Quantum technology (QT) applies quantum mechanical properties such as quantum entanglement, quantum superposition, and No-cloning theorem to quantum systems such as atoms, ions, electrons, photons, or molecules. Quantum bit is the basic unit of quantum information. Whereas in a classical system, a bit is either in one state or the another. However, quantum qubits can exist in large number of states simultaneously, property called Superposition.
Quantum entanglement is a phenomenon where entangled particles can stay connected in the sense that the actions performed on one of the particles affects the other no matter what’s the distance between them. No-cloning theorem tells us that quantum information (qubit) cannot be copied.
Quantum technologies have the potential to spur revolutions in computing, sensing, cryptography and beyond. By taking advantage of those properties, quantum computers can process information in new ways, potentially performing calculations far beyond the reach of even the fastest of today’s supercomputers. They can simultaneously consider different possible solutions to a problem and quickly converge on the correct solution without checking each possibility individually. This dramatically speed up certain calculations, such as number factoring.
The power of quantum computers depends on the number of qubits and their quality measured by coherence, and gate fidelity. Qubit is very fragile, can be disrupted by things like tiny changes in temperature or very slight vibrations. Coherence measures the time during which quantum information is preserved. The gate fidelity uses distance from ideal gate to decide how noisy a quantum gate is.
However, quantum mechanics allows the qubit to be in a coherent superposition of both states simultaneously, a property which is fundamental to quantum mechanics and quantum computing. A quantum mode, or qumode, spans the full spectrum of variables between one and zero — the values to the right of the decimal point.
Quantum computers also utilize another aspect of quantum mechanics known as entanglement. Its qubits are highly connected. This generally refers to different particles having correlated quantum states, known as Entanglement. This allows operations to act on multiple qubits and increases the information density of a quantum computer.
We are now in era of Noisy intermediate-scale quantum (NISQ) in which quantum computers are composed of hundreds of noisy qubits that are not error-corrected. They Physical qubits are realized using superconducting Josephson junction qubits and the trapped-ion qubits. Other promising Qubits are Semiconductor based qubits; Topological qubits; and Photonic qubits.
Calculations using these noisy qubits can introduce errors and make long computations impossible. However, these computers still can demonstrate the advantages of quantum computing and various algorithms are being developed in disciplines such as machine learning, quantum chemistry and optimization.
For future fault-tolerant quantum computers Researchers have devised error-correction schemes such as surface code, that store data redundantly with information spread over tens of thousands of entangled physical qubits. These combined bits are collectively known as a logical qubit or perfect qubit. Previous research has found that a quantum computer with 300 perfect qubits could perform more calculations in an instant than there are atoms in the universe.
In race to develop first large-scale programmable quantum computer, Google took lead in 2019 when it launched Sycamore, a 53 physical superconducting qubits Quantum computer. Further it claimed quantum supremacy by carrying out a calculation in 200 seconds compared to estimated 10,000 years what the world’s most powerful supercomputer Summit, would take.
Recently IBM has unveiled Eagle, a 127-qubit quantum processor. Expectations are to reach 1 million physical qubits in 10 years
In 2021, China claimed to test two different quantum computers on more challenging tasks than Sycamore faced and showed faster results. Zuchongzi, a 56 superconducting qubits computer completed a task of sampling, a given spread of probabilities. In another study, the scientists tested Jiuzhang 2.0, a photonic quantum computer, on Gaussian boson sampling, task where the machine analyzes random patches of data. They estimated Jiuzhang 2.0 could solve the problem roughly 10 raised to 24 times faster than classical supercomputers. They note their work points to “an unambiguous quantum computational advantage.”
Requirements of Qubits for implementation of Quantum computer
Qubits represent atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. Examples include: the spin of the electron in which the two levels can be taken as spin up and spin down; or the polarization of a single photon in which the two states can be taken to be the vertical polarization and the horizontal polarization. In a classical system, a bit would have to be in one state or the other.
Any system with two possible quantum mechanical states — such as the oscillations in a superconducting loop or energy levels of an ion — could form a qubit, but all hardware types have pros and cons, and each faces substantial hurdles to forming a full-blown quantum computer. A machine capable of living up to the original promise of quantum computing by, for example, cracking conventional encryption, would require millions of individually controllable qubits. But size is not the only issue: the quality of the qubits and how well they connect to each other are just as important.
