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Quantum computing – Wikipedia

Study of a model of computation

Quantum Computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically[1]:I-5 There are two main approaches to physically implementing a quantum computer currently, analog and digital. Analog approaches are further divided into quantum simulation, quantum annealing, and adiabatic quantum computation. Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits.[1]:213

Qubits are fundamental to quantum computing and are somewhat analogous to bits in a classical computer. Qubits can be in a 1 or 0 quantum state. But they can also be in a superposition of the 1 and 0 states. However, when qubits are measured the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Quantum computing began in the early 1980s, when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine.[2]Richard FeynmanandYuri Maninlater suggested that a quantum computer had the potential to simulate things that a classical computer could not.[3][4] In 1994, Peter Shor developed a quantum algorithm for factoring integers that had the potential to decrypt all secured communications.[5]

Despite ongoing experimental progress since the late 1990s, most researchers believe that "fault-tolerant quantum computing [is] still a rather distant dream".[6] On 23 October 2019, Google AI, in partnership with the U.S. National Aeronautics and Space Administration (NASA), published a paper in which they claimed to have achieved quantum supremacy.[7] While some have disputed this claim, it is still a significant milestone in the history of quantum computing.[8]

The field of quantum computing is a subfield of quantum information science, which includes quantum cryptography and quantum communication.

The prevailing model of quantum computation describes the computation in terms of a network of quantum logic gates. What follows is a brief treatment of the subject based upon Chapter 4 of Nielsen and Chuang.[9]

A memory consisting of n {textstyle n} bits of information has 2 n {textstyle 2^{n}} possible states. A vector representing all memory states has hence 2 n {textstyle 2^{n}} entries (one for each state). This vector should be viewed as a probability vector and represents the fact that the memory is to be found in a particular state.

In the classical view, one entry would have a value of 1 (i.e. a 100% probability of being in this state) and all other entries would be zero. In quantum mechanics, probability vectors are generalized to density operators. This is the technically rigorous mathematical foundation for quantum logic gates, but the intermediate quantum state vector formalism is usually introduced first because it is conceptually simpler. This article focuses on the quantum state vector formalism for simplicity.

We begin by considering a simple memory consisting of only one bit. This memory may be found in one of two states: the zero state or the one state. We may represent the state of this memory using Dirac notation so that

The state of this one-qubit quantum memory can be manipulated by applying quantum logic gates, analogous to how classical memory can be manipulated with classical logic gates. One important gate for both classical and quantum computation is the NOT gate, which can be represented by a matrix

The mathematics of single qubit gates can be extended to operate on multiqubit quantum memories in two important ways. One way is simply to select a qubit and apply that gate to the target qubit whilst leaving the remainder of the memory unaffected. Another way is to apply the gate to its target only if another part of the memory is in a desired state. These two choices can be illustrated using another example. The possible states of a two-qubit quantum memory are

In summary, a quantum computation can be described as a network of quantum logic gates and measurements. Any measurement can be deferred to the end of a quantum computation, though this deferment may come at a computational cost. Because of this possibility of deferring a measurement, most quantum circuits depict a network consisting only of quantum logic gates and no measurements. More information can be found in the following articles: universal quantum computer, Shor's algorithm, Grover's algorithm, DeutschJozsa algorithm, amplitude amplification, quantum Fourier transform, quantum gate, quantum adiabatic algorithm and quantum error correction.

Any quantum computation can be represented as a network of quantum logic gates from a fairly small family of gates. A choice of gate family that enables this construction is known as a universal gate set. One common such set includes all single-qubit gates as well as the CNOT gate from above. This means any quantum computation can be performed by executing a sequence of single-qubit gates together with CNOT gates. Though this gate set is infinite, it can be replaced with a finite gate set by appealing to the Solovay-Kitaev theorem.

Integer factorization, which underpins the security of public key cryptographic systems, is believed to be computationally infeasible with an ordinary computer for large integers if they are the product of few prime numbers (e.g., products of two 300-digit primes).[10] By comparison, a quantum computer could efficiently solve this problem using Shor's algorithm to find its factors. This ability would allow a quantum computer to break many of the cryptographic systems in use today, in the sense that there would be a polynomial time (in the number of digits of the integer) algorithm for solving the problem. In particular, most of the popular public key ciphers are based on the difficulty of factoring integers or the discrete logarithm problem, both of which can be solved by Shor's algorithm. In particular, the RSA, DiffieHellman, and elliptic curve DiffieHellman algorithms could be broken. These are used to protect secure Web pages, encrypted email, and many other types of data. Breaking these would have significant ramifications for electronic privacy and security.

