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As Artificial Intelligence (AI) and natural language processing advance, people often do not know if they are talking to a person or an AI-powered chatbot. What matters more than who or what is on the other side of the chat is the perceived humanness of the interaction.

With text-based bots becoming ubiquitous and AI-powered voice systems emerging, consumers of everything from shoes to insurance may find themselves talking to non-humans. Companies will have to decide when bots are appropriate and effective and when they are not. Researchers then developed a measurement for perceived humanness.

In the study, participants chatted with bots or human agents from companies and rated them on humanness. Sixty-three of 172 participants could not identify whether they were interacting with a human or a machine. But whether the interaction featured AI or not, higher scores of perceived humanness led to greater consumer trust in the companies.

If people felt like if it was humaneither with really good AI or with a real personthen they felt like the organisation was investing in the relationship. People think that the company is trying to create good interaction and the company put some time or resources into this, and therefore they trust the organisation.

Tom Kelleher, Ph.D., Advertising Professor, College of Journalism and Communications, University of Florida

Researchers started studying how language affects customer trust more than a decade ago when blogging culture introduced a conversational approach to the stuffy, stilted language businesses tended to bludgeon their customers with.

Companies noticed that as jargon waned, consumer trust, satisfaction and commitment grew. The new study shows that the same holds true with chatbots and other online interactions, and can be applied to bots and humans alike.

As AI-powered interfaces blossom, even expanding to include animated avatars that look human, ethical issues will follow. Should companies disclose when customers are interacting with a non-human agent? What if the helper is a hybrid: A person assisted by AI? Are there areas where consumers will not accept bots, such as health care, or situations where they might prefer a non-human?

If Im just trying to get an insurance quote, I would almost rather put something into an app than have to make small talk about the weather. But later on, if my house floods, Im going to want to talk to a real person. As the metaverse evolves, understanding when to employ AI and when to employ real people will be an increasingly important business decision, Kelleher said.

As reported by OpenGov Asia, a new report showed that Artificial Intelligence (AI) has reached a critical turning point in its evolution. Substantial advances in language processing, computer vision and pattern recognition mean that AI is touching peoples lives dailyfrom helping people to choose a movie to aid in medical diagnoses.

With that success, however, comes a renewed urgency to understand and mitigate the risks and downsides of AI-driven systems, such as algorithmic discrimination or the use of AI for deliberate deception. Computer scientists must work with experts in the social sciences and law to assure that the pitfalls of AI are minimised.

The report Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report aims to monitor the progress of AI and guide its future development. This new report, the second to be released by the AI100 project, assesses developments in AI between 2016 and 2021.

In terms of AI advances, the panel noted substantial progress across subfields of AI, including speech and language processing, computer vision and other areas. Much of this progress has been driven by advances in machine learning techniques, particularly deep learning systems, which have leapt in recent years from the academic setting to everyday applications.

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3 Phases on the Journey to Multicloud OpenGov Asia - OpenGov Asia

As the pandemic shifted more work online and remote work became the norm, government agencies move to a multi-cloud environment quickly. However, many agencies are realising that not all apps and workloads are suited to the cloud. Skyrocketing costs due to data egress, poor performance from lack of in-house skills to manage workloads in public clouds and security complications related to compliance demands have made many cloud workloads problematic.

This year will be the year of multi-cloud strategy when federal IT leaders take a step back and prioritise creating a comprehensive plan for deploying multicloud environments. There are three critical phases to thoughtful deployment that will allow agencies to reap all the benefits that multicloud has to offer. These three stages include assessing the current IT system, determining where workloads belong and mapping out plans and achieving business goals set out in those plans.

Phase 1: Assess the current IT ecosystem

Throughout this process, agencies may determine that some apps must be repatriated, or moved back from the cloud to on-premises. For best decision-making, this is the time to examine data egress (when applications send data back and forth from clouds or downloads and files are moved to external storage).