Some of the requirements for Quantum computation are: a scalable physical system containing a collection of well characterized qubits; the ability to initialize the state of the qubits to a simple fiducial state; Long relevant decoherence times, much longer than the gate operation time; a “universal” set of quantum gates (single qubit rotations + C-Not / C-Phase / …. )
The qubits need to stay coherent for long periods of time. This is when their state is still in Superposition – i.e. hasn’t collapsed to zero or one which is known as decoherence. Quantum coherence is at the heart of quantum information technology. The latter can only be realised as long as quantum coherence is preserved. In fact, as quantum applications increase in complexity, coherence time needs to be extended. In a similar manner, longer coherence times reveal higher performance and higher quantum operation fidelity, which is extremely important especially for quantum computing.
Decoherence is caused by the qubit interacting with the outside world, and explains why much of today’s quantum computers are large engineering feats – much of the work goes into keeping qubits isolated from the outside world.
One of the major differences between a classical computer and a quantum computer is in how it handles small unwanted variations, or noise, in the system. Since a classical bit is either one or zero, even if the value is slightly off (some noise in the system) it is easy for the operations on that signal to remove that noise. In fact, today’s classical gates, which operate on bits and are used to create computers, have very large noise margins—they can reject large variations in their inputs and still produce clean, noise-free outputs. Because a qubit can be any combination of one and zero, qubits and quantum gates cannot readily reject small errors (noise) that occur in physical circuits. As a result, small errors in creating the desired quantum operations, or any stray signals that couple into the physical system, can eventually lead to wrong outputs appearing in the computation. Thus, one of the most important design parameters for systems that operate on physical qubits is their error rate.
Although the physical qubit operations are sensitive to noise, it is possible to run a quantum error correction (QEC) algorithm on a physical quantum computer to emulate a noise-free, or “fully error corrected,” quantum computer. Much research is going into understanding how to reduce noise through error correction. Once we have worked out how to build noise-free qubits (known as logical qubits) a key focus of further development will simply be on how we can scale up quantum computers to include more qubits (whilst preserving connectivity and minimising noise).
Quantum Computers will only be useful if we can achieve high gate fidelity and connectivity. The more logic gates you add to a quantum computer, the more noise builds up (which by definition cannot be attenuated). Therefore a good quantum computer demonstrates high gate fidelity where information is precisely preserved between operations.
Applying quantum computing to practical problems hinges on the ability to scale to and control thousands – if not millions – of qubits at the same time with high levels of fidelity. However, current quantum systems designs are limited by overall system size, qubit fidelity and especially the complexity of control electronics required to manage the quantum at large scale.
In quantum computing and specifically the quantum circuit model of computation, a quantum logic gate (or simply quantum gate) is a basic quantum circuit operating on a small number of qubits. They are the building blocks of quantum circuits, like classical logic gates are for conventional digital circuits. Quantum gates can leverage two key aspects of quantum mechanics that are entirely out of reach for classical gates: superposition and entanglement. It needs to achieves quick gate operations:
Some of the possible Qubits and implementations are Neutral atoms, Trapped ions, Colour centres (e.g., NV-centers in diamond), Electron spins (e.g,. quantum dots), Superconducting qubits (charge, phase, flux), NMR, Optical qubits and Topological qubits among others.
There have been two leading approaches for building general purpose Quantum computer. One approach, adopted by Google, IBM, Rigetti and Quantum Circuits involves encoding quantum states as oscillating currents in superconducting loops. The other, pursued by IonQ and several major academic labs, is to encode qubits in single ions held by electric and magnetic fields in vacuum traps.
“Right now I think both superconductors and ion traps have shown a lot of progress and demonstrated a large number of algorithms. The advantage of trapped ions is that every ion is the same. For these small chains [of ions in the trap] you do get this advantage of basically being able to achieve communication between any pair. In superconducting devices, typically, you are only able to talk to sort of neighbor qubits. So if you have an algorithm which requires a longer distance communication between qubits, there is some cost you have to pay to get the information from one to the other,” Kenneth Brown of Duke University.
Trapped atomic ions, on the other hand, feature virtually identical qubits, and their wiring can be reconfigured by modifying externally applied electromagnetic fields. However, atomic qubit switching speeds are generally much slower than solid state devices, and the development of engineering infrastructure for trapped ion quantum computers and the mitigation of noise and decoherence from the applied control fields is just beginning.”