However, other cryptographic algorithms do not appear to be broken by those algorithms.[11][12] Some public-key algorithms are based on problems other than the integer factorization and discrete logarithm problems to which Shor's algorithm applies, like the McEliece cryptosystem based on a problem in coding theory.[11][13] Lattice-based cryptosystems are also not known to be broken by quantum computers, and finding a polynomial time algorithm for solving the dihedral hidden subgroup problem, which would break many lattice based cryptosystems, is a well-studied open problem.[14] It has been proven that applying Grover's algorithm to break a symmetric (secret key) algorithm by brute force requires time equal to roughly 2n/2 invocations of the underlying cryptographic algorithm, compared with roughly 2n in the classical case,[15] meaning that symmetric key lengths are effectively halved: AES-256 would have the same security against an attack using Grover's algorithm that AES-128 has against classical brute-force search (see Key size).

Quantum cryptography could potentially fulfill some of the functions of public key cryptography. Quantum-based cryptographic systems could, therefore, be more secure than traditional systems against quantum hacking.[16]

Besides factorization and discrete logarithms, quantum algorithms offering a more than polynomial speedup over the best known classical algorithm have been found for several problems,[17] including the simulation of quantum physical processes from chemistry and solid state physics, the approximation of Jones polynomials, and solving Pell's equation. No mathematical proof has been found that shows that an equally fast classical algorithm cannot be discovered, although this is considered unlikely.[18] However, quantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover's algorithm using quadratically fewer queries to the database than that are required by classical algorithms. In this case, the advantage is not only provable but also optimal, it has been shown that Grover's algorithm gives the maximal possible probability of finding the desired element for any number of oracle lookups. Several other examples of provable quantum speedups for query problems have subsequently been discovered, such as for finding collisions in two-to-one functions and evaluating NAND trees.

Problems that can be addressed with Grover's algorithm have the following properties:

For problems with all these properties, the running time of Grover's algorithm on a quantum computer will scale as the square root of the number of inputs (or elements in the database), as opposed to the linear scaling of classical algorithms. A general class of problems to which Grover's algorithm can be applied[19] is Boolean satisfiability problem. In this instance, the database through which the algorithm is iterating is that of all possible answers. An example (and possible) application of this is a password cracker that attempts to guess the password or secret key for an encrypted file or system. Symmetric ciphers such as Triple DES and AES are particularly vulnerable to this kind of attack.[citation needed] This application of quantum computing is a major interest of government agencies.[20]

Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate in an efficient manner classically, many believe quantum simulation will be one of the most important applications of quantum computing.[21] Quantum simulation could also be used to simulate the behavior of atoms and particles at unusual conditions such as the reactions inside a collider.[22]

Quantum annealing or Adiabatic quantum computation relies on the adiabatic theorem to undertake calculations. A system is placed in the ground state for a simple Hamiltonian, which is slowly evolved to a more complicated Hamiltonian whose ground state represents the solution to the problem in question. The adiabatic theorem states that if the evolution is slow enough the system will stay in its ground state at all times through the process.

The Quantum algorithm for linear systems of equations or "HHL Algorithm", named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.[23]

John Preskill has introduced the term quantum supremacy to refer to the hypothetical speedup advantage that a quantum computer would have over a classical computer in a certain field.[24] Google announced in 2017 that it expected to achieve quantum supremacy by the end of the year though that did not happen. IBM said in 2018 that the best classical computers will be beaten on some practical task within about five years and views the quantum supremacy test only as a potential future benchmark.[25] Although skeptics like Gil Kalai doubt that quantum supremacy will ever be achieved,[26][27] in October 2019, a Sycamore processor created in conjunction with Google AI Quantum was reported to have achieved quantum supremacy,[28] with calculations more than 3,000,000 times as fast as those of Summit, generally considered the world's fastest computer.[29] Bill Unruh doubted the practicality of quantum computers in a paper published back in 1994.[30] Paul Davies argued that a 400-qubit computer would even come into conflict with the cosmological information bound implied by the holographic principle.[31]