This is especially important for agencies that have experienced unforeseen cloud costs due to unanticipated egress costs and misunderstanding how chatty their on-premises apps would be with apps in the public cloud. The key to success for this phase will be less about applications and more about where workloads and corresponding data reside.

Phase 2: Determine where workloads belong and map out a plan

After thoroughly vetting the current IT environment and app workloads, its time to map out getting from an as-is state to the to be state. This process will encompass gauging what workloads need to be containerized and ported, which refactored and which rewritten entirely.

Phase 3: Achieve goals set out in the plan

This phase should also be iterative, never stopping after implementation, and aim to reduce time to value, minimize risk and manage costs more effectively. Navigating multicloud is not simply a matter of technology. Successful transitions involve people, processes and technology. Agencies will have to prepare for a cultural shift, process changes and be equipped with the necessary technologies and training to enable successful multi-cloud deployment.

To effectively manage costs, agencies will need automated continuous monitoring that focuses on instances. Too often, organizations have been surprised by shadow IT where employees knowingly or unknowingly use cloud services that contribute to exhausting the cloud budget. Actively managing instances and services in the multi-cloud environment is vital to monitoring costs.

By incorporating a more thoughtful approach to multi-cloud, federal agencies stand to glean more of its benefits in the coming year, including increased agility, flexibility, efficiency, performance, security and cost management.

As reported by OpenGov Asia, a report titled Government Cloud Platforms 20212022 RadarView evaluated 15 providers based on product maturity, enterprise adaptability and future readiness. The report identifies four trends that are shaping the market. The first is the increasing compliance needs that are accelerating the shift to the cloud. The cloud helps agencies address sensitive workloads, such as those involving health care data while complying with requirements.

The second trend is the emergence of tailored cloud regions for communities such as defence and intelligence. Such regions can address the level of sensitive data that these communities work with, and these users can look to these isolated cloud resources to deploy workloads securely and compliantly.

The third trend is the fact that convergence with emerging technologies is driving change. Fourth, government cloud providers are expanding their influence by growing into new regions and helping the public sector shift to cloud while maintaining data governance and sovereignty.

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Taiwan University Unveils Heart Arrhythmia Detection App - OpenGov Asia

Proposed quantum computer implementation

A trapped ion quantum computer is one proposed approach to a large-scale quantum computer. Ions, or charged atomic particles, can be confined and suspended in free space using electromagnetic fields. Qubits are stored in stable electronic states of each ion, and quantum information can be transferred through the collective quantized motion of the ions in a shared trap (interacting through the Coulomb force). Lasers are applied to induce coupling between the qubit states (for single qubit operations) or coupling between the internal qubit states and the external motional states (for entanglement between qubits).[1]

The fundamental operations of a quantum computer have been demonstrated experimentally with the currently highest accuracy in trapped ion systems. Promising schemes in development to scale the system to arbitrarily large numbers of qubits include transporting ions to spatially distinct locations in an array of ion traps, building large entangled states via photonically connected networks of remotely entangled ion chains, and combinations of these two ideas. This makes the trapped ion quantum computer system one of the most promising architectures for a scalable, universal quantum computer. As of April 2018, the largest number of particles to be controllably entangled is 20 trapped ions.[2][3][4]

The first implementation scheme for a controlled-NOT quantum gate was proposed by Ignacio Cirac and Peter Zoller in 1995,[5] specifically for the trapped ion system. The same year, a key step in the controlled-NOT gate was experimentally realized at NIST Ion Storage Group, and research in quantum computing began to take off worldwide.[citation needed]

In 2021, researchers from the University of Innsbruck presented a quantum computing demonstrator that fits inside two 19-inch server racks, the world's first quality standards-meeting compact trapped ion quantum computer.[7][6]

The electrodynamic ion trap currently used in trapped ion quantum computing research was invented in the 1950s by Wolfgang Paul (who received the Nobel Prize for his work in 1989[8]). Charged particles cannot be trapped in 3D by just electrostatic forces because of Earnshaw's theorem. Instead, an electric field oscillating at radio frequency (RF) is applied, forming a potential with the shape of a saddle spinning at the RF frequency. If the RF field has the right parameters (oscillation frequency and field strength), the charged particle becomes effectively trapped at the saddle point by a restoring force, with the motion described by a set of Mathieu equations.[1]