Atoms have many energy levels that have been studied extensively over the past century, and some of these energy levels are extremely stable. Indeed, with accuracies better than one part in 10−15, atomic clocks provide the best available time and frequency standards.
The qubits encoded in the atomic energy levels can be initialized by optical pumping and laser cooling, manipulated with electromagnetic radiation, and then measured via laser-induced fluorescence. In short, atoms provide clean, well-defined qubits.
Neutral atoms make attractive qubit candidates also because of their weak interaction with the environment, leading to long coherence times They can be cooled down to nK temperatures and trapped in very large numbers (millions) in microscopic arrays created by laser beams (called optical lattices). The trapping and manipulation of atoms can be done with high precision. While one-qubit gates can be implemented with very high fidelity, realizing twoqubit gates or many-qubit entangled states is challenging because the atoms interact
very weakly with each other
• Production of large quantum registers
• Massive parallelism in gate operations
• Long coherence times (>20s)
• Gates typically slower than other implementations (~ms for collisional gates) (Rydberg gates can be somewhat faster)
• Individual addressing (but recently achieved)
What: Neutral atoms are a similar approach to Ion Traps but instead of using ionized atoms and exploiting their charge to hold the qubits in place, neutral atoms and laser tweezers are used.
Who: Atom Computing; PASQAL; QuEra.
Pros: Neutral atoms benefit from the same long coherence times as ions (used in Ion Trap quantum computers). Its unique feature compared to Ion Traps is its potential for building multidimensional arrays. You can read more in our exclusive interview with the CEO of Atom Computing.
Cons: Scaling up a neutral atom system faces the issues that arise when scaling a trapped ion computer. This approach continues to be highly nascent.
While neutral atoms interact weakly among themselves, ions, being charged, interact rather strongly via Coulomb repulsion. This facilitates the implementation of twoqubits gates without compromising the long coherence times. Also thanks to their charge, the motion and position of the ions can be well controlled. Ions can be trapped by electrical (or magnetic) fields, laser-cooled and manipulated with high precision. Entangled (Greenberger-Horne-Zeilinger (GHZ) and W) states of up to 14 qubits have been realized .
What: Ion Trap quantum computers work by trapping ions (charged atoms) using electric fields and holding them in place, the outermost electron orbiting the nucleus can then be put in different states and used as a qubit.
Who: IonQ; Alpine Quantum Technologies; Honeywell. But a separate approach, using ions trapped in electric fields, is gaining traction in the quest to make a commercial quantum computer. Honeywell launched its first quantum computer using trapped ions as the basis of its quantum bits or ‘qubits’, which it had been working on quietly for more than a decade. Honeywell, headquartered in Charlotte, North Carolina, is the first established company to take this route. In October, seven months after the launch, the firm unveiled an upgraded machine; it already has plans to scale this up.
Pros: A big feature of Ion Trap computers is their stability; the qubits have much longer “coherence times” than those used in Superconducting quantum computers. While an Ion Trap computer can operate at room temperature, to get the best performance the ions need to be cooled, but not to the extent a Superconducting quantum computer requires. The connections between Ion Trap qubits can be reconfigured meaning each qubit can interact with each other qubit in the computer, avoiding some of the computational overhead found with superconducting chips.
Cons: Ion trap computers are generally significantly slower than their superconducting counterparts. While they do not need to be kept as cold, the ions do need to be in a high vacuum. The technology involved in creating ion traps is not as mature as with superconducting qubits, we will need to see large improvements in the area before we can imagine a scalable system. Building a Ion Trap quantum computer requires integration of technologies from a wide range of domains, including vacuum, laser, and optical systems, radio frequency and microwave technology, and coherent electronic controllers.
A UCLA-led interdisciplinary research team including collaborators at Harvard University has now developed a fundamentally new strategy for building these computers. While the current state of the art employs circuits, semiconductors and other tools of electrical engineering, the team has produced a game plan based in chemists’ ability to custom-design atomic building blocks that control the properties of larger molecular structures when they’re put together.
The findings, published in Nature Chemistry in July 2022, could ultimately lead to a leap in quantum processing power. “The idea is, instead of building a quantum computer, to let chemistry build it for us,” said Eric Hudson, UCLA’s David S. Saxon Presidential Professor of Physics and corresponding author of the study. “All of us are still learning the rules for this type of quantum technology, so this work is very sci-fi right now.