There are a number of technical challenges in building a large-scale quantum computer,.[32] David DiVincenzo listed the following requirements for a practical quantum computer:[33]

Sourcing parts for quantum computers is very difficult: Quantum computers need Helium-3, a nuclear research byproduct, and special cables that are only made by a single company in Japan.[34]

One of the greatest challenges is controlling or removing quantum decoherence. This usually means isolating the system from its environment as interactions with the external world cause the system to decohere. However, other sources of decoherence also exist. Examples include the quantum gates, and the lattice vibrations and background thermonuclear spin of the physical system used to implement the qubits. Decoherence is irreversible, as it is effectively non-unitary, and is usually something that should be highly controlled, if not avoided. Decoherence times for candidate systems in particular, the transverse relaxation time T2 (for NMR and MRI technology, also called the dephasing time), typically range between nanoseconds and seconds at low temperature.[35] Currently, some quantum computers require their qubits to be cooled to 20 millikelvins in order to prevent significant decoherence.[36]

As a result, time-consuming tasks may render some quantum algorithms inoperable, as maintaining the state of qubits for a long enough duration will eventually corrupt the superpositions.[37]

These issues are more difficult for optical approaches as the timescales are orders of magnitude shorter and an often-cited approach to overcoming them is optical pulse shaping. Error rates are typically proportional to the ratio of operating time to decoherence time, hence any operation must be completed much more quickly than the decoherence time.

As described in the Quantum threshold theorem, if the error rate is small enough, it is thought to be possible to use quantum error correction to suppress errors and decoherence. This allows the total calculation time to be longer than the decoherence time if the error correction scheme can correct errors faster than decoherence introduces them. An often cited figure for the required error rate in each gate for fault-tolerant computation is 103, assuming the noise is depolarizing.

Meeting this scalability condition is possible for a wide range of systems. However, the use of error correction brings with it the cost of a greatly increased number of required qubits. The number required to factor integers using Shor's algorithm is still polynomial, and thought to be between L and L2, where L is the number of qubits in the number to be factored; error correction algorithms would inflate this figure by an additional factor of L. For a 1000-bit number, this implies a need for about 104 bits without error correction.[38] With error correction, the figure would rise to about 107 bits. Computation time is about L2 or about 107 steps and at 1MHz, about 10 seconds.

A very different approach to the stability-decoherence problem is to create a topological quantum computer with anyons, quasi-particles used as threads and relying on braid theory to form stable logic gates.[39][40]

Physicist Mikhail Dyakonov has expressed skepticism of quantum computing as follows:

There are a number of quantum computing models, distinguished by the basic elements in which the computation is decomposed. The four main models of practical importance are:

The quantum Turing machine is theoretically important but the direct implementation of this model is not pursued. All four models of computation have been shown to be equivalent; each can simulate the other with no more than polynomial overhead.

For physically implementing a quantum computer, many different candidates are being pursued, among them (distinguished by the physical system used to realize the qubits):

A large number of candidates demonstrates that the topic, in spite of rapid progress, is still in its infancy. There is also a vast amount of flexibility.

The class of problems that can be efficiently solved by quantum computers is called BQP, for "bounded error, quantum, polynomial time". Quantum computers only run probabilistic algorithms, so BQP on quantum computers is the counterpart of BPP ("bounded error, probabilistic, polynomial time") on classical computers. It is defined as the set of problems solvable with a polynomial-time algorithm, whose probability of error is bounded away from one half.[61] A quantum computer is said to "solve" a problem if, for every instance, its answer will be right with high probability. If that solution runs in polynomial time, then that problem is in BQP.

BQP is contained in the complexity class #P (or more precisely in the associated class of decision problems P#P),[62] which is a subclass of PSPACE.

