical relationship between this measure and the purity of the photon source. In the new study, simply put, physicists flashed a specifically tuned laser onto two crystals, of lithium niobate. The crystals then emitted two photons each, which the physicists called the ‘signal’ and the ‘idler’. They directd the signal photons from both crystals to an interferometer – a device that splits a beam of light into two and recombines them to produce an interference pattern – to observe the characteristic proof of wave-like behaviour; they also directed the two idler photons to two detectors, to confirm their particle-like behaviour.
Each pair of signal and idler photons produced by each crystal would be entangled. Wikipedia: “Quantum entanglement is a physical phenomenon that occurs when a group of particles are generated, interact or share spatial proximity in a way such that the quantum state of each particle of the group cannot be described independently of the state of the others, including when the particles are separated by a large distance.” One implication of this relationship is that if we discover, or observe, one of two entangled particles in a certain quantum state, we can determine the state of the other particle without observing it.
In their experiment, the physicists effectively mapped source purity with “the likelihood that a particular crystal source will be the one that emits light” (source). That is, by increasing or decreasing the chances of one of the two crystals emitting photons – by adjusting the strength of the incident laser – the physicists could control the value of the source purity they needed to plug into the equation. They found that when one of the crystals became very likely to emit paired photons, the interference pattern became very feeble – i.e. the photons at the interferometer were behaving like particles. The interference pattern was sharpest when both crystals were equally likely to emit paired photons. These results confirmed the (theoretical) findings of the February 2020 paper, but the physicists were able to do one better.
The February 2020 paper posited that source purity (µ), interference visibility (V) and ‘particle location distinguishability’ (D) were related thus: V2 + D2 = µ2. The new paper also found that µ2 = 1 – E2, where E is a measure of the extent of entanglement between an idler photon and the detector detecting it. This is new, and we don’t yet know how other physicists will exploit it to delve even more into the seemingly bottomless pit that is wave-particle duality. Equally, the experiment also demonstrates, according to Xiaofeng Qian, one of the authors of the February 2020 paper, that a “quantum particle can behave simultaneously, but partially, as both” wave and light.


A quantum computer doesn’t need to be a single large device but could be built from a network of small parts, new research from the University of Bristol has demonstrated. As a result, building such a computer would be easier to achieve.
Many groups of research scientists around the world are trying to build a quantum computer to run algorithms that take advantage of the strange effects of quantum mechanics such as entanglement and superposition. A quantum computer could solve problems in chemistry by simulating many body quantum systems, or break modern cryptographic schemes by quickly factorising large numbers.
Previous research shows that if a quantum algorithm is to offer an exponential speed-up over classical computing, there must be a large entangled state at some point in the computation and it was widely believed that this translates into requiring a single large device.
In a paper published in the Proceedings of the Royal Society A, Dr Steve Brierley of Bristol’s School of Mathematics and colleagues show that, in fact, this is not the case. A network of small quantum computers can implement any quantum algorithm with a small overhead.
The key breakthrough was learning how to efficiently move quantum data between the many sites without causing a collision or destroying the delicate superposition needed in the computation. This allows the different sites to communicate with each other during the computation in much the same way a parallel classical computer would do.
We provide algorithms for eﬃciently moving and addressing quantum memory in parallel. These imply that the standard circuit model can be simulated with low overhead by the more realistic model of a distributed quantum computer. As a result, the circuit model can be used by algorithm designers without worrying whether the underlying architecture supports the connectivity of the circuit. In addition, we apply our results to existing memory intensive quantum algorithms. We present a parallel quantum search algorithm and improve the time-space trade-oﬀ for the Element Distinctness and Collision Finding problems.
In classical parallel computing, sorting networks provide an elegant solution to the routing problem and simulation of the parallel RAM model. In this paper, we have demonstrated that they can be applied to quantum computing too. The information about the connectivity of a quantum circuit is available before we run the algorithm (at compile time). Using this classical information we have designed an eﬃcient scheme for routing quantum packets. The application of this data-moving algorithm is to distributed quantum computing. We provide an eﬃcient way of mapping arbitrary unconstrained circuits to limited circuits respecting the locality of a graph.