The scientists developed small molecules that include calcium and oxygen atoms and act as qubits. These calcium-oxygen structures form what chemists call a functional group, meaning that it can be plugged into almost any other molecule while also conferring its own properties to that molecule.
The team showed that their functional groups maintained their desired structure even when attached to much larger molecules. Their qubits can also stand up to laser cooling, a key requirement for quantum computing.
“If we can bond a quantum functional group to a surface or some long molecule, we might be able to control more qubits,” Hudson said. “It should also be cheaper to scale up, because an atom is one of the cheapest things in the universe. You can make as many as you want.”
In addition to its potential for next-generation computing, the quantum functional group could be a boon for basic discovery in chemistry and the life sciences, for instance by helping scientists uncover more about the structure and function of various molecules and chemicals in the human body.
“Qubits can also be exquisitely sensitive tools for measurement,” said study co-author Justin Caram, a UCLA assistant professor of chemistry and biochemistry. “If we could protect them so they can survive in complex environments such as biological systems, we would be armed with so much new information about our world.”
Hudson said that the development of a chemically based quantum computer could realistically take decades and is not certain to succeed. Future steps include anchoring qubits to larger molecules, coaxing tethered qubits to interact as processors without unwanted signaling, and entangling them so that they work as a system.
Superconducting circuits are typically µm-scale circuits operated at mK temperatures. Although macroscopic, they can still exhibit quantum behavior, which can be harnessed for QC . Superconducting circuits are RLC circuits that also include nonlinear elements, called Josephson junctions. Thanks to superconductivity, the resistance vanishes (R = 0), eliminating the most serious source of dissipation and noise. Now, the LC circuit is a harmonic oscillator. The problem with harmonic oscillators is that they have an infinite number of equally-spaced energy levels and therefore it is not possible to target only the lowest two energy levels. By introducing nonlinearity through the Josephson junction, the energy-level separation becomes nonuniform, and the lowest two levels can be used to encode the qubit.
This qubit implementation is the most common, and is the main focus of IBM and Google’s universal quantum computers. Others are Rigetti; Alibaba; Intel; Quantum Circuits; Oxford Quantum Circuits.
Quantum information can be encoded in different ways: in the number of superconducting electrons on a small island (charge qubit), in the direction of a current around a loop (flux qubit), or in oscillatory states of the circuit (phase qubit). These qubits can be controlled by microwaves, voltages, magnetic fields, and currents as well as measured with high accuracy using integrated on-chip instruments. Superconducting qubits have coherence times that can reach tens of µs , the coupling between qubits can be made strong and can be turned on and off electronically.
Pros: Superconducting qubits have fast gate times (faster operation time), meaning similar computations can be performed much more quickly than on other qubits (e.g. Ion Trap). This is important since useful quantum computations will likely have millions of logical gates (operations). Additionally, the technology behind superconducting qubits can take advantage of existing methods and processes (such as printable circuits) that we have already spent decades improving. As a result it is easier to envisage a scalable superconducting quantum computer than with some other existing methods.
Cons: Superconducting qubits have fast decoherence times, meaning their ‘memory’ is very short lived and we need more error correcting qubits to compensate. Since superconducting qubits can normally only interact with the handful of qubits next to them on the device, we need extra operations to perform most algorithms. They also must be kept very cold (below 100mK, or 0.1 degrees above absolute zero) which can be expensive and inconvenient. Finally, each superconducting qubit is slightly different and must be calibrated which could cause problems on larger systems.
Spins in solids
Coherent control and measurement of single spins in solids is now possible, and this allows using electron spins in semiconductor quantum dots, or electron spins together with nuclear spins in nitrogen-vacancy (NV) color centers in diamond for QC purposes.
Quantum dots are nanoscale structures in which electrons are trapped in all three dimensions. They can be fabricated in several ways, for example, by growth or with electrode gates in a two-dimensional electron-gas. The material of choice is usually GaAs. On the other hand, NV centers are point defects in the diamond lattice, consisting of a nearest-neighbor pair made of a nitrogen atom, substituting a carbon atom, and a lattice vacancy. Although in its early stages, quantum computing with electronic and nuclear spins in an array of phosphorus donor atoms embedded in a pure silicon lattice (P:Si) has recently achieved very encouraging results.
Solid state qubits such as quantum dots are attractive because, like superconducting circuits, they could be designed to have certain characteristics and assembled in large arrays. Furthermore, they require temperatures of up to a few K (NV centers in diamond could operate even at room temperature). The manipulation and readout can be done both electrically and optically.