BQP is suspected to be disjoint from NP-complete and a strict superset of P, but that is not known. Both integer factorization and discrete log are in BQP. Both of these problems are NP problems suspected to be outside BPP, and hence outside P. Both are suspected to not be NP-complete. There is a common misconception that quantum computers can solve NP-complete problems in polynomial time. That is not known to be true, and is generally suspected to be false.[62]

The capacity of a quantum computer to accelerate classical algorithms has rigid limitsupper bounds of quantum computation's complexity. The overwhelming part of classical calculations cannot be accelerated on a quantum computer.[63] A similar fact prevails for particular computational tasks, like the search problem, for which Grover's algorithm is optimal.[64]

Bohmian Mechanics is a non-local hidden variable interpretation of quantum mechanics. It has been shown that a non-local hidden variable quantum computer could implement a search of an N-item database at most in O ( N 3 ) {displaystyle O({sqrt[{3}]{N}})} steps. This is slightly faster than the O ( N ) {displaystyle O({sqrt {N}})} steps taken by Grover's algorithm. Neither search method will allow quantum computers to solve NP-Complete problems in polynomial time.[65]

Although quantum computers may be faster than classical computers for some problem types, those described above cannot solve any problem that classical computers cannot already solve. A Turing machine can simulate these quantum computers, so such a quantum computer could never solve an undecidable problem like the halting problem. The existence of "standard" quantum computers does not disprove the ChurchTuring thesis.[66] It has been speculated that theories of quantum gravity, such as M-theory or loop quantum gravity, may allow even faster computers to be built. Currently, defining computation in such theories is an open problem due to the problem of time, i.e., there currently exists no obvious way to describe what it means for an observer to submit input to a computer and later receive output.[67][68]

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Quantum computing - Wikipedia

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What Is Quantum Computing? The Next Era of Computational …

When you first stumble across the term quantum computer, you might pass it off as some far-flung science fiction concept rather than a serious current news item.

But with the phrase being thrown around with increasing frequency, its understandable to wonder exactly what quantum computers are, and just as understandable to be at a loss as to where to dive in. Heres the rundown on what quantum computers are, why theres so much buzz around them, and what they might mean for you.

All computing relies on bits, the smallest unit of information that is encoded as an on state or an off state, more commonly referred to as a 1 or a 0, in some physical medium or another.

Most of the time, a bit takes the physical form of an electrical signal traveling over the circuits in the computers motherboard. By stringing multiple bits together, we can represent more complex and useful things like text, music, and more.

The two key differences between quantum bits and classical bits (from the computers we use today) are the physical form the bits take and, correspondingly, the nature of data encoded in them. The electrical bits of a classical computer can only exist in one state at a time, either 1 or 0.

Quantum bits (or qubits) are made of subatomic particles, namely individual photons or electrons. Because these subatomic particles conform more to the rules of quantum mechanics than classical mechanics, they exhibit the bizarre properties of quantum particles. The most salient of these properties for computer scientists is superposition. This is the idea that a particle can exist in multiple states simultaneously, at least until that state is measured and collapses into a single state. By harnessing this superposition property, computer scientists can make qubits encode a 1 and a 0 at the same time.

The other quantum mechanical quirk that makes quantum computers tick is entanglement, a linking of two quantum particles or, in this case, two qubits. When the two particles are entangled, the change in state of one particle will alter the state of its partner in a predictable way, which comes in handy when it comes time to get a quantum computer to calculate the answer to the problem you feed it.

A quantum computers qubits start in their 1-and-0 hybrid state as the computer initially starts crunching through a problem. When the solution is found, the qubits in superposition collapse to the correct orientation of stable 1s and 0s for returning the solution.

Aside from the fact that they are far beyond the reach of all but the most elite research teams (and will likely stay that way for a while), most of us dont have much use for quantum computers. They dont offer any real advantage over classical computers for the kinds of tasks we do most of the time.

However, even the most formidable classical supercomputers have a hard time cracking certain problems due to their inherent computational complexity. This is because some calculations can only be achieved by brute force, guessing until the answer is found. They end up with so many possible solutions that it would take thousands of years for all the worlds supercomputers combined to find the correct one.

The superposition property exhibited by qubits can allow supercomputers to cut this guessing time down precipitously. Classical computings laborious trial-and-error computations can only ever make one guess at a time, while the dual 1-and-0 state of a quantum computers qubits lets it make multiple guesses at the same time.

