After a lot of theorising, companies and research institutions are now announcing plans that bring real-world quantum computers close to reality.
Quantum computing is coming – but what will it mean for organisations and industries?
What is quantum computing?
Quantum computing uses the laws of quantum mechanics to solve problems too complex for traditional computers.
Quantum bits (or qubits) are the basic unit of information in quantum computing but, unlike the traditional bit, it can be a one, a zero or both at the same time, so quantum computing breaks free from the constraints of traditional binary code. These qubits can be entangled into multi-qubit states, enabling powerful quantum computation.
Today’s digital computers struggle with calculations that involve finding the optimal arrangements of items, because they must work through each permutation to find the best. This can take an enormous number of calculations and, the more complicated the code, the more processing power required, and the longer the processing takes.
A quantum computer, on the other hand, uses the multiplicity of states in the quantum world to work through possibilities simultaneously and at much greater speed.
The real-life applications this opens up will benefit a wide range of industries in a multitude of ways, some of which we’re yet to fathom. However, the industry uses are likely to focus on two key benefits:
- the ability to handle complexity and vast amounts of data
- the sheer speed of calculations, scenario assessment and pattern recognition.
Quantum computing is adept at handling complexity
Perhaps the most astounding benefit of quantum computing is its capacity to process complex problems beyond the capabilities of today’s digital computers – even down to the molecular level. Chemical and biological engineering researchers are excited about the possibilities of discovering, mapping and manipulating molecules, using quantum computing to understand the motion and interaction of subatomic particles. This could open the door to being able to create a room-temperature superconductor, removing carbon dioxide for a better climate, and creating solid-state batteries.
And over in pharmacology, researchers will be able to model and simulate interactions between drugs and all 20,000+ proteins encoded in the human genome, pushing knowledge forward and accelerating current efforts in materials discovery and drug development. Quantum computing will provide an effective way of understanding drugs and their reactions on humans, making more drug options available.
Artificial intelligence and machine learning will also benefit from quantum developments that can process complex problems and recognise patterns in less time than conventional computers.
As a result, diagnostics in healthcare will improve and healthcare professionals will be able to optimise targeted treatments such as radiotherapy by modelling complex scenarios to identify the optimum treatment path. Fraud detection in finance, too, is reliant upon pattern recognition, and quantum computing can potentially improve detection rates.
However, quantum computing’s ability to process complex calculations will also force industries to change how they do things, most notably so far in the arena of cybersecurity. Quantum computing has the potential to be able to crack the mathematics that underpins much of the current cryptography that’s used to secure networks. The type of algorithms that are most affected are ‘asymmetric’ algorithms used in key exchange, digital signatures and Public Key Infrastructure certificate-based authentication. But, as well as a threat, quantum computing can also be the solution. Post-Quantum Cryptography will use quantum computing to keep all the functionality of existing cryptography, at the same time as upgrading it to be much harder for quantum computers to break.
Quantum computing unlocks record calculation speeds
For other industries, the sheer speed of quantum computing is the leading draw. Computing speed has long been a source of advantage in financial markets where hedge funds compete to achieve millisecond advantages in obtaining price information. Quantum computing means the faster calculation of massive, complex scenarios to find the right mix for fruitful investments based on expected returns. Plus, quantum algorithms can increase the speed of market variables analysis, automatically triggering high-frequency trading.
Calculation speed is also important in areas such as weather forecasting. Currently, the process of analysing weather conditions by traditional computers can sometimes take longer than the weather itself does to change and, at best, limits forecasters’ ability to warn of weather events. Quantum computing will be able to crunch huge amounts of data at speed, enhancing weather modelling by improving pattern recognition to make prediction more accurate, increasing the amount of warning that can be given. It’ll also be able to generate greater insight into climate change and mitigation.
Sifting through possibilities at speed is vital in sectors such as manufacturing and industrial design, too. Developing a working product traditionally takes draft after draft and test after test. However, quantum computing can identify the most effective option rapidly, delivering better designs for a better product.
Rapid modelling also has significant, positive implications for logistics and supply chain efficiency. It’ll unlock real-time optimisation, allowing continuous calculation of optimal routes of traffic management, fleet operations, air traffic control and freight and distribution.
Welcome to a quantum-powered world
The possibilities of quantum computing are, perhaps, beyond full comprehension right now, but there’s clear potential in every industry.
In fact, our early trial with the consultancy company, EY, is already yielding some promising results. For more insight into how we’re harnessing quantum computing in our solutions, download our whitepaper.


Whenever you take a closer look at quantum computers and how they work, it’s almost surreal. Not many of us are aware of the massive leap these supercomputers represent in processing power.
As mysterious as quantum physics has become to the average person, the same can be said about quantum computers. Their future trajectory will easily surpass today’s most powerful computers.
The role of quantum computers
Bear in mind that quantum computers aren’t here to replace conventional computers. There is no way that could happen because traditional computers solve too many everyday problems. And they’re also too economical and too easy to use.
Instead, quantum computers will be reserved to address the cutting-edge projects in virtually every field – ranging from social issues to engineering methods to pharmaceuticals research. Many companies are already designing and experimenting with research projects that are ideally suited for the newest supercomputer.
A prevailing problem for our society, in general, has been our inability to process the mountains of data we generate. We need the processing power to analyze data in a timelier manner. There is where quantum computing methods will earn their keep.
The real secret behind the power of a quantum computer is how it generates and processes quantum bits, also known as qubits.
What is a qubit?
We are familiar with the term ‘bit’ because we define bits of information generated in today’s conventional computers. A bit is simply a stream of electrical pulses that represent either a 1 or 0. All things created by current computers are a long string of bits.
Rather than bits, a quantum computer uses qubits, which are comprised of subatomic particles like photons and electrons. As you might imagine, creating and processing qubits is both an engineering and scientific challenge.
Some companies choose to use superconductors that can be cooled to temperatures that are colder than deep space. Other companies approach the process by trapping individual atoms in electromagnetic fields that exist on silicon chips within ultra-high-vacuum chambers. Both approaches have the same goals of isolating qubits within a controlled quantum state.
Qubits possess some rather outrageous quantum properties that allow them to generate far more computing power than the same number of ordinary binary bits. One such property is called superposition, and another known as entanglement.
What is superposition?
Qubits are capable of representing countless potential combinations of 1 and 0 simultaneously. This unique ability to represent multiple states is superposition. To lift qubits into superposition, scientists manipulate them with microwave beams and precision lasers.
Because of this counterintuitive property, quantum computers with many qubits in superposition can blast through massive numbers of possible outcomes simultaneously. The result of a calculation is rendered only when the qubits have been measured. After this, they collapse out of their quantum state into either a 1 or 0.
What is entanglement?
Entanglement is where pairs of qubits exist in one quantum state. Thus, whenever the state of one qubit is changed, the same change is instantly applied to the other entangled qubit. Researchers have discovered that this will happen in a predictable way – even when extremely long distances separate them.
As to how and why entangled qubits work like this remains a mystery. However, it’s one of the critical reasons that quantum computers are so powerful. In conventional computers, when you double the number of bits, you double its overall processing power. But because of entanglement, the addition of qubits to a quantum computer creates an exponential increase in its ability to process data and information.
Imagining the plethora of quantum algorithms that could be designed and executed at this quantum level explains all the excitement about them.
But it’s not all good news. One problem with them is they are far more prone to errors than conventional processors. The reason for this is something called ‘decoherence.’
What is decoherence?
Decoherence is when the quantum behavior of qubits decays and disappears. Because their quantum state is so fragile, the slightest change in temperature or a tiny vibration – known as outside ‘noise’ – can cause them to fall out of superposition before it has finished its assigned task.
Because of their fragile nature, we’ve seen scientists attempt to protect qubits by putting them in vacuum chambers and supercooled environments. Despite these precautions, noise still manages to induce errors in calculations.
Quantum algorithms that are cleverly designed can compensate for some of these errors, and the addition of more qubits also seems to help. But as it stands, thousands of standard qubits are required to generate a single, reliable one – and these are called ‘logical’ qubits. Thus, just to get an adequate number of logical qubits, a quantum computer must devote lots of its computational capacity.