This saddle point is the point of minimized energy magnitude, | E ( x ) | {displaystyle |E(mathbf {x} )|} , for the ions in the potential field.[9] The Paul trap is often described as a harmonic potential well that traps ions in two dimensions (assume x ^ {displaystyle {hat {x}}} and y ^ {displaystyle {widehat {y}}} without loss of generality) and does not trap ions in the z ^ {displaystyle {widehat {z}}} direction. When multiple ions are at the saddle point and the system is at equilibrium, the ions are only free to move in z ^ {displaystyle {widehat {z}}} . Therefore, the ions will repel each other and create a vertical configuration in z ^ {displaystyle {widehat {z}}} , the simplest case being a linear strand of only a few ions.[10] Coulomb interactions of increasing complexity will create a more intricate ion configuration if many ions are initialized in the same trap.[1] Furthermore, the additional vibrations of the added ions greatly complicate the quantum system, which makes initialization and computation more difficult.[10]

Once trapped, the ions should be cooled such that k B T z {displaystyle k_{rm {B}}Tll hbar omega _{z}} (see Lamb Dicke regime). This can be achieved by a combination of Doppler cooling and resolved sideband cooling. At this very low temperature, vibrational energy in the ion trap is quantized into phonons by the energy eigenstates of the ion strand, which are called the center of mass vibrational modes. A single phonon's energy is given by the relation z {displaystyle hbar omega _{z}} . These quantum states occur when the trapped ions vibrate together and are completely isolated from the external environment. If the ions are not properly isolated, noise can result from ions interacting with external electromagnetic fields, which creates random movement and destroys the quantized energy states.[1]

The full requirements for a functional quantum computer are not entirely known, but there are many generally accepted requirements. David DiVincenzo outlined several of these criterion for quantum computing.[1]

Any two-level quantum system can form a qubit, and there are two predominant ways to form a qubit using the electronic states of an ion:

Hyperfine qubits are extremely long-lived (decay time of the order of thousands to millions of years) and phase/frequency stable (traditionally used for atomic frequency standards).[10] Optical qubits are also relatively long-lived (with a decay time of the order of a second), compared to the logic gate operation time (which is of the order of microseconds). The use of each type of qubit poses its own distinct challenges in the laboratory.

Ionic qubit states can be prepared in a specific qubit state using a process called optical pumping. In this process, a laser couples the ion to some excited states which eventually decay to one state which is not coupled to the laser. Once the ion reaches that state, it has no excited levels to couple to in the presence of that laser and, therefore, remains in that state. If the ion decays to one of the other states, the laser will continue to excite the ion until it decays to the state that does not interact with the laser. This initialization process is standard in many physics experiments and can be performed with extremely high fidelity (>99.9%).[11]

The system's initial state for quantum computation can therefore be described by the ions in their hyperfine and motional ground states, resulting in an initial center of mass phonon state of | 0 {displaystyle |0rangle } (zero phonons).[1]

Measuring the state of the qubit stored in an ion is quite simple. Typically, a laser is applied to the ion that couples only one of the qubit states. When the ion collapses into this state during the measurement process, the laser will excite it, resulting in a photon being released when the ion decays from the excited state. After decay, the ion is continually excited by the laser and repeatedly emits photons. These photons can be collected by a photomultiplier tube (PMT) or a charge-coupled device (CCD) camera. If the ion collapses into the other qubit state, then it does not interact with the laser and no photon is emitted. By counting the number of collected photons, the state of the ion may be determined with a very high accuracy (>99.9%).[citation needed]

One of the requirements of universal quantum computing is to coherently change the state of a single qubit. For example, this can transform a qubit starting out in 0 into any arbitrary superposition of 0 and 1 defined by the user. In a trapped ion system, this is often done using magnetic dipole transitions or stimulated Raman transitions for hyperfine qubits and electric quadrupole transitions for optical qubits. The term "rotation" alludes to the Bloch sphere representation of a qubit pure state. Gate fidelity can be greater than 99%.