So, what kind of problems require all this time-consuming guesswork calculation? One example is simulating atomic structures, especially when they interact chemically with those of other atoms. With a quantum computer powering the atomic modeling, researchers in material science could create new compounds for use in engineering and manufacturing. Quantum computers are well suited to simulating similarly intricate systems like economic market forces, astrophysical dynamics, or genetic mutation patterns in organisms, to name only a few.

Amidst all these generally inoffensive applications of this emerging technology, though, there are also some uses of quantum computers that raise serious concerns. By far the most frequently cited harm is the potential for quantum computers to break some of the strongest encryption algorithms currently in use.

In the hands of an aggressive foreign government adversary, quantum computers could compromise a broad swath of otherwise secure internet traffic, leaving sensitive communications susceptible to widespread surveillance. Work is currently being undertaken to mature encryption ciphers based on calculations that are still hard for even quantum computers to do, but they are not all ready for prime-time, or widely adopted at present.

A little over a decade ago, actual fabrication of quantum computers was barely in its incipient stages. Starting in the 2010s, though, development of functioning prototype quantum computers took off. A number of companies have assembled working quantum computers as of a few years ago, with IBM going so far as to allow researchers and hobbyists to run their own programs on it via the cloud.

Despite the strides that companies like IBM have undoubtedly made to build functioning prototypes, quantum computers are still in their infancy. Currently, the quantum computers that research teams have constructed so far require a lot of overhead for executing error correction. For every qubit that actually performs a calculation, there are several dozen whose job it is to compensate for the ones mistake. The aggregate of all these qubits make what is called a logical qubit.

Long story short, industry and academic titans have gotten quantum computers to work, but they do so very inefficiently.

Fierce competition between quantum computer researchers is still raging, between big and small players alike. Among those who have working quantum computers are the traditionally dominant tech companies one would expect: IBM, Intel, Microsoft, and Google.

As exacting and costly of a venture as creating a quantum computer is, there are a surprising number of smaller companies and even startups that are rising to the challenge.

The comparatively lean D-Wave Systems has spurred many advances in the fieldand proved it was not out of contention by answering Googles momentous announcement with news of a huge deal with Los Alamos National Labs. Still, smaller competitors like Rigetti Computing are also in the running for establishing themselves as quantum computing innovators.

Depending on who you ask, youll get a different frontrunner for the most powerful quantum computer. Google certainly made its case recently with its achievement of quantum supremacy, a metric that itself Google more or less devised. Quantum supremacy is the point at which a quantum computer is first able to outperform a classical computer at some computation. Googles Sycamore prototype equipped with 54 qubits was able to break that barrier by zipping through a problem in just under three-and-a-half minutes that would take the mightiest classical supercomputer 10,000 years to churn through.

Not to be outdone, D-Wave boasts that the devices it will soon be supplying to Los Alamos weigh in at 5000 qubits apiece, although it should be noted that the quality of D-Waves qubits has been called into question before. IBM hasnt made the same kind of splash as Google and D-Wave in the last couple of years, but they shouldnt be counted out yet, either, especially considering their track record of slow and steady accomplishments.

Put simply, the race for the worlds most powerful quantum computer is as wide open as it ever was.

The short answer to this is not really, at least for the near-term future. Quantum computers require an immense volume of equipment, and finely tuned environments to operate. The leading architecture requires cooling to mere degrees above absolute zero, meaning they are nowhere near practical for ordinary consumers to ever own.

But as the explosion of cloud computing has proven, you dont need to own a specialized computer to harness its capabilities. As mentioned above, IBM is already offering daring technophiles the chance to run programs on a small subset of its Q System Ones qubits. In time, IBM and its competitors will likely sell compute time on more robust quantum computers for those interested in applying them to otherwise inscrutable problems.

But if you arent researching the kinds of exceptionally tricky problems that quantum computers aim to solve, you probably wont interact with them much. In fact, quantum computers are in some cases worse at the sort of tasks we use computers for every day, purely because quantum computers are so hyper-specialized. Unless you are an academic running the kind of modeling where quantum computing thrives, youll likely never get your hands on one, and never need to.

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Explainer: What is a quantum computer? – MIT Technology Review

This is the first in a series of explainers on quantum technology. The other two are on quantum communication and post-quantum cryptography.

A quantum computer harnesses some of the almost-mystical phenomena of quantum mechanics to deliver huge leaps forward in processing power. Quantum machines promise to outstrip even the most capable of todaysand tomorrowssupercomputers.

