A quantum computer is any gadget for calculation that utilizes unmistakably quantum mechanical wonders, for example, superposition and ensnarement, to perform the procedure on the information. In an old-style (or regular) computer, data is put away as bits; in quantum computers, it is put away as qubits (quantum bits).
The essential guideline of quantum calculation is that the quantum properties can be utilized to speak to and structure information and that quantum instruments can be formulated and worked to perform tasks with this information. Even though quantum registering google is still in its early stages, tests have been done in which quantum computational activities were executed on a few google quantum computers qubits.
Research in both hypothetical and reasonable zones proceeds at a distracted pace, and numerous national governments and military subsidizing organizations bolster quantum processing google research to create google quantum computers for both regular citizen and national security purposes, for example, cryptanalysis. On the off chance that enormous scope google quantum computers can be fabricated, they will have the option to take care of
specific issues exponentially quicker than any of our present traditional computers (for instance Shor's calculation).
Quantum computers are unique about different computers, for example, DNA computers and customary computers dependent on transistors. Some processing structures, optical computers may utilize old-style superposition of
electromagnetic waves, however without some explicitly quantum mechanical assets, for example, trap, they have less potential for computational accelerate than quantum computers.
The intensity of quantum computers Integer factorization is accepted to be computationally infeasible with normal computers for huge whole numbers that are the result of just a couple of prime numbers (e.g., results of two 300-digit primes).
How does Quantum Computer work?
Here we will talk about how quantum computers work and on which quantum algorithm. Quantum Computers works on Grover's Algorithm. Quantum computers perform computations dependent on the likelihood of an item's state before it is estimated – rather than only 1s or 0s – which implies they can process a large amount of information as compared to classical computers. Old style computers complete sensible activities utilizing
the unmistakable situation of a physical state. These are typically double, which means its activities depend on one of two positions.
A solitary state -, for example, on or off, up or down, 1 or 0 – is known as a piece. In quantum registering, activities rather utilize the quantum condition of an article to create what's known as a qubit. These states are the unclear properties of an article before they' ve been distinguished, for example, the turn of an electron
or the polarization of a photon. Instead of having an unmistakable position, unmeasured
quantum states happen in a blended 'superposition' much the same as a coin turning through the air before it arrives in your grasp.
These superpositions can be caught with those of different articles, which means their ultimate results will be numerically related regardless of whether we don't have the foggiest idea yet what they are. The intricate arithmetic behind these disrupted conditions of snared ' turning coins' can be connected to uncommon
calculations to make short work of issues that would take a traditional computer quite a while to work out… if they would ever ascertain them by any stretch of the imagination. Such calculations would be valuable in taking care of complex numerical issues, delivering hard- to-break security codes, or foreseeing different molecule collaborations in substance responses.
Types of Quantum Computers Processors:
IBM IBM Q 53 53 QB
Intel 17-Qubit Superconducting
Intel Tangle Lake 49 QB
Rigetti 8Q Agave 8 QB
Comparison Between Quantum and Classic Computers:
By comparison, a quantum computer could take care of this issue more effectively than old-style computers utilizing Grover’s algorithm calculation to discover its elements, and consequently, there is google quantum matchless quality. This capacity would permit quantum computers to "break" huge numbers of the cryptographic frameworks being used today, as in there would be a polynomial-time (in the number of bits of the whole number) calculation for taking care of the issue.
Specifically, the greater part of the well known open key figures depends on the trouble of calculating whole numbers, including types of RSA. These are utilized to ensure secure Web pages, encoded email, and numerous different kinds of information. Breaking these would have noteworthy consequences for electronic protection
and security. The best way to expand the security of a calculation like RSA is to increment the key size and expectation that a foe doesn't have the assets to fabricate and utilize an amazing enough quantum computer. It appears to be conceivable that it will consistently be conceivable to fabricate old-style computers that have a greater number of bits than the number of qubits in the biggest quantum computers.
Why Quantum computing is the Supreme?
Now, we will discuss google quantum supremacy. For the present, old-style innovation can deal with any errand tossed at a quantum computer. Quantum matchless quality depicts the capacity of quantum computers to beat their old style partners. A few organizations, for example, IBM and Google, guarantee we may be close, as they keep on packing more qubits together and fabricate increasingly exact gadgets. Not every person is persuaded that quantum computers merit the exertion.
There is a term called quantum tempering which is a significant factor. Quantum annealing (QA) is a metaheuristic for finding the worldwide least of a given target work over a given arrangement of competitor arrangements (applicant states), by a procedure utilizing quantum variances (as such, a meta-system for finding a technique that finds a flat out least size/length/cost/good ways from inside a perhaps extremely huge, yet regardless limited arrangement of potential arrangements utilizing quantum change based calculation rather than old-style calculation). A few mathematicians accept some hindrances that are difficult to survive, putting quantum figuring always far off.
What's Quantum Computer price?
Today, a solitary qubit will interfere with you $10,000 – and that is before you consider innovative work costs. At that value, helpful all-inclusive quantum computers – equipment alone – comes in at any rate of $10bn. This for a machine whose genuine business esteem is a long way from ensuring. To make quantum computers industrially reasonable, the expense per qubit should drop significantly. However, how this will occur, nobody knows.
Quantum computing Google benefits:
Following are quantum computing benefits
1. Quantum computers can take care of issues that are inconceivable or would take a customary computer an unfeasible measure of time (a billion years) to fathom.
2. Quantum computers are extraordinary for taking care of streamlining issues from making sense of the most ideal approach to plan trips at an air terminal to deciding the best conveyance courses for the FedEx truck.
3. Quantum computers will change the scene of information security. Even though quantum computers would have the option to split a significant number of the present encryption methods, forecasts are that they would make hack-confirmation substitutions.
Five ways Quantum Computers will change the world:
Here are five different ways that show how will quantum computing change the world. Let us discuss it in brief.
1. Make life-sparing medications and comprehend a portion of science's most mind-boggling issues:
Quantum registering will alter Artificial Intelligence(AI). The fundamental standard of AI is that the more input you give a computer program, the more precise it becomes. This criticism figures probability from numerous potential decisions and results in the program showing "intelligence" and improved execution.
Quantum computers quickly dissect gigantic amounts of information so they can fundamentally abbreviate the AI expectation to absorb information. If innovation turns out to be increasingly instinctive it will make a colossal
effect in each Industry. We' ll have the option to do things we never thought conceivable from making life-sparing meds to tackling a portion of science's most mind-boggling issues.
2. A genuine discussion with AI
Quantum Computing will change man-made brainpower by giving huge figuring capacity to empower a quicker and increasingly hearty AI particularly in characteristic language handling and general AI. we have achieved an extraordinary arrangement just in the previous barely any years with the present advances in registering power. Quantum Computing is a far cry further developed than anything we have today. An AI on a Quantum Computers can hold a genuine discussion with people and comprehend what is being said.
3. Critical danger to digital security
The intensity of quantum computers will predominate our present preparing abilities, introducing another time of information and revelation. On the drawback, that force postures such a critical danger to digital security that we will need to revaluate how we secure business exchanges (and every single other datum moves), or none of them will be protected.
4. No exchanging
Let's be honest — quantum registering will make human exchanging outdated. We saw what high-recurrence exchanging did to the intensity of primate-slid entertainers in the money related markets. Quantum calculations are going to exploit to an unheard-of level.
5. Undermine web-based financial exchanges, every one of our interchanges, driverless vehicles, and even our races:
Quantum Computing will compel us to re-examine the central ideal models of our advanced security. Known quantum computer assaults — which are simply trusting that genuine quantum computers will show up — will break quite a bit of the present broadly utilized.
cryptography. For what reason do we give it a second thought? Since this crypto supports the security of pretty much all that we currently underestimate: from web-based financial exchanges to every one of our interchanges, to the trust we have in our driverless vehicles and even in our races.


Back in 1958, in the earliest days of the computing revolution, the US Office of Naval Research organized a press conference to unveil a device invented by a psychologist named Frank Rosenblatt at the Cornell Aeronautical Laboratory. Rosenblatt called his device a perceptron, and the New York Times reported that it was “the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself, and be conscious of its existence.”
Those claims turned out to be somewhat overblown. But the device kick-started a field of research that still has huge potential today.
A perceptron is a single-layer neural network. The deep-learning networks that have generated so much interest in recent years are direct descendants. Although Rosenblatt’s device never achieved its overhyped potential, there is great hope that one of its descendants might.
Today, there is another information processing revolution in its infancy: quantum computing. And that raises an interesting question: is it possible to implement a perceptron on a quantum computer, and if so, how powerful can it be?
Today we get an answer of sorts thanks to the work of Francesco Tacchino and colleagues at the University of Pavia in Italy. These guys have built the world’s first perceptron implemented on a quantum computer and then put it through its paces on some simple image processing tasks.
In its simplest form, a perceptron takes a vector input—a set of numbers—and multiplies it by a weighting vector to produce a single-number output. If this number is above a certain threshold the output is 1, and if it is below the threshold the output is 0.
That has some useful applications. Imagine a pixel array that produces a set of light intensity levels—one for each pixel—when imaging a particular pattern. When this set of numbers is fed into a perceptron, it produces a 1 or 0 output. The goal is to adjust the weighting vector and threshold so that the output is 1 when it sees, say a cat, and 0 in all other cases.
Tacchino and co have repeated Rosenblatt’s early work on a quantum computer. The technology that makes this possible is IBM’s Q-5 “Tenerife” superconducting quantum processor. This is a quantum computer capable of processing five qubits and programmable over the web by anyone who can write a quantum algorithm.
Tacchino and co have created an algorithm that takes a classical vector (like an image) as an input, combines it with a quantum weighting vector, and then produces a 0 or 1 output.
The big advantage of quantum computing is that it allows an exponential increase in the number of dimensions it can process. While a classical perceptron can process an input of N dimensions, a quantum perceptron can process 2N dimensions.
Tacchino and co demonstrate this on IBM’s Q-5 processor. Because of the small number of qubits, the processor can handle N = 2. This is equivalent to a 2×2 black-and-white image. The researchers then ask: does this image contain horizontal or vertical lines, or a checkerboard pattern?
It turns out that the quantum perceptron can easily classify the patterns in these simple images. “We show that this quantum model of a perceptron can be used as an elementary nonlinear classifier of simple patterns,” say Tacchino and co.
They go on to show how it could be used in more complex patterns, albeit in a way that is limited by the number of qubits the quantum processor can handle.
That’s interesting work with significant potential. Rosenblatt and others soon discovered that a single perceptron can only classify very simple images, like straight lines. However, other scientists found that combining perceptrons into layers has much more potential. Various other advances and tweaks have led to machines that can recognize objects and faces as accurately as humans can, and even thrash the best human players of chess and Go.
Tacchino and co’s quantum perceptron is at a similarly early stage of evolution. Future goals will be to encode the equivalent of gray-scale images and to combine quantum perceptrons into many-layered networks.
This group’s work has that potential. “Our procedure is fully general and could be implemented and run on any platform capable of performing universal quantum computation,” they say.
Of course, the limiting factor is the availability of more powerful quantum processors capable of handling larger numbers of qubits. But most quantum researchers agree that this kind of capability is close.
Indeed, since Tacchino and co did their work, IBM has already made a 16-qubit quantum processor available via the web. It’s only a matter of time before quantum perceptrons become much more powerful.
This article was originally published by: https://www.technologyreview.com/s/612435/machine-learning-meet-quantum-computing/


Graphene strips folded in similar fashion to origami paper could be used to build microchips that are up to 100 times smaller than conventional chips, found physicists – and packing phones and laptops with those tiny chips could significantly boost the performance of our devices.
New research from the University of Sussex in the UK shows that changing the structure of nanomaterials like graphene can unlock electronic properties and effectively enable the material to act like a transistor.
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The scientists deliberately created kinks in a layer of graphene and found that the material could, as a result, be made to behave like an electronic component. Graphene, and its nano-scale dimensions, could therefore be leveraged to design the smallest microchips yet, which will be useful to build faster phones and laptops.
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Alan Dalton, professor at the school of mathematical and physics sciences at the University of Sussex, said: “We’re mechanically creating kinks in a layer of graphene. It’s a bit like nano-origami.
“This kind of technology – ‘straintronics’ using nanomaterials as opposed to electronics – allows space for more chips inside any device. Everything we want to do with computers – to speed them up – can be done by crinkling graphene like this.”
Discovered in 2004, graphene is an atom-thick sheet of carbon atoms, which, due to its nano-sized width, is effectively a 2D material. Graphene is best known for its exceptional strength, but also for the material’s conductivity properties, which has already generated much interest in the electronics industry including from Samsung Electronics.
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The field of straintronics has already shown that deforming the structure of 2D nanomaterials like graphene, but also molybdenum disulfide, can unlock key electronic properties, but the exact impact of different “folds” remains poorly understood, argued the researchers.
Yet the behavior of those materials offers huge potential for high-performance devices: for example, changing the structure of a strip of 2D material can change its doping properties, which correspond to electron density, and effectively convert the material into a superconductor.
The researchers carried an in-depth study of the impact of structural changes on properties, such as doping in strips of graphene and of molybdenum disulfide. From kinks and wrinkles to pit-holes, they observed how the materials could be twisted and turned to eventually be used to design smaller electronic components.
Manoj Tripathi, research fellow in nano-structured materials at the University of Sussex, who led the research, said: “We’ve shown we can create structures from graphene and other 2D materials simply by adding deliberate kinks into the structure. By making this sort of corrugation we can create a smart electronic component, like a transistor, or a logic gate.”
The findings are likely to resonate in an industry pressed to conform to Moore’s law, which holds that the number of transistors on a microchip doubles every two years, in response for growing demand for faster computing services. The problem is, engineers are struggling to find ways to fit much more processing power into tiny chips, creating a big problem for the traditional semiconducting industry.
A tiny graphene-based transistor could significantly help overcome these hurdles. “Using these nanomaterials will make our computer chips smaller and faster. It is absolutely critical that this happens as computer manufacturers are now at the limit of what they can do with traditional semiconducting technology. Ultimately, this will make our computers and phones thousands of times faster in the future,” said Dalton.
Since it was discovered over 15 years ago, graphene has struggled to find as many applications as was initially hoped for, and the material has often been presented as a victim of its own hype. But then, it took over a century for the first silicon chip to be created after the material was discovered in 1824. Dalton and Tripathi’s research, in that light, seems to be another step towards finding a potentially game-changing use for graphene.
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Graphene Processors and Quantum Gates Since the 1960s, Moore’s law has accurately predicted the evolution trend of processors as to the amount of transistor doubling every 2 years. But lately we’ve seen something odd happening, processor clocks aren’t getting any faster. This has to do with another law called Dennard Scaling and it seems that the good old days with silicon chips are over. Hello everyone, subject zero here! Thankfully the solution might have been available for quite some time now and Graphene offers something quite unique to this problem, not only for your everyday processor types, but also Quantum computing. In 2009 it was speculated that by now we would have the famous 400GHz processors, but this technology has proven itself to be a bit more complicated than previously thought however most scientists including me, believe that in the next 5 years we will see the first Graphene commercial hardware come to reality. References https://en.wikipedia.org/wiki/Quantum… https://www.nature.com/articles/s4153… https://www.hpcwire.com/2019/05/08/gr… https://en.wikipedia.org/wiki/Graphen… https://www.computerhope.com/history/… http://www.tfcbooks.com/teslafaq/q&a_… https://www.rambus.com/blogs/understa… https://www.technologyreview.com/s/51… https://arxiv.org/ftp/arxiv/papers/13… https://www.sciencedaily.com/releases… https://www.nature.com/articles/srep2… http://infowebbie.com/scienceupdate/s… https://graphene-flagship.eu/field-ef… https://github.com/karlrupp/microproc… https://aip.scitation.org/doi/full/10… https://www.theglobeandmail.com/repor…


Author: Sarah Kearns
Editors: David Mertz, Zuleirys Santana Rodriguez, and Scott Barolo
In a previous post, we discussed how proteins fold into unique shapes that allow them to perform their biological functions. Through many physical and chemical properties, like hydrogen bonding and hydrophobicity, proteins are able to fold correctly. However, proteins can fold improperly, and sometimes these malformed peptides aggregate, leading to diseases like Alzheimer’s.
How can we figure out when the folding process goes wrong? Can we use computers to figure out the folding/misfolding process and develop methods to prevent or undo the damage done by protein aggregates?
In the late 1960s, a scientist named Cyrus Levinthal noted that protein folding is different from regular chemical reactions. Chemical reactions proceed from a reactant to a product via a set pathway of structures and intermediates. Proteins do not do this because a protein doesn’t find just one intermediate shape as it folds — it can potentially find millions. Levinthal concluded that a new protein, moving through so many intermediate structures, must take an enormously long time to find its final native state.
To understand the vast number of conformational possibilities, let’s take a polypeptide of 101 amino acids. There will be a total of 100 bonds connecting amino acids, each bond having six possible conformations (see Figure 1). This means that a protein of 101 amino acids has 3100, or 5*1047, configurations—and some proteins are five or ten times longer!
Even if our 101-amino acid protein were able to sample 1013 conformations per second, it would still need 1027 years to try all possible shapes. However, in reality, it takes seconds, not eons, for a protein to find its native conformation. This leads to a big question: Can humans predict how proteins will fold? Even with the help of computers, which can test each possible shape in microseconds, testing them all would require 30 years of computation just for one protein.
Simplifying Structure Prediction
Protein structures, such as hydrogen and ionic bonding and hydrophobic interactions, are difficult to predict rationally just based on the amino acid sequence. Instead, a database of protein structures found by x-ray crystallography, called the Protein Data Bank, has been more helpful in determining the rules of protein folding. Still, determining protein structures accurately is difficult and time-consuming. Some computational shortcuts have made the process simpler, but the predicted folds still are not exact.
The biggest simplifications are made by assuming a lattice structure or using a coarse-grained representation. The former takes a globular protein that typically has variable bond lengths between each amino acid into a lattice (has uniform bond lengths) and places each residue into a 3D grid structure thus limiting the number of possibilities the possible placements of each amino acid. A coarse-grained model would simplify a protein structure by representing amino acids as a single point (see Figure 2).
So far, computational prediction of protein structures is limited to these simpler models because more realistic all-atom energy diagrams are too complex and computationally heavy. In our protein of 101 amino acids, there are close to 2000 atoms to move around in 3100 configurations. With the advent of quantum computing, such problems are becoming easier to solve, but for now, they still use coarse-grained representations.
How Your PC Can Help Mine Data
Some researchers have turned such computational problems into citizen science projects. Perhaps the most famous of these is FoldIt, developed by the Center for Game Science and the Department of Biochemistry at the University of Washington. Foldit is an online game where players compete to create accurate protein structures by moving around the backbone chain, amino acid residues, and domains. Players score points by packing the protein, hiding hydrophobic residues, and clearing any clashes between side chains to minimize the energy of the overall structure. The lowest-energy conformations from the game are then collected and analyzed to improve real-life folding algorithms.
A less hands-on folding program is Folding@home from Stanford University, which borrows unused processors on your personal computer to work on a folding algorithm. While users check their emails or listen to music, or even when the screensaver runs, their computers solve structures and compute minimization functions.
All this data has gone towards the goal of figuring out both how malformed proteins aggregate and how to design drugs that will prevent misfolding. FoldIt has already produced a retrovirus structure that is being used to determine inhibitors of HIV. One of the labs behind FoldIt has been focusing on proteins involved in cancer, AIDS, and other diseases. The Folding@home project has produced about 130 peer-reviewed papers describing its accomplishments in simulating, not only protein folding but also molecular dynamics, which help determine the ability for drugs to bind.
Having an idea of what the protein does and where it does it, without having to use expensive machines to do crystallography (to get the structure of a protein) or high-throughput screening (to find the substrates of a protein), saves both time and resources when developing a drug. More work has to be done before computational predictions perfectly line up with crystal structures. But when that day comes, we will be much closer to understanding how proteins work, and how to cure diseases of protein folding and function.
About the author
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Figure 1: Sarah Kearns
Figure 2: Sarah Kearns


Quantum computers are expected to play a crucial role in Machine Learning (ML) and AI. But qubit counts and more-sophisticated algorithms alone will not deliver quantum advantage. Toolkits and a unified experience are needed to bring it to the masses.
Think of Quantum computing as a new kind of computing, using the same physical rules that atoms follow in order to manipulate information. At this fundamental level, quantum computers execute quantum circuits—like a computer's logical circuits but now using the physical phenomena of superposition, entanglement, and interference to implement mathematical calculations out of the reach of even the most advanced supercomputers.
All computing systems rely on a fundamental ability to store and manipulate information. Current computers manipulate individual bits, which store information as binary 0 and 1 states. Quantum computers leverage quantum mechanical phenomena to manipulate information. To do this, they rely on quantum bits, or qubits.
Superposition refers to a combination of states we would ordinarily describe independently. To make a classical analogy, if you play two musical notes at once, what you will hear is a superposition of the two notes.
Entanglement is a famously counterintuitive quantum phenomenon describing behavior we never see in the classical world. Entangled particles behave together as a system in ways that cannot be explained using classical logic.
Finally, quantum states can undergo interference due to a phenomenon known as phase. Quantum interference can be understood similarly to wave interference; when two waves are in phase, their amplitudes add, and when they are out of phase, their amplitudes cancel.
Quantum and AI
There are high hopes that quantum computing’s tremendous processing power will unleash exponential advances in artificial intelligence (AI). AI systems thrive when machine-learning algorithms used to train them are given massive amounts of data to ingest, classify, and analyze. The more precisely that data can be classified according to specific characteristics, or features, the better AI will perform. Quantum computers are expected to play a crucial role in machine learning, including the crucial aspect of accessing more computationally complex feature spaces—the fine-grain aspects of data that could lead to new insights.
Machine learning is changing the way we use computers in our everyday lives and in science. It is natural to seek connections between these two emerging approaches, in the hope of reaping multiple benefits.
IBM is testing quantum systems to train and run machine-learning algorithms to dramatically improve tasks such as classification of data. This could allow us to solve complex problems more quickly, potentially improving applications like disease diagnosis, fraud detection, efficient energy management, and more.
Scaling Quantum Systems
For commercial application, quantum computers will need to demonstrate fault tolerance, just as we expect our existing computing systems to. What does it take to create a fault-tolerant quantum system? To increase the computational power of a quantum computer, improvements are needed along two dimensions.
- Qubit count: The more qubits you have, the more states can in principle be manipulated and stored.
- Low error rates: These are needed to manipulate qubit states accurately and perform sequential operations that provide answers, not “noise.”
A useful metric for understanding quantum capability is quantum volume. This measures the relationship between number and quality of qubits, circuit connectivity, and error rates of operations. Developing systems with larger quantum volume will lead to discovering the first instances of applications where quantum computers can offer a computational advantage for solving real problems.
Organizations across a wide array of industries are partnering with IBM to explore a broad set of quantum computing applications. Carmakers, airlines, energy companies, healthcare providers, financial services firms, and world-class research organizations are considering new solutions and services that until recently were unthought of. Organizations are now able to start their quantum journey with access to advanced systems and a comprehensive software stack, supported by a large quantum development community. These projects help showcase quantum computing’s power to solve real-world problems too complex for even today’s most-powerful supercomputers.
The annual IBM Quantum Summit looked at the promise of quantum computing for industry looking at several business challenges that quantum computers are well-suited to tackle. Foremost among them are the ability to help researchers create simulations of complex chemical compounds and reactions out of reach for today’s computers. Such simulations are expected to have a profound impact on the development of new materials that improve battery technology, resist corrosion, and make renewable energy more efficient and less expensive.
IBM announced a roadmap at the annual IBM Quantum Summit to reach 1,000+ qubits by 2023. Qubit counts and more-sophisticated algorithms alone will not deliver Quantum Advantage (the point where certain information-processing tasks can be performed more efficiently or cost-effectively on a quantum computer versus a classical one).
The roadmap aims to take the technology from today’s noisy, small-scale devices to the million-plus qubit devices of the future. This is essential if quantum computers are to help industry and research organizations tackle some of the world’s biggest challenges, across industry, government, and research. Here are five things you should know about the roadmap:
- IBM quantum scientists are building a quantum computer with a 1,121-qubit processor, called Condor, inside a large dilution "super-fridge." The Condor processor-based quantum computer will be online and capable of exploring Quantum Advantage by 2023.
- Condor lays the groundwork for scaling to fully error-corrected, interconnected, 1-million-plus-qubit quantum computers. These multi-million-qubit super-fridges, connected via intranets, will make the exploration of classically intractable problems possible for any number of industries, including finance, chemistry, and AI.
- In 2021, IBM will debut the 127-qubit "Eagle" chip. Eagle features several upgrades to reduce qubit errors, including its unique layout, which will allow for scaling the number of qubits that work together as logical qubits—the "fault tolerant" qubits needed to reach Quantum Advantage. With the Eagle processor, IBM will also introduce concurrent real-time classical compute capabilities that will allow for execution of a broader family of quantum circuits and codes.
- Eagle will be followed by the 433-qubit "Osprey" processor in 2022. Osprey continues to push the boundaries of fabrication techniques to build a smaller chip to ensure more logical qubits that don't sacrifice performance. Its more-efficient and denser controls and cryogenic infrastructure will ensure that scaling up future processors doesn’t sacrifice the performance of individual qubits, introduce further sources of noise, or take up too large a footprint.
- These advances are necessary to establish a Quantum Industry. Over the next three years, IBM's multidisciplinary team of scientists will work alongside academia and industry to help solve the challenges of fabrication, cryogenics, and electronics, as well as improve software capabilities, such as error-correction coding.
MIT has introduced a Quantum Computing Online Curriculum for professionals and leaders in business, government, and technology to deliver a better understanding of the business and technical implications of quantum computing. The online courses will apply the principles of quantum computing to real-world examples utilizing a state-of-the-art web-available quantum computer: IBM’s Quantum Experience.
MIT’s quantum learning initiative is created in collaboration with IBM Q, and the MIT-IBM Watson AI Lab. The MIT-IBM Watson AI Lab is focused on fundamental AI research with the goal of propelling scientific breakthroughs that unlock the potential of AI. A key initiative of the lab is the intersection of quantum computing and machine learning.
Currently, quantum computing researchers and enthusiasts need to know quantum programming; it’s simply a must. Soon, though, all they will need is a quantum app store and a line of code. Not an app store like in your smartphone, but similar to a code repository of today, such as GitHub—a type of digital library where software developers make the code they have written available to anyone. And in the near future, developers will be able to put in their lines of code that will call on quantum computers to deal with specific tasks a regular computer can’t.
The quantum research field has undergone dramatic changes in the last few decades, but only recently have quantum scientists released easy-to-use tools to make this discipline accessible to everyone. IBM offers all the quantum programming tools you need with Qiskit and makes it easy to get started running quantum circuits on our systems with the IBM Q Experience quantum cloud platform. Users have already run over 28 million experiments and simulations.
Your Next Steps
Consider applying for the IBM Quantum Challenge: “Programming for the Not-So-Distant Quantum Future,” a three-week quantum computing educational challenge starting on November 8 or November 9, 2020 (depending on your time zone). More information is available here.
In addition, IBM Quantum will sponsor 5,000 students to attend an eight-month intensive quantum computing course from The Coding School.
Finally, if AI is an area you are interested in, you can learn more about it in the book I co-authored, Artificial Intelligence: Evolution and Revolution.
Special thanks to the IBM quantum team and their blogs, a key source for this article.


Leonard Susskind, a pioneer of string theory, the holographic principle and other big physics ideas spanning the past half-century, has proposed a solution to an important puzzle about black holes. The problem is that even though these mysterious, invisible spheres appear to stay a constant size as viewed from the outside, their interiors keep growing in volume essentially forever. How is this possible?
In a series of recent papers and talks, the 78-year-old Stanford University professor and his collaborators conjecture that black holes grow in volume because they are steadily increasing in complexity — an idea that, while unproven, is fueling new thinking about the quantum nature of gravity inside black holes.
Black holes are spherical regions of such extreme gravity that not even light can escape. First discovered a century ago as shocking solutions to the equations of Albert Einstein’s general theory of relativity, they’ve since been detected throughout the universe. (They typically form from the inward gravitational collapse of dead stars.) Einstein’s theory equates the force of gravity with curves in space-time, the four-dimensional fabric of the universe, but gravity becomes so strong in black holes that the space-time fabric bends toward its breaking point — the infinitely dense “singularity” at the black hole’s center.
According to general relativity, the inward gravitational collapse never stops. Even though, from the outside, the black hole appears to stay a constant size, expanding slightly only when new things fall into it, its interior volume grows bigger and bigger all the time as space stretches toward the center point. For a simplified picture of this eternal growth, imagine a black hole as a funnel extending downward from a two-dimensional sheet representing the fabric of space-time. The funnel gets deeper and deeper, so that infalling things never quite reach the mysterious singularity at the bottom. In reality, a black hole is a funnel that stretches inward from all three spatial directions. A spherical boundary surrounds it called the “event horizon,” marking the point of no return.
Since at least the 1970s, physicists have recognized that black holes must really be quantum systems of some kind — just like everything else in the universe. What Einstein’s theory describes as warped space-time in the interior is presumably really a collective state of vast numbers of gravity particles called “gravitons,” described by the true quantum theory of gravity. In that case, all the known properties of a black hole should trace to properties of this quantum system.
Indeed, in 1972, the Israeli physicist Jacob Bekenstein figured out that the area of the spherical event horizon of a black hole corresponds to its “entropy.” This is the number of different possible microscopic arrangements of all the particles inside the black hole, or, as modern theorists would describe it, the black hole’s storage capacity for information.
Bekenstein’s insight led Stephen Hawking to realize two years later that black holes have temperatures, and that they therefore radiate heat. This radiation causes black holes to slowly evaporate away, giving rise to the much-discussed “black hole information paradox,” which asks what happens to information that falls into black holes. Quantum mechanics says the universe preserves all information about the past. But how does information about infalling stuff, which seems to slide forever toward the central singularity, also evaporate out?
The relationship between a black hole’s surface area and its information content has kept quantum gravity researchers busy for decades. But one might also ask: What does the growing volume of its interior correspond to, in quantum terms? “For whatever reason, nobody, including myself for a number of years, really thought very much about what that means,” said Susskind. “What is the thing which is growing? That should have been one of the leading puzzles of black hole physics.”
In recent years, with the rise of quantum computing, physicists have been gaining new insights about physical systems like black holes by studying their information-processing abilities — as if they were quantum computers. This angle led Susskind and his collaborators to identify a candidate for the evolving quantum property of black holes that underlies their growing volume. What’s changing, the theorists say, is the “complexity” of the black hole — roughly a measure of the number of computations that would be needed to recover the black hole’s initial quantum state, at the moment it formed. After its formation, as particles inside the black hole interact with one another, the information about their initial state becomes ever more scrambled. Consequently, their complexity continuously grows.
Using toy models that represent black holes as holograms, Susskind and his collaborators have shown that the complexity and volume of black holes both grow at the same rate, supporting the idea that the one might underlie the other. And, whereas Bekenstein calculated that black holes store the maximum possible amount of information given their surface area, Susskind’s findings suggest that they also grow in complexity at the fastest possible rate allowed by physical laws.
John Preskill, a theoretical physicist at the California Institute of Technology who also studies black holes using quantum information theory, finds Susskind’s idea very interesting. “That’s really cool that this notion of computational complexity, which is very much something that a computer scientist might think of and is not part of the usual physicist’s bag of tricks,” Preskill said, “could correspond to something which is very natural for someone who knows general relativity to think about,” namely the growth of black hole interiors.
Researchers are still puzzling over the implications of Susskind’s thesis. Aron Wall, a theorist at Stanford (soon moving to the University of Cambridge), said, “The proposal, while exciting, is still rather speculative and may not be correct.” One challenge is defining complexity in the context of black holes, Wall said, in order to clarify how the complexity of quantum interactions might give rise to spatial volume.
A potential lesson, according to Douglas Stanford, a black hole specialist at the Institute for Advanced Study in Princeton, New Jersey, “is that black holes have a type of internal clock that keeps time for a very long time. For an ordinary quantum system,” he said, “this is the complexity of the state. For a black hole, it is the size of the region behind the horizon.”
If complexity does underlie spatial volume in black holes, Susskind envisions consequences for our understanding of cosmology in general. “It’s not only black hole interiors that grow with time. The space of cosmology grows with time,” he said. “I think it’s a very, very interesting question whether the cosmological growth of space is connected to the growth of some kind of complexity. And whether the cosmic clock, the evolution of the universe, is connected with the evolution of complexity. There, I don’t know the answer.”


The RSA algorithm is the basis of a cryptosystem -- a suite of cryptographic algorithms that are used for specific security services or purposes -- which enables public key encryption and is widely used to secure sensitive data, particularly when it is being sent over an insecure network such as the internet.
RSA was first publicly described in 1977 by Ron Rivest, Adi Shamir and Leonard Adleman of the Massachusetts Institute of Technology, though the 1973 creation of a public key algorithm by British mathematician Clifford Cocks was kept classified by the U.K.'s GCHQ until 1997.
Public key cryptography, also known as asymmetric cryptography, uses two different but mathematically linked keys -- one public and one private. The public key can be shared with everyone, whereas the private key must be kept secret.
In RSA cryptography, both the public and the private keys can encrypt a message; the opposite key from the one used to encrypt a message is used to decrypt it. This attribute is one reason why RSA has become the most widely used asymmetric algorithm: It provides a method to assure the confidentiality, integrity, authenticity, and non-repudiation of electronic communications and data storage.
Many protocols like secure shell, OpenPGP, S/MIME, and SSL/TLS rely on RSA for encryption and digital signature functions. It is also used in software programs -- browsers are an obvious example, as they need to establish a secure connection over an insecure network, like the internet, or validate a digital signature. RSA signature verification is one of the most commonly performed operations in network-connected systems.
Why the RSA algorithm is used
RSA derives its security from the difficulty of factoring large integers that are the product of two large prime numbers. Multiplying these two numbers is easy, but determining the original prime numbers from the total -- or factoring -- is considered infeasible due to the time it would take using even today's supercomputers.
The public and private key generation algorithm is the most complex part of RSA cryptography. Two large prime numbers, p and q, are generated using the Rabin-Miller primality test algorithm. A modulus, n, is calculated by multiplying p and q. This number is used by both the public and private keys and provides the link between them. Its length, usually expressed in bits, is called the key length.
The public key consists of the modulus n and a public exponent, e, which is normally set at 65537, as it's a prime number that is not too large. The e figure doesn't have to be a secretly selected prime number, as the public key is shared with everyone.
The private key consists of the modulus n and the private exponent d, which is calculated using the Extended Euclidean algorithm to find the multiplicative inverse with respect to the totient of n.
Read on or watch the video below for a more detailed explanation of how the RSA algorithm works.
How does the RSA algorithm work?
Alice generates her RSA keys by selecting two primes: p=11 and q=13. The modulus is n=p×q=143. The totient is n ϕ(n)=(p−1)x(q−1)=120. She chooses 7 for her RSA public key e and calculates her RSA private key using the Extended Euclidean algorithm, which gives her 103.
Bob wants to send Alice an encrypted message, M, so he obtains her RSA public key (n, e) which, in this example, is (143, 7). His plaintext message is just the number 9 and is encrypted into ciphertext, C, as follows:
Me mod n = 97 mod 143 = 48 = C
When Alice receives Bob's message, she decrypts it by using her RSA private key (d, n) as follows:
Cd mod n = 48103 mod 143 = 9 = M
To use RSA keys to digitally sign a message, Alice would need to create a hash -- a message digest of her message to Bob -- encrypt the hash value with her RSA private key, and add the key to the message. Bob can then verify that the message has been sent by Alice and has not been altered by decrypting the hash value with her public key. If this value matches the hash of the original message, then only Alice could have sent it -- authentication and non-repudiation -- and the message is exactly as she wrote it -- integrity.
Alice could, of course, encrypt her message with Bob's RSA public key -- confidentiality -- before sending it to Bob. A digital certificate contains information that identifies the certificate's owner and also contains the owner's public key. Certificates are signed by the certificate authority that issues them, and they can simplify the process of obtaining public keys and verifying the owner.
RSA security relies on the computational difficulty of factoring large integers. As computing power increases and more efficient factoring algorithms are discovered, the ability to factor larger and larger numbers also increases.
Encryption strength is directly tied to key size, and doubling key length can deliver an exponential increase in strength, although it does impair performance. RSA keys are typically 1024- or 2048-bits long, but experts believe that 1024-bit keys are no longer fully secure against all attacks. This is why the government and some industries are moving to a minimum key length of 2048-bits.
Barring an unforeseen breakthrough in quantum computing, it will be many years before longer keys are required, but elliptic curve cryptography (ECC) is gaining favor with many security experts as an alternative to RSA to implement public key cryptography. It can create faster, smaller and more efficient cryptographic keys.
Modern hardware and software are ECC-ready, and its popularity is likely to grow, as it can deliver equivalent security with lower computing power and battery resource usage, making it more suitable for mobile apps than RSA. Finally, a team of researchers, which included Adi Shamir, a co-inventor of RSA, has successfully created a 4096-bit RSA key using acoustic cryptanalysis; however, any encryption algorithm is vulnerable to attack.


You probably learned about nuclear fusion in high school environmental science: It was built up as a clean, high-yield, virtually limitless source of power. Then, the bell rang, you went to lunch, and it was never spoken of again.
For decades, commercial fusion energy was a great idea handicapped by the limits of plasma physics. But recent advances in material science and fusion reactors could change that
One very, very hot bottle
Nuclear power plants, like the one Homer Simpson works at, use fission, the splitting of uranium atoms to generate energy. Fusion does the opposite, fusing hydrogen nuclei together to release helium and energy in the process. Well-known practitioners include our very own sun, other stars in the galaxy, and Matthew McConaughey in Interstellar.
The science underpinning fusion is well understood, but making it happen here on Earth is quite tricky. Scientists are looking to “basically take a star and put it in a bottle,” according to fusion expert Brandon Sorbom.
- Engineering hurdles include heating the plasma up to 100 million °C (which we can do), sustaining these temperatures for extended periods of time (still working on this), and building a device capable of withstanding the pummeling of a million Arizona summers all at once (also a work in progress).
- Another key challenge is creating a system that generates more power that it consumes, says Dennis Whyte, MIT professor and director of the Plasma Science and Fusion Center. The good news is, once the reaction is going, as long as fuel is continuously supplied you can laissez les bon temp[erature]s rouler.
Today there are two main approaches to fusion, according to the World Nuclear Association: magnetic confinement (which, you guessed it, uses magnetic fields to contain plasma) and inertial confinement (which uses lasers or particle beams). Most of the academic fusion community has focused on magnetic confinement through tokamaks (donut-shaped containment chambers), Sorbom said.
- Vocab lesson: Tokamaks derive their name from “toroidalnya kamera ee magnetnaya katushka”—Russian for the no-less-confusing “torus-shaped magnetic chamber.” For obvious reasons, we’ll be sticking to the abbreviation.
These devices take a lot of manpower and resources to build, so good luck getting a large-scale project off the ground without the help of government funding or billionaire philanthropists.
In the south of France, scientists from around the world are forgoing romantic walks along the Riviera to build ITER, an international fusion project that will create not only the world’s largest tokamak, but (fingers crossed) the first fusion device to generate net energy. With 35 countries collaborating—including the U.S., Russia, China, India, and EU members—it might be one of the only areas of peaceful international collaboration left.
- By the late 2030s, the ITER tokamak is expected to produce up to 500 megawatts of fusion power in pulses that last 400 seconds, Danas Ridikas, head of the physics at the International Atomic Energy Agency, told the Brew.
- ITER may a science-driven venture, but any effort to move the R&D needle forward benefits commercial ventures as well.
Other technologies, such as supercomputing, big data analysis, and 3D printing could help accelerate progress in the field, said Ridikas. Quantum computing, which we profiled earlier this week, is expected to drive breakthroughs in fields like high energy physics.
Sun’s in the bottle, so what’s next?
After scientists prove they can maintain plasma for extended periods of time and generate a working device, they can then work on a fusion demonstration power plant that is able to connect to the power grid, said Ridikas. And once they have that, they can work on future commercial fusion power plants. Easy peasy, right?
Sorbom, who’s chief science officer at MIT-spinoff Commonwealth Fusion Systems, was recently recognized for a breakthrough in tokamak electromagnetic systems that could make tokamaks or fusion devices smaller (and cheaper) to build. With tokamaks the size of a house instead of a football field…that could open up the field for more players and speed the path to commercial fusion energy on the grid.
- By 2025, CFS and MIT are trying to build a power plant prototype (called Sparc) using the new electromagnetic system. Sparc = Kitty Hawk for fusion energy, proving it can be done but only flying a few hundred feet.
- Five to 10 years after Sparc is working, Sorbom and the CFS team hope to complete Arc (a demonstration power plant that can put electricity on the grid). Arc = the transatlantic flight.
Transitioning the world’s current energy production to renewables is a massive undertaking, and fusion power will help not only clean up energy production, but scale it tenfold worldwide, according to Sorbom. It will be “almost like solar energy, but you control the light switch on the sun, and you also have the dimmer switch.” `
- Bonus: Fuel (hydrogen isotopes) is theoretically limitless.
- Double bonus: Nuclear energy has a bad rep, and as much as we want another season of Chernobyl to binge watch, no one wants that happening in their backyard. But with fusion, “there is no risk for a meltdown accident,” Ridikas said. “If any disturbance occurs, the plasma cools within seconds and the fusion reaction stops.” Maybe an HBO special about a power outage instead?
Why now? Until recently, fusion energy was dominated by plasma physics, which kept it a pretty niche field, Sorbom said. Now on the slow and steady path to #mainstream, the community needs people of all backgrounds to get involved.
- Yes, this means engineers and scientists who can help build a fusion reactor. But it also means business people who can scale, commercialize, and get fusion energy out into the real world.
- One of the most exciting things about fusion R&D today is its focus on the ecosystem—figuring out what a fusion-driven economy would look like as well as the economic targets and applications, said MIT’s Whyte.
A healthy dose of reality: There’s a running joke that fusion is the energy of the future…and always will be. Even if projects like CFS’s hit their benchmarks on time, fusion energy is years or decades away from realization, let alone from grid integration and global reach. But Sorbom and Whyte were both optimistic that they would see functioning fusion energy in their lifetime.
Because no matter how you frame it, the promise of a clean, carbon-free baseload power that could yield four times as much as fission reactors is a good deal and worth trying for.
Did you get all that? Here are some refreshers just in case
The promise: Clean, carbon-free, energy with a theoretically limitless source of fuel, capable of higher yields that existing fission energy.
The roadblocks: Scientists can heat plasma up to 100 million °C, but they’re still working on sustaining these temperatures for extended periods of time and building reactors that can withstand the heat.
The timeline: Experts believe we’ll see working fusion energy in our lifetime. They’re still trying to build better reactors today (and after that, they have to tackle demonstration then commercial power plants), but recent breakthroughs have many optimistic.
The players: Governments (U.S., EU, Russia, Japan, China, Brazil, Canada, Korea), companies (Lockheed Martin, Commonwealth Fusion Systems, General Fusion, Tokamak Energy, AGNI Energy), Academia (MIT, Princeton), and billionaires (Jeff Bezos, Bill Gates, Peter Thiel).


The best place to start our journey through quantum computing is to recall how classical computing works and try to extend it. Since our final quantum computing model will be a circuit model, we should informally discuss circuits first.
A circuit has three parts: the “inputs,” which are bits (either zero or one); the “gates,” which represent the lowest-level computations we perform on bits; and the “wires,” which connect the outputs of gates to the inputs of other gates. Typically the gates have one or two input bits and one output bit, and they correspond to some logical operation like AND, NOT, or XOR.
If we want to come up with a different model of computing, we could start regular circuits and generalize some or all of these pieces. Indeed, in our motivational post we saw a glimpse of a probabilistic model of computation, where instead of the inputs being bits they were probabilities in a probability distribution, and instead of the gates being simple boolean functions they were linear maps that preserved probability distributions (we called such a matrix “stochastic”).
Rather than go through that whole train of thought again let’s just jump into the definitions for the quantum setting. In case you missed last time, our goal is to avoid as much physics as possible and frame everything purely in terms of linear algebra.
Qubits are Unit Vectors
The generalization of a bit is simple: it’s a unit vector in . That is, our most atomic unit of data is a vector with the constraints that are complex numbers and . We call such a vector a qubit.
A qubit can assume “binary” values much like a regular bit, because you could pick two distinguished unit vectors, like and , and call one “zero” and the other “one.” Obviously there are many more possible unit vectors, such as and . But before we go romping about with what qubits can do, we need to understand how we can extract information from a qubit. The definitions we make here will motivate a lot of the rest of what we do, and is in my opinion one of the major hurdles to becoming comfortable with quantum computing.
A bittersweet fact of life is that bits are comforting. They can be zero or one, you can create them and change them and read them whenever you want without an existential crisis. The same is not true of qubits. This is a large part of what makes quantum computing so weird: you can’t just read the information in a qubit! Before we say why, notice that the coefficients in a qubit are complex numbers, so being able to read them exactly would potentially encode an infinite amount of information (in the infinite binary expansion)! Not only would this be an undesirably powerful property of a circuit, but physicists’ experiments tell us it’s not possible either.
So as we’ll see when we get to some algorithms, the main difficulty in getting useful quantum algorithms is not necessarily figuring out how to compute what you want to compute, it’s figuring out how to tease useful information out of the qubits that otherwise directly contain what you want. And the reason it’s so hard is that when you read a qubit, most of the information in the qubit is destroyed. And what you get to see is only a small piece of the information available. Here is the simplest example of that phenomenon, which is called the measurement in the computational basis.
Definition: Let be a qubit. Call the standard basis vectors the computational basis of . The process of measuring in the computational basis consists of two parts.
- You observe (get as output) a random choice of or . The probability of getting is , and the probability of getting is .
- As a side effect, the qubit instantaneously becomes whatever state was observed in 1. This is often called a collapse of the waveform by physicists.
There are more sophisticated ways to measure, and more sophisticated ways to express the process of measurement, but we’ll cover those when we need them. For now this is it.
Why is this so painful? Because if you wanted to try to estimate the probabilities or , not only would you get an estimate at best, but you’d have to repeat whatever computation prepared for measurement over and over again until you get an estimate you’re satisfied with. In fact, we’ll see situations like this, where we actually have a perfect representation of the data we need to solve our problem, but we just can’t get at it because the measurement process destroys it once we measure.
Before we can talk about those algorithms we need to see how we’re allowed to manipulate qubits. As we said before, we use unitary matrices to preserve unit vectors, so let’s recall those and make everything more precise.
Qubit Mappings are Unitary Matrices
Suppose is a qubit. If we are to have any mapping between vector spaces, it had better be a linear map, and the linear maps that send unit vectors to unit vectors are called unitary matrices. An equivalent definition that seems a bit stronger is:
Definition: A linear map is called unitary if it preserves the inner product on .
Let’s remember the inner product on is defined by and has some useful properties.
- The square norm of a vector is .
- Swapping the coordinates of the complex inner product conjugates the result:
- The complex inner product is a linear map if you fix the second coordinate, and a conjugate-linear map if you fix the first. That is, and
By the first bullet, it makes sense to require unitary matrices to preserve the inner product instead of just the norm, though the two are equivalent (see the derivation on page 2 of these notes). We can obviously generalize unitary matrices to any complex vector space, and unitary matrices have some nice properties. In particular, if is a unitary matrix then the important property is that the columns (and rows) of form an orthonormal basis. As an immediate result, if we take the product , which is just the matrix of all possible inner products of columns of , we get the identity matrix. This means that unitary matrices are invertible and their inverse is .
Already we have one interesting philosophical tidbit. Any unitary transformation of a qubit is reversible because all unitary matrices are invertible. Apparently the only non-reversible thing we’ve seen so far is measurement.
Recall that is the conjugate transpose of the matrix, which I’ll often write as . Note that there is a way to define without appealing to matrices: it is a notion called the adjoint, which is that linear map such that for all . Also recall that “unitary matrix” for complex vector spaces means precisely the same thing as “orthogonal matrix” does for real numbers. The only difference is the inner product being used (indeed, if the complex matrix happens to have real entries, then orthogonal matrix and unitary matrix mean the same thing).
Definition: A single qubit gate is a unitary matrix .
So enough with the properties and definitions, let’s see some examples. For all of these examples we’ll fix the basis to the computational basis . One very important, but still very simple example of a single qubit gate is the Hadamard gate. This is the unitary map given by the matrix
It’s so important because if you apply it to a basis vector, say, , you get a uniform linear combination . One simple use of this is to allow for unbiased coin flips, and as readers of this blog know unbiased coins can efficiently simulate biased coins. But it has many other uses we’ll touch on as they come.
Just to give another example, the quantum NOT gate, often called a Pauli X gate, is the following matrix
It’s called this because, if we consider to be the “zero” bit and to be “one,” then this mapping swaps the two. In general, it takes to .
As the reader can probably imagine by the suggestive comparison with classical operations, quantum circuits can do everything that classical circuits can do. We’ll save the proof for a future post, but if we want to do some kind of “quantum AND” operation, we get an obvious question. How do you perform an operation that involves multiple qubits? The short answer is: you represent a collection of bits by their tensor product, and apply a unitary matrix to that tensor.
We’ll go into more detail on this next time, and in the mean time we suggest checking out this blog’s primer on the tensor product. Until then!


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”.
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.”
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.


Let's get Started!
So, what are Quantum computers?
Understanding Quantum computers are pretty complicated and quite confusing. So I'm going to break it down in an easy way to understand it.
We all know that regular computers use bits 0 and 1 for storing data and processing tasks so for example if I have four bits in a row I can represent a bunch of numbers.
Quantum bits know as Qubits are either 0 or 1 but it may still be 0 & 1 at the same time.
I know! I know! it sounds very strange and confusing because it's not really quite plain to grasp, for example, let's imagine that a bit is sort of like a coin, it can either be heads or tails just like 0 or 1.
it can only be either heads or tails right?
What is it right now?
while it's in the air Is it Heads or tails ?
it's heads or tails now in a strange manner, it's heads and tails at the same moment, that doesn't even make sense before it falls in my palm as I see the coin, so then I can see what it's like, and that's the theory behind the quantum bit so it should be a 0 and a 1 at the same moment.
In simple words qubits are bits with two state and each state have some probability just like in the case of coins
You get it right!
How it is going to change the world?
Well now is where the fun begins let's suppose we have 4 bits with this we have 16 possibilities( means we can have 16 numbers)
let's say that I'm trying to crack a password the password I'm trying to crack is one of the numbers we can get with this bits.
A normal computer will take one number at a time and put it in the normal machine one by one till we got the right answer
what if we use a quantum computer so instead of putting in these four regular bits, We put in four quantum bits, Now remember each bit is both a 0 and a 1 that means these quantum bits are all the numbers all at the same time
So, when I put the quantum bits into my machine to find the right password what comes off the other end of machine is saying that I'm both right and wrong because We gave it both right and wrong answers at the same time.
We still want to know what the correct password is? right?
Well there's a technique called a growver operator, this is a real thing where you can sweep away all the wrong answers and what you're left behind with is the right answer
So that's the beauty of Quantum computing
I heard some people say that it will take the age of the universe to try and crack these codes
That's how secure they are but with a quantum computer you can try them all at the same time use the growver operator to sweep away all the wrong answers and what you're left with is the right answer.
So instead of taking millions of years with a regular computer you can do it in seconds with quantum computers.
Are you excited to write you first quantum program
Lets get started
# install latest version !pip install cirq
import cirq import numpy as np from cirq import Circuit from cirq.devices import GridQubit
# creating circuit with 5 qubit length = 5 qubits = [cirq.GridQubit(i, j) for i in range(length) for j in range(length)] print(qubits)
Applying Hadamard operation on every qubit
circuit = cirq.Circuit() H1 = cirq.H(qubits) H2 = cirq.H(qubits) H3 = cirq.H(qubits) H4 = cirq.H(qubits) H5 = cirq.H(qubits)
Apply CNOT operation on (0, 1), (1,2), (2,3), (3,4) , swap at (0,4) , rotating X by with pi/2
C1 = cirq.CNOT(qubits,qubits) C2 = cirq.CNOT(qubits,qubits) C3 = cirq.CNOT(qubits,qubits) C4 = cirq.CNOT(qubits,qubits) #swap S1 = cirq.SWAP(qubits,qubits) #Rotation X1 = cirq.X(qubits) X2 = cirq.X(qubits) X3 = cirq.X(qubits) X4 = cirq.X(qubits) X5 = cirq.X(qubits)
Creating the moment and printing the circuit
moment1 = cirq.Moment([H1]) moment2 = cirq.Moment([H2]) moment3 = cirq.Moment([H3]) moment4 = cirq.Moment([H4]) moment5 = cirq.Moment([H5]) moment6 = cirq.Moment([C1]) moment7 = cirq.Moment([C2]) moment8 = cirq.Moment([C3]) moment9 = cirq.Moment([S1]) moment10 = cirq.Moment([X1]) moment11 = cirq.Moment([X2]) moment12 = cirq.Moment([X3]) moment13 = cirq.Moment([X4]) moment14 = cirq.Moment([X5]) #circuit circuit = cirq.Circuit((moment1, moment2, moment3, moment4, moment5 ,moment6 ,moment7, moment8, moment9, moment10, moment11, moment12, moment13, moment14)) print(circuit)
This is the quantum circuit you get, I will recommend you to try and play with it.
I hope it's helped you in some way, Thanks for reading!
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IBM announced last year that they had conclusively demonstrated “quantum advantage” for the first time, proving that quantum computers are better than classical computers for some types of problems.
This announcement is one of many exciting developments in quantum computing over the last few years, and comes at a time when both private and public investment in the space is accelerating. Household names like IBM, Intel, Google, and Microsoft are investing around $100MM annually to develop their own quantum systems, and well-funded startups like Rigetti and IonQ are developing systems and software of their own to compete with big-name players.
Governments are also getting more involved. The EU, UK, and China have all made significant investments into quantum technologies, and the US House of Representatives recently passed a measure to provide $1.3 billion to fund a National Quantum Initiative. While it is encouraging to see so much activity, it can be difficult to look past the hype surrounding the industry and understand what it all means. In this post, I will describe where quantum computing is today, and how it is likely to be used in the coming years.
What is quantum computing?
Before we dive in, it is important to understand what a quantum computer is. Quantum computers are devices that take advantage of quantum mechanics to perform calculations. Whereas classical computers store data in “bits” – binary units of information that are always either 1 or 0 – quantum computers store data as qubits, which have several unusual properties.
For one thing, qubits can exist in superpositions, meaning that instead of being either 1 or 0, a qubit can be a mixture of both at the same time (this comic explains it in a simple and fun way). Qubits can also be entangled with each other so their individual states are perfectly correlated. This means that measuring the state of one qubit reveals information about the state of the other. It also means that performing a calculation on the first qubit will affect the state of the second qubit in a predictable way.
The future vision for quantum computing – and hurdles to getting there
Thanks to properties like qubit superposition and entanglement, quantum computers are really good at many things that classical computers are bad at – like handling complex algorithms. One such algorithm that gets a lot of attention due to its potential impact on data security is Shor’s algorithm. This algorithm describes how a quantum computer could be used to find the prime factors of a large number much faster than any known classical algorithm.
RSA, one of the most commonly used encryption methods in the world, relies on the fact that classical computers can easily multiply very large prime numbers, but can’t do the reverse operation (find prime factors) very well without a key. This asymmetry in difficulty is what protects RSA-encrypted data from being decrypted by anyone other than the intended recipient (who already knows what key to use).
A quantum computer capable of running Shor’s algorithm would be able to decode encrypted information easily, posing a significant security threat. However, quantum computing is in its early days, and many signs indicate that it will be a long time before quantum computers achieve that particular capability. The first of these signs is the fact that quantum computing companies haven’t settled on a standard qubit. Superconducting circuit qubits and trapped ion qubits are leading now, but other technologies (e.g., silicon spin qubits, topological qubits, and photonic qubits) are being explored as well.
Current systems are also very small compared to what would be needed for the problems quantum computing promises to solve. Current systems have around 100 qubits, with IonQ’s 160 trapped-ion qubit system leading the field, but a quantum computer that can run Shor’s algorithm and factor numbers that are hundreds of digits long will likely require millions of qubits. Even the most optimistic industry experts don’t think quantum computers will reach this point for at least a decade.
The value of quantum computing in the near term
That’s not to say that quantum computers won’t be useful until they have millions of qubits. To the contrary, some of the most exciting applications for our specialty materials clients are much nearer term. Applications in materials simulation, chemical modeling, and process optimization can be addressed by systems with just a few hundred qubits – a target that may be achieved in the next 5 years.
Additionally, developments in adjacent markets are making quantum computing increasingly accessible. Many major players offer software to help people familiarize themselves with writing code for quantum computers and are working to engage directly with future users. Companies like Zapata Computing and QC Ware, who base their business models on writing algorithms and software for quantum computers, have popped up in the last few years. The growth of this ecosystem is enabling companies to explore how they will use increasingly powerful quantum computers, and understand and prepare for the impact of these systems.
Deciphering the hype around a topic as complicated as quantum physics can seem overwhelming. When we’re constantly bombarded by headlines promising that quantum computing is almost ready for business, or threatening imminent security breaches, it’s hard to know whether to panic or write the whole thing off as ludicrous. Our experiences in this industry indicates the threat of RSA decryption is real and deserves to be taken seriously, but is at least a decade away. Applications for smaller quantum computers, on the other hand, have the potential to meaningfully affect many companies’ business in the near future. The ecosystem is growing, investment is accelerating, and we are rapidly nearing a time when quantum computers will solve valuable problems. Now is the perfect time to start thinking about how your business can take advantage of this exciting technology.


Locke influenced that form of government, as well. 1470 Words6 Pages The Founding Fathers of the United States relied heavily on many of the principles taught by John Locke. Firstly authority of government proceeds from the people. However, I would argue that factions should play some role in our government due to the massive size and scope of the government itself and its electorate. What characteristics of the state exemplify its legitimacy? In this revolution we saw a rising movement from the people to oppose monarchy and demand a rule by the people. John Locke was a philosophical influence in both political theory and theoretical philosophy, which was embraced among the era of 1789-1914 and, The Enlightenment thinker, John Locke, greatly influenced movements like the American and French Revolutions. John Locke’s philosophy of government and the governed influenced our founding fathers as expressed in our 3 founding documents; Declaration, Constitution and Bill of Rights (first 10 amendments). Many of the principles of Locke’s Second Treatise of Government may easily be discovered in the Declaration of Independence with some minor differences in wording and order. Thomas Jefferson was not the only founding father who subscribed to these beliefs. In this revolution we saw, “Golden Era”. Jefferson received a great deal of inspiration from Locke in writing the Declaration of Independence. Locke stressed that the role of the state is to protect each individual from the will and desires of others. rest of the founding fathers demanded from the King of England stemmed from a basic desire for rights and liberties for all people, not just the wealthy barons. He single handedly developed a political system that had a focus on liberty, his work would help influence many men from both sides of the Atlantic. Did Hamilton, Madison, Jefferson, and various other so-called Founding Fathers look to the right person for inspiration? Sometimes the majority is just plain wrong, especially when reflecting on history and various social issues that have sprung up. Jefferson had many philosophical minds to ponder when writing the document, such as Aristotle and most importantly John Locke. Leonardo da Vinci opened the door to the Renaissance and William Shakespeare treated us to the best writings and plays in the English language. Because of his past occupation, who used to persuade to become a doctor, he understood how people's lives, and what was the best form of government that they need. I would argue that this system is one of the most vital aspects of our own system of government. Enlighten Thinker: John Locke John Locke (1632-1704) is a Philosopher and Physician. While he does not specifically call out Hobbes in this work, he does seem to be responding to Hobbes, passively. He was known as one of the most affective Founding Father of Enlighten movement. Looking back centuries later, how has this influence played out? Thus, men “unite for the mutual preservation of their lives, liberties, and estates” (Locke 1690, IX, 123). Copyright 2020 | MH Newsdesk lite by MH Themes, Jimmy Fallon and Bruce Springsteen Mock Chris Christie with “Born to Run” Cover, Amy Poehler Wins Golden Globe for Best Actress in Parks and Recreation – Video, Texas Gun Show Shoots Self in Foot and Shuts Down Over Background Checks, Late Night Political Jokes of the Week – GWB Bridgegate, Cold Snap, Letterman Top 10, 2014 Political Cartoons – NJ Gov. Many of the ideas of the proper role of, Finally, towards the end of the Declaration, Jefferson wrote that they were “appealing to the Supreme Judge of the World, for the Rectitude of our Intentions…And for the support of this Declaration, with a firm Reliance on the Protection of divine Providence, we mutually pledge to each other our Lives, our Fortunes, and our sacred Honor.” Again, the similarity to Locke is found. In contrast, Hobbes was more focused on the dangers of nature and the need to form a sovereign state to aid in the interest of man’s self-preservation. “Secondly, there wants a, Writing Devices in Romeo and Juliet Essay, Strategies to Solve Addition and Subtraction Essay, Shakespeare In Love -Combination of Romantic Comedy and Shakespearean Tragedy, Inferno as a Manifestation of the Pain of Dante Alighieri Essay, Essay on The True Villian in Frankenstein. Locke had three main philosophies, religious tolerance, all men are born a blank slate, and that the divine right to rule is incorrect. Imagine if civil rights laws in the South during the 1960s were put up to a majority vote. John Locke was born on August 29, 1632 in Wrington, England, nation. James Madison's writings were also heavily influenced by Locke. November 12, 2013. He believed that people should have a direct say in the government and that absolute monarchy should not be a factor that rules everyday people in their everyday lives. Likewise, John Locke is a man who accomplished what many men could not. I would argue that they did. Locke insists upon a separation of powers that influenced the United State’s own system of checks and balances. Much like Hobbes, Locke starts with man’s original place in nature. John Locke and his ideas contributed in a major way towards the Enlightenment. It has been said that “Locke’s justification of revolt, as based on his theory of natural rights, was the background from which the Declaration sprang.” Locke’s influence appears in countless speeches and writings of the Founding Fathers. Thomas Hobbes and John Locke had very similar views on natural law and natural right. He was a very intelligent man and grew up with a good education that led him to have many opportunities not just for himself but many others too.
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Our pocket computers – you know them as smartphones – are small, fast and powerful thanks to the billions of bits and transistors packed into the chips they use.
Bits make up a computer’s memory and are the smallest building blocks of traditional computing. They can have one of two values, usually represented as either a 0 or a 1. Your phone has about 256 billion bits (or 32 gigabytes).
Transistors act as switches. Think of them as a roadway with a gate. A binary signal of 1 will tell the gate to allow the cars to flow down the roadway. A binary signal of 0 will tell the gate to stop all cars from traveling on the roadway. My iPhone 11 has 8.5 billion transistors.
We’re now at a crossroads. And we’re bumping up against the limits of bits and transistors. Either our ability to increase the power and speed of computers will dramatically decline or we’ll discover entirely new and revolutionary technologies.
The most fascinating and game-changing of those technologies is quantum computing.
First, some background. Quantum computers rely on qubits instead of bits. The qubit is a combination of “quantum” and “bit.”
Qubits are based on a somewhat complicated concept called quantum superposition. It’s the idea that a quantum object can be in multiple states at the same time. A qubit doesn’t have to be only a 0 or a 1. Once it is measured, it will take one of those two forms. But until it is measured, it can exist as a range of probabilities between those two results.
And that’s important. Two bits taken together can be represented as either 00 or 01 or 10 or 11. But through the lens of superposition, two qubits can store all four values simultaneously. Three qubits can hold eight different values. As the number of qubits increases, the number of values increases exponentially.
So compared with bits, it takes a much smaller number of qubits to give a computer incredible power.
Quantum computing is an amazing theoretical construct. But can it become a reality?
Incredibly, some are saying it’s already arrived.
Google recently published a paper claiming it achieved a real quantum computing breakthrough. Using a machine made up of 53 qubits, the company performed a calculation in three minutes and 20 seconds that Google claims would take the world’s best “normal” supercomputer 10,000 years to complete.
Other legacy companies are in various stages of developing quantum computing capability – most notably IBM and Microsoft. IBM calls Google’s claim bogus, saying a traditional supercomputer could have completed the calculation in just 2.5 days.
Still, the difference between 3.5 minutes and 2.5 days is enormous. I’d like to see more than one “demo” by a single company “prove” quantum computing.
But it feels real to me. We could very well be on the precipice of a new computer age. And you can be sure that dozens of startups will be leading the charge.
The day before Google’s big announcement, quantum computing startup IonQ raised $55 million. IonQ wants to make its quantum computers available to other companies via the cloud. And earlier this year, it announced its own major quantum breakthrough.
Another startup, a German company called HQS Quantum Simulations, raised $2.6 million in seed financing. HQS wants to use quantum computers to run simulations that can discover new materials and substances with commercial potential. It cites batteries and more efficient solar cells as two examples.
The possibilities are endless. Any industry that involves highly complex calculations and simulations could be disrupted, from finance to artificial intelligence (AI). Think of all the new medicines that could be created. On the downside, quantum computers would also make current encryption practices obsolete. A whole new world of possibility is about to open up.
In the meantime, the transistor side of chip technology is also making huge strides.
Untether AI just raised $13 million from Intel and others to develop a new type of chip for AI that can transfer data to different parts of the chip 1,000 times more quickly than a conventional AI chip. Untether AI uses “near-memory computing” to reduce the physical distance between memory and the processing tasks, speeding up data transfer and lowering power consumption.
We’re witnessing a Kitty Hawk flight moment in computing. Just as the Wright brothers proved flight was possible, Google and Untether AI are showing us that computing power the likes of which we’ve never seen is also becoming a reality.
Air transit opened up a world of unimaginable uses… many good but some not so good. It created entire industries. Changed the way we travel. Transformed warfare. And made the world a much smaller place.
What will these breakthroughs in computing give us? We’re about to find out.