The rotation operators R x ( ) {displaystyle R_{x}(theta )} and R y ( ) {displaystyle R_{y}(theta )} can be applied to individual ions by manipulating the frequency of an external electromagnetic field from and exposing the ions to the field for specific amounts of time. These controls create a Hamiltonian of the form H I i = / 2 ( S + exp ( i ) + S exp ( i ) ) {displaystyle H_{I}^{i}=hbar Omega /2(S_{+}exp(iphi )+S_{-}exp(-iphi ))} . Here, S + {displaystyle S_{+}} and S {displaystyle S_{-}} are the raising and lowering operators of spin (see Ladder operator). These rotations are the universal building blocks for single-qubit gates in quantum computing.[1]

To obtain the Hamiltonian for the ion-laser interaction, apply the JaynesCummings model. Once the Hamiltonian is found, the formula for the unitary operation performed on the qubit can be derived using the principles of quantum time evolution. Although this model utilizes the rotating wave approximation, it proves to be effective for the purposes of trapped-ion quantum computing.[1]

Besides the controlled-NOT gate proposed by Cirac and Zoller in 1995, many equivalent, but more robust, schemes have been proposed and implemented experimentally since. Recent theoretical work by JJ. Garcia-Ripoll, Cirac, and Zoller have shown that there are no fundamental limitations to the speed of entangling gates, but gates in this impulsive regime (faster than 1 microsecond) have not yet been demonstrated experimentally. The fidelity of these implementations has been greater than 99%.[12]

Quantum computers must be capable of initializing, storing, and manipulating many qubits at once in order to solve difficult computational problems. However, as previously discussed, a finite number of qubits can be stored in each trap while still maintaining their computational abilities. It is therefore necessary to design interconnected ion traps that are capable of transferring information from one trap to another. Ions can be separated from the same interaction region to individual storage regions and brought back together without losing the quantum information stored in their internal states. Ions can also be made to turn corners at a "T" junction, allowing a two dimensional trap array design. Semiconductor fabrication techniques have also been employed to manufacture the new generation of traps, making the 'ion trap on a chip' a reality. An example is the quantum charge-coupled device (QCCD) designed by D. Kielpinski, C. Monroe, and D.J. Wineland.[13] QCCDs resemble mazes of electrodes with designated areas for storing and manipulating qubits.

The variable electric potential created by the electrodes can both trap ions in specific regions and move them through the transport channels, which negates the necessity of containing all ions in a single trap. Ions in the QCCD's memory region are isolated from any operations and therefore the information contained in their states is kept for later use. Gates, including those that entangle two ion states, are applied to qubits in the interaction region by the method already described in this article.[13]

When an ion is being transported between regions in an interconnected trap and is subjected to a nonuniform magnetic field, decoherence can occur in the form of the equation below (see Zeeman effect).[13] This is effectively changes the relative phase of the quantum state. The up and down arrows correspond to a general superposition qubit state, in this case the ground and excited states of the ion.

| + | exp ( i ) | + | {displaystyle left|uparrow rightrangle +left|downarrow rightrangle longrightarrow exp(ialpha )left|uparrow rightrangle +left|downarrow rightrangle }

Additional relative phases could arise from physical movements of the trap or the presence of unintended electric fields. If the user could determine the parameter , accounting for this decoherence would be relatively simple, as known quantum information processes exist for correcting a relative phase.[1] However, since from the interaction with the magnetic field is path-dependent, the problem is highly complex. Considering the multiple ways that decoherence of a relative phase can be introduced in an ion trap, reimagining the ion state in a new basis that minimizes decoherence could be a way to eliminate the issue.