They wont wipe out conventional computers, though. Using a classical machine will still be the easiest and most economical solution for tackling most problems. But quantum computers promise to power exciting advances in various fields, from materials science to pharmaceuticals research. Companies are already experimenting with them to develop things like lighter and more powerful batteries for electric cars, and to help create novel drugs.

The secret to a quantum computers power lies in its ability to generate and manipulate quantum bits, or qubits.

What is a qubit?

Today's computers use bitsa stream of electrical or optical pulses representing1s or0s. Everything from your tweets and e-mails to your iTunes songs and YouTube videos are essentially long strings of these binary digits.

Quantum computers, on the other hand, usequbits, whichare typically subatomic particles such as electrons or photons. Generating and managing qubits is a scientific and engineering challenge. Some companies, such as IBM, Google, and Rigetti Computing, use superconducting circuits cooled to temperatures colder than deep space. Others, like IonQ, trap individual atoms in electromagnetic fields on a silicon chip in ultra-high-vacuum chambers. In both cases, the goal is to isolate the qubits in a controlled quantum state.

Qubits have some quirky quantum properties that mean a connected group of them can provide way more processing power than the same number of binary bits. One of those properties is known as superposition and another is called entanglement.

Qubits can represent numerous possible combinations of 1and 0 at the same time. This ability to simultaneously be in multiple states is called superposition. To put qubits into superposition, researchers manipulate them using precision lasers or microwave beams.

Thanks to this counterintuitive phenomenon, a quantum computer with several qubits in superposition can crunch through a vast number of potential outcomes simultaneously. The final result of a calculation emerges only once the qubits are measured, which immediately causes their quantum state to collapse to either 1or 0.

Researchers can generate pairs of qubits that are entangled, which means the two members of a pair exist in a single quantum state. Changing the state of one of the qubits will instantaneously change the state of the other one in a predictable way. This happens even if they are separated by very long distances.

Nobody really knows quite how or why entanglement works. It even baffled Einstein, who famously described it as spooky action at a distance. But its key to the power of quantum computers. In a conventional computer, doubling the number of bits doubles its processing power. But thanks to entanglement, adding extra qubits to a quantum machine produces an exponential increase in its number-crunching ability.

Quantum computers harness entangled qubits in a kind of quantum daisy chain to work their magic. The machines ability to speed up calculations using specially designed quantum algorithms is why theres so much buzz about their potential.

Thats the good news. The bad news is that quantum machines are way more error-prone than classical computers because of decoherence.

The interaction of qubits with their environment in ways that cause their quantum behavior to decay and ultimately disappear is called decoherence. Their quantum state is extremely fragile. The slightest vibration or change in temperaturedisturbances known as noise in quantum-speakcan cause them to tumble out of superposition before their job has been properly done. Thats why researchers do their best to protect qubits from the outside world in those supercooled fridges and vacuum chambers.

But despite their efforts, noise still causes lots of errors to creep into calculations. Smart quantum algorithmscan compensate for some of these, and adding more qubits also helps. However, it will likely take thousands of standard qubits to create a single, highly reliable one, known as a logical qubit. This will sap a lot of a quantum computers computational capacity.

And theres the rub: so far, researchers havent been able to generate more than 128 standard qubits (see our qubit counter here). So were still many years away from getting quantum computers that will be broadly useful.

That hasnt dented pioneers hopes of being the first to demonstrate quantum supremacy.

What is quantum supremacy?

Its the point at which a quantum computer can complete a mathematical calculation that is demonstrably beyond the reach of even the most powerful supercomputer.

Its still unclear exactly how many qubits will be needed to achieve this because researchers keep finding new algorithms to boost the performance of classical machines, and supercomputing hardware keeps getting better. But researchers and companies are working hard to claim the title, running testsagainst some of the worlds most powerful supercomputers.

Theres plenty of debate in the research world about just how significant achieving this milestone will be. Rather than wait for supremacy to be declared, companies are already starting to experiment with quantum computers made by companies like IBM, Rigetti, and D-Wave, a Canadian firm. Chinese firms like Alibaba are also offering access to quantum machines. Some businesses are buying quantum computers, while others are using ones made available through cloud computing services.