Nowadays, Nuclear Magnetic Resonance (NMR) techniques are extensively used in the context of nuclear spins in semiconductors. NMR techniques have been used for the control of nuclear spins in molecules , which proved very successful for realizing QC with such nuclear spin qubits
What: Artificial atoms made by adding an electron to a small piece of silicon. Microwaves are used to control the electrons state.
Who: Intel; Silicon Quantum Computing.
Pros: If successful, this approach should allow for longer coherence times than the superconducting approach. Working with silicon builds on decades of research from the existing semiconductor industry.
Cons: Unlike Ion Traps, the silicon approach requires cooling. The technology is still highly nascent.
Microsoft’s approach to building a quantum computer is based on a type of qubit – or unit of quantum information – called a topological qubit. These operate on a different principle to the other qubits The Microsoft team believes that topological qubits are better able to withstand challenges such as heat or electrical noise, allowing them to remain in a quantum state longer. That, in turn, makes them much more practical and effective. “A topological design is less impacted by changes in its environment,” Holmdahl said.
Topological quantum computing seeks to implement a more resilient qubit by utilizing non-Abelian forms of matter to store quantum information. Non-Abelian forms of matter are quasiparticles, like Majorana Fermions and Anyons (non-Abelian quasiparticles who are neither bosons or fermions). The most promising topological quantum computing developments derives from a non-Abelian braiding of chiral Majorana Fermions by Quantum Dots. The type of anyon needed to create a universal quantum computer have not been experimentally confirmed yet (but we have some tentative signs). For now, this model of quantum computation is purely theoretical.
Pros: Topological qubits are protected from noise due to their values existing at two distinct points, making our quantum computer more robust against outside interference. This increased stability will help the quantum computer scale to complete longer, more complex computations, bringing the solutions we need within reach. Topological qubits should demonstrate high coherence times and much higher fidelities (lower errors) than other implementations.
Photons can also make good qubits and they can carry quantum information over long distances hardly being affected by noise or decoherence. The qubit states can be encoded, for example, in the polarization of a single photon, and one-qubit gates can be easily realized with optical elements.
Unfortunately optical QC has a serious drawback: the difficulty in implementing two-qubit gates. Realizing the nonlinearity required for entangling two qubits is challenging, so alternatives such as the teleportation of nondeterministic quantum gates have been investigated. While this approach is still impractical due to the large amount of required resources, another solution may be found in measurement-based QC.
In 2014, Pfister’s group succeeded in generating more than 3,000 quantum modes in a bulk optical system. However, using this many quantum modes requires a large footprint to contain the thousands of mirrors, lenses and other components that would be needed to run an algorithm and perform other operations.
What: Photonic qubits are single particles of light (photons) operating on silicon chips pathways.
Who: PsiQuantum; Xanadu.
Pros: The main advantage marketed by industry players is that this implementation does not require extreme cooling, and thus allows for more energy-efficient computation. In addition, given the use of silicon chips, the approach is seen as highly scalable (as it can use existing semiconductor industry infrastructure) rather than having to develop new atomic-scale fabrication techniques.
Cons: The technology is still nascent and key areas such as qubit connectivity are to be demonstrated.
A research team led by Xu Yi, assistant professor of electrical and computer engineering at the University of Virginia School of Engineering and Applied Science, has carved a niche in the physics and applications of photonic devices, which detect and shape light for a wide range of uses including communications and computing. His research group has created a scalable quantum computing platform, which drastically reduces the number of devices needed to achieve quantum speed, on a photonic chip the size of a penny. Through multiplexing, Yi’s team verified the generation of 40 qumodes from a single microresonator on a chip, proving that multiplexing of quantum modes can work in integrated photonic platforms. This is just the number they are able to measure.
Exploiting the advantages of both natural and artificial atoms in hybrid systems provides exciting prospects for realizing QC. For instance, ions and atoms interfaced with superconducting circuits are now being investigated. As recent results point out neutral atoms and ions could also be interfaced with each other
Natural atoms, with their long decoherence times, are envisaged by many as quantum memories , while the tunable artificial atoms may be used for the “quantum processing unit”. Both natural and artificial atoms may be coupled with photons via a cavity. Note that a necessary requirement is for the coupling timescale to be shorter than the decoherence time. Such cavities could be used as input/output interfaces and for long distance communication. Perhaps the first functional quantum computer will be a complex hybrid system made of natural atoms, artificial atoms, and photons.