One way to combat decoherence is to represent the quantum state in a new basis called the decoherence-free subspaces, or DFS., with basis states | {displaystyle left|uparrow downarrow rightrangle } and | {displaystyle left|downarrow uparrow rightrangle } . The DFS is actually the subspace of two ion states, such that if both ions acquire the same relative phase, the total quantum state in the DFS will be unaffected.[13]

Trapped ion quantum computers theoretically meet all of DiVincenzo's criteria for quantum computing, but implementation of the system can be quite difficult. The main challenges facing trapped ion quantum computing are the initialization of the ion's motional states, and the relatively brief lifetimes of the phonon states.[1] Decoherence also proves to be challenging to eliminate, and is caused when the qubits interact with the external environment undesirably.[5]

The controlled NOT gate is a crucial component for quantum computing, as any quantum gate can be created by a combination of CNOT gates and single-qubit rotations.[10] It is therefore important that a trapped-ion quantum computer can perform this operation by meeting the following three requirements.

First, the trapped ion quantum computer must be able to perform arbitrary rotations on qubits, which are already discussed in the "arbitrary single-qubit rotation" section.

The next component of a CNOT gate is the controlled phase-flip gate, or the controlled-Z gate (see quantum logic gate). In a trapped ion quantum computer, the state of the center of mass phonon functions as the control qubit, and the internal atomic spin state of the ion is the working qubit. The phase of the working qubit will therefore be flipped if the phonon qubit is in the state | 1 {displaystyle |1rangle } .

Lastly, a SWAP gate must be implemented, acting on both the ion state and the phonon state.[1]

Two alternate schemes to represent the CNOT gates are presented in Michael Nielsen and Isaac Chuang's Quantum Computation and Quantum Information and Cirac and Zoller's Quantum Computation with Cold Trapped Ions.[1][5]

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Trapped ion quantum computer - Wikipedia

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Yale Quantum Institute

By Shannon Brescher Shea, U.S. Department of EnergyJanuary 3, 2022

A computer is suspended from the ceiling. Delicate lines and loops of silvery wires and tubes connect gold-colored platforms. It seems to belong in a science-fiction movie, perhaps a steam-punk cousin of HAL in 2001: A Space Odyssey. But as the makers of that 1968 movie imagined computers the size of a spaceship, this technology would have never crossed their minds a quantum computer.

Quantum computers have the potential to solve problems that conventional computers cant. Conventional computer chips can only process so much information at one time and were coming very close to reaching their physical limits. In contrast, the unique properties of materials for quantum computing have the potential to process more information much faster.

These advances could revolutionize certain areas of scientific research. Identifying materials with specific characteristics, understanding photosynthesis, and discovering new medicines all require massive amounts of calculations. In theory, quantum computing could solve these problems faster and more efficiently. Quantum computing could also open up possibilities we never even considered. Its like a microwave oven versus a conventional oven different technologies with different purposes.

But were not there yet. So far, one company has claimed its quantum computer can complete a specific calculation faster than the worlds fastest conventional supercomputers. Scientists routinely using quantum computers to answer scientific questions is a long way off.

To use quantum computers on a large scale, we need to improve the technology at their heart qubits. Qubits are the quantum version of conventional computers most basic form of information, bits. The DOEs Office of Science is supporting research into developing the ingredients and recipes to build these challenging qubits.

DOEs Lawrence Berkeley National Laboratory is using a sophisticated cooling system to keep qubits the heart of quantum computers cold enough for scientists to study them for use in quantum computers. Credit: Image courtesy of Lawrence Berkeley National Laboratory

At the atomic scale, physics gets very weird. Electrons, atoms, and other quantum particles interact with each other differently than ordinary objects. In certain materials, we can harness these strange behaviors. Several of these properties particularly superposition and entanglement can be extremely useful in computing technology.

The principle of superposition is the idea that a qubit can be in multiple states at once. With traditional bits, you only have two options: 1 or 0. These binary numbers describe all of the information in any computer. Qubits are more complicated.