Our results already apply to nearest neighbour architectures in the case of a circuit that is highly parallel. The case of emulating a circuit with many concurrent operations on a 1D nearest neighbour machine was covered by Hirata et al. The approach is to use the Insertion/Bubble sort to perform all of the operations in O(N) time-steps which compares favorably to performing each gate in turn in O(N2) depth. We put this idea in a general framework applying to any (connected) graph. Along the way we are able to prove that up to polylogarithmic factors, this approach is optimal.
We have shown how the addition of a few long-range (or ﬂying) qubits dramatically increases the power of a distributed quantum computer. Using only O(logN) connections per node enables eﬃcient sorting over the hypercube. A distributed quantum computer with nodes connected according to the hypercube graph would be able to emulate arbitrary quantum circuits with only O(log2 N) overhead. One might expect that a quantum computer requires O(N) connections per node so that each qubit can potentially interact with any other qubit. Our result demonstrates that this is not the case: for a small overhead O(logN) connections suﬃce.
We have presented a new algorithm for accessing quantum memory in parallel. The algorithm is a modiﬁcation of the data-moving algorithm used in Sections 2 and 3 but where the destinations are quantum data and no longer restricted to form a permutation.
The algorithm is extremely eﬃcient; it has an overhead that is scarcely larger than any algorithm capable of accessing even a single entry from memory. Theorem 5 implies that N processors can have unrestricted access to a shared quantum memory. It tells us that the quantum parallel RAM and the circuit models are equivalent up to logarithmic factors.
Finally, we demonstrated that the parallel look-up algorithm can be used to optimize existing quantum algorithms. We provided an extension of Grover’s algorithm that eﬃciently performs multiple simultaneous searches over a physical database, and answered an open problem posed by Grover and Rudolph by demonstrating an improved spacetime trade-oﬀ for the Element Distinctness problem. It seems likely that this framework for eﬃcient communication in parallel quantum computing will be a useful subroutine in other memory-intensive quantum algorithms, such as triangle ﬁnding, or more generally for frameworks such as learning graphs.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.


How feasible is it to build a Jupiter brain, a computer the size of a planet? Just in the past few decades, the amount of computational power that’s available to humanity has increased dramatically. Your smartphone is millions of times more powerful than the NASA computers used to send astronauts to the moon on the Apollo 11 mission in 1969. Computers have become integral to our lives, becoming the backbone of our communications, finances, education, art, health care, military, and entertainment. In fact, it would be hard to find an area of our lives that computers didn’t affect.
Now imagine that one day we make a computer that’s the size of an entire planet. And we’re not talking Earth, but larger, a megastructure the size of a gas giant like Jupiter. What would be the implications for humans to operate a computer that size, with an absolutely enormous, virtually limitless, amount of computing power? How would our lives change? One certainly begins to conjure up the transformational effects of having so much oomph, from energy generation to space travel and colonization to a fundamental change in the lifespan and abilities of future humans.
But while speculation of that sort can easily lead us into the fictional realm, what are the known facts about creating such an impressive computer? How hard would it be?
The limits of a Jupiter brain
Building a Jupiter brain would be dependent on specific factors that limit the power of a computer, as outlined by the Swedish computational neuroscientist and transhumanist Anders Sandberg in his seminal 1999 paper on the subject. His work, titled “The Physics of Informational Processing Superobjects: Daily Life Among the Jupiter Brains,” focused on the stipulations of building such an enormous computer. As Anders writes in his paper, the “laws of physics impose constraints on the activities of intelligent beings regardless of their motivations, culture or technology.” Even more specifically, he argues, each civilization is also limited by the physics of information processing.
The specific physical constraints Sanders found in supersizing a computer are the following:
1. Processing and memory density
The elements that constitute a computer and its memory units, all the chips and circuits involved, have a finite size, which is limited by physics. This fact creates “an upper limit” on the processing and memory density of any computing system. In other words, you can’t create computer parts that are smaller than a certain shape, beyond a certain size they will stop functioning reliably.
2. Processing speed
The speed of information processing or memory retrieval is related to how fast electrical signals can travel through the computer, determined by the “natural timescales of physical processes,” writes Sandberg.
