Daily Archives: June 15, 2024

Quantum computing is like Forrest Gumps box of chocolates: You never know what youre gonna get. Quantum phenomena the behavior of matter and energy at the atomic and subatomic levels are not definite, one thing or another. They are opaque clouds of possibility or, more precisely, probabilities. When someone observes a quantum system, it loses its quantum-ness and collapses into a definite state.

Quantum phenomena are mysterious and often counterintuitive. This makes quantum computing difficult to understand. People naturally reach for the familiar to attempt to explain the unfamiliar, and for quantum computing this usually means using traditional binary computing as a metaphor. But explaining quantum computing this way leads to major conceptual confusion, because at a base level the two are entirely different animals.

This problem highlights the often mistaken belief that common metaphors are more useful than exotic ones when explaining new technologies. Sometimes the opposite approach is more useful. The freshness of the metaphor should match the novelty of the discovery.

The uniqueness of quantum computers calls for an unusual metaphor. As a communications researcher who studies technology, I believe that quantum computers can be better understood as kaleidoscopes.

The gap between understanding classical and quantum computers is a wide chasm. Classical computers store and process information via transistors, which are electronic devices that take binary, deterministic states: one or zero, yes or no. Quantum computers, in contrast, handle information probabilistically at the atomic and subatomic levels.

Classical computers use the flow of electricity to sequentially open and close gates to record or manipulate information. Information flows through circuits, triggering actions through a series of switches that record information as ones and zeros. Using binary math, bits are the foundation of all things digital, from the apps on your phone to the account records at your bank and the Wi-Fi signals bouncing around your home.

In contrast, quantum computers use changes in the quantum states of atoms, ions, electrons or photons. Quantum computers link, or entangle, multiple quantum particles so that changes to one affect all the others. They then introduce interference patterns, like multiple stones tossed into a pond at the same time. Some waves combine to create higher peaks, while some waves and troughs combine to cancel each other out. Carefully calibrated interference patterns guide the quantum computer toward the solution of a problem.

The term bit is a metaphor. The word suggests that during calculations, a computer can break up large values into tiny ones bits of information which electronic devices such as transistors can more easily process.

Using metaphors like this has a cost, though. They are not perfect. Metaphors are incomplete comparisons that transfer knowledge from something people know well to something they are working to understand. The bit metaphor ignores that the binary method does not deal with many types of different bits at once, as common sense might suggest. Instead, all bits are the same.

The smallest unit of a quantum computer is called the quantum bit, or qubit. But transferring the bit metaphor to quantum computing is even less adequate than using it for classical computing. Transferring a metaphor from one use to another blunts its effect.

The prevalent explanation of quantum computing is that while classical computers can store or process only a zero or one in a transistor or other computational unit, quantum computers supposedly store and handle both zero and one and other values in between at the same time through the process of superposition.

Superposition, however, does not store one or zero or any other number simultaneously. There is only an expectation that the values might be zero or one at the end of the computation. This quantum probability is the polar opposite of the binary method of storing information.

Driven by quantum sciences uncertainty principle, the probability that a qubit stores a one or zero is like Schroedingers cat, which can be either dead or alive, depending on when you observe it. But the two different values do not exist simultaneously during superposition. They exist only as probabilities, and an observer cannot determine when or how frequently those values existed before the observation ended the superposition.

Leaving behind these challenges to using traditional binary computing metaphors means embracing new metaphors to explain quantum computing.

The kaleidoscope metaphor is particularly apt to explain quantum processes. Kaleidoscopes can create infinitely diverse yet orderly patterns using a limited number of colored glass beads, mirror-dividing walls and light. Rotating the kaleidoscope enhances the effect, generating an infinitely variable spectacle of fleeting colors and shapes.

The shapes not only change but cant be reversed. If you turn the kaleidoscope in the opposite direction, the imagery will generally remain the same, but the exact composition of each shape or even their structures will vary as the beads randomly mingle with each other. In other words, while the beads, light and mirrors could replicate some patterns shown before, these are never absolutely the same.

Using the kaleidoscope metaphor, the solution a quantum computer provides the final pattern depends on when you stop the computing process. Quantum computing isnt about guessing the state of any given particle but using mathematical models of how the interaction among many particles in various states creates patterns, called quantum correlations.

Each final pattern is the answer to a problem posed to the quantum computer, and what you get in a quantum computing operation is a probability that a certain configuration will result.

Metaphors make the unknown manageable, approachable and discoverable. Approximating the meaning of a surprising object or phenomenon by extending an existing metaphor is a method that is as old as calling the edge of an ax its bit and its flat end its butt. The two metaphors take something we understand from everyday life very well, applying it to a technology that needs a specialized explanation of what it does. Calling the cutting edge of an ax a bit suggestively indicates what it does, adding the nuance that it changes the object it is applied to. When an ax shapes or splits a piece of wood, it takes a bite from it.

Metaphors, however, do much more than provide convenient labels and explanations of new processes. The words people use to describe new concepts change over time, expanding and taking on a life of their own.

When encountering dramatically different ideas, technologies or scientific phenomena, its important to use fresh and striking terms as windows to open the mind and increase understanding. Scientists and engineers seeking to explain new concepts would do well to seek out originality and master metaphors in other words, to think about words the way poets do.

Originally posted here:

Quantum computers are like kaleidoscopes why unusual metaphors help illustrate science and technology - The Conversation

June 14, 2024by Henning Soller and Niko Mohr with Elisa Becker-Foss, Kamalika Dutta, Martina Gschwendtner, Mena