Imagine a pot with water in it. When you have water in a pot with a top on it, you dont know if its boiling or not. Real water is either boiling or not; looking at it doesnt change its state. But if the pot was in the quantum realm, the water (representing a quantum particle) could both be boiling and not boiling at the same time or any linear superposition of these two states. If you took the lid off of that quantum pot, the water would immediately be one state or the other. The measurement forces the quantum particle (or water) into a specific observable state.

Entanglement is when qubits have a relationship to each other that prevents them from acting independently. It happens when a quantum particle has a state (such as spin or electric charge) thats linked to another quantum particles state. This relationship persists even when the particles are physically far apart, even far beyond atomic distances.

These properties allow quantum computers to process more information than conventional bits that can only be in a single state and only act independently from each other.

But to get any of these great properties, you need to have fine control over a materials electrons or other quantum particles. In some ways, this isnt so different from conventional computers. Whether electrons move or not through a conventional transistor determines the bits value, making it either 1 or 0.

Rather than simply switching electron flow on or off, qubits require control over tricky things like electron spin. To create a qubit, scientists have to find a spot in a material where they can access and control these quantum properties. Once they access them, they can then use light or magnetic fields to create superposition, entanglement, and other properties.

In many materials, scientists do this by manipulating the spin of individual electrons. Electron spin is similar to the spin of a top; it has a direction, angle, and momentum. Each electrons spin is either up or down. But as a quantum mechanical property, spin can also exist in a combination of up and down. To influence electron spin, scientists apply microwaves (similar to the ones in your microwave oven) and magnets. The magnets and microwaves together allow scientists to control the qubit.

Since the 1990s, scientists have been able to gain better and better control over electron spin. Thats allowed them to access quantum states and manipulate quantum information more than ever before.

To see where thats gone today, its remarkable, said David Awschalom, a quantum physicist at DOEs Argonne National Laboratory and the University of Chicago as well as Director of the Chicago Quantum Exchange.

Whether they use electron spin or another approach, all qubits face major challenges before we can scale them up. Two of the biggest ones are coherence time and error correction.

When you run a computer, you need to be able to create and store a piece of information, leave it alone, and then come back later to retrieve it. However, if the system that holds the information changes on its own, its useless for computing. Unfortunately, qubits are sensitive to the environment around them and dont maintain their state for very long.

Right now, quantum systems are subject to a lot of noise, things that cause them to have a low coherence time (the time they can maintain their condition) or produce errors. Making sure that you get the right answer all of the time is one of the biggest hurdles in quantum computing, said Danna Freedman, an associate professor in chemistry at Northwestern University.

Even if you can reduce that noise, there will still be errors. We will have to build technology that is able to do error correction before we are able to make a big difference with quantum computing, said Giulia Galli, a quantum chemist and physicist at DOEs Argonne National Laboratory and the University of Chicago.

The more qubits you have in play, the more these problems multiply. While todays most powerful quantum computers have about 50 qubits, its likely that they will need hundreds or thousands to solve the problems that we want them to.

The jury is still out on which qubit technology will be the best. No real winner has been identified, said Galli. [Different ones] may have their place for different applications. In addition to computing, different quantum materials may be useful for quantum sensing or networked quantum communications.

To help move qubits forward, DOEs Office of Science is supporting research on a number of different technologies. To realize quantum computings enormous scientific potential, we need to reimagine quantum R&D by simultaneously exploring a range of possible solutions, said Irfan Siddiqi, a quantum physicist at the DOE Lawrence Berkeley National Laboratory and the University of California, Berkeley.

Superconducting Qubits

Superconducting qubits are currently the most advanced qubit technology. Most existing quantum computers use superconducting qubits, including the one that beat the worlds fastest supercomputer. They use metal-insulator-metal sandwiches called Josephson junctions. To turn these materials into superconductors materials that electricity can run through with no loss scientists lower them to extremely cold temperatures. Among other things, pairs of electrons coherently move through the material as if theyre single particles. This movement makes the quantum states more long-lived than in conventional materials.

