Category Archives: Quantum Computing

UIUC, UT Austin, and Rutgers University Win a $5.8 Million Grant to Research Fluxonium Qubits and Modular Quantum Processor Architectures  Quantum Computing Report

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UIUC, UT Austin, and Rutgers University Win a $5.8 Million Grant to Research Fluxonium Qubits and Modular Quantum Processor Architectures - Quantum...

HCLTech Trends Report: Al, multi-cloud and quantum computing to drive change in 2023  CNBCTV18

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HCLTech Trends Report: Al, multi-cloud and quantum computing to drive change in 2023 - CNBCTV18

For years, quantum computings news cycle was dominated by headlines about record-setting systems. Researchers at Google and IBM have had spats over who achieved whatand whether it was worth the effort. But the time for arguing over whos got the biggest processor seems to have passed: firms are heads-down and preparing for life in the real world. Suddenly, everyone is behaving like grown-ups.

As if to emphasize how much researchers want to get off the hype train, IBM is expected to announce a processor in 2023 that bucks the trend of putting ever more quantum bits, or qubits, into play. Qubits, the processing units of quantum computers, can be built from a variety of technologies, including superconducting circuitry, trapped ions, and photons, the quantum particles of light.

IBM has long pursued superconducting qubits, and over the years the company has been making steady progress in increasing the number it can pack on a chip. In 2021, for example, IBM unveiled one with a record-breaking 127 of them. In November, it debuted its 433-qubit Osprey processor, and the company aims to release a 1,121-qubit processor called Condor in 2023.

But this year IBM is also expected to debut its Heron processor, which will have just 133 qubits. It might look like a backwards step, but as the company is keen to point out, Herons qubits will be of the highest quality. And, crucially, each chip will be able to connect directly to other Heron processors, heralding a shift from single quantum computing chips toward modular quantum computers built from multiple processors connected togethera move that is expected to help quantum computers scale up significantly.

Heron is a signal of larger shifts in the quantum computing industry. Thanks to some recent breakthroughs, aggressive roadmapping, and high levels of funding, we may see general-purpose quantum computers earlier than many would have anticipated just a few years ago, some experts suggest. Overall, things are certainly progressing at a rapid pace, says Michele Mosca, deputy director of the Institute for Quantum Computing at the University of Waterloo.

Here are a few areas where experts expect to see progress.

IBMs Heron project is just a first step into the world of modular quantum computing. The chips will be connected with conventional electronics, so they will not be able to maintain the quantumness of information as it moves from processor to processor. But the hope is that such chips, ultimately linked together with quantum-friendly fiber-optic or microwave connections, will open the path toward distributed, large-scale quantum computers with as many as a million connected qubits. That may be how many are needed to run useful, error-corrected quantum algorithms. We need technologies that scale both in size and in cost, so modularity is key, says Jerry Chow, director at IBMQuantum Hardware System Development.

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What's next for quantum computing | MIT Technology Review

Quantum search algorithm

In quantum computing, Grover's algorithm, also known as the quantum search algorithm, refers to a quantum algorithm for unstructured search that finds with high probability the unique input to a black box function that produces a particular output value, using just O ( N ) {displaystyle O({sqrt {N}})} evaluations of the function, where N {displaystyle N} is the size of the function's domain. It was devised by Lov Grover in 1996.[1]

The analogous problem in classical computation cannot be solved in fewer than O ( N ) {displaystyle O(N)} evaluations (because, on average, one has to check half of the domain to get a 50% chance of finding the right input). Charles H. Bennett, Ethan Bernstein, Gilles Brassard, and Umesh Vazirani proved that any quantum solution to the problem needs to evaluate the function ( N ) {displaystyle Omega ({sqrt {N}})} times, so Grover's algorithm is asymptotically optimal.[2] Since classical algorithms for NP-complete problems require exponentially many steps, and Grover's algorithm provides at most a quadratic speedup over the classical solution for unstructured search, this suggests that Grover's algorithm by itself will not provide polynomial-time solutions for NP-complete problems (as the square root of an exponential function is an exponential, not polynomial, function).[3]

Unlike other quantum algorithms, which may provide exponential speedup over their classical counterparts, Grover's algorithm provides only a quadratic speedup. However, even quadratic speedup is considerable when N {displaystyle N} is large, and Grover's algorithm can be applied to speed up broad classes of algorithms.[3] Grover's algorithm could brute-force a 128-bit symmetric cryptographic key in roughly 264 iterations, or a 256-bit key in roughly 2128 iterations. As a result, it is sometimes suggested[4] that symmetric key lengths be doubled to protect against future quantum attacks.

Grover's algorithm, along with variants like amplitude amplification, can be used to speed up a broad range of algorithms.[5][6][7] In particular, algorithms for NP-complete problems generally contain exhaustive search as a subroutine, which can be sped up by Grover's algorithm.[6] The current best algorithm for 3SAT is one such example. Generic constraint satisfaction problems also see quadratic speedups with Grover.[8] These algorithms do not require that the input be given in the form of an oracle, since Grover's algorithm is being applied with an explicit function, e.g. the function checking that a set of bits satisfies a 3SAT instance.

Grover's algorithm can also give provable speedups for black-box problems in quantum query complexity, including element distinctness[9] and the collision problem[10] (solved with the BrassardHyerTapp algorithm). In these types of problems, one treats the oracle function f as a database, and the goal is to use the quantum query to this function as few times as possible.

Grover's algorithm essentially solves the task of function inversion. Roughly speaking, if we have a function y = f ( x ) {displaystyle y=f(x)} that can be evaluated on a quantum computer, Grover's algorithm allows us to calculate x {displaystyle x} when given y {displaystyle y} . Consequently, Grover's algorithm gives broad asymptotic speed-ups to many kinds of brute-force attacks on symmetric-key cryptography, including collision attacks and pre-image attacks.[11] However, this may not necessarily be the most efficient algorithm since, for example, the parallel rho algorithm is able to find a collision in SHA2 more efficiently than Grover's algorithm.[12]

Grover's original paper described the algorithm as a database search algorithm, and this description is still common. The database in this analogy is a table of all of the function's outputs, indexed by the corresponding input. However, this database is not represented explicitly. Instead, an oracle is invoked to evaluate an item by its index. Reading a full database item by item and converting it into such a representation may take a lot longer than Grover's search. To account for such effects, Grover's algorithm can be viewed as solving an equation or satisfying a constraint. In such applications, the oracle is a way to check the constraint and is not related to the search algorithm. This separation usually prevents algorithmic optimizations, whereas conventional search algorithms often rely on such optimizations and avoid exhaustive search.[13]

The major barrier to instantiating a speedup from Grover's algorithm is that the quadratic speedup achieved is too modest to overcome the large overhead of near-term quantum computers.[14] However, later generations of fault-tolerant quantum computers with better hardware performance may be able to realize these speedups for practical instances of data.

As input for Grover's algorithm, suppose we have a function f : { 0 , 1 , , N 1 } { 0 , 1 } {displaystyle fcolon {0,1,ldots ,N-1}to {0,1}} . In the "unstructured database" analogy, the domain represent indices to a database, and f(x) = 1 if and only if the data that x points to satisfies the search criterion. We additionally assume that only one index satisfies f(x) = 1, and we call this index . Our goal is to identify .

We can access f with a subroutine (sometimes called an oracle) in the form of a unitary operator U that acts as follows:

This uses the N {displaystyle N} -dimensional state space H {displaystyle {mathcal {H}}} , which is supplied by a register with n = log 2 N {displaystyle n=lceil log _{2}Nrceil } qubits.This is often written as

Grover's algorithm outputs with probability at least 1/2 using O ( N ) {displaystyle O({sqrt {N}})} applications of U. This probability can be made arbitrarily large by running Grover's algorithm multiple times. If one runs Grover's algorithm until is found, the expected number of applications is still O ( N ) {displaystyle O({sqrt {N}})} , since it will only be run twice on average.

This section compares the above oracle U {displaystyle U_{omega }} with an oracle U f {displaystyle U_{f}} .

U is different from the standard quantum oracle for a function f. This standard oracle, denoted here as Uf, uses an ancillary qubit system. The operation then represents an inversion (NOT gate) on the main system conditioned by the value of f(x) from the ancillary system:

or briefly,

These oracles are typically realized using uncomputation.

If we are given Uf as our oracle, then we can also implement U, since U is Uf when the ancillary qubit is in the state | = 1 2 ( | 0 | 1 ) = H | 1 {displaystyle |-rangle ={frac {1}{sqrt {2}}}{big (}|0rangle -|1rangle {big )}=H|1rangle } :

So, Grover's algorithm can be run regardless of which oracle is given.[3] If Uf is given, then we must maintain an additional qubit in the state | {displaystyle |-rangle } and apply Uf in place of U.

The steps of Grover's algorithm are given as follows:

For the correctly chosen value of r {displaystyle r} , the output will be | {displaystyle |omega rangle } with probability approaching 1 for N 1. Analysis shows that this eventual value for r ( N ) {displaystyle r(N)} satisfies r ( N ) 4 N {displaystyle r(N)leq {Big lceil }{frac {pi }{4}}{sqrt {N}}{Big rceil }} .

Implementing the steps for this algorithm can be done using a number of gates linear in the number of qubits.[3] Thus, the gate complexity of this algorithm is O ( log ( N ) r ( N ) ) {displaystyle O(log(N)r(N))} , or O ( log ( N ) ) {displaystyle O(log(N))} per iteration.

There is a geometric interpretation of Grover's algorithm, following from the observation that the quantum state of Grover's algorithm stays in a two-dimensional subspace after each step. Consider the plane spanned by | s {displaystyle |srangle } and | {displaystyle |omega rangle } ; equivalently, the plane spanned by | {displaystyle |omega rangle } and the perpendicular ket | s = 1 N 1 x | x {displaystyle textstyle |s'rangle ={frac {1}{sqrt {N-1}}}sum _{xneq omega }|xrangle } .

Grover's algorithm begins with the initial ket | s {displaystyle |srangle } , which lies in the subspace. The operator U {displaystyle U_{omega }} is a reflection at the hyperplane orthogonal to | {displaystyle |omega rangle } for vectors in the plane spanned by | s {displaystyle |s'rangle } and | {displaystyle |omega rangle } , i.e. it acts as a reflection across | s {displaystyle |s'rangle } . This can be seen by writing U {displaystyle U_{omega }} in the form of a Householder reflection:

The operator U s = 2 | s s | I {displaystyle U_{s}=2|srangle langle s|-I} is a reflection through | s {displaystyle |srangle } . Both operators U s {displaystyle U_{s}} and U {displaystyle U_{omega }} take states in the plane spanned by | s {displaystyle |s'rangle } and | {displaystyle |omega rangle } to states in the plane. Therefore, Grover's algorithm stays in this plane for the entire algorithm.

It is straightforward to check that the operator U s U {displaystyle U_{s}U_{omega }} of each Grover iteration step rotates the state vector by an angle of = 2 arcsin 1 N {displaystyle theta =2arcsin {tfrac {1}{sqrt {N}}}} .So, with enough iterations, one can rotate from the initial state | s {displaystyle |srangle } to the desired output state | {displaystyle |omega rangle } . The initial ket is close to the state orthogonal to | {displaystyle |omega rangle } :

In geometric terms, the angle / 2 {displaystyle theta /2} between | s {displaystyle |srangle } and | s {displaystyle |s'rangle } is given by

We need to stop when the state vector passes close to | {displaystyle |omega rangle } ; after this, subsequent iterations rotate the state vector away from | {displaystyle |omega rangle } , reducing the probability of obtaining the correct answer. The exact probability of measuring the correct answer is

where r is the (integer) number of Grover iterations. The earliest time that we get a near-optimal measurement is therefore r N / 4 {displaystyle rapprox pi {sqrt {N}}/4} .

To complete the algebraic analysis, we need to find out what happens when we repeatedly apply U s U {displaystyle U_{s}U_{omega }} . A natural way to do this is by eigenvalue analysis of a matrix. Notice that during the entire computation, the state of the algorithm is a linear combination of s {displaystyle s} and {displaystyle omega } . We can write the action of U s {displaystyle U_{s}} and U {displaystyle U_{omega }} in the space spanned by { | s , | } {displaystyle {|srangle ,|omega rangle }} as:

So in the basis { | , | s } {displaystyle {|omega rangle ,|srangle }} (which is neither orthogonal nor a basis of the whole space) the action U s U {displaystyle U_{s}U_{omega }} of applying U {displaystyle U_{omega }} followed by U s {displaystyle U_{s}} is given by the matrix

This matrix happens to have a very convenient Jordan form. If we define t = arcsin ( 1 / N ) {displaystyle t=arcsin(1/{sqrt {N}})} , it is

It follows that r-th power of the matrix (corresponding to r iterations) is

Using this form, we can use trigonometric identities to compute the probability of observing after r iterations mentioned in the previous section,

Alternatively, one might reasonably imagine that a near-optimal time to distinguish would be when the angles 2rt and 2rt are as far apart as possible, which corresponds to 2 r t / 2 {displaystyle 2rtapprox pi /2} , or r = / 4 t = / 4 arcsin ( 1 / N ) N / 4 {displaystyle r=pi /4t=pi /4arcsin(1/{sqrt {N}})approx pi {sqrt {N}}/4} . Then the system is in state

A short calculation now shows that the observation yields the correct answer with error O ( 1 N ) {displaystyle Oleft({frac {1}{N}}right)} .

If, instead of 1 matching entry, there are k matching entries, the same algorithm works, but the number of iterations must be 4 ( N k ) 1 / 2 {textstyle {frac {pi }{4}}{left({frac {N}{k}}right)^{1/2}}} instead of 4 N 1 / 2 . {textstyle {frac {pi }{4}}{N^{1/2}}.}

There are several ways to handle the case if k is unknown.[15] A simple solution performs optimally up to a constant factor: run Grover's algorithm repeatedly for increasingly small values of k, e.g., taking k = N, N/2, N/4, ..., and so on, taking k = N / 2 t {displaystyle k=N/2^{t}} for iteration t until a matching entry is found.

With sufficiently high probability, a marked entry will be found by iteration t = log 2 ( N / k ) + c {displaystyle t=log _{2}(N/k)+c} for some constant c. Thus, the total number of iterations taken is at most

A version of this algorithm is used in order to solve the collision problem.[16][17]

A modification of Grover's algorithm called quantum partial search was described by Grover and Radhakrishnan in 2004.[18] In partial search, one is not interested in finding the exact address of the target item, only the first few digits of the address. Equivalently, we can think of "chunking" the search space into blocks, and then asking "in which block is the target item?". In many applications, such a search yields enough information if the target address contains the information wanted. For instance, to use the example given by L. K. Grover, if one has a list of students organized by class rank, we may only be interested in whether a student is in the lower 25%, 2550%, 5075% or 75100% percentile.

To describe partial search, we consider a database separated into K {displaystyle K} blocks, each of size b = N / K {displaystyle b=N/K} . The partial search problem is easier. Consider the approach we would take classically we pick one block at random, and then perform a normal search through the rest of the blocks (in set theory language, the complement). If we don't find the target, then we know it's in the block we didn't search. The average number of iterations drops from N / 2 {displaystyle N/2} to ( N b ) / 2 {displaystyle (N-b)/2} .

Grover's algorithm requires 4 N {textstyle {frac {pi }{4}}{sqrt {N}}} iterations. Partial search will be faster by a numerical factor that depends on the number of blocks K {displaystyle K} . Partial search uses n 1 {displaystyle n_{1}} global iterations and n 2 {displaystyle n_{2}} local iterations. The global Grover operator is designated G 1 {displaystyle G_{1}} and the local Grover operator is designated G 2 {displaystyle G_{2}} .

The global Grover operator acts on the blocks. Essentially, it is given as follows:

The optimal values of j 1 {displaystyle j_{1}} and j 2 {displaystyle j_{2}} are discussed in the paper by Grover and Radhakrishnan. One might also wonder what happens if one applies successive partial searches at different levels of "resolution". This idea was studied in detail by Vladimir Korepin and Xu, who called it binary quantum search. They proved that it is not in fact any faster than performing a single partial search.

Grover's algorithm is optimal up to sub-constant factors. That is, any algorithm that accesses the database only by using the operator U must apply U at least a 1 o ( 1 ) {displaystyle 1-o(1)} fraction as many times as Grover's algorithm.[19] The extension of Grover's algorithm to k matching entries, (N/k)1/2/4, is also optimal.[16] This result is important in understanding the limits of quantum computation.

If the Grover's search problem was solvable with logc N applications of U, that would imply that NP is contained in BQP, by transforming problems in NP into Grover-type search problems. The optimality of Grover's algorithm suggests that quantum computers cannot solve NP-Complete problems in polynomial time, and thus NP is not contained in BQP.

It has been shown that a class of non-local hidden variable quantum computers could implement a search of an N {displaystyle N} -item database in at most O ( N 3 ) {displaystyle O({sqrt[{3}]{N}})} steps. This is faster than the O ( N ) {displaystyle O({sqrt {N}})} steps taken by Grover's algorithm.[20]

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Grover's algorithm - Wikipedia

quantum computer, device that employs properties described by quantum mechanics to enhance computations.

As early as 1959 the American physicist and Nobel laureate Richard Feynman noted that, as electronic components begin to reach microscopic scales, effects predicted by quantum mechanics occurwhich, he suggested, might be exploited in the design of more powerful computers. In particular, quantum researchers hope to harness a phenomenon known as superposition. In the quantum mechanical world, objects do not necessarily have clearly defined states, as demonstrated by the famous experiment in which a single photon of light passing through a screen with two small slits will produce a wavelike interference pattern, or superposition of all available paths. (See wave-particle duality.) However, when one slit is closedor a detector is used to determine which slit the photon passed throughthe interference pattern disappears. In consequence, a quantum system exists in all possible states before a measurement collapses the system into one state. Harnessing this phenomenon in a computer promises to expand computational power greatly. A traditional digital computer employs binary digits, or bits, that can be in one of two states, represented as 0 and 1; thus, for example, a 4-bit computer register can hold any one of 16 (24) possible numbers. In contrast, a quantum bit (qubit) exists in a wavelike superposition of values from 0 to 1; thus, for example, a 4-qubit computer register can hold 16 different numbers simultaneously. In theory, a quantum computer can therefore operate on a great many values in parallel, so that a 30-qubit quantum computer would be comparable to a digital computer capable of performing 10 trillion floating-point operations per second (TFLOPS)comparable to the speed of the fastest supercomputers.

During the 1980s and 90s the theory of quantum computers advanced considerably beyond Feynmans early speculations. In 1985 David Deutsch of the University of Oxford described the construction of quantum logic gates for a universal quantum computer, and in 1994 Peter Shor of AT&T devised an algorithm to factor numbers with a quantum computer that would require as few as six qubits (although many more qubits would be necessary for factoring large numbers in a reasonable time). When a practical quantum computer is built, it will break current encryption schemes based on multiplying two large primes; in compensation, quantum mechanical effects offer a new method of secure communication known as quantum encryption. However, actually building a useful quantum computer has proved difficult. Although the potential of quantum computers is enormous, the requirements are equally stringent. A quantum computer must maintain coherence between its qubits (known as quantum entanglement) long enough to perform an algorithm; because of nearly inevitable interactions with the environment (decoherence), practical methods of detecting and correcting errors need to be devised; and, finally, since measuring a quantum system disturbs its state, reliable methods of extracting information must be developed.

Plans for building quantum computers have been proposed; although several demonstrate the fundamental principles, none is beyond the experimental stage. Three of the most promising approaches are presented below: nuclear magnetic resonance (NMR), ion traps, and quantum dots.

In 1998 Isaac Chuang of the Los Alamos National Laboratory, Neil Gershenfeld of the Massachusetts Institute of Technology (MIT), and Mark Kubinec of the University of California at Berkeley created the first quantum computer (2-qubit) that could be loaded with data and output a solution. Although their system was coherent for only a few nanoseconds and trivial from the perspective of solving meaningful problems, it demonstrated the principles of quantum computation. Rather than trying to isolate a few subatomic particles, they dissolved a large number of chloroform molecules (CHCL3) in water at room temperature and applied a magnetic field to orient the spins of the carbon and hydrogen nuclei in the chloroform. (Because ordinary carbon has no magnetic spin, their solution used an isotope, carbon-13.) A spin parallel to the external magnetic field could then be interpreted as a 1 and an antiparallel spin as 0, and the hydrogen nuclei and carbon-13 nuclei could be treated collectively as a 2-qubit system. In addition to the external magnetic field, radio frequency pulses were applied to cause spin states to flip, thereby creating superimposed parallel and antiparallel states. Further pulses were applied to execute a simple algorithm and to examine the systems final state. This type of quantum computer can be extended by using molecules with more individually addressable nuclei. In fact, in March 2000 Emanuel Knill, Raymond Laflamme, and Rudy Martinez of Los Alamos and Ching-Hua Tseng of MIT announced that they had created a 7-qubit quantum computer using trans-crotonic acid. However, many researchers are skeptical about extending magnetic techniques much beyond 10 to 15 qubits because of diminishing coherence among the nuclei.

Just one week before the announcement of a 7-qubit quantum computer, physicist David Wineland and colleagues at the U.S. National Institute for Standards and Technology (NIST) announced that they had created a 4-qubit quantum computer by entangling four ionized beryllium atoms using an electromagnetic trap. After confining the ions in a linear arrangement, a laser cooled the particles almost to absolute zero and synchronized their spin states. Finally, a laser was used to entangle the particles, creating a superposition of both spin-up and spin-down states simultaneously for all four ions. Again, this approach demonstrated basic principles of quantum computing, but scaling up the technique to practical dimensions remains problematic.

Quantum computers based on semiconductor technology are yet another possibility. In a common approach a discrete number of free electrons (qubits) reside within extremely small regions, known as quantum dots, and in one of two spin states, interpreted as 0 and 1. Although prone to decoherence, such quantum computers build on well-established, solid-state techniques and offer the prospect of readily applying integrated circuit scaling technology. In addition, large ensembles of identical quantum dots could potentially be manufactured on a single silicon chip. The chip operates in an external magnetic field that controls electron spin states, while neighbouring electrons are weakly coupled (entangled) through quantum mechanical effects. An array of superimposed wire electrodes allows individual quantum dots to be addressed, algorithms executed, and results deduced. Such a system necessarily must be operated at temperatures near absolute zero to minimize environmental decoherence, but it has the potential to incorporate very large numbers of qubits.

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Quantum computer | Description & Facts | Britannica

What do you do at QSYS?

The Quantum School for Young Students (formerly the Quantum Cryptography school for Young Students) will provide you with the necessary mathematical background to tackle some of the largest topics in modern physics.You bring your scientific curiosity and love of learning, and we'll show you how to use mathematical tools to explore.Interested in getting a head start? Download the free QSYS (QCSYS) quantum primer and begin building your linear algebra and arithmetic skills, as well as your understanding of quantum mechanics.

During the program, you will learn about:

QSYS students can expect a full day of activities every day of the program. Students will tour the quantum labs at IQC, work together on advanced problems, experiment with real quantum lab equipment, and participate in interactive lectures from quantum expected. Social activities will be held in the evenings, and students will have an opportunity to visit Niagara Falls on Sunday August 13th.

You are an ideal candidate, if you are:

Experience with quantum physics is not required, just curiosity and interest in exploring new scientific concepts.Exceptional Grade 10 students may be accepted, space permitting.

There is no registration fee for QSYS 2023. All meals and accommodations are included during QSYS, and bursaries are available to help cover the costs of travel. You can apply for a bursary on your application.

QSYS 2023 will take place Wednesday August 9th to Thursday August 17th. Students attending QSYS will arrive on Tuesday August 8th and depart on Friday August 18th.

QSYS will be run on-campus at the University of Waterloo, at the Mike and Ophelia Lazaridis Quantum-Nano Centre (QNC). All students (including those from the Waterloo Region) will stay in residence on the University of Waterloo campus with QSYS chaperones.

Check out our Frequently Asked Questions page.

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Quantum School for Young Students | Institute for Quantum Computing

Why IBM is no longer interested in breaking patent recordsand how it plans to measure innovation in the age of open source and quantum computing  Fortune

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Why IBM is no longer interested in breaking patent recordsand how it plans to measure innovation in the age of open source and quantum computing -...

Two years ago, we issued our first draft of that map to take our first steps: our ambitious three-year plan to develop quantum computing technology, called our development roadmap. Since then, our exploration has revealed new discoveries, gaining us insights that have allowed us to refine that map and travel even further than wed planned. Today, were excited to present to you an update to that map: our plan to weave quantum processors, CPUs, and GPUs into a compute fabric capable of solving problems beyond the scope of classical resources alone.

Our goal is to build quantum-centric supercomputers. The quantum-centric supercomputer will incorporate quantum processors, classical processors, quantum communication networks, and classical networks, all working together to completely transform how we compute. In order to do so, we need to solve the challenge of scaling quantum processors, develop a runtime environment for providing quantum calculations with increased speed and quality, and introduce a serverless programming model to allow quantum and classical processors to work together frictionlessly.

But first: where did this journey begin? We put the first quantum computer on the cloud in 2016, and in 2017, we introduced an open source software development kit for programming these quantum computers, called Qiskit. We debuted the first integrated quantum computer system, called the IBM Quantum System One, in 2019, then in 2020 we released our development roadmap showing how we planned to mature quantum computers into a commercial technology.

As part of that roadmap, in 2021 we released our IBM Quantum broke the 100qubit processor barrier in 2021. Read more about Eagle.127-qubit IBM Quantum Eagle processor and launched Qiskit Runtime, a runtime environment of co-located classical systems and quantum systems built to support containerized execution of quantum circuits at speed and scale. The first version gave a In 2021, we demonstrated a 120x speedup in simulating molecules thanks to a host of improvements, including the ability to run quantum programs entirely on the cloud with Qiskit Runtime.120x speedup on a research-grade quantum workload. Earlier this year, we launched the Qiskit Runtime Services with primitives: pre-built programs that allow algorithm developers easy access to the outputs of quantum computations without requiring intricate understanding of the hardware.

Now, our updated map will show us the way forward.

In order to benefit from our world-leading hardware, we need to develop the software and infrastructure so that our users can take advantage of it. Different users have different needs and experiences, and we need to build tools for each persona: kernel developers, algorithm developers, and model developers.

For our kernel developers those who focus on making faster and better quantum circuits on real hardware well be delivering and maturing Qiskit Runtime. First, we will add dynamic circuits, which allow for feedback and feedforward of quantum measurements to change or steer the course of future operations. Dynamic circuits extend what the hardware can do by reducing circuit depth, by allowing for alternative models of constructing circuits, and by enabling parity checks of the fundamental operations at the heart of quantum error correction.

To continue to increase the speed of quantum programs in 2023, we plan to bring threads to the Qiskit Runtime, allowing us to operate parallelized quantum processors, including automatically distributing work that is trivially parallelizable. In 2024 and 2025, well introduce error mitigation and suppression techniques into Qiskit Runtime so that users can focus on improving the quality of the results obtained from quantum hardware. These techniques will help lay the groundwork for quantum error correction in the future.

However, we have work to do if we want quantum will find broader use, such as among our algorithm developers those who use quantum circuits within classical routines in order to make applications that demonstrate quantum advantage.

For our algorithm developers, well be maturing the Qiskit Runtime Services primitives. The unique power of quantum computers is their ability to generate non-classical probability distributions at their outputs. Consequently, much of quantum algorithm development is related to sampling from, or estimating properties of these distributions. The primitives are a collection of core functions to easily and efficiently work with these distributions.

Typically, algorithm developers require breaking problems into a series of smaller quantum and classical programs, with an orchestration layer to stitch the data streams together into an overall workflow. We call the infrastructure responsible for this stitching To bring value to our users, we need our programing model to fit seamlessly into their workflows, where they can focus on their code and not have to worry about the deployment and infrastructure. We need a serverless architecture.Quantum Serverless. Quantum Serverless centers around enabling flexible quantum-classical resource combinations without requiring developers to be hardware and infrastructure experts, while allocating just those computing resources a developer needs when they need them. In 2023, we plan to integrate Quantum Serverless into our core software stack in order to enable core functionality such as circuit knitting.

What is circuit knitting? Circuit knitting techniques break larger circuits into smaller pieces to run on a quantum computer, and then knit the results back together using a classical computer.

Earlier this year, we demonstrated a circuit knitting method called entanglement forging to double the size of the quantum systems we could address with the same number of qubits. However, circuit knitting requires that we can run lots of circuits split across quantum resources and orchestrated with classical resources. We think that parallelized quantum processors with classical communication will be able to bring about quantum advantage even sooner, and a recent paper suggests a path forward.

With all of these pieces in place, well soon have quantum computing ready for our model developers those who develop quantum applications to find solutions to complex problems in their specific domains. We think by next year, well begin prototyping quantum software applications for specific use cases. Well begin to define these services with our first test case machine learning working with partners to accelerate the path toward useful quantum software applications. By 2025, we think model developers will be able to explore quantum applications in machine learning, optimization, natural sciences, and beyond.

Of course, we know that central to quantum computing is the hardware that makes running quantum programs possible. We also know that a quantum computer capable of reaching its full potential could require hundreds of thousands, maybe millions of high-quality qubits, so we must figure out how to scale these processors up. With the 433-qubit Osprey processor and the 1,121-qubit Condor processors slated for release in 2022 and 2023, respectively we will test the limits of single-chip processors and controlling large-scale quantum systems integrated into the IBM Quantum System Two. But we dont plan to realize large-scale quantum computers on a giant chip. Instead, were developing ways to link processors together into a modular system capable of scaling without physics limitations.

To tackle scale, we are going to introduce three distinct approaches. First, in 2023, we are introducing Heron: a 133-qubit processor with control hardware that allows for real-time classical communication between separate processors, enabling the knitting techniques described above. The second approach is to extend the size of quantum processors by enabling multi-chip processors. Crossbill, a 408 qubit processor, will be made from three chips connected by chip-to-chip couplers that allow for a continuous realization of the heavy-hex lattices across multiple chips. The goal of this architecture is to make users feel as if theyre just using just one, larger processor.

Along with scaling through modular connection of multi-chip processors, in 2024, we also plan to introduce our third approach: quantum communication between processors to support quantum parallelization. We will introduce the 462-qubit Flamingo processor with a built-in quantum communication link, and then release a demonstration of this architecture by linking together at least three Flamingo processors into a 1,386-qubit system. We expect that this link will result in slower and lower-fidelity gates across processors. Our software needs to be aware of this architecture consideration in order for our users to best take advantage of this system.

Our learning about scale will bring all of these advances together in order to realize their full potential. So, in 2025, well introduce the Kookaburra processor. Kookaburra will be a 1,386 qubit multi-chip processor with a quantum communication link. As a demonstration, we will connect three Kookaburra chips into a 4,158-qubit system connected by quantum communication for our users.

The combination of these technologies classical parallelization, multi-chip quantum processors, and quantum parallelization gives us all the ingredients we need to scale our computers to wherever our roadmap takes. By 2025, we will have effectively removed the main boundaries in the way of scaling quantum processors up with modular quantum hardware and the accompanying control electronics and cryogenic infrastructure. Pushing modularity in both our software and our hardware will be key to achieving scales well ahead of our competitors, and were excited to deliver it to you.

Our updated roadmap takes us as far as 2025 but development wont stop there. By then, we will have removed some of the biggest roadblocks in the way of scaling quantum hardware, while developing the tools and techniques capable of integrating quantum into computing workflows. This sea change will be the equivalent of replacing paper maps with GPS satellites as we navigate into the quantum future.

This sea change will be the equivalent of replacing paper maps with GPS satellites.

We arent just thinking about quantum computers, though. Were trying to induce a paradigm shift in computing overall. For many years, CPU-centric supercomputers were societys processing workhorse, with IBM serving as a key developer of these systems. In the last few years, weve seen the emergence of AI-centric supercomputers, where CPUs and GPUs work together in giant systems to tackle AI-heavy workloads.

Now, IBM is ushering in the age of the quantum-centric supercomputer, where quantum resources QPUs will be woven together with CPUs and GPUs into a compute fabric. We think that the quantum-centric supercomputer will serve as an essential technology for those solving the toughest problems, those doing the most ground-breaking research, and those developing the most cutting-edge technology.

We may be on track, but exploring uncharted territory isnt easy. Were attempting to rewrite the rules of computing in just a few years. Following our roadmap will require us to solve some incredibly tough engineering and physics problems.

But were feeling pretty confident weve gotten this far, after all, with the new help of our world-leading team of researchers, the IBM Quantum Network, the Qiskit open source community, and our growing community of kernel, algorithm, and model developers. Were glad to have you all along for the ride as we continue onward.

Quantum Chemistry: Few fields will get value from quantum computing as quickly as chemistry. Even todays supercomputers struggle to model a single molecule in its full complexity. We study algorithms designed to do what those machines cant.

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IBM Quantum roadmap to build quantum-centric supercomputers | IBM ...

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From Quantum Computing to 5G and IoT, 5 biggest tech trends of 2022 | Tech Bytes year-ender - The Financial Express

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