3. Communication delays
If we build a gigantic computer the size of a planet, it might experience delays in communication between its various extended parts due to the speed of light. In fact, the faster its processing speed, the longer the delays might feel “from an internal subjective point of view,” as the scientist describes. If we want to have fewer delays, the distances in the system need to be as small as possible, or else not need to utilize communication over long distances.
4. Energy supply
As you might imagine, an extremely large computing system would be a major power hog. Computation on such a scale would need tremendous amounts of energy and the management of heat dissipation. In fact, looking for the heat emissions from large computing system is one potential way to scour the sky for advanced alien civilizations.
Sandberg suggests some ways to deal with these challenges. While the power and speed of individual processors may have a limit, we must turn our focus to figuring out how to make parallel systems where all the disparate elements work in unison. He gives the example of the human brain where “even fairly slow and inefficient elements can produce a very powerful computing system.”
The processing factors and the delays in communication may have to be handled by creating a computing system that’s more concentrated and modular. Among other considerations, he also proposes giving “reversible computing” (a theoretical form of quantum computing in which the computational process is to some extent time-reversible) a closer look, as it may be possible to achieve this type of computation without having to expend additional energy. It involves no bits being erased and is based on reversible physics. An example of this would be copying and pasting a record, along with its inverse. Such machines could be potentially built by utilizing reversible circuits and logical boards as well as quantum computation, among several other approaches proposed by Sanders.
Technologies you would need
One of the fun parts of trying to design a Jupiter brain is figuring out the technology that would be necessary to accomplish this mammoth task. Besides the potential army of self-replicating swarms of nanorobots that would need to be employed to put this immense computer together; in an appendix to his paper, Sanders suggests a design for what it would take to make a Jupiter brain he called “Zeus.”
Zeus would be a sphere 11,184 miles (18,000 kilometers) in diameter, weighing about 1.8 times the mass of Earth. This super-object would be made out of nano diamonds called diamondoids. These would form a network of nodes around a central energy core consisting of quantum dot circuits and molecular storage systems. Another way to organize the nodes and distribute information could be through a cortex “with connections through the interior” which Sanders finds most “volume-efficient” and best for cooling.
Each node would be a processing element, a memory storage system, or both, meant to act with relative independence. Internal connections between the nodes would be optical, employing fiber optics/waveguides or utilizing “directional signals sent through vacuum.”
Around the sphere would be a concentric shield whose function would be to offer protection from radiation and dissipate heat into space via radiators. Zeus would be powered by nuclear fusion reactors dispersed on the outside of that shield. This would make a Jupiter brain particularly distinct from other hypothetical megastructures like a Dyson Sphere or a Matrioshka Brain that Type II civilizations on the Kardashev Scale could theoretically create to harness energy from stars.
Where would we get the supplies to make a Jupiter brain? Sanders proposes gathering the carbon located in gas giant cores or through star lifting, any one of several hypothetical processes that would allow Type II civilizations to repurpose stellar matter.
If planet-size computers are not enough of a challenge, Sanders also proposes some information processing solutions that even he termed “exotica”, as they involve developing or purely theoretical technologies. Among these are using quantum computers, which are not only quantitatively but “qualitatively more powerful than classical computers.” Sanders also believes they allow for reversible computation and are the “natural choice” when it comes to computing systems on the nanoscale or the even smaller femtoscale.
Black Holes could potentially be used as processing elements if they do not destroy information, a currently contested notion. If information is released from black holes via Hawking radiation, they could possibly be tapped as information processors, conjectures the scientist.
A network of wormholes, theoretical tunnels that connect distant parts of the space and time continuum, is another yet-to-be-proven hypothetical structure that may serve as “extremely useful” for information processing and communications.
Another philosophical nugget that would be at home in any discussion involving The Matrix also emerged from Sandberg’s paper: As a civilization grows and expands its information processes to the limits of physical laws and technology, it will at some point become “advantageous in terms of flexibility and efficiency for individual beings to exist as software rather than (biological) hardware."
Why is that so? Fewer of the increasingly scarce resources would be required to sustain such a being, which will evolve automatically as code. The limits of this virtual existence are bounded by the computing system it exists in. “As technology advances the being will be extended too,” writes Sanders.