Where is a quantum computer likely to be most useful first?

One of the most promising applications of quantum computers is for simulating the behavior of matterdown to the molecular level. Auto manufacturers like Volkswagen and Daimler are using quantum computers to simulate the chemical composition of electrical-vehicle batteries to help find new ways to improve their performance. And pharmaceutical companies are leveraging them to analyze and compare compounds that could lead to the creation of new drugs.

The machines are also great for optimization problems because they can crunch through vast numbers of potential solutions extremely fast. Airbus, for instance, is using them to help calculate the most fuel-efficient ascent and descent paths for aircraft. And Volkswagen has unveiled a service that calculates the optimal routes for buses and taxis in cities in order to minimize congestion. Some researchers also think the machines could be used to accelerate artificial intelligence.

It could take quite a few years for quantum computers to achieve their full potential. Universities and businesses working on them are facing a shortage of skilled researchersin the fieldand a lack of suppliersof some key components. But if these exotic new computing machines live up to their promise, they could transform entire industries and turbocharge global innovation.

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What Is Quantum Computing? A Super-Easy Explanation For Anyone

Its fascinating to think about the power in our pockettodays smartphones have the computing power of a military computer from 50 years ago that was the size of an entire room. However, even with the phenomenal strides we made in technology and classical computers since the onset of the computer revolution, there remain problems that classical computers just cant solve. Many believe quantum computers are the answer.

The Limits of Classical Computers

Now that we have made the switching and memory units of computers, known as transistors, almost as small as an atom, we need to find an entirely new way of thinking about and building computers. Even though a classical computer helps us do many amazing things, under the hood its really just a calculator that uses a sequence of bitsvalues of 0 and 1 to represent two states (think on and off switch) to makes sense of and decisions about the data we input following a prearranged set of instructions. Quantum computers are not intended to replace classical computers, they are expected to be a different tool we will use to solve complex problems that are beyond the capabilities of a classical computer.

Basically, as we are entering a big data world in which the information we need to store grows, there is a need for more ones and zeros and transistors to process it. For the most part classical computers are limited to doing one thing at a time, so the more complex the problem, the longer it takes. A problem that requires more power and time than todays computers can accommodate is called an intractable problem. These are the problems that quantum computers are predicted to solve.

The Power of Quantum Computers

When you enter the world of atomic and subatomic particles, things begin to behave in unexpected ways. In fact, these particles can exist in more than one state at a time. Its this ability that quantum computers take advantage of.

Instead of bits, which conventional computers use, a quantum computer uses quantum bitsknown as qubits. To illustrate the difference, imagine a sphere. A bit can be at either of the two poles of the sphere, but a qubit can exist at any point on the sphere. So, this means that a computer using qubits can store an enormous amount of information and uses less energy doing so than a classical computer. By entering into this quantum area of computing where the traditional laws of physics no longer apply, we will be able to create processors that are significantly faster (a million or more times) than the ones we use today. Sounds fantastic, but the challenge is that quantum computing is also incredibly complex.

The pressure is on the computer industry to find ways to make computing more efficient, since we reached the limits of energy efficiency using classical methods. By 2040, according to a report by the Semiconductor Industry Association, we will no longer have the capability to power all of the machines around the world. Thats precisely why the computer industry is racing to make quantum computers work on a commercial scale. No small feat, but one that will pay extraordinary dividends.

How our world will change with quantum computing

Its difficult to predict how quantum computing will change our world simply because there will be applications in all industries. Were venturing into an entirely new realm of physics and there will be solutions and uses we have never even thought of yet. But when you consider how much classical computers revolutionized our world with a relatively simple use of bits and two options of 0 or 1, you can imagine the extraordinary possibilities when you have the processing power of qubits that can perform millions of calculations at the same moment.

What we do know is that it will be game-changing for every industry and will have a huge impact in the way we do business, invent new medicine and materials, safeguard our data, explore space, and predict weather events and climate change. Its no coincidence that some of the worlds most influential companies such as IBM and Google and the worlds governments are investing in quantum computing technology. They are expecting quantum computing to change our world because it will allow us to solve problems and experience efficiencies that arent possible today. In another post, I dig deeper into how quantum computing will change our world.

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Why Quantum Computing Gets Special Attention In The Trump Administration’s Budget Proposal – Texas Standard

The Trump administrations fiscal year 2021 budget proposal includes significant increases in funding for artificial intelligence and quantum computing, while cutting overall research and development spending. If Congress agrees to it, artificial intelligence, or AI, funding would nearly double, and quantum computing would receive a 50% boost over last years budget, doubling in 2022, to $860 million. The administration says these two fields of research are important to U.S. national security, in part because China also invests heavily in these fields.

Quantum computing uses quantum mechanics to solve highly complex problems more quickly than they can be solved by standard or classical computers. Though fully functional quantum computers dont yet exist, scientists at academic institutions, as well as at IBM, Google and other companies, are working to build such systems.

Scott Aaronson is a professor of computer science and the founding director of the Quantum Information Center at the University of Texas at Austin. He says applications for quantum computing include simulation of chemistry and physics problems. These simulations enable scientists to design new materials, drugs, superconductors and solar cells, among other applications.

Aaronson says the governments role is to support basic scientific research the kind needed to build and perfect quantum computers.

We do not yet know how to build a fully scalable quantum computer. The quantum version of the transistor, if you like, has not been invented yet, Aaronson says.

On the software front, researchers have not yet developed applications that take full advantage of quantum computings capabilities.

Thats often misrepresented in the popular press, where its claimed that a quantum computer is just a black box that does everything, Aaronson says.

Competition between the U.S. and China in quantum computing revolves, in part, around the role such a system could play in breaking the encryption that makes things secure on the internet.

Truly useful quantum computing applications could be as much as a decade away, Aaronson says. Initially, these tools would be highly specialized.

The way I put it is that were now entering the very, very early, vacuum-tube era of quantum computers, he says.

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The $600 quantum computer that could spell the end for conventional encryption – BetaNews

Concerns that quantum computing could place current encryption techniques at risk have been around for some time.

But now cybersecurity startup Active Cypher has built a password-hacking quantum computer to demonstrate that the dangers are very real.

Using easily available parts costing just $600, Active Cyphers founder and CTO, Dan Gleason, created a portable quantum computer dubbed QUBY (named after qubits, the basic unit of quantum information). QUBY runs recently open-sourced quantum algorithms capable of executing within a quantum emulator that can perform cryptographic cracking algorithms. Calculations that would have otherwise taken years on conventional computers are now performed in seconds on QUBY.

Gleason explains, "After years of foreseeing this danger and trying to warn the cybersecurity community that current cybersecurity protocols were not up to par, I decided to take a week and move my theory to prototype. I hope that QUBY can increase awareness of how the cyberthreats of quantum computing are not reserved to billion-dollar state-sponsored projects, but can be seen on much a smaller, localized scale."

The concern is that quantum computing will lead to the sunset of AES-256 (the current encryption standard), meaning all encrypted files could one day be decrypted. "The disruption that will come about from that will be on an unprecedented, global scale. It's going to be massive," says Gleason. Modelled after the SADM, a man-portable nuclear weapon deployed in the 1960s, QUBY was downsized so that it fits in a backpack and is therefore untraceable. Low-level 'neighborhood hackers' have already been using portable devices that can surreptitiously swipe credit card information from an unsuspecting passerby. Quantum compute emulating devices will open the door for significantly more cyberthreats.

In response to the threat, Active Cypher has developed advanced dynamic cyphering encryption that is built to be quantum resilient. Gleason explains that, "Our encryption is not based on solving a mathematical problem. It's based on a very large, random key which is used in creating the obfuscated cyphertext, without any key information within the cyphertext, and is thus impossible to be derived through prime factorization -- traditional brute force attempts which use the cyphertext to extract key information from patterns derived from the key material."

Active Cypher's completely random cyphertext cannot be deciphered using even large quantum computers since the only solution to cracking the key is to try every possible combination of the key, which will produce every known possible output of the text, without knowledge of which version might be the correct one. "In other words, you'll find a greater chance of finding a specific grain of sand in a desert than cracking this open," says Gleason.

Active Cypher showcased QUBY in early February at Ready -- an internal Microsoft conference held in Seattle. The prototype will also be presented at RSA in San Francisco later this month.

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The $600 quantum computer that could spell the end for conventional encryption - BetaNews

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