This brings us to the quantum brick wall that scientists are facing. Thus far, researchers have not been able to create more than 128 standard qubits. So we’re quite a few years away from developing that quantum computer that’ll be useful.
Fortunately, this quantum brick wall hasn’t slowed down the efforts of computer researchers who seek to find an answer.
What is quantum supremacy?
Quantum supremacy is the ultimate goal of researchers. It represents that point where quantum computers can perform mathematical calculations that are way beyond the capabilities of the most powerful supercomputers. And to do so reliably.
Currently, no one yet knows how many qubits would be required to achieve such a goal. One reason is that the goalposts keep moving. Other researchers continue to find new ways and algorithms that boost the power of classical computers, and the hardware of supercomputers keeps improving as well.
Nonetheless, quantum computer researchers and their sponsors continue working diligently to reach quantum supremacy as they compete against the most powerful supercomputers on the planet.
And the research world at large hasn’t given up the cause either. Many companies continue experimenting with them now – instead of waiting for supremacy. Several firms have even allowed outside access to their quantum machines.
What will the first quantum computer be used for?
There are many promising applications of quantum computers. One such approach is the simulation of matter at the molecular level. Automakers are experimenting with quantum computers to simulate the chemical composition of things like electrical-vehicle batteries to enhance performance.
Major pharmaceutical firms use them to compare and analyze compounds that may find new drugs. They can accomplish what previously took humans years to accomplish using conventional methods in just a few days.
These quantum machines are phenomenal at analyzing numbers and solving optimization problems incredibly fast. This ability alone makes them extremely valuable in a broad spectrum of disciplines.
Even though it could take several years for quantum computers to reach their full potential, it appears to be well worth the effort.
One thing that is working against them is that businesses and universities experience a growing shortage of researchers with the required skills. Efforts to bolster STEM candidates are falling woefully short of expectations.
Secondly, there are not enough suppliers of the required vital components to support quantum computers. And many companies have shifted their priorities elsewhere at present.
Hopefully, we will be able to overcome these obstacles and reach our quantum goals. These new computing machines could completely revolutionize entire industries and boost global innovation to levels never seen before.


Those of us who grew up learning about the double slit experiment, know that the Copenhagen interpretation, very simplified, dictates that an electron can be said to behave as both a particle and a wave…but that we mustn’t care. The interference patterns generated on the detector screen supposedly force us to accept that an electron physically moves through the two slits in the apparatus at once. Statistically, it must, and it is only by measuring that we can find out if we are dealing with a particle or wave behavior. What happens to the electron before the detector screen measures a wave or particle behavior is undetermined. It is a question that cannot be asked (under the Copenhagen view) because we can’t find out.
Even stranger, the fact that a detector is or is not observing this experiment, changes the outcome of the experiment at the quantum level. The act of observing is said to alter the experiment. Usually, this is when the teachers stops explaining (and sweating).
This is all very unsatisfying. Students want to understand what happens at the slits. Does the particle split in two? Does it change into a wave and back? And what is up with that observer? Why is there only a statistical and not an intuitive explanation?
For most people, this is an incredibly difficult concept to grasp. Most students -and teachers- never get beyond this point. It usually takes an advanced course in physics to find out about the underlying principles that allow these experimental results to manifest. You need to be introduced to the idea that everything in nature is a manifestation of some field.
Both particles and waves are constituted ‘only’ of fields in various forms. Furthermore, a detector can only “detect” because it generates a ‘force field’ that a particle or wave to be detected can bounce into and interact with. Once a student learns this, it is immediately understood that the particle-wave behavior is just another way the same fields manifest and interact. It again becomes intuitive. (They are usually also very angry not learning about fields immediately.) The ‘observer effect’ is just an extra field, that of the detector, you need to account for in your experimental setup. If you don’t understand this. Don’t worry. Just think about the invisible force field you notice when you play with magnets. That is one of the fields ‘stuff’ is made off. Other types of fields in nature manifest in different ways but they too are detectable. (All of them are part of the standard model of physics. Google it. Find out.)
Vice versa. The double slit experimental setup does have a practical use. It can be used as a detector. It detects changes in the surrounding fields.
An interesting question is if this so-called ‘observer’, or better ‘detector field’ effect, can also be influenced by people. Our brains and heart muscles create detectable small currents and electric fields, and electric fields interact, so our presence or nearness to the electrical field of a detector must influence what the double slit experiment measures. Correct?
This has been tested over and again over the last decades in hundreds of experiments and…apparently it does. An interesting result.
Even stranger, it works at large distances. A person doesn’t necessarily need to be in the same room… or even country as the experiment setup. You just need to focus your brain on the little box running the double slit experiment. Euh. What?
Wait a minute… Electric fields taper off with distance. Does this mean that the electric fields in our brain our doing something…like quantum entanglement at a distance? Does this mean that the mind interacts with matter… at a distance?
Well. On the one hand, the answer is: of course it does. Our mind is just the manifestation of electrical pulses racing around and interacting in the neural network of our brain. Depending on whether you believe our brain is just a glorified ‘wet’ computer, this view suffices to most material reductionists, but religion, philosophical traditions and human experiences have always informed us that science doesn’t really get the human experience. Reductionism, although it does bring many technological advances, seems to overshoot in many cases. Our intuition and mind seem to be able of a lot more than science gives it credit for. Science seems to throw out the baby with the bath water, and even open-minded investigators feel their credibility will suffer when they tackle these subjects, and in doing so damage their career prospects.
But what if the reach of our electric fields, and maybe our consciousness reaches much further? Is there something as a consciousness that extends beyond our body? Couldn’t we benefit from finding out? Are we now in a position to ask these questions beyond mere philosophical discourse? Well. We know of at least person who is not afraid to ask.
If you are interested in how precisely this question can be asked and tested in a scientific and empirical manner, I recommend the lecture below, given by Dean Radin of the Institute of Noetic Sciences (IONS). He shows there is a way and the results are very intriguing to say the least.
For other people who prefer to have a wet computer for a brain and do not believe in consciousness interacting at a distance: Noticing the progress around the world with brain steered prostheses, implanting that wifi-router is less than a decade away.
Select your provider wisely.


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.


While the scientific community holds its breath for a large-scale quantum computer that could carry out useful calculations, a team of IBM researchers has approached the problem with an entirely different vision: to achieve more and better results right now, even with the limited quantum resources that exist today.
By tweaking their method, the scientists successfully simulated some molecules with a higher degree of accuracy than before, with no need for more qubits. The researchers effectively managed to pack more information into the mathematical functions that were used to carry out the simulation, meaning that the outcome of the process was far more precise, and yet came at no extra computational cost.
"We demonstrate that the properties for paradigmatic molecules such as hydrogen fluoride (HF) can be calculated with a higher degree of accuracy on today's small quantum computers," said the researchers, at the same time priding themselves on helping quantum computers "punch above their weight".
SEE: Hiring Kit: Computer Hardware Engineer (TechRepublic Premium)
Car manufacturer Daimler, a long-term quantum research partner of IBM's, has shown a strong interest in the results, which could go a long way in developing higher-performing, longer-lasting and less expensive batteries.
Since 2015, Daimler has been working on upgrading lithium-ion batteries to lithium-sulfur ones – a non-toxic and easily available material that would increase the capacity and speed-of-charging of electric vehicles.
Designing a battery based on new materials requires an exact understanding of which compounds should come together and how. The process involves accurately describing all the characteristics of all the molecules that make up the compound, as well as the particles that make up these molecules, to simulate how the compound will react in many different environments. In other words, it is an incredibly data-heavy job, with infinite molecular combinations to test before the right one is found.
The classical methods that exist today fail to render these simulations with the precision that is required for a breakthrough such as the one Daimler is working towards. "This is a big problem to develop next-generation batteries," Heike Riel, IBM Research quantum lead, told ZDNet. "Classical computers, and the models we've developed in physics and chemistry for many years still cannot solve those problems."
But the task could be performed at speed by quantum computers. Qubits, and their ability to encode different information at the same time, enable quantum algorithms to run several calculations at once – and are expected, one day, to enable quantum computers to tackle problems that are seemingly impossible, in a matter of minutes.
To do that, physicists need quantum computers that support many qubits; but scaling qubits is no piece of cake. Most quantum computers, including IBM's, work with less than 100 qubits, which is nowhere near enough to simulate the complex molecules that are needed for breakthroughs, such as lithium-sulfur car batteries.
Some of the properties of these molecules are typically represented in computer experiments with a mathematical function called a Hamiltonian, which represents particles' spatial functions, also called orbitals. In other words, the larger the molecule, the larger the orbital, and the more qubits and quantum operations will be needed.
"We currently can't represent enough orbitals in our simulations on quantum hardware to correlate the electrons found in complex molecules in the real world," said IBM's team.
Instead of waiting for a larger quantum computer that could take in weighty calculations, the researchers decided to see what they could do with the technology as it stands. To compensate for resource limitations, the team created a so-called "transcorrelated" Hamiltonian – one that was transformed to contain additional information about the behavior of electrons in a particular molecule.
This information, which concerns the propensity of negatively charged electrons to repel each other, cannot usually fit on existing quantum computers, because it requires too much extra computation. By incorporating the behavior of electrons directly into a Hamiltonian, the researchers, therefore, increased the accuracy of the simulation, yet didn't create the need for more qubits.
The method is a new step towards calculating materials' properties with accuracy on a quantum computer, despite the limited resources available to date. "The more orbitals you can simulate, the closer you can get to reproducing the results of an actual experiment," said the scientists. "Better modelling and simulations will ultimately result in the prediction of new materials with specific properties of interest."
SEE: Quantum computers are coming. Get ready for them to change everything
IBM's findings might accelerate the timeline of events for quantum applications, therefore, with new use cases emerging even while quantum computers work with few qubits. According to the researchers, companies like Daimler are already keen to find out more about the breakthrough.
This is unlikely to shift IBM's focus on expanding the scale of its quantum computer. The company recently unveiled a roadmap to a million-qubit system, and said that it expects a fault-tolerant quantum computer to be an achievable goal for the next ten years. According to Riel, quantum simulation is likely to be one of the first applications of the technology to witness real-world impacts.
"The car batteries are a good example of this," she said. "Soon, the number of qubits will be enough to generate valuable insights with which you can develop new materials. We'll see quantum advantage soon in the area of quantum simulation and new materials."
IBM's roadmap announces that the company will reach 1,000 qubits in 2023, which could mark the start of early value creation in pharmaceuticals and chemicals, thanks to the simulation of small molecules.


The underlying principles of modern cryptography rely on fascinating mathematical problems which removes the dependency of covert key sharing between parties to communicate with utmost privacy even in the presence of an adversary.
To formulate such a system, Public Key Cryptography/Asymmetric Cryptography was released which used a pair of keys (pk, sk) where pk denotes the public key and (sk) is the secret key.
The Public Key is available to all the users but the private key is kept hidden. Both the keys are intertwined through a mathematical function such that encryption can be performed with the public key that can only be decrypted with the private key and vice versa.
The relationship between the public key and the private key is through a special kind of one-way functions called Trapdoor functions.
What are One way Functions?
One way functions possess this hardness property such that it is easy to compute the one-way function but hard to invert. ”Easy” corresponds to the fact that the function can be computed efficiently and ”hard” means that any algorithm attempting to invert it will succeed with a very small probability.
Trapdoor functions are one-way functions with additional trapdoor information which allows the inverse to be easily computed. Secure Public-Key Crypto systems are built using a one-way function that has a trapdoor.
Consider a public-key crypto system with a pair of keys generated by a key generation algorithm as (pk, sk). If Alice wants to send a message to Bob she uses the public key of Bob to encrypt the message. On other hand, Bob uses his private key pk to decrypt the message. Here pk is the trapdoor information with which the legitimate user Bob could decrypt the message in polynomial time.
The candidates of one-way functions and trapdoor functions used in modern cryptography are derived from number theory. Some of the examples of such functions are
* Discrete Logarithm Problem,
* Factorization and RSA
Discrete Logarithm Problem
Discrete Logarithm Problem is the construction behind the very first public-key cryptosystem primitive ”Diffie Hellman Key Exchange”.
It is based on finding x in the following equation where G is an element in a special algebraic structure called Fields where any power of x of G generates an element in that field.
Factorization and RSA
Imagine factoring 12 into its prime parts. Within a matter of seconds, one can properly answer 3 and 12. Again for a healthy brain exercise try to calculate the factorization of 128 -- may require some piece of paper, after computing that goes on to a number like 14567978. Here is where the problem starts.
Without a calculator or a program, it may take more than a few minutes to solve it. Similarly what if I say factorize a 1024 bits number? Now the situation is out of hand and it seems impossible without a method to solve it. RSA is based on this difficulty of factoring a large number. Of course with a trapdoor as a secret key which is the inverse of the number.
Can TrapDoors Expire?
Trapdoor functions are the basis of modern cryptography and in turn the heart of cryptographic applications like Blockchains. These functions have single-handedly upheld the security of such networks with goals ranging from encryption to identity management and authenticated transfers. These paradigms have been securing our data since its inception by Diffie-Hellman in the 1990s by iterating to better possibilities and to be universally applicable. But a series of twists have altered the thoughts from cryptographic agility to mere doubts on its security. In 1996, Peter Shor proposed a quantum algorithm that was able to solve the sorcery behind modern cryptography within a feasible time.
More precisely, to factor a number N, it takes a time complexity of O(log N).
In layman terms, it means an attacker can hijack your account within two hours.
So, How are we going to secure our credit card and other sensitive information while purchasing online? What about cryptocurrencies that at the core rely on these paradigms?
The answer to the above question lies in the concept of Post-Quantum cryptography which promises to secure our infrastructure from the quantum apocalypse.
Archethic (public blockchain by Uniris) realises this threat posed by quantum cryptography and has progressed in parallel to research in the field of cryptography by adding backward compatibility and giving choice to the users for the algorithms.
The 2nd article in this series will cover in detail more about Post-quantum cryptography and how it helps in Quantum-Resistant Blockchains.
Archethic Public Blockchain
Archethic is a Layer 1 aiming to create a new Decentralized Internet.
Its blockchain infrastructure is the most scalable, secure & energy-efficient solution on the market thanks to the implementation of a new consensus: "ARCH".
Archethic smart contracts expand developers' boundaries by introducing internal oracle, time-triggers, editable content & interpreted language.
Through native integration for DeFi, NFTs & decentralized identity; Archethic offers an inclusive and interoperable ecosystem for all blockchains.
In order to achieve the long-term vision of an autonomous network in the hands of the world population, we developed a biometric device respecting personal data privacy (GDPR compliant).
Making the blockchain world accessible with the tip of a finger. Be the only key! https://www.archethic.net/
Archethic Foundation Non-profit in order to manage decentralized governance of the public blockchain


The roots of quantum computing come from quantum mechanics, which deals with atomic and subatomic level energy interactions. Quantum mechanics can explain unsolved theoretical physics, chemistry, and technology problems mathematically. Thus, it is considered the steppingstone for the unified theory of the Universe.
The major challenge when dealing with quantum mechanics is its inability to simulate the quantum models. A simple quantum system with few interactions requires enormous computing power to simulate. Richard Feynman and Yuri Manin studied this knowledge gap, and they postulated a new genre of computing known as quantum computing. Their introduction to quantum computing relies on the Controlled NOT (CNOT) gate, which can simulate any arbitrary quantum circuit.
What is Quantum Computing?
When the problem is very complex, and the system needs to process massive data, quantum computers can show the magic. The prominent feature of quantum computing is leveraging the uncertainties, whereas classical computing cannot accept uncertainties. Conventional computing relies on bits with two states, 1 and 0. Quantum computing leverages the Qubits (Quantum bits), taking the states 0, 1, and quantum superpositions of 0 and 1.
Arguably, the increased states can increase the computational delay, thus reducing the processing speed. Considering a single operation, this argument is valid, and it may take more time. But the number of processes required for solving a computationally intricate problem can be significantly reduced. That’s how quantum computers can successfully simulate subatomic particle interactions. Apart from Qubits, many other quantum principles such as superposition, quantum entanglement, interference, and coherence are also utilized in quantum architecture.
What Change Can Quantum Computing Bring?
Even though Quantum computing is in the infant stage, as the technology evolves, it can produce significant advancements in machine learning, material science, Theoretical physics, nuclear physics, chemistry, energy, medicine design, etc.
One of the significant threats that scientists are expecting is information security. When quantum computers are in full swing, our existing cryptographic techniques built on the complexity of the mathematical problem will become a cakewalk for quantum computers. This scenario demands a complete restructuring of our information security architecture and existing cryptocurrency architecture.
How to Get Your Hands on Quantum Computing?
Many cloud-based quantum computing solutions are available for users. The major players are IBM quantum, Google Quantum Computing Service (Cirq), Amazon Bracket, and Microsoft Azure Quantum. Azure quantum is the only service that is not limited to single quantum hardware for implementing your quantum logic. Azure quantum computing service has been in its public preview since early 2021.
Why Azure Quantum?
Azure quantum computing can present you Quantum Development kit, the best development environment out of the whole spectra of quantum services. The flexibility and adaptability of multiple quantum hardware usages are significant advantages.
Quantinum, IONQ, Pasqal, Rigetti, And Quantum circuits are the major quantum service providers in Azure Quantum. In Azure quantum, You can work with popular quantum programming frameworks such as Cirq, Qiskit, and Q#. It provides Quantum Inspired Optimization (QIO), apart from quantum computing service. The major QIO service providers in Azure Quantum are IQbit, Microsoft QIO, and Toshiba SBM.
What is Quantum Development Kit?
Microsoft Quantum Development Kit (QDK) is the open-source environment for your Microsoft Azure Quantum. You can use python-based SDKs like Qiskit or Cirq. Otherwise, users can leverage the high-level quantum programing language Q#. The resource estimator facility will help you forecast the cost of running your code.
How to Develop a Quantum Application Using Azure Quantum?
As a baby step, let us consider the creation of random binary bits 0 and 1 in a qubit place holder.
Step 1: – Create Azure Quantum workspace.
Open Quantum workspace from Azure portal
For free usage, you can select the Quick create option. Advanced create contains a few paid hardware services. Azure quantum pricing is significantly less than other quantum computing services in the cloud computing environment.
Populate the required detail fields
Click on create and wait for deployment
Step 2: – Create Jupyter notebook in Azure quantum workspace.
Select Notebooks from the Operations section of the left blade. You can select the sample jobs from the sample gallery.
For a new notebook, click on the three dots near my notebooks
Select new notebook
You can select Kernel type, either IPython or IQ#, and provide a file name.
Step 3: – Import Workspace
Here the coding is based on Python and Q#. The new notebook will contain the code to connect to your quantum workspace by default. You can use connect function to connect to the workspace.
You can see the available target instances using the code given below and verify that the required hardware is available.
Step 4: – Implement your quantum logic
The next step is to build your business logic using quantum programming languages such as Q#, Cirq, and Qiskit. The given example generates a random bit using Q#.
We utilized the Measurement, Arrays, and Convert modules for random qubit generation.
We submit the job to IonQ hardware, a general-purpose trapped ion quantum computer that executes the function. Before selecting the target, make sure the in the available target list ionq simulator is available.
Wait until the job is succeeded. For a better understanding of the result, we can plot the probability of 1 and 0 bits using matplotlib.
The process is similar in all the languages like Qiskit and Cirq.
MS Learn: https://docs.microsoft.com/en-us/learn/paths/quantum-computing-fundamentals/
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Sometimes, it’s easy for a computer to predict the future. Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what mathematicians call linear differential equations. But in nonlinear systems, interactions can affect themselves: When air streams past a jet’s wings, the air flow alters molecular interactions, which alter the air flow, and so on. This feedback loop breeds chaos, where small changes in initial conditions lead to wildly different behavior later, making predictions nearly impossible — no matter how powerful the computer.
“This is part of why it’s difficult to predict the weather or understand complicated fluid flow,” said Andrew Childs, a quantum information researcher at the University of Maryland. “There are hard computational problems that you could solve, if you could [figure out] these nonlinear dynamics.”
That may soon be possible. In separate studies posted in November, two teams — one led by Childs, the other based at the Massachusetts Institute of Technology — described powerful tools that would allow quantum computers to better model nonlinear dynamics.
Quantum computers take advantage of quantum phenomena to perform certain calculations more efficiently than their classical counterparts. Thanks to these abilities, they can already topple complex linear differential equations exponentially faster than classical machines. Researchers have long hoped they could similarly tame nonlinear problems with clever quantum algorithms.
The new approaches disguise that nonlinearity as a more digestible set of linear approximations, though their exact methods vary considerably. As a result, researchers now have two separate ways of approaching nonlinear problems with quantum computers.
“What is interesting about these two papers is that they found a regime where, given some assumptions, they have an algorithm that is efficient,” said Mária Kieferová, a quantum computing researcher at the University of Technology Sydney who is not affiliated with either study. “This is really exciting, and [both studies] use really nice techniques.”
The Cost of Chaos
Quantum information researchers have tried to use linear equations as a key to unlock nonlinear differential ones for over a decade. One breakthrough came in 2010, when Dominic Berry, now at Macquarie University in Sydney, built the first algorithm for solving linear differential equations exponentially faster on quantum, rather than on classical, computers. Soon, Berry’s own focus shifted to nonlinear differential equations as well.
“We had done some work on that before,” Berry said. “But it was very, very inefficient.”
The problem is, the physics underlying quantum computers is itself fundamentally linear. “It’s like teaching a car to fly,” said Bobak Kiani, a co-author of the MIT study.
So the trick is finding a way to mathematically convert a nonlinear system into a linear one. “We want to have some linear system because that’s what our toolbox has in it,” Childs said. The groups did this in two different ways.
Childs’ team used Carleman linearization, an out-of-fashion mathematical technique from the 1930s, to transform nonlinear problems into an array of linear equations.
Unfortunately, that list of equations is infinite. Researchers have to figure where they can cut off the list to get a good-enough approximation. “Do I stop at equation number 10? Number 20?” said Nuno Loureiro, a plasma physicist at MIT and a co-author of the Maryland study. The team proved that for a particular range of nonlinearity, their method could truncate that infinite list and solve the equations.
The MIT-led paper took a different approach. It modeled any nonlinear problem as a Bose-Einstein condensate. This is a state of matter where interactions within an ultracold group of particles cause each individual particle to behave identically. Since the particles are all interconnected, each particle’s behavior influences the rest, feeding back to that particle in a loop characteristic of nonlinearity.
The MIT algorithm mimics this nonlinear phenomenon on a quantum computer, using Bose-Einstein math to connect nonlinearity and linearity. So by imagining a pseudo Bose-Einstein condensate tailor made for each nonlinear problem, this algorithm deduces a useful linear approximation. “Give me your favorite nonlinear differential equation, then I’ll build you a Bose-Einstein condensate that will simulate it,” said Tobias Osborne, a quantum information scientist at Leibniz University Hannover who was not involved in either study. “This is an idea I really loved.”
Berry thinks both papers are important in different ways (he wasn’t involved with either). “But ultimately the importance of them is showing that it’s possible to take advantage of [these methods] to get the nonlinear behavior,” he said.
Knowing One’s Limits
While these are significant steps, they are still among the first in cracking nonlinear systems. More researchers will likely analyze and refine each method — even before the hardware needed to implement them becomes a reality. “With both of these algorithms, we are really looking in the future,” Kieferová said. Using them to solve practical nonlinear problems requires quantum computers with thousands of qubits to minimize error and noise — far beyond what’s possible today.
And both algorithms can realistically handle only mildly nonlinear problems. The Maryland study quantifies exactly how much nonlinearity it can handle with a new parameter, R, which represents the ratio of a problem’s nonlinearity to its linearity — its tendency toward chaos versus the friction keeping the system on the rails.
“[Childs’ study is] mathematically rigorous. He gives very clear statements of when it will work and when it won’t work,” Osborne said. “I think that’s really, really interesting. That’s the core contribution.”
The MIT-led study doesn’t rigorously prove any theorems to bound its algorithm, according to Kiani. But the team plans to learn more about the algorithm’s limitations by running small-scale tests on a quantum computer before moving to more challenging problems.
The most significant caveat for both techniques is that quantum solutions fundamentally differ from classical ones. Quantum states correspond to probabilities rather than to absolute values, so instead of visualizing air flow around every segment of a jet’s fuselage, for example, you extract average velocities or detect pockets of stagnant air. “This fact that the output is quantum mechanical means that you still have to do a lot of stuff afterwards to analyze that state,” Kiani said.
It’s vital to not overpromise what quantum computers can do, Osborne said. But researchers are bound to test many successful quantum algorithms like these on practical problems in the next five to 10 years. “We’re going to try all kinds of things,” he said. “And if we think about the limitations, that might limit our creativity.”


Coffee cups and donuts are one and the same, according to mathematicians. While they might have some serious physical differences (crunching on a coffee cup would be significantly less pleasant than biting into a donut,) their underlying topology, or the way their surfaces bend, are the same because they both have only one hole. These similarities may seem semantic, but making use of topology has enabled scientists to explore futuristic materials and their uses in high-performance technologies and quantum computing. But, a golden-child of topological technology, a material called a topological insulator, might be more mysterious than physicists first believed.
In recent years scientists have found that this material, which is prized for its ability to conduct electron flow on its surface while insulating flow from its center, may actually be more fragile than it seems and that its topological barriers may in fact be more free-flowing and less predictable. But, far from letting that news discourage them, scientists in a pair of new studies have worked to uncover how such fragile topology may actually be useful.
The first study, published Thursday in the journal Science, used mathematical modeling in order to better understand this material's fragile properties. The team focused on the quantum behavior of electrons both on the surface and in the interior of the topological insulator and how it might be possible in some cases for this strict topological barrier to breakdown. Typically in these materials, the wave function (think an electron GPS signal) of electrons in the center of the insulator spread neatly to the edge of the surface, creating a boundary-correspondence that allows for free flow on the surface and insulation in the center. But, by looking at these electrons more closely, the team discovered that this wasn't always the case. The researchers call this scenario a "twisted bulk-boundary-correspondence" and it creates a fragile topology in which electrons cannot flow on the surface of the material.
B. Andrei Bernevig, a professor of physics at Princeton and co-author on both papers, said in a statement that this phenomenon is a breakdown of how researchers typically believe these materials behave.
"Fragile topology is a strange beast: It is now predicted to exist in hundreds of materials," said Bernevig. "It is as if the usual principle that we have been relying on to experimentally determine a topological state breaks down."
In addition to identifying mechanisms that might be causing these strange fragile topologies, the authors also identified that these wave functions could actually be manipulated to change the boundary-correspondence conditions of the material. The researchers say that this ability to tune the material, essentially turning on and off the conductivity of its surface, could be a useful design element for electronic and optical technologies.
However, being based in mathematics, the results of this first study were intriguing but still highly theoretical. It took a second paper, complete with a life-sized, 3D printed crystalline structure, to bring the results to life. In the second paper, also published Thursday in the journal Science, researchers explored how twisted bulk-boundary-correspondence could be physically demonstrated. Being in the world of large, Newtonian physics and no longer the small, quantum world of electrons, the research team used sound waves to represent minuscule electron wave functions. Through experiments, the team was able to demonstrate how twisted bulk-boundary-correspondence could occur on the surface as well as how manipulating the sound waves could change the flow of "electrons" on the surface as well.
The lead author of the second paper and physicist at ETH Zurich, Sebastian Huber, said in a statement that these unusual theoretical and experimental results point toward a new overarching understanding of these kinds of materials.
"This was a very left-field idea and realization," Huber said. "We can now show that virtually all topological states that have been realized in our artificial systems are fragile, and not stable as was thought in the past. This work provides that confirmation, but much more, it introduces a new overarching principle."
And when it comes to further research of this property, Bernevig tells Inverse that there's much left to be explored.
"Many things [are left to explore]," says Bernevig. "[W]e know absolutely nothing abt how these states respond to other stimuli: disorder, electric and magnetic fields etc. we know nothing about what happens to these states in the presence of strong interactions. Moreover, the physics breakthrough of last year, a material called twisted bilayer graphene, is theoretically thought to exhibit fragile topology in the bands of interest. Understanding how the topology adds to the remarkable properties of such a material will be crucial to figuring out its puzzles"
Abstract for paper 1: A topological insulator reveals its nontrivial bulk through the presence of gapless edge states: This is called the bulk-boundary correspondence. However, the recent discovery of “fragile” topological states with no gapless edges casts doubt on this concept. We propose a generalization of the bulk-boundary correspondence: a transformation under which the gap between the fragile phase and other bands must close. We derive specific twisted boundary conditions (TBCs) that can detect all the two-dimensional eigenvalue fragile phases. We develop the concept of real-space invariants, local good quantum numbers in real space, which fully characterize these phases and determine the number of gap closings under the TBCs. Realizations of the TBCs in metamaterials are proposed, thereby providing a route to their experimental verification.
Abstract for paper 2: Symmetries crucially underlie the classification of topological phases of matter. Most materials, both natural as well as architectured, possess crystalline symmetries. Recent theoretical works unveiled that these crystalline symmetries can stabilize fragile Bloch bands that challenge our very notion of topology: Although answering to the most basic definition of topology, one can trivialize these bands through the addition of trivial Bloch bands. Here, we fully characterize the symmetry properties of the response of an acoustic metamaterial to establish the fragile nature of the low-lying Bloch bands. Additionally, we present a spectral signature in the form of spectral flow under twisted boundary conditions
This article has been updated to include original comment from the researcher.


The telecommunications industry has seen rapid growth in recent years, as more and more people use the internet and phone services to stay connected. This growth has led to increased competition, which has caused telecom systems to become more reliable and efficient. Today, telecom systems are used to connect businesses and individuals all over the world.
WHAT NEW TELECOM TECHNOLOGIES ARE ON THE HORIZON?
One such technology is 5G.5G is considered the next generation of wireless telecommunications. It utilizes higher frequencies and offers greater speeds and capacity than current wireless networks. Another upcoming technology is blockchain. It helps to secure deals, reduce fraud, and prevent third-party interference.
A final technology that is set to revolutionize telecom is quantum computing. Quantum computers are able to perform certain tasks much faster than classical computers. This could lead to significant changes in how we use telecom services, as well as other industries.
HOW TELECOM SYSTEMS OPERATE?
Telecommunications systems operate through the transmission and reception of signals. Signals are created when an electric current is passed through a conductor, such as wire, cable, or telephone line. The type of signal that is transmitted depends on the type of electrical equipment used to create it. For example, a voice using plain old telephone service (POTS) uses an analog signal. Analog signals can be transmitted over shorter distances because they do not lose their clarity as they travel down the line. Digital signals use pulses of electricity and are more efficient over long distances because they can be compressed into smaller packages. They are also easier to interpret than analog signals.
WHAT OUR CURRENT TELECOM SYSTEMS DO AND HOW THEY CAN BE IMPROVED UPON?
Telecommunications systems are ubiquitous and fundamental to modern life. They enable people to communicate with each other, exchange information, and access the internet. Today’s telecommunications systems use a variety of technologies, but they all share some common features.
First, telecommunications systems use wires to transmit signals between devices. These wires can be situated in different locations, which allows telecom systems to reach far-flung corners of the world. Second, telecommunications systems use centralized servers to store and manage data. This allows telecom systems to keep track of important information and make it available to users in a quick and reliable manner.
Third, telecommunications systems use algorithms to determine what data should be sent over which wire. This process is known as transmission scheduling and it determines how much bandwidth each device will get during a given moment in time.
HOW TELECOM SYSTEMS ARE USED TO COMMUNICATE?
Telecommunications have been around for more than a century and are used to communicate through signals that are transmitted through the air or over water. These signals can be sent through wires, satellites, or radio waves. Telecommunications systems use different technologies to send and receive messages. The most common telecommunications system is the telephone system. Telephone systems use copper wires to transmit voice and data. Telephone networks use switches to routes calls between customers. The telephone system is reliable and easy to use, but it can be slow.
HOW TELEPHONE SYSTEMS HAVE EVOLVED OVER THE YEARS?
Telephones have evolved over the years in terms of design, features and functionality. The earliest phones were simple devices that allowed people to make and receive calls. Over time, phones became more sophisticated and allowed for more features, such as voice recognition and messaging. In recent years, phones have even become embedded into our everyday lives by being integrated into our vehicles and homes. As phone technology continues to evolve, we can expect even more amazing advances in the future!
THE IMPACT OF TELECOMMUNICATIONS ON BUSINESS?
Telecommunications are essential for businesses of all sizes, as they provide a platform for communication, networking and collaboration. Telecommunication technology has had a dramatic impact on business in recent years, with many companies now relying on telecommunications to run their operations. Here are some of the ways telecommunications have helped businesses:
Employees can stay connected with family and friends while at work. This allows businesses to keep employees organized and productive, while also reducing stress levels.
Businesses can communicate with customers and other stakeholders easily and quickly. This allows companies to resolve issues quickly and ensure that everyone is on the same page. Telecommunications allow businesses to conduct business remotely from any location.
HOW TELECOM PROVIDERS ARE USING BIG DATA TO IMPROVE CUSTOMER SERVICE AND MARKETING EFFORTS?
Telecommunications providers are using big data to improve customer service and marketing efforts. By understanding customer behavior, providers can better serve customers and create more engaging marketing campaigns. For example, Verizon has used data analytics to identify areas of its network where congestion is most likely to occur. The company then creates plans that prioritize traffic flows during times of congestion so that customers have the best possible experience. Similarly, AT&T uses big data to target advertising to specific demographics. By understanding who watches particular shows or reads specific magazines, the company can create ads that are more likely to be seen by those interested in those topics.
THE EVOLUTION OF TELECOM SYSTEMS?
Telecommunications systems have evolved dramatically over the past few decades. For one, the use of technology has allowed for telecommunications to become more widespread and accessible. Additionally, advancements in technology have allowed telecommunications providers to offer a wider range of services and products. In recent years, telecom providers have also started offering new technologies such as voice over internet protocol (VoIP) and mobile broadband. As telecom systems continue to evolve, it is important for businesses and consumers to keep up with the changes in order to stay ahead of the competition.
Telecom systems have evolved over the years to accommodate ever-growing demand for communication. Advances in technology and the need for faster and more reliable service have led to the development of new telecom systems. As always, innovation is key to keeping customers happy and meeting their needs. So, stay ahead of the curve and keep your telecom system up to date with the latest advancements.


In the previous article, I explained the reason why quantum computing is interesting and I simply explained how they’re conceptually different from traditional computers. Recently, I have been wondering how hardware could change when it comes to Quantum computers, so I tried to find answers for this question. In this article I will try cover the difference I found and give some technical details about hardware differences.
For instance, a computer is essentially made of:
- Motherboard (main board with circuits and electrical CMOS transistors)
- Storage (HDD)
- Network card
- Graphics card
- Power supply
- Eventually all ports with I/O accessories (monitor, keyboard, mouse…etc)
(This is a very simplified summary of main components found in our PCs)
- Storage is measured in (mega)bits/bytes.
- Network transmissions is measure in (mega)Bits/bytes Per Second.
- CPUs are made of multiple cores with their performance measured by their frequency (in Hz/GHz). This means the processing speed is a few billion simple logic operations per second.
- Evolution is limited by Moore’s law.
If we’re ever to turn to Quantum computers how will these concepts
Here are some answers (as far as we know at the time of writing):
Please note that quantum computers we have at the moment are usually hybrid, which means we use their computational power in conjunction with a classic computer. They are not meant to replace Classical computers and therefore we don’t really have a classical component Vs. Quantum component for each item. However I will cover what makes a Quantum computer (in the widely used Hybrid model like IBM’s machines)
Quantum computers are made of:
- Main board with SQUID – “a quantum transistor”
- QPU (quantum processing unit), AKA co-processor.
- No Storage: By its nature, you can’t save or duplicate information on a quantum computer. There is some work on quantum hard drives and there are workaround involving using DNA or converting qubits to bit and store them on regular HDD.
- No RAM: During calculations the qubits themselves hold the data required. There is the qram concept but I could not find any proofs that is being used in practice in current QCs.
- Network card: Current (hybrid) Quantum Computers are not networked and they communicate through the paired classical computer. Quantum networks via fiber links as a medium are currently a possible solution. This topic is highly active in the QC research field!
- No Graphics card: quantum computers are mainly used for calculations. You’re not going to play games watch videos on them. They’re not designed to perform such tasks. The classical part takes care of that.
- Power supply: it differs a bit from the classical one, but it still runs on normal electricity to power the cooling system.
- I/O is managed by converting them from/to binary through quantum measurement (The Quantum processors we currently have are always used along with a classical computer to control them.)
Differences from the common classical concepts:
- Storage is measured in quantum bit qubit (aka: Qbit – the equivalent of bit).
- I could not find enough information on quantum networks speed measurements. Afaik, the existing QCs are not directly connected to internet and there is no information regarding the efficiency of a network between two connected quantum computers in the experiments I found in Delft and tum. I am not aware of 2 distant Quantum computers that have been directly connected.
- To compare performance, we can’t talk about GHz anymore. However, we need to compare efficiency in terms of time-complexity. The processing power of QCs is measured in teraflops – rate of flipping (trillions of logic operations per second). Each QPU has a finite number of qubits. The more qubits the faster its performance. The fastest I heard of is 2000 Qubits computer. (not universal)
- Quantum computing still can’t break Moore’s law, since it is a law about transistors in a dense integrated circuit. Therefore, the law does not even apply to Quantum computers (QCs) and thus QCs are not affected by it. However, another law exists for QCs, it’s called rose’s law.
Some information about quantum storage technologies:
In order to find the answers I was looking for and write this article I had to read some resources, here’s the list:
I hope you liked my article! I’m thinking on writing my next article on Quantum networks and quantum cloud computing. I’m always wondering what happens if we host websites on quantum computers? How can quantum computers revolutionize the web? Is such a thing possible or useful? Maybe I will gather more information and write my next article to talk about that (spoiler: remember we said quantum won’t replace classical) 🙄


Scanning electron microscope image of a device, similar to the one used. Highlighted are the positions of the tuning gates (red), the microwave antenna (blue), and the single electron transistor used for spin readout (yellow). Credit: Guilherme Tosi & Arne Laucht/UNSW
Australian engineers have created a new quantum bit which remains in a stable superposition for 10 times longer than previously achieved, dramatically expanding the time during which calculations could be performed in a future silicon quantum computer.
The new quantum bit, made up of the spin of a single atom in silicon and merged with an electromagnetic field – known as ‘dressed qubit’ – retains quantum information for much longer that an ‘undressed’ atom, opening up new avenues to build and operate the superpowerful quantum computers of the future.
The result by a team at Australia’s University of New South Wales (UNSW), appears today in the online version of the international journal, Nature Nanotechnology.
“We have created a new quantum bit where the spin of a single electron is merged together with a strong electromagnetic field,” said Arne Laucht, a Research Fellow at the School of Electrical Engineering & Telecommunications at UNSW, and lead author of the paper. “This quantum bit is more versatile and more long-lived than the electron alone, and will allow us to build more reliable quantum computers.”
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Building a quantum computer has been called the ‘space race of the 21st century’ – a difficult and ambitious challenge with the potential to deliver revolutionary tools for tackling otherwise impossible calculations, such as the design of complex drugs and advanced materials, or the rapid search of massive, unsorted databases.
Its speed and power lie in the fact that quantum systems can host multiple ‘superpositions’ of different initial states, which in a computer are treated as inputs which, in turn, all get processed at the same time.
“The greatest hurdle in using quantum objects for computing is to preserve their delicate superpositions long enough to allow us to perform useful calculations,” said Andrea Morello, leader of the research team and a Program Manager in the Centre for Quantum Computation & Communication Technology (CQC2T) at UNSW.
“Our decade-long research program had already established the most long-lived quantum bit in the solid state, by encoding quantum information in the spin of a single phosphorus atom inside a silicon chip, placed in a static magnetic field,” he said.
What Laucht and colleagues did was push this further: “We have now implemented a new way to encode the information: we have subjected the atom to a very strong, continuously oscillating electromagnetic field at microwave frequencies, and thus we have ‘redefined’ the quantum bit as the orientation of the spin with respect to the microwave field.”
The results are striking: since the electromagnetic field steadily oscillates at a very high frequency, any noise or disturbance at a different frequency results in a zero net effect. The researchers achieved an improvement by a factor of 10 in the time span during which a quantum superposition can be preserved.
Specifically, they measured a dephasing time of T2*=2.4 milliseconds – a result that is 10-fold better than the standard qubit, allowing many more operations to be performed within the time span during which the delicate quantum information is safely preserved.
“This new ‘dressed qubit’ can be controlled in a variety of ways that would be impractical with an ‘undressed qubit’,”, added Morello. “For example, it can be controlled by simply modulating the frequency of the microwave field, just like in an FM radio. The ‘undressed qubit’ instead requires turning the amplitude of the control fields on and off, like an AM radio.
“In some sense, this is why the dressed qubit is more immune to noise: the quantum information is controlled by the frequency, which is rock-solid, whereas the amplitude can be more easily affected by external noise”.
Since the device is built upon standard silicon technology, this result paves the way to the construction of powerful and reliable quantum processors based upon the same fabrication process already used for today’s computers.
The UNSW team leads the world in developing quantum computing in silicon, and Morello’s team is part of the consortium of UNSW researchers who have struck a A$70 million deal between UNSW, the researchers, business and the Australian government to develop a prototype silicon quantum integrated circuit – the first step in building the world’s first quantum computer in silicon.
A functional quantum computer would allow massive increases in speed and efficiency for certain computing tasks – even when compared with today’s fastest silicon-based ‘classical’ computers. In a number of key areas – such as searching large databases, solving complicated sets of equations, and modelling atomic systems such as biological molecules and drugs – they would far surpass today’s computers.They would also be enormously useful in the finance and healthcare industries, and for government, security and defence organisations.
Quantum computers could identify and develop new medicines by greatly accelerating the computer-aided design of pharmaceutical compounds (and minimising lengthy trial and error testing), and develop new, lighter and stronger materials spanning consumer electronics to aircraft. They would also make possible new types of computational applications and solutions that are beyond our ability to foresee.
Source: University of New South Wales
Arne Laucht et al, A dressed spin qubit in silicon, Nature Nanotechnology (2016). DOI: 10.1038/nnano.2016.178


Quantum computers have the potential to drive major advances in everything from medicine, to manufacturing, to the way we produce materials.
But while quantum computers could help us solve problems currently out of reach of conventional computers, they are also much more prone to making mistakes—mainly because they are incredibly sensitive to the smallest changes in their environment.
Amazon launched the AWS Center for Quantum Computing in 2019 with a goal of accelerating the development of quantum computing technologies and applications. Now, the company is launching a new facility for quantum computing at the California Institute of Technology (Caltech) with the ambitious goal of building a "fault-tolerant" quantum computer. Teams will focus on developing more powerful quantum computing hardware and identifying new applications for quantum technologies.
Here are five challenges they'll be wrapping their heads around:
Making more and better qubits
Conventional computers use bits—usually represented as a value of 1 or 0 in code—as their most basic unit of information. A bit can be anything with two distinct states. For example, a light that is either on or off, or a door that is either open or closed. But quantum computers use quantum bits, or "qubits"—usually elementary particles such as electrons or photons—to make calculations. Unlike bits, qubits can be manipulated to exist in a quantum state known as superposition, where they are both 1 and 0 at the same time, as well as all the possible states in between. This, along with some other equally mind-bending behaviors of qubits in a quantum state, allow quantum computers to perform certain calculations exponentially more efficiently than any current or future conventional computer. The AWS team will construct qubits from superconducting materials, such as aluminum patterned into electrical circuits on silicon microchips. That's because the techniques to manufacture these are well understood, making it possible to produce many more qubits, in a repeatable way, and at scale.
Keeping the noise down
The ability of qubits to exist in a quantum state is what gives quantum computers the potential to be massively more powerful than conventional computers when performing certain calculations. But keeping qubits in this state is—to put it mildly—a massive headache. Even the tiniest changes in their environment (referred to by quantum scientists as "noise"), such as vibrations or heat, can knock them out of superposition, causing them to lose information and become more error prone. The key to building successful quantum computers lies in controlling these errors. One area AWS will be investing in is material improvements to reduce noise, such as superconductors with surfaces prepared one atomic layer at a time, to minimize defects.
Developing a bigger quantum computer
One of the most challenging aspects of building quantum computers is how to scale them up. To go beyond the realms of what's already possible with conventional computers, they will need to be much, much larger than current machines. Today's quantum computers are "noisy" and error prone. The goal for quantum researchers is to scale from a handful of noisy qubits to a machine with hundreds and then thousands of very low-noise qubits. The new AWS facility includes everything the teams need to push the boundaries of quantum research and development, including technologies required to support bigger quantum devices, such as cryogenic cooling systems to protect devices from thermal noise and nanoscale fabrication tools required to construct new forms of quantum circuits.
Reducing the cost of error correction
Aside from investing in innovations to reduce noise, AWS will also be working on building error correction into quantum computing hardware, using redundant sets of physical qubits to form so-called "logical" qubits, which encode quantum information and can be used to detect and correct errors. Performing error correction in this way is typically very expensive and resource intensive, due the large amount of physical hardware required to generate logical qubits. AWS is researching ways to reduce these costs by designing more efficient methods of implementing error correction into quantum hardware.
Speeding up the clock
There's more to building a useful quantum computer than simply increasing the number of qubits. Another important metric is the computer's clock speed, or the time it takes to perform "quantum gate operations" and do so accurately. (Quantum gates are essentially the building blocks of quantum circuits—the models by which quantum computers make calculations.) This is where superconducting qubits offer an advantage, because they make it easier to speed up quantum gates. As AWS tries to build better qubits, its ultimate measure of success will be the extent to which it can speed up the clock while reducing quantum gate errors.
The AWS Center for Quantum Computing
The AWS Center for Quantum Computing brings together quantum computing experts from Amazon, Caltech, and other top academic research institutions. The center also offers scholarships and training opportunities for students and young faculty members, helping to support the quantum scientists of the future.
The center's ultimate goal is to build an entirely new type of computer: a fault-tolerant quantum machine able to perform accurate computations beyond anything offered by conventional computing technology at the scale needed to solve complex problems that could have a major impact on how we all live and work.
Learn more about the AWS Center for Quantum Computing.


What is Quantum Computing?
Quantum Computers or Quantum Computing machines are the small machines that perform basic computations that are based on different properties of quantum physics, classical computers, and many more which include a wide variety of gadgets like smartphones, laptops convert the information in binary which are 0’s and 1’s (bits). In Quantum computers each basic unit of memory is a quantum bit or qubit.
What is Qubit?
A qubit or a quantum bit is derived from a unit of a memory which is a basic unit of quantum information. It is a quantum version of the classical binary bit which physically realized in two different states.
To understand further let’s see a quick example Suppose let’s consider a mobile phone. Let it be a modern smartphone, knows where you are on the planet, all the time often to within meters.
That’s very useful if you are trying to find your way in some unfamiliar part of town, but if you pause for a moment to think about this, it’s actually a bit creepy how does your phone do?
Global Position System
The answer is of course by a GPS (Global Position System). At any time there are between 24 to 34 working GPS satellites in the orbit, with an altitude of 20,000 KM and if your phone receives signals from at least 4 of them, it can work out where it is. That’s because the satellite knows where they will tell your mobile phone. The time delay in receiving a message from the satellite to phone determines how far away the satellite is from your phone which means distance is time delay times the speed of light. Knowing the distance of four satellites then allows your phone to triangulate and establish where it is. However, light travels about 30cm in one nanosecond, so in order to get your location down to within a meter, the error in the time delay can only be few nanoseconds. So, the precise normal clocks are not sufficient we need to use much more precise atomic clocks. So, to monitor how atomic clock takes transitions of an atom we need quantum mechanics so, you know where you are on the planet every time you check your phone’s map because of quantum mechanics which is a part of quantum computing.
Another way in which quantum mechanics impact our life is via transistors. These are tiny devices a few tense of nanometers which are typically made from silicon, gallium, or some other semiconducting material, transistors are used as very fast current switches in microchips and they can be made to perform logic operations. Very large computations can be performed in seconds on a chip consisting of transistors. A typical mobile phone chip has several billion transistors.
What is a Transister?
A semiconductor transistor is made up of two types of semi conducting material called p-type and n-type. The type indicates whether the current in the semiconductor is carried by electrons (n-type) or holes (p-type). A hole are where an electron is supposed to sit to fill the shell, but is missing instead, in a transistor we can either have n type between the two layers of p-type or the other way around gives us npn or pnp types of transistors. Depending on the voltage that we apply to the middle layer we can open or close the flow of current between the outer layers.
What is a Bit?
Even though the computer operates on the principle of quantum mechanics, the actual logic carried out on the computers is zeros and ones. The unit of information in computers, the “bit”, is an abstract idea that represents a physical system that can be in two distinct states. For example, a light bulb can be on or off, wire in a computer can carry a current, a capacitor can carry a charge or no charge. In every case, the system is either on or off we can apply logic to these states as follows if we have three light bulbs A, B&C and set up a circuit such that c is on whenever both A& B are on, then the state of light bulb C is the logical value of A&B.
So, in the previous example, let’s return to the atomic transitions, every transition has two states ground state and excited state. When we return an atom to the atomic clock then the transition takes place. We can also call these two states zero and one, giving us a tiny bit. But it gives us more, the atom can be in one of these two states and also in a quantum superposition of these two states. This is in fact how quantum mechanics allows us to calculate how the atom jumps between these states in an atomic clock.
Now we can see that at a fundamental quantum mechanical level, a bit is not just a system with two states that is labeled zero and one but also allows superpositions between zero and one. This gives us how to manipulate the information stored in the system and it turns out that computers built on quantum principles are more powerful than ordinary computers. To distinguish the fundamental quantum systems in this new type of computer, we call them quantum bits or qubits.
There are many ways in which we can construct qubits besides the atomic levels they are electronic spin qubits, photon polarization qubits, and superconducting qubits, and so on.
How does Quantum computing work with python?
The operations which take place from one classical computing state to superposition state is just only one which mostly looks either a 0 or 1. The combination of information also is in the form of 0 and 1. It only takes two operations to create entanglement between two qubits.
IBM was the first company to put a first quantum computer on cloud extending the reach of technologies beyond the research laboratories. Nowadays there is a high scope of quantum developers in the real world, wondering if you, too, should get quantum ready? The short answer will be always YES!
Quantum Computing Using Python
In quantum computing using python, developers mostly use Qiskit. Qiskit is an open-source Programming platform that uses python language. If you are aware of Python, then you’re on the right path to take advantage of superposition, entanglement, and interface in quantum programs.
For seasoned developers in the industry looking to explore the potential applications of quantum computing, the Qiskit element Aqua (algorithms for quantum computing applications) offers a library of algorithms for artificial intelligence, chemistry, finance, and optimization. For example, there are a number of finance-related tutorials to experiment with credit risk analysis, fixed income pricing, basket option pricing, and others.
The field of Quantum computing is developing more and more the above content was just a small part of quantum computing works with python.
This post is contributed by Syed Rizwan.
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Scientists have shown how an optical chip can simulate the motion of atoms within molecules at the quantum level, which could lead to better ways of creating chemicals for use as pharmaceuticals.
An optical chip uses light to process information, instead of electricity, and can operate as a quantum computing circuit when using single particles of light, known as photons. Data from the chip allows a frame-by-frame reconstruction of atomic motions to create a virtual movie of a molecule’s quantum vibrations, which is what lies at the heart of the research published today in Nature.
“We program the chip, mapping its components to the structure of a particular molecule, say ammonia, then simulate how a particular vibrational pattern evolves over some time interval.”
These findings are the result of a collaboration between researchers at the University of Bristol, MIT, IUPUI, Nokia Bell Labs, and NTT. As well as paving the way for more efficient pharmaceutical developments, the research could prompt new methods of molecular modelling for industrial chemists.
When lasers were invented in the 1960s, experimental chemists had the idea of using them to break apart molecules. However, the vibrations within molecules rapidly redistribute the laser energy before the intended molecular bond is broken. Controlling the behaviour of molecules requires an understanding of how they vibrate at the quantum level. But modelling these dynamics requires massive computational power, beyond what we can expect from coming
generations of supercomputers.
The Quantum Engineering and Technology Labs at Bristol have pioneered the use of optical chips, controlling single photons of light, as basic circuitry for quantum computers. Quantum computers are expected to be exponentially faster than conventional supercomputers at solving certain problems. Yet constructing a quantum computer is a highly challenging long-term goal.
As reported in Nature, the team demonstrated a new route to molecular modelling that could become an early application of photonic quantum technologies. The new methods exploit a similarity between the vibrations of atoms in molecules and photons of light in optical chips.
Bristol physicist Dr Anthony Laing, who led the project, explained: “We can think of the atoms in molecules as being connected by springs. Across the whole molecule, the connected atoms will collectively vibrate, like a complicated dance routine. At a quantum level, the energy of the dance goes up or down in well-defined levels, as if the beat of the music has moved up or down a notch. Each notch represents a quantum of vibration.
“Light also comes in quantised packets called photons. Mathematically, a quantum of light is like a quantum of molecular vibration. Using integrated chips, we can control the behaviour of photons very precisely. We can program a photonic chip to mimic the vibrations of a molecule.
“We program the chip, mapping its components to the structure of a particular molecule, say ammonia, then simulate how a particular vibrational pattern evolves over some time interval. By taking many time intervals, we essentially build up a movie of the molecular dynamics.”
First author Dr Chris Sparrow, who was a student on the project, spoke of the simulator’s versatility: “The chip can be reprogrammed in a few seconds to simulate different molecules. In these experiments we simulated the dynamics of ammonia and a type of formaldehyde, and other more exotic molecules. We simulated a water molecule reaching thermal equilibrium with its environment, and energy transport in a protein fragment.
“In this type of simulation, because time is a controllable parameter, we can immediately jump to the most interesting points of the movie. Or play the simulation in slow motion. We can even rewind the simulation to understand the origins of a particular vibrational pattern.”
Joint first author, Dr Enrique Martín-Lopéz, now a Senior Researcher with Nokia Bell Labs, added: “We were also able to show how a machine learning algorithm can identify the type of vibration that best breaks apart an ammonia molecule.
A key feature of the photonic simulator that enables this is its tracking of energy moving through the molecule, from one localised vibration to another. Developing these quantum simulation techniques further has clear industrial relevance.”
The photonic chip used in the experiments was fabricated by Japanese Telecoms company NTT.
Dr Laing explained the main directions for the future of the research: “Scaling up the simulators to a size where they can provide an advantage over conventional computing methods will likely require error correction or error mitigation techniques. And we want to further develop the sophistication of molecular model that we use as the program for the simulator. Part of this study was to demonstrate techniques that go beyond the standard harmonic approximation of molecular dynamics. We need to push these methods to increase the real-world accuracy of our models.
“This approach to quantum simulation uses analogies between photonics and molecular vibrations as a starting point. This gives us a head start in being able to implement interesting simulations. Building on this, we hope that we can realise quantum simulation and modelling tools that provide a practical advantage in the coming years.”
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