The Swedish philosopher and computational neuroscientist Nick Bostrom wrote a now-famous paper on the Simulation Hypothesis titled “Are we living in a computer simulation?” In it, he estimates that all the brain activity by all the humans who ever lived would amount to somewhere between 1033 and 1036 operations. By comparison, a planet-sized computer like a Jupiter brain would be able to execute 1042 operations per second. It would be able to simulate all of human brain activity ever, all the consciousnesses of all the people who ever lived, “by using less than one millionth of its processing power for one second,” writes Bostrom.
Certainly, these technologies and their implications are highly speculative at this point, but visualizing the futuristic gadgetry is one step in making it real eventually, as has happened with other tech developments. If we can imagine it, well, perhaps we can build it.


TABLE OF CONTENT
Computing technology is about to reach a landmark moment in its growth. As the academic and business realms brace for the dawn of the quantum age, there is understandable apprehension about what lies ahead, which brings up questions like “what is quantum computing, its applications, and implications?” One fact is for sure – the world is about to see a new generation that’s tied to quantum computing security threats. The main danger is the disruption of the traditional encryption systems, resulting in all kinds of private information being revealed.
Although brute force attacks will take months to break through high-level algorithms, quantum attacks can break traditional public-key encryptions in a fraction of the time. Even if quantum computers do not arrive for another decade, today's public key encryption has yet to be proven reliable against mathematical attacks. Furthermore, the challenge faced by a potential quantum computer has an immediate impact on data: the “download now, decode afterward” attack vector ensures that (encrypted) confidential information can be collected now and processed offline until quantum computers emerge.
However, there are ways to protect your data, called quantum security. Quantum-safe encryption is the best way to maintain the protection of encryption keys and achieve long-term privacy. Quantum computing security would secure the data from today's brute force attacks, guarantee that long-living data is protected from potential attacks, and shield high-value data in a post-quantum computing environment.
Dangers Posed by Quantum Computer Hacking
Quantum computers are data storage and computing devices that make use of quantum mechanical properties. This can be immensely beneficial for some projects, where they can significantly outperform even one of the most powerful supercomputers.
Traditional devices, such as phones and laptops, store data in binary "bits" that can be either 0s or 1s. A quantum bit, or qubit, is the fundamental memory device of a quantum computer. Physical systems, including the spin of an electron or the photon's angle, are used to create qubits. Quantum superposition is a property that causes these devices to be in several configurations at the same time. Quantum entanglement is a process that allows qubits to be inextricably bound. As a consequence, a set of qubits will represent several items at the same time. A traditional device, for example, can reflect any figure between 0 and 255 with only eight bits. However, an eight-qubit quantum machine will simultaneously represent all numbers between 0 and 255. This is crucial for massively faster processing speeds, which are needed to replicate quantum mechanics at the molecular level.
Now a vital question arises – what is quantum computing used for?
In 2019, Google made headlines for declaring quantum superiority, claiming that its machines could execute tasks that a traditional machine couldn’t. IBM has also been reporting about its efforts to build a 1000-qubit quantum computer by 2023. Below are some of the most common quantum computing technologies in practice:
- Artificial Intelligence (AI) and Machine Learning (ML): Speech, image, and handwriting recognition are only a few of the typical AI and ML applications we see daily. And this is where quantum computation could help solve difficult problems in a fraction of the time, whereas it would take conventional computers thousands of years to solve.
- Computer-Aided Chemistry: The number of quantum states, also in the tiniest of molecules, is thought to be enormous, making it impossible for traditional computational memory to process. Creating a superconductor (at room temperature), eliminating carbon dioxide for a healthier atmosphere, and optimizing the nitrogen-fixation method for ammonia-based fertilizer are only a few of the crucial problems that quantum computing could solve.
- Drug Development & Design: Researchers agree that quantum computation can be a valuable method for understanding medications and their effects on humans, saving pharmaceutical companies a lot of money and time.
- Cryptography & Cybersecurity: Thanks to the number of cyber-attacks that occur regularly worldwide, the online security environment has become very fragile. Quantum computing cybersecurity, combined with machine learning, will help develop different strategies to fight cyber threats.
- Financial Analysis: Companies can increase their solutions' accuracy and lessen the time needed to produce them by using quantum technology to execute large and complicated calculations.