A team led by researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, in close collaboration with FAMU-FSU College of Engineering Associate Professor of Mechanical Engineering Wei Guo, has announced the creation of a new qubit platform that shows great promise to be developed into future quantum computers. Their work is published in Nature.
While there are many choices of qubit types, the team chose the simplest one — a single electron. The team created its qubit by freezing neon gas into a solid at very low temperatures, spraying electrons from a light bulb onto the solid and trapping a single electron there.
Defects in the qubit system can significantly reduce the coherence time. For that reason, the team chose to trap an electron on an ultrapure solid neon surface in a vacuum. Neon is one of only six inert elements, meaning it does not react with other elements.
By using a chip-scale superconducting resonator — like a miniature microwave oven — the team was able to manipulate the trapped electrons, allowing them to read and store information from the qubit, thus making it useful for use in future quantum computers.
Previous research used liquid helium as the medium for holding electrons. That material was easy to make free of defects, but vibrations of the liquid-free surface could easily disturb the electron state and hence compromise the performance of the qubit.
Solid neon offers a material with few defects that doesn’t vibrate like liquid helium. After building their platform, the team performed real-time qubit operations using microwave photons on a trapped electron and characterized its quantum properties. These tests demonstrated that solid neon provided a robust environment for the electron with very low electric noise to disturb it. Most importantly, the qubit attained coherence times in the quantum state competitive with other state-of-the-art qubits.
The simplicity of the qubit platform should also lend itself to simple, low-cost manufacturing, Jin said.
Silicon carbide qubit coherence time for a record five seconds, reported in Feb 2022
A team of researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago were able to read out their qubit on demand and then keep the quantum state intact for over five seconds — a new record for this class of devices. Additionally, the researchers’ qubits are made from an easy-to-use material called silicon carbide, which is widely found in lightbulbs, electric vehicles and high-voltage electronics.
“This essentially brings silicon carbide to the forefront as a quantum communication platform,” said University of Chicago graduate student Elena Glen, co-first author on the paper. “This is exciting because it’s easy to scale up, since we already know how to make useful devices with this material.”
The researchers grew highly purified samples of silicon carbide that reduced the background noise that tends to interfere with their qubit functioning. Then, by applying a series of microwave pulses to the qubit, they extended the amount of time that their qubits preserved their quantum information, a concept referred to as “coherence.”
“These pulses decouple the qubit from noise sources and errors by rapidly flipping the quantum state,” said Chris Anderson of the University of Chicago, co-first author on the paper. “Each pulse is like hitting the undo button on our qubit, erasing any error that may have happened between pulses.”
The researchers think that even longer coherences should be possible. Extending coherence time has significant ramifications, such as how complex an operation a future quantum computer can handle or how small a signal a quantum sensor can detect.
“For example, this new record time means we can perform over 100 million quantum operations before our state gets scrambled,” Anderson said.
Every computer needs a way to read information encoded into its bits. For semiconductor qubits, like the ones measured by the team, the typical readout method is to address the qubits with lasers and measure the light emitted back. This procedure is challenging, however, because it requires detecting single particles of light called photons very efficiently.
Instead, the researchers use carefully designed laser pulses to add a single electron to their qubit depending on its initial quantum state, either zero or one. Then the qubit is read out in the same way as before — with a laser.
“Only now, the emitted light reflects the absence or presence of the electron, and with almost 10,000 times more signal,” Glen said. “By converting our fragile quantum state into stable electronic charges, we can measure our state much, much more easily. With this signal boost, we can get a reliable answer every time we check what state the qubit is in. This type of measurement is called ‘single shot readout,’ and with it, we can unlock a lot of useful quantum technologies.”
“The ability to perform single shot readout unlocks a new opportunity: using the light emitted from silicon carbide qubits to help develop a future quantum internet,” Glen said. “Essential operations such as quantum entanglement, where the quantum state of one qubit can be known by reading out the state of another, are now in the cards for silicon carbide-based systems.”
“We’ve essentially made a translator to convert from quantum states to the realm of electrons, which are the language of classical electronics, like what’s in your smartphone,” Anderson said. “We want to create a new generation of devices that are sensitive to single electrons, but that also host quantum states. Silicon carbide can do both, and that’s why we think it really shines.”
References and resources also include: