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Superconducting quantum computing – Wikipedia

Quantum computing implementation

Superconducting quantum computing is an implementation of a quantum computer in superconducting electronic circuits. Research in superconducting quantum computing is conducted by Google,[1] IBM,[2] BBN Technologies,[3] Rigetti,[4] and Intel.[5] as of May2016[update], up to nine fully controllable qubits are demonstrated in a 1D array,[6] up to sixteen in a 2D architecture.[2]

More than two thousand superconducting qubits are in a commercial product by D-Wave Systems, however these qubits implement quantum annealing instead of a universal model of quantum computation.

Classical computation models rely on physical implementations consistent with the laws of classical mechanics.[8] It is known, however, that the classical description is only accurate for specific cases, while the more general description of nature is given by quantum mechanics. Quantum computation studies the application of quantum phenomena, that are beyond the scope of classical approximation, for information processing and communication. Various models of quantum computation exist, however the most popular models incorporate the concepts of qubits and quantum gates. A qubit is a generalization of a bit - a system with two possible states, that can be in a quantum superposition of both. A quantum gate is a generalization of a logic gate: it describes the transformation that one or more qubits will experience after the gate is applied on them, given their initial state. The physical implementation of qubits and gates is difficult, for the same reasons that quantum phenomena are hard to observe in everyday life. One approach is to implement the quantum computers in superconductors, where the quantum effects become macroscopic, though at a price of extremely low operation temperatures.

In a superconductor, the basic charge carriers are pairs of electrons (known as Cooper pairs), rather than the single electrons in a normal conductor. The total spin of a Cooper pair is an integer number, thus the Cooper pairs are bosons (while the single electrons in the normal conductor are fermions). Cooled bosons, contrary to cooled fermions, are allowed to occupy a single quantum energy level, in an effect known as the Bose-Einstein condensate. In a classical interpretation it would correspond to multiple particles occupying the same position in space and having an equal momentum, effectively behaving as a single particle.

At every point of a superconducting electronic circuit (that is a network of electrical elements), the condensate wave function describing the charge flow is well-defined by a specific complex probability amplitude. In a normal conductor electrical circuit, the same quantum description is true for individual charge carriers, however the various wave functions are averaged in the macroscopic analysis, making it impossible to observe quantum effects. The condensate wave function allows designing and measuring macroscopic quantum effects. For example, only a discrete number of magnetic flux quanta penetrates a superconducting loop, similarly to the discrete atomic energy levels in the Bohr model. In both cases, the quantization is a result of the complex amplitude continuity. Differing from the microscopic quantum systems (such as atoms or photons) used for implementations of quantum computers, the parameters of the superconducting circuits may be designed by setting the (classical) values of the electrical elements that compose them, e.g. adjusting the capacitance or inductance.

In order to obtain a quantum mechanical description of an electrical circuit a few steps are required. First, all the electrical elements are described with the condensate wave function amplitude and phase, rather than with the closely related macroscopic current and voltage description used for classical circuits. For example, a square of the wave function amplitude at some point in space is the probability of finding a charge carrier there, hence the square of the amplitude corresponds to the classical charge distribution. Second, generalized Kirchhoff's circuit laws are applied at every node of the circuit network to obtain the equations of motion. Finally, the equations of motion are reformulated to Lagrangian mechanics and a quantum Hamiltonian is derived.

The devices are typically designed in the radio-frequency spectrum, cooled down in dilution refrigerators below 100mK and addressed with conventional electronic instruments, e.g. frequency synthesizers and spectrum analyzers. Typical dimensions on the scale of micrometers, with sub-micrometer resolution, allow a convenient design of a quantum Hamiltonian with the well-established integrated circuit technology.

A distinguishing feature of superconducting quantum circuits is the usage of a Josephson junction - an electrical element non existent in normal conductors. A junction is a weak connection between two leads of a superconducting wire, usually implemented as a thin layer of insulator with a shadow evaporation technique. The condensate wave functions on the two sides of the junction are weakly correlated - they are allowed to have different superconducting phases, contrary to the case of a continuous superconducting wire, where the superconducting wave function must be continuous. The current through the junction occurs by quantum tunneling. This is used to create a non-linear inductance which is essential for qubit design, as it allows a design of anharmonic oscillators. A quantum harmonic oscillator cannot be used as a qubit, as there is no way to address only two of its states.

The three superconducting qubit archetypes are the phase, charge and flux qubits, though many hybridizations exist (Fluxonium,[9] Transmon,[10] Xmon,[11] Quantronium[12]). For any qubit implementation, the logical quantum states { | 0 , | 1 } {displaystyle {|0rangle ,|1rangle }} are to be mapped to the different states of the physical system, typically to the discrete (quantized) energy levels or to their quantum superpositions. In the charge qubit, different energy levels correspond to an integer number of Cooper pairs on a superconducting island. In the flux qubit, the energy levels correspond to different integer numbers of magnetic flux quanta trapped in a superconducting ring. In the phase qubit, the energy levels correspond to different quantum charge oscillation amplitudes across a Josephson junction, where the charge and the phase are analogous to momentum and position correspondingly of a quantum harmonic oscillator. Note that the phase here is the complex argument of the superconducting wavefunction, also known as the superconducting order parameter, not the phase between the different states of the qubit.

In the table below, the three archetypes are reviewed. In the first row, the qubit electrical circuit diagram is presented. In the second, the quantum Hamiltonian derived from the circuit is shown. Generally, the Hamiltonian can be divided to a "kinetic" and "potential" parts, in analogy to a particle in a potential well. The particle mass corresponds to some inverse function of the circuit capacitance, while the shape of the potential is governed by the regular inductors and Josephson junctions. One of the first challenges in qubit design is to shape the potential well and to choose the particle mass in a way that the energy separation between specific two of the energy levels will differ from all other inter-level energy separations in the system. These two levels will be used as the logical states of the qubit. The schematic wave solutions in the third row of the table depict the complex amplitude of the phase variable. In other words, if a phase of the qubit is measured while the qubit is in a specific state, there is a non-zero probability to measure a specific value only where the depicted wave function oscillates. All three rows are essentially three different presentations of the same physical system.



A superconducting island (encircled with a dashed line) defined between the leads of a capacitor with capacitance C {displaystyle C} and a Josephson junction with energy E J {displaystyle E_{J}} is biased by voltage U {displaystyle U}

A superconducting loop with inductance L {displaystyle L} is interrupted by a junction with Josephson energy E J {displaystyle E_{J}} . Bias flux {displaystyle Phi } is induced by a flux line with a current I 0 {displaystyle I_{0}}

Josephson junction with energy parameter E J {displaystyle E_{J}} is biased by a current I 0 {displaystyle I_{0}}

H = E C ( N N g ) 2 E J cos {displaystyle H=E_{C}(N-N_{g})^{2}-E_{J}cos phi } ,where N {displaystyle N} is the number of Cooper pairs to tunnel the junction, N g = C V 0 / 2 e {displaystyle N_{g}=CV_{0}/2e} is the charge on the capacitor in units of Cooper pairs number, E C = ( 2 e ) 2 / 2 ( C J + C ) {displaystyle E_{C}=(2e)^{2}/2(C_{J}+C)} is the charging energy associated with both the capacitance C {displaystyle C} and the Josephson junction capacitance C J {displaystyle C_{J}} , and {displaystyle phi } is the superconducting wave function phase difference across the junction.

H = q 2 2 C J + ( 0 2 ) 2 2 2 L E J cos [ 2 0 ] {displaystyle H={frac {q^{2}}{2C_{J}}}+left({frac {Phi _{0}}{2pi }}right)^{2}{frac {phi ^{2}}{2L}}-E_{J}cos left[phi -Phi {frac {2pi }{Phi _{0}}}right]} ,where q {displaystyle q} is the charge on the junction capacitance C J {displaystyle C_{J}} and {displaystyle phi } is the superconducting wave function phase difference across the Josephson junction. {displaystyle phi } is allowed to take values greater than 2 {displaystyle 2pi } , and thus is alternatively defined as the time integral of voltage along the inductance L {displaystyle L} .

H = ( 2 e ) 2 2 C J q 2 I 0 0 2 E J cos {displaystyle H={frac {(2e)^{2}}{2C_{J}}}q^{2}-I_{0}{frac {Phi _{0}}{2pi }}phi -E_{J}cos phi } , where C J {displaystyle C_{J}} is the capacitance associated with the Josephson junction, 0 {displaystyle Phi _{0}} is the magnetic flux quantum, q {displaystyle q} is the charge on the junction capacitance C J {displaystyle C_{J}} and {displaystyle phi } is the phase across the junction.

The potential part of the Hamiltonian, E J cos {displaystyle -E_{J}cos phi } , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. The bias voltage is set so that N g = 1 2 {displaystyle N_{g}={frac {1}{2}}} , minimizing the energy gap between | 0 {displaystyle |0rangle } and | 1 {displaystyle |1rangle } , thus making the gap different from other energy gaps (e.g. the gap between | 1 {displaystyle |1rangle } and | 2 {displaystyle |2rangle } ). The difference in gaps allows addressing transitions from | 0 {displaystyle |0rangle } to | 1 {displaystyle |1rangle } and vice versa only, without populating other states, thus effectively treating the circuit as a two-level system (qubit).

The potential part of the Hamiltonian, ( 0 2 ) 2 2 2 L E J cos [ 2 0 ] {displaystyle left({frac {Phi _{0}}{2pi }}right)^{2}{frac {phi ^{2}}{2L}}-E_{J}cos left[phi -Phi {frac {2pi }{Phi _{0}}}right]} , plotted for the bias flux = 0 / 2 {displaystyle Phi =Phi _{0}/2} , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. Different wells correspond to a different number of flux quanta trapped in the superconducting loops. The two lower states correspond to a symmetrical and an antisymmetrical superposition of zero or single trapped flux quanta, sometimes denoted as clockwise and counterclockwise loop current states: | 0 = [ | + | ] / 2 {displaystyle |0rangle =left[|circlearrowleft rangle +|circlearrowright rangle right]/{sqrt {2}}} and | 1 = [ | | ] / 2 {displaystyle |1rangle =left[|circlearrowleft rangle -|circlearrowright rangle right]/{sqrt {2}}} .

The so-called "washboard" potential part of the Hamiltonian, I 0 0 2 E J cos {displaystyle -I_{0}{frac {Phi _{0}}{2pi }}phi -E_{J}cos phi } , is depicted with the thick red line. Schematic wave function solutions are depicted with thin lines, lifted to their appropriate energy level for clarity. Only the solid wave functions are used for computation. The bias current is adjusted to make the wells shallow enough to contain exactly two localized wave functions. A slight increase in the bias current causes a selective "spill" of the higher energy state ( | 1 {displaystyle |1rangle } ), expressed with a measurable voltage spike - a mechanism commonly used for phase qubit measurement.

The GHz energy gap between the energy levels of a superconducting qubit is intentionally designed to be compatible with available electronic equipment, due to the terahertz gap - lack of equipment in the higher frequency band. In addition, the superconductor energy gap implies a top limit of operation below ~1THz (beyond it, the Cooper pairs break). On the other hand, the energy level separation cannot be too small due to cooling considerations: a temperature of 1K implies energy fluctuations of 20GHz. Temperatures of tens of mili-Kelvin achieved in dilution refrigerators allow qubit operation at a ~5GHz energy level separation. The qubit energy level separation may often be adjusted by means of controlling a dedicated bias current line, providing a "knob" to fine tune the qubit parameters.

An arbitrary single qubit gate is achieved by rotation in the Bloch sphere. The rotations between the different energy levels of a single qubit are induced by microwave pulses sent to an antenna or transmission line coupled to the qubit, with a frequency resonant with the energy separation between the levels. Individual qubits may be addressed by a dedicated transmission line, or by a shared one if the other qubits are off resonance. The axis of rotation is set by quadrature amplitude modulation of the microwave pulse, while the pulse length determines the angle of rotation.[14]

More formally, following the notation of,[14] for a driving signal

E ( t ) = E x ( t ) cos ( d t ) + E y ( t ) sin ( d t ) {displaystyle {mathcal {E}}(t)={mathcal {E}}^{x}(t)cos(omega _{d}t)+{mathcal {E}}^{y}(t)sin(omega _{d}t)}

of frequency d {displaystyle omega _{d}} , a driven qubit Hamiltonian in a rotating wave approximation is

H R / = ( d ) | 1 1 | + E x ( t ) 2 x + E y ( t ) 2 y {displaystyle H^{R}/hbar =(omega -omega _{d})|1rangle langle 1|+{frac {{mathcal {E}}^{x}(t)}{2}}sigma _{x}+{frac {{mathcal {E}}^{y}(t)}{2}}sigma _{y}} ,

where {displaystyle omega } is the qubit resonance and x , y {displaystyle sigma _{x},sigma _{y}} are Pauli matrices.

In order to implement a rotation about the X {displaystyle X} axis, one can set E y ( t ) = 0 {displaystyle {mathcal {E}}^{y}(t)=0} and apply the microwave pulse at frequency d = {displaystyle omega _{d}=omega } for time t g {displaystyle t_{g}} . The resulting transformation is

U x = exp { i 0 t g H R d t } = exp { i 0 t g E x ( t ) d t x / 2 } {displaystyle U_{x}=exp left{-{frac {i}{hbar }}int _{0}^{t_{g}}H^{R}dtright}=exp left{-iint _{0}^{t_{g}}{mathcal {E}}^{x}(t)dtcdot sigma _{x}/2right}} ,

that is exactly the rotation operator R X ( ) {displaystyle R_{X}(theta )} by angle = 0 t g E x ( t ) d t {displaystyle theta =int _{0}^{t_{g}}{mathcal {E}}^{x}(t)dt} about the X {displaystyle X} axis in the Bloch sphere. An arbitrary rotation about the Y {displaystyle Y} axis can be implemented in a similar way. Showing the two rotation operators is sufficient for universality, as every single qubit unitary operator U {displaystyle U} may be presented as U = R X ( 1 ) R Y ( 2 ) R X ( 3 ) {displaystyle U=R_{X}(theta _{1})R_{Y}(theta _{2})R_{X}(theta _{3})} (up to a global phase, that is physically unimportant) by a procedure known as the X Y {displaystyle X-Y} decomposition.[15]

For example, setting 0 t g E x ( t ) d t = {displaystyle int _{0}^{t_{g}}{mathcal {E}}^{x}(t)dt=pi } results with a transformation

U x = exp { i 0 t g E x ( t ) d t x / 2 } = e i x / 2 = i x {displaystyle U_{x}=exp left{-iint _{0}^{t_{g}}{mathcal {E}}^{x}(t)dtcdot sigma _{x}/2right}=e^{-ipi sigma _{x}/2}=-isigma _{x}} ,

that is known as the NOT gate (up to the global phase i {displaystyle -i} ).

Coupling qubits is essential for implementing 2-qubit gates. Coupling two qubits may be achieved by connecting them to an intermediate electrical coupling circuit. The circuit might be a fixed element, such as a capacitor, or controllable, such as a DC-SQUID. In the first case, decoupling the qubits (during the time the gate is off) is achieved by tuning the qubits out of resonance one from another, i.e. making the energy gaps between their computational states different.[16] This approach is inherently limited to allow nearest-neighbor coupling only, as a physical electrical circuit is to be lay out in between the connected qubits. Notably, D-Wave Systems' nearest-neighbor coupling achieves a highly connected unit cell of 8 qubits in the Chimera graph configuration. Generally, quantum algorithms require coupling between arbitrary qubits, therefore the connectivity limitation is likely to require multiple swap operations, limiting the length of the possible quantum computation before the processor decoherence.

Another method of coupling two or more qubits is by coupling them to an intermediate quantum bus. The quantum bus is often implemented as a microwave cavity, modeled by a quantum harmonic oscillator. Coupled qubits may be brought in and out of resonance with the bus and one with the other, hence eliminating the nearest-neighbor limitation. The formalism used to describe this coupling is cavity quantum electrodynamics, where qubits are analogous to atoms interacting with optical photon cavity, with the difference of GHz rather than THz regime of the electromagnetic radiation.

One popular gating mechanism includes two qubits and a bus, all tuned to different energy level separations. Applying microwave excitation to the first qubit, with a frequency resonant with the second qubit, causes a x {displaystyle sigma _{x}} rotation of the second qubit. The rotation direction depends on the state of the first qubit, allowing a controlled phase gate construction.[17]

More formally, following the notation of,[17] the drive Hamiltonian describing the system excited through the first qubit driving line is

H D / = A ( t ) cos ( ~ 2 t ) ( x I J 12 z x + m 12 I x ) {displaystyle H_{D}/hbar =A(t)cos({tilde {omega }}_{2}t)left(sigma _{x}otimes I-{frac {J}{Delta _{12}}}sigma _{z}otimes sigma _{x}+m_{12}Iotimes sigma _{x}right)} ,

where A ( t ) {displaystyle A(t)} is the shape of the microwave pulse in time, ~ 2 {displaystyle {tilde {omega }}_{2}} is the resonance frequency of the second qubit, { I , x , y , z } {displaystyle {I,sigma _{x},sigma _{y},sigma _{z}}} are the Pauli matrices, J {displaystyle J} is the coupling coefficient between the two qubits via the resonator, 12 1 2 {displaystyle Delta _{12}equiv omega _{1}-omega _{2}} is the qubit detuning, m 12 {displaystyle m_{12}} is the stray (unwanted) coupling between qubits and {displaystyle hbar } is Planck constant divided by 2 {displaystyle 2pi } . The time integral over A ( t ) {displaystyle A(t)} determines the angle of rotation. Unwanted rotations due to the first and third terms of the Hamiltonian can be compensated with single qubit operations. The remaining part is exactly the controlled-X gate.

Architecture-specific readout (measurement) mechanisms exist. The readout of a phase qubit is explained in the qubit archetypes table above. A state of the flux qubit is often read by an adjust DC-SQUID magnetometer. A more general readout scheme includes a coupling to a microwave resonator, where the resonance frequency of the resonator is shifted by the qubit state.[18]

The list of DiVincenzo's criteria for a physical system to implement a logical qubit is satisfied by the superconducting implementation. The challenges currently faced by the superconducting approach are mostly in the field of microwave engineering.[18]

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Superconducting quantum computing - Wikipedia

Quantum computing | MIT News

Researchers integrate diamond-based sensing components onto a chip to enable low-cost, high-performance quantum hardware.

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Shining light through household bleach creates fluorescent quantum defects in carbon nanotubes for quantum computing and biomedical imaging.

MITs Senthil Todadri and Xiao-Gang Wen will study highly entangled quantum matter in a collaboration supported by the Simons Foundation.

New dual-cavity design emits more single photons that can carry quantum information at room temperature.

Shor awarded the $150,000 prize, named after a fifth-century B.C. Chinese scientist, for his groundbreaking theoretical work in the field of quantum computation.

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Efficient chip enables low-power devices to run todays toughest quantum encryption schemes.

The prestigious awards are supporting five innovative projects that challenge established norms and have the potential to be world-changing.

Approach developed by MIT engineers surmounts longstanding problem of light scattering within biological tissue and other complex materials.

William Oliver says a lack of available quantum scientists and engineers may be an inhibitor of the technologys growth.

Eleven new professors join the MIT community.

First measurement of its kind could provide stepping stone to practical quantum computing.

MIT researchers have demonstrated that a tungsten ditelluride-based transistor combines two different electronic states of matter.

Professors Daniel Harlow, Aram Harrow, Hong Liu, and Jesse Thaler among the first recipients of new honor for advances in quantum understanding.

PhD student David Layden in the Quantum Engineering Group has a new approach to spatial noise filtering that boosts development of ultra-sensitive quantum sensors.

Scientists find a theoretical optical device may have uses in quantum computing.

New York Times op-ed by MIT president says a national focus on innovation and research is more effective than only playing defense on trade practices.

Math and physics major Shaun Datta wraps up four years of pushing himself beyond his comfort zone by singing a cappella with the MIT Logarhythms.

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Quantum computing | MIT News

How Do Quantum Computers Work? –

Quantum computers perform calculations based on the probability of an object's state before it is measured - instead of just 1s or 0s - which means they have the potential to process exponentially more data compared to classical computers.

Classical computers carry out logical operations using the definite position of a physical state. These are usually binary, meaning its operations are based on one of two positions. A single state - such as on or off, up or down, 1 or 0 - is called a bit.

In quantum computing, operations instead use the quantum state of an object to produce what's known as a qubit. These states are the undefined properties of an object before they've been detected, such as the spin of an electron or the polarisation of a photon.

Rather than having a clear position, unmeasured quantum states occur in a mixed 'superposition', not unlike a coin spinning through the air before it lands in your hand.

These superpositions can be entangled with those of other objects, meaning their final outcomes will be mathematically related even if we don't know yet what they are.

The complex mathematics behind these unsettled states of entangled 'spinning coins' can be plugged into special algorithms to make short work of problems that would take a classical computer a long time to work out... if they could ever calculate them at all.

Such algorithms would be useful in solving complex mathematical problems, producing hard-to-break security codes, or predicting multiple particle interactions in chemical reactions.

Building a functional quantum computer requires holding an object in a superposition state long enough to carry out various processes on them.

Unfortunately, once a superposition meets with materials that are part of a measured system, it loses its in-between state in what's known as decoherence and becomes a boring old classical bit.

Devices need to be able to shield quantum states from decoherence, while still making them easy to read.

Different processes are tackling this challenge from different angles, whether it's to use more robust quantum processes or to find better ways to check for errors.

For the time being, classical technology can manage any task thrown at a quantum computer. Quantum supremacy describes the ability of a quantum computer to outperform their classical counterparts.

Some companies, such as IBM and Google, claim we might be close, as they continue to cram more qubits together and build more accurate devices.

Not everybody is convinced that quantum computers are worth the effort. Some mathematicians believe there are obstacles that are practically impossible to overcome, putting quantum computing forever out of reach.

Time will tell who is right.

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How Do Quantum Computers Work? -

What is Quantum Computing? – Definition from Techopedia

A traditional computer works on bits of data that are binary, or Boolean, with only two possible values: 0 or 1. In contrast, a quantum bit, or "qubit," has possible values of 1, 0 or a superposition of 1 and 0, in the case of an unknown value. According to scientists, qubits are based on physical atoms and molecular structures. However, many find it helpful to theorize a qubit as a binary data unit with superposition.

The use of qubits makes the practical quantum computer model quite difficult. Traditional hardware requires altering to read and use these unknown values. Another idea, known as entanglement, uses quantum theory to suggest that accurate values cannot be obtained in the ways that traditional computers read binary bits. It also has been suggested that a quantum computer is based on a non-deterministic model, where the computer has more than one possible outcome for any given case or situation. Each of these ideas provides a foundation for the theory of actual quantum computing, which is still problematic in todays tech world.

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What is Quantum Computing? - Definition from Techopedia

How Quantum Computers Work | HowStuffWorks

The massive amount of processing power generated by computer manufacturers has not yet been able to quench our thirst for speed and computing capacity. In 1947, American computer engineer Howard Aiken said that just six electronic digital computers would satisfy the computing needs of the United States. Others have made similar errant predictions about the amount of computing power that would support our growing technological needs. Of course, Aiken didn't count on the large amounts of data generated by scientific research, the proliferation of personal computers or the emergence of the Internet, which have only fueled our need for more, more and more computing power.

Will we ever have the amount of computing power we need or want? If, as Moore's Law states, the number of transistors on a microprocessor continues to double every 18 months, the year 2020 or 2030 will find the circuits on a microprocessor measured on an atomic scale. And the logical next step will be to create quantum computers, which will harness the power of atoms and molecules to perform memory and processing tasks. Quantum computers have the potential to perform certain calculations significantly faster than any silicon-based computer.

Scientists have already built basic quantum computers that can perform certain calculations; but a practical quantum computer is still years away. In this article, you'll learn what a quantum computer is and just what it'll be used for in the next era of computing.

You don't have to go back too far to find the origins of quantum computing. While computers have been around for the majority of the 20th century, quantum computing was first theorized less than 30 years ago, by a physicist at the Argonne National Laboratory. Paul Benioff is credited with first applying quantum theory to computers in 1981. Benioff theorized about creating a quantum Turing machine. Most digital computers, like the one you are using to read this article, are based on the Turing Theory. Learn what this is in the next section.

How Quantum Computers Work | HowStuffWorks

Quantum computing could change everything, and IBM is …

In January, IBM made waves when it announced its IBM Q System One, the world's first gate model quantum computer available to businesses a system housed in a sleek, 9-cubic-foot glass case.

It's a major milestone for quantum computers, which had to date mostly been found in research labs. Already, IBM says, customers are lining up to figure out how to get their hands on this technology, which shows promise in fields as varied as chemistry, materials science, food production, aerospace, drug discovery, predicting the stock market and even fighting climate change.

The reason for excitement: a quantum computer has seemingly-magical properties that allow it to process exponentially more information than a conventional system. A quantum computer isn't just a much faster computer. Rather, it's an entirely different paradigm of computing that requires some radical rethinking.

Now, the race is on to be the first company to conquer the massive opportunity presented by this technology. IBM, Microsoft, Google, and other tech titans and startups alike are all placing big bets on the technology. Meanwhile, in December, the U.S. government passed the National Quantum Initiative Act, which proposes spending $1.2 billion over the next five years on labs, academia and companies to advance quantum computing technologies.

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Quantum computing could change everything, and IBM is ...

Quantum Computing – Intel

Ongoing Development in Partnership with Industry and AcademiaThe challenges in developing functioning quantum computing systems are manifold and daunting. For example, qubits themselves are extremely fragile, with any disturbance including measurement causing them to revert from their quantum state to a classical (binary) one, resulting in data loss. Tangle Lake also must operate at profoundly cold temperatures, within a small fraction of one kelvin from absolute zero.

Moreover, there are significant issues of scale, with real-world implementations at commercial scale likely requiring at least one million qubits. Given that reality, the relatively large size of quantum processors is a significant limitation in its own right; for example, Tangle Lake is about three inches square. To address these challenges, Intel is actively developing design, modeling, packaging, and fabrication techniques to enable the creation of more complex quantum processors.

Intel began collaborating with QuTech, a quantum computing organization in the Netherlands, in 2015; that involvement includes a US$50M investment by Intel in QuTech to provide ongoing engineering resources that will help accelerate developments in the field. QuTech was created as an advanced research and education center for quantum computing by the Netherlands Organisation for Applied Research and the Delft University of Technology. Combined with Intels expertise in fabrication, control electronics, and architecture, this partnership is uniquely suited to the challenges of developing the first viable quantum computing systems.

Currently, Tangle Lake chips produced in Oregon are being shipped to QuTech in the Netherlands for analysis. QuTech has developed robust techniques for simulating quantum workloads as a means to address issues such as connecting, controlling, and measuring multiple, entangled qubits. In addition to helping drive system-level design of quantum computers, the insights uncovered through this work contribute to faster transition from design and fabrication to testing of future generations of the technology.

In addition to its collaboration with QuTech, Intel Labs is also working with other ecosystem members both on fundamental and system-level challenges on the entire quantum computing stack. Joint research being conducted with QuTech, the University of Toronto, the University of Chicago, and others builds upward from quantum devices to include mechanisms such as error correction, hardware- and software-based control mechanisms, and approaches and tools for developing quantum applications.

Beyond Superconduction: The Promise of Spin QubitsOne approach to addressing some of the challenges that are inherent to quantum processors such as Tangle Lake that are based on superconducting qubits is the investigation of spin qubits by Intel Labs and QuTech. Spin qubits function on the basis of the spin of a single electron in silicon, controlled by microwave pulses. Compared to superconducting qubits, spin qubits far more closely resemble existing semiconductor components operating in silicon, potentially taking advantage of existing fabrication techniques. In addition, this promising area of research holds the potential for advantages in the following areas:

Operating temperature:Spin qubits require extremely cold operating conditions, but to a lesser degree than superconducting qubits (approximately one degree kelvin compared to 20 millikelvins); because the difficulty of achieving lower temperatures increases exponentially as one gets closer to absolute zero, this difference potentially offers significant reductions in system complexity.

Stability and duration:Spin qubits are expected to remain coherent for far longer than superconducting qubits, making it far simpler at the processor level to implement them for algorithms.

Physical size:Far smaller than superconducting qubits, a billion spin qubits could theoretically fit in one square millimeter of space. In combination with their structural similarity to conventional transistors, this property of spin qubits could be instrumental in scaling quantum computing systems upward to the estimated millions of qubits that will eventually be needed in production systems.

To date, researchers have developed a spin qubit fabrication flow using Intels 300-millimeter process technology that is enabling the production of small spin-qubit arrays in silicon. In fact, QuTech has already begun testing small-scale spin-qubit-based quantum computer systems. As a publicly shared software foundation, QuTech has also developed the Quantum Technology Toolbox, a Python package for performing measurements and calibration of spin-qubits.

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Quantum Computing - Intel

IBM expands universities in its quantum computing research …

IBM said its commercial quantum computing program, called IBM Q Network, is expanding to more universities in North America, including Notre Dame, Florida State, and Virginia Tech.

The company's IBM Q Network is designed to develop curricula for students and forge research partnerships with academia. The additions of the aforementioned universities as well as Stony Brook University and the University of Tokyo will round out a list that already includes Duke, Harvard, and the University of Waterloo.

According to IBM, each university will have a different research focus and angle. For instance, Stony Brook will focus on preparing students for working in the quantum technologies field. Notre Dame will look a quantum applications in chemistry, physics, and engineering and Virginia Tech is looking at new algorithms for quantum chemistry.

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Big Blue earlier this year outlined its latest Q System One and showed off hardware designs for the system. Most quantum computing applications are likely to be consumed as a cloud service through multiple clouds.

The research collaboration network for IBM's Q Network will also includethe University of Colorado Boulder, the University of Waterloo, as well as the University of Chicago and the University of Illinois via the Chicago Quantum Exchange, a research hub for quantum technology.

While these research areas are fluid and early stages, IBM is laying down the groundwork for quantum advances and ensuring there are people able to work in the field and ultimately expand it.

IBM has recently said that its possible that quantum computing will hit so-called Quantum Advantage in the 2020s. Quantum Advantage is where quantum computing leaves the lab for more practical applications.

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IBM expands universities in its quantum computing research ...

Quantum computing is a marathon not a sprint | VentureBeat

If you watch the technology headlines you might think something called quantum computing is the Next Big Thing. In January, USA Today declared IBMs new quantum computer one of the four most wow worthy announcements at CES, the annual gadget fest in Las Vegas. Gartner also listed quantum computing as one of the top technology trends for 2019, joining fan favorites like blockchain and virtual reality.

Ive spent more than 25 years as a physicist researching quantum computers machines that store and process information on individual atoms or particles, like photons and Ive started a company that is building them. I am convinced quantum computing is in fact a breakthrough technology that offers the only known way to attack some of the worlds hardest problems in medicine, transportation, computer security, and other areas we havent yet foreseen.

We must be clear, however, about what is and isnt happening next. The big quantum computing discoveries that will most impact society are still years away. In the meantime, we will see breathless announcements of records broken as the technology rapidly develops. These incremental advances are important for government, which has a role in encouraging this research, as well as for industries that need to start developing ways to use quantum computers as they become more powerful. But too much hype risks disillusionment that may slow the progress.

The first thing to know about quantum computers is that they are not a faster, better version of the computers we have now. Youll never trade in your laptop or smartphone for a quantum version. Quantum computers almost certainly wont run social networks, animate Pixar movies, or keep track of airline reservations. They solve different problems in different ways.

Quantum computers were proposed in 1982 by Richard Feynman, the Nobel prize winning physicist, who worried that conventional computers could never tackle problems in quantum mechanics, the well-established theory that predicts the behavior of small isolated particles such as atoms or electrons. Today, we do use conventional computers to simulate quantum models of material and chemical processes, but these simulations grind to a halt when faced with all the possible arrangements of electrons in even a small molecule or chunk of material.

Feynmans idea was simple: build a computer that stores information on individual particles later named qubits that already follow the very rules of quantum mechanics that seem to perplex conventional computers.

Whats the difference? Ordinary computers think in certainties, digitizing every aspect of the world to well-defined numbers. Quantum computers probe all possibilities, constantly updating the probabilities of multiple scenarios. Add more qubits, and they can consider exponentially more scenarios. A quantum computer is programmed to consider all these possibilities and narrow them down to just a few, and then when the output is measured, it can tell us information about all those scenarios. It is critical that a quantum computer not be measured or looked at while it considers the uncountable number of possibilities. For that reason, qubits are like senators before a controversial vote: They shouldnt reveal their position until they are forced to.

Our world is filled with uncertainty, and quantum computers can be very helpful in selecting the best of several options. Thus a bank wouldnt use a quantum computer to track checking accounts. When you look at your balance, you want a single answer you can count on. But the bank might use a quantum computer to estimate how much money you will have in your account a year from now, based on the probability you will get a raise or get fired, whether your teenager will crash the car, if the stock market will crash, and how these factors interact.

To be clear, nobody has yet written a program that makes financial projections on a quantum computer. One reason is that, until now, there havent been any quantum computers to try them out on. But after a lot of work, thats changed. Over the last few years, corporate, academic, and government groups have built machines that can isolate and manipulate particles or other types of qubits well enough to handle basic programs.

It takes exacting precision and extreme conditions to isolate and control qubits. Some quantum computers freeze solid-state circuits to close to absolute zero. Others uses electric fields to levitate atoms in a vacuum that is more pure than deep space, while using lasers to manipulate them with an accuracy of 1/10,000 the width of a human hair. These atomic qubits in particular can scale to much larger systems because they are all the same isolated atomic element, perfectly replicable, and they are so well isolated that they never reveal their qubit states until forced to.

In 3-5 years, these machines will perform certain calculations that would not be possible using ordinary computers. But it may be 5-10 years before any of these machines have the capacity and accuracy to solve useful problems. Along the way, I worry that some who read about quantum computing being the next big thing will feel let down and lose interest. We cant let that happen. Government needs to continue to support basic research, as Congress did passing the National Quantum Initiative Act last year. And the industrial community needs to start working with the current generation of quantum computers so they can develop the know-how and the software that will give them an edge as the technology improves.

Even then, you wont have a quantum computer on your desk or in your pocket. But you may start to see better drugs, more flexible materials, and organizations running more efficiently. All that will definitely be wow worthy.

Christopher Monroe is the Bice Zorn Professor of Physics and Distinguished Professor at the University of Maryland and co-founder and CEO of IonQ, a quantum computing startup.

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The CIO’s Guide to Quantum Computing – Smarter With Gartner

It makes sense that sci-fi-level myths might surround a technology that must be stored in a container colder than interstellar space and has the potential to solve some of the worlds most challenging problems.

CIOs have been inundated with quantum computing hype: Quantum computers will operate faster than the speed of light, or Quantum computers will replace conventional systems or Quantum computing will render all security encryption algorithms obsolete.

Quantum solutions could revolutionize the entire IT industry

The truth is that quantum solutions could revolutionize the entire IT industry with major economic, industrial, academic and societal impacts. But they wont operate faster than light travels or replace current computing systems, and although theyll challenge some security encryptions, they wont render them all obsolete overnight.

Quantum computing is heavily hyped and evolving at different rates, but it should not be ignored, says Matthew Brisse, VP Analyst, Gartner. It holds great promise, especially in the areas of chemistry, optimization, machine learning and AI to name a few. Todays data scientists simply cannot address key opportunities in these areas because of the compute limitations of classic computer architectures.

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Align quantum computing with business needs

Some of these problems may take todays fastest supercomputers months, or even years, to run through a series of permutations, making it impractical to attempt, says Brisse. Quantum computers have the potential to run complex calculations that classical systems could literally never complete. This potential for compute acceleration, as well as the ability to address difficult and complex problems, is what is driving so much interest from CEOs in a variety of industries.

Quantum computing is a type of nonclassical computing based on the quantum state of subatomic particles. Quantum computing is fundamentally different from classic computers, which operate using binary bits. This means the bits are either 0 or 1, true or false, positive or negative. However, in quantum computing, the bit is referred to as a quantum bit, or qubit. Unlike the strictly binary bits of classic computing, qubits can, strangely, represent a range of values in one qubit. This representation is called superpositioning.

Superpositioning is what gives quantum computers speed and parallelism, as each qubit can represent a quantitative solution to a problem. Further, qubits can be linked with other qubits in a process called entanglement; each entangled qubit adds two more dimensions to the system. When combined with superposition, quantum computers can process a massive number of possible outcomes at the same time.

The number of high-quality qubits necessary to make a viable quantum computer depends on the problem.

The ability for a quantum computer to outperform a classical computer is called quantum supremacy. While it may sound like a sci-fi dream, experts believe that for a limited number of computing problems, quantum supremacy will be a reality in a matter of years.

Applications for quantum computing will be narrow and focused, as general-purpose quantum computing will most likely never be economical. However, the technology does hold the potential to revolutionize certain industries. Quantum computing could enable breakthroughs by:

Researchers have shown how quantum computing could kill, or at least significantly weaken, current cryptography systems. If true, this would jeopardize any business that relies on encryption. If a sufficiently powerful quantum computer becomes available within 10 or so years, any data that has been published or intercepted is subject to cryptanalysis by a future quantum computer. Most security professionals speculate that quantum computing will eventually render RSA cryptography and ECC useless but will not be able to effectively counter hash, code, lattice-based or multivariate-quadratic-equations cryptography. Symmetric key cryptographic systems like Advanced Encryption Standard (AES), SNOW 3G, 3GPP and Kerberos are resistant to a quantum computing attack if they use a large-enough key size. The problem is, researchers keep coming up with new key cracking algorithms. For this reason, governments are investing in a cousin to quantum computing quantum key distribution.

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The physics, materials and control systems of quantum computers remain uncertain, but the potential for disruption is driving large organizations like IBM, Google, Intel and Microsoft to heavily invest in quantum hardware and software. Startups in multiple industries are emerging, alongside new skill sets from quantum algorithm experts and designers to quantum circuit engineers and applied physicists.

CIOs should view quantum computing as a competitive advantage, as new quantum-inspired algorithms could bring innovative solutions and approaches to product development. It could also reduce time to market and optimize customer delivery.

Additionally, waiting or ignoring quantum computing might place intellectual property (IP) and patent portfolios at risk. Early organizations will have the competitive advantage by patenting quantum algorithms within their specific domain. For example, a rival company could develop a quantum algorithm patent that improves Monte Carlo simulations by 1,000% or a pharmaceutical company could shorten the time to market for new drugs.

As with any new technological innovation, there is a risk that the hype outpaces product development, which could negatively impact perceptions and investments. In the case of quantum computing, this is called quantum winter. Hype in the media is creating awareness and advancement, but also setting unrealistic expectations for timing and capabilities. This level of hype inevitably leads to disillusionment, which is dangerous, as quantum computing requires sustained, focused investment for the long term.

The hype around quantum computing makes it interesting as an investment. However, the fundamental physics are still in development, and consistent results wont appear for at least 5 to 10 years and possibly much longer. Therefore, any investments made in pursuit of quantum computing opportunities must pay off in monetizable discoveries.

By 2023, 95% of organizations researching quantum computing strategies will utilize QCaaS

Logistically, quantum computers are difficult to maintain and require specialized environments cooled to .015 Kelvin. The quantum processor must be placed in a dilution refrigerator shielded to 50,000 times less than the earths magnetic field and placed in a high vacuum to 10 billion times lower than atmospheric pressure. It will also need calibration several times per day. For most organizations, this is not feasible. Gartner recommends that organizations interested in quantum computing leverage quantum computing as a service (QCaaS) to minimize risk and contain costs. By 2023, 95% of organizations researching quantum computing strategies will utilize QCaaS.

Overall, it remains safer to underinvest in the technology or to invest in skilled personnel who can be fully productive as product managers in revenue-bearing areas. As quantum computing opportunities arise, these product managers will have the skills to address them. Gartner has found surprising numbers of degreed quantum physicists in product management roles.

Gartner projections should be used to manage expectations inside the organization. Take this time to identify opportunities to provide support to clients or customers, or leverage industry breakthroughs. Consider looking to the R&D group for support and ensure you have access to a resource who can help you translate quantum technology into opportunities in your business.

By 2023, 90% of enterprise quantum computing investments will engage quantum consulting organizations to help shape problems that can leverage quantum algorithms. Knowing how to identify and extract business value from a quantum computing initiative is a key skill to develop. IBM, Microsoft and others have customer engagement services for organizations interested in identifying potential business opportunities that quantum computing could someday address.

Gartner predicts that by 2023, 20% of organizations will be budgeting for quantum computing projects, compared to less than 1% today. CIOs should look for potential opportunities from quantum computing and be ready to help the business leverage them.

By 2023, 20% of organizations will be budgeting for quantum computing projects

These opportunities will need to be fully integrated with traditional IT, and will require new cross-collaboration from research scientists, computational data scientists and quantum data scientists. This new development paradigm is critical to the success of any quantum program.

It is time to learn more about quantum computing.

This article has been updated from the original, published on November 29, 2017, to reflect new events, conditions or research.

The CIO's Guide to Quantum Computing - Smarter With Gartner