To scale up superconducting qubits, Siddiqi and his colleagues are studying how to build them even better with support from the Office of Science. His team has examined how to make improvements to a Josephson junction, a thin insulating barrier between two superconductors in the qubit. By affecting how electrons flow, this barrier makes it possible to control electrons energy levels. Making this junction as consistent and small as possible can increase the qubits coherence time. In one paper on these junctions, Siddiqis team provides a recipe to build an eight-qubit quantum processor, complete with experimental ingredients and step-by-step instructions.

Qubits Using Defects

Defects are spaces where atoms are missing or misplaced in a materials structure. These spaces change how electrons move in the materials. In certain quantum materials, these spaces trap electrons, allowing researchers to access and control their spins. Unlike superconductors, these qubits dont always need to be at ultra-low temperatures. They have the potential to have long coherence times and be manufactured at scale.

While diamonds are usually valued for their lack of imperfections, their defects are actually quite useful for qubits. Adding a nitrogen atom to a place where there would normally be a carbon atom in diamonds creates whats called a nitrogen-vacancy center. Researchers using the Center for Functional Nanomaterials, a DOE Office of Science user facility, found a way to create a stencil just two nanometers long to create these defect patterns. This spacing helped increase these qubits coherence time and made it easier to entangle them.

But useful defects arent limited to diamonds. Diamonds are expensive, small, and hard to control. Aluminum nitride and silicon carbide are cheaper, easier to use, and already common in everyday electronics. Galli and her team used theory to predict how to physically strain aluminum nitride in just the right way to create electron states for qubits. As nitrogen vacancies occur naturally in aluminum nitride, scientists should be able to control electron spin in it just as they do in diamonds. Another option, silicon carbide, is already used in LED lights, high-powered electronics, and electronic displays. Awschaloms team found that certain defects in silicon carbide have coherence times comparable to or longer than those in nitrogen-vacancy centers in diamonds. In complementary work, Gallis group developed theoretical models explaining the longer coherence times.

Based on theoretical work, we began to examine these materials at the atomic scale. We found that the quantum states were always there, but no one had looked for them, said Awschalom. Their presence and robust behavior in these materials were unexpected. We imagined that their quantum properties would be short-lived due to interactions with nearby nuclear spins. Since then, his team has embedded these qubits in commercial electronic wafers and found that they do surprisingly well. This can allow them to connect the qubits with electronics.

Materials by Design

While some scientists are investigating how to use existing materials, others are taking a different tack designing materials from scratch. This approach builds custom materials molecule by molecule. By customizing metals, the molecules or ions bound to metals, and the surrounding environment, scientists can potentially control quantum states at the level of a single particle.

When youre talking about both understanding and optimizing the properties of a qubit, knowing that every atom in a quantum system is exactly where you want it is very important, said Freedman.

With this approach, scientists can limit the amount of nuclear spin (the spin of the nucleus of an atom) in the qubits environment. A lot of atoms that contain nuclear spin cause magnetic noise that makes it hard to maintain and control electron spin. That reduces the qubits coherence time. Freedman and her team developed an environment that had very little nuclear spin. By testing different combinations of solvents, temperatures, and ions/molecules attached to the metal, they achieved a 1 millisecond coherence time in a molecule that contains the metal vanadium. That was a much longer coherence time than anyone had achieved in a molecule before. While previous molecular qubits had coherence times that were five times shorter than diamond nitrogen-vacancy centers times, this matched coherence times in diamonds.

That was genuinely shocking to me because I thought molecules would necessarily be the underdogs in this game, said Freedman. [It] opens up a gigantic space for us to play in.

The surprises in quantum just keep coming. Awschalom compared our present-day situation to the 1950s when scientists were exploring the potential of transistors. At the time, transistors were less than half an inch long. Now laptops have billions of them. Quantum computing stands in a similar place.

The overall idea that we could completely transform the way that computation is done and the way nature is studied by doing quantum simulation is really very exciting, said Galli. Our fundamental way of looking at materials, based on quantum simulations, can finally be useful to develop technologically relevant devices and materials.

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Creating the Heart of a Quantum Computer: Developing Qubits - SciTechDaily