Quantum computers could, however, assist hackers in gaining access to our most sensitive information by breaking cryptography that would usually take thousands of years to crack, including with supercomputers. To demonstrate that traditional encryption (RSA + AES) is approaching its limit, Active Cypher designed QUBY, a mini-quantum computer, by repurposing hardware running quantum algorithms. With hardware costing $600, QUBY is compact enough to fit in a backpack. Performing a vast superposition of potential outcomes for modern encryption algorithms requires a quantum processor with millions of qubits — and the biggest quantum machine on the market currently has just 72 qubits. However, quantum emulators can already speed up the breaching of encryption protocols by using sophisticated cracking algorithms on non-quantum hardware platforms. This fact makes matters of quantum security even more important today.
Present defense measures would be vulnerable to new forms of cyber threats due to quantum computing, posing a serious problem for advanced technical networks such as networked cars or industrial control systems. Cyber-attacks on industrial facilities may result in the theft of trade secrets or disruption of the manufacturing cycle, resulting in colossal economic damage. These aspects also increase the significance of quantum security.
Importance of Quantum Security
Without any quantum-safe encryption, any data transferred on public media now and even in the future would be susceptible to spying. Also, material protected from current attacks could be stored for later decryption until a functional quantum computer is accessible. It could also result in undetected data manipulations. It would be difficult to claim the credibility and authenticity of transmitted content. This would breach existing regulatory standards for data confidentiality and safety from a business, ethical, and legal viewpoint.
Security specialists are concerned that today’s common algorithms protection is based on one of three difficult mathematical problems: integer factorization, discrete logarithm, or elliptic-curve discrete algorithm. Most of these challenges can be easily resolved using Shor's algorithm on a reasonably efficient quantum computer. Although existing theoretical quantum computers lack the computational power to hack any actual cryptographic algorithm, many cryptographers are developing new algorithms to plan for the day when quantum computing becomes a threat.
Encryption efficiently preserves privacy because data encryption and decryption are relatively simple programming processes, but cracking an encryption scheme is incredibly difficult. A brute force attack on the encryption key is incredibly difficult. Since an intruder can't access data without the encryption keys, they'll want to steal them. If the hacker cannot access the keys by any means, they will resort to a "brute force" attack, in which they try all available encryption keys. Quantum security algorithms keep the principle that without an encryption key, an intruder will be unable to decrypt data without compromising the data's integrity.
When addressing encryption, it's important to understand that most security schemes employ one of two encryption forms – Asymmetric Encryption and Symmetric Encryption.
- Asymmetric Encryption: This encryption utilizes a key pair that comprises a public and private key. Every node will have its pair of keys. The public key can be exchanged with other nodes; however, the private key should be kept confidential. A negative aspect of asymmetric encryption is that it can use 100 times the amount of CPU cycles as symmetric encryption. The solution is to establish an initial encrypted link for sharing a hidden symmetric key by first running an asymmetric session.
- Symmetric Encryption: It allows the use of a single hidden key that all interacting participants obtain. The question is how to share keys without them being intercepted. This is where asymmetric encryption is used, which allows for the safe sharing of the symmetric encryption key.
Discovering, developing, and implementing modern quantum-safe encryption algorithms has become a priority for academic, technology, and government organizations worldwide. The aim is to develop one or more algorithms that can withstand quantum computation with certainty. Quantum encryption is accomplished using a mathematical approach that is impossible to overcome with both standard and a quantum computer. The present RSA and ECC encryption algorithms are focused on algebraic problems involving very big random numbers. They are extended to both public and private keys so that the private key is never revealed.
Surprisingly, symmetric encryption and hash algorithms are currently untouched by quantum computing and do not require replacement. Many encryption schemes that rely on asymmetric encryption to create keys for symmetric encryption operations are at risk of being broken. Quantum-safe encryption aims to replace the asymmetric encryption algorithms currently used for key exchange and digital signatures.


Alternate format: ITSAP.40.016 Using encryption to keep your sensitive data secure (PDF, 391 KB)
Encryption technologies are used to secure many applications and websites that you use daily. For example, online banking or shopping, email applications, and secure instant messaging use encryption. Encryption technologies secure information while it is in transit (e.g. connecting to a website) and while it is at rest (e.g. stored in encrypted databases). Many up-to-date operating systems, mobile devices, and cloud services offer built-in encryption, but what is encryption? How is it used? And what should you and your organization consider when using it?
What is encryption?
Encryption encodes (or scrambles) information. Encryption protects the confidentiality of information by preventing unauthorized individuals from accessing it.
For example, Alice wants to send Bob a message, and she wants to ensure only he can read it. To keep the information confidential and private, she encrypts the message using a secret key. Once encrypted, this message can only be read by someone who has the secret key to decode it. In this case, Bob has the secret key.
Eve is intentionally trying to intercept the message and read it. However, the message is encrypted, and even if Eve gets a copy of it, she can’t read it without acquiring the secret key.
If an individual accidentally receives a message that includes encrypted information, they will be unable to read the encrypted contents without the key to decrypt the message.
How is encryption used?
Encryption is an important part of cyber security. It is used in a variety of ways to keep data confidential and private, such as in HTTPS websites, secure messaging applications, email services, and virtual private networks. Encryption is used to protect information while it is actively moving from one location to another (i.e. in transit) from sender to receiver. For example, when you connect to your bank’s website using a laptop or a smartphone, the data that is transmitted between your device and the bank’s website is encrypted. Encryption is also used to protect information while it is at rest. For example, when information is stored in an encrypted database, it is stored in an unreadable format. Even if someone gains access to that database, there’s an additional layer of security for the stored information. Encryption is also used to protect personal information that you share with organizations. For example, when you share your personal information (e.g. birthdate, banking or credit card information) with an online retailer, you should make sure they are protecting your information with encryption by using secure browsing.
Many cloud service providers offer encryption to protect your data while you are using cloud based services. These services offer the ability to keep data encrypted when uploading or downloading files, as well as storing the encrypted data to keep it protected while at rest.
When properly implemented, encryption is a mechanism that you and your organization can use to keep data private. Encryption is seamlessly integrated into many applications to provide a secure user experience.
How can I use encryption?
Your organization likely already uses encryption for many applications, such as secure browsing and encrypted messaging applications.
If you access a website with padlock icon and HTTPS in front of the web address, the communication (i.e. the data exchanged between your device and the website’s servers) with the website is encrypted.
To protect your organization’s information and systems, we recommend that you use HTTPS wherever possible. To ensure that users are accessing only HTTPS-supported websites, your organization should implement the web security policy tool HTTP Strict Transport Security (HSTS). HSTS offers additional security by forcing users’ browsers to load HTTPS supported websites and ignore unsecured websites (e.g. HTTP).
Encrypted messaging applications
Most instant messaging applications offer a level of encryption to protect the confidentiality of your information. In some cases, messages are encrypted between your device and the cloud storage used by the messaging service provider. In other cases, the messages are encrypted from your device to the recipient’s device (i.e. end-to-end encryption). When using end-to-end encryption services, not even the messaging service provider can read your encrypted messages.
In deciding which tools to use, you need to consider both the functionality of the service and the security and privacy requirements of your information and activities. For further information, refer to protect how you connect.
Encryption is just one of many security controls necessary to protect the confidentiality of data.
What else should I consider?
Encryption is integrated into many products that are commonly used by individuals and organizations to run daily operations. When choosing a product that uses encryption, we recommend that you choose a product that is certified through the Common Criteria (CC) and the Cryptographic Module Validation Program (CMVP). The CC and the CMVP list cryptographic modules that conform to Federal Information Processing Standards. Although the CC and the CMVP are used to vet products for federal government use, we recommend that everyone uses these certified products.
The CCCS recommends
The cccs recommends
- Evaluate the sensitivity of your information (e.g. personal and proprietary data) to determine where it may be at risk and implement encryption accordingly.
- Choose a vendor that uses standardized encryption algorithms (e.g. CC and CMVP supported modules).
- Review your IT lifecycle management plan and budget to include software and hardware updates for your encryption products.
- Update and patch your systems frequently.
Prepare and plan for the quantum threat to cyber security. For more information, please see ITSE.00.017 Addressing the Quantum Computing Threat to Cryptography.
Encryption for highly sensitive data
Systems that contain highly sensitive information (e.g. financial, medical, and government institutions) require additional security considerations. Contact us for further guidance on cryptographic solutions for high-sensitivity systems and information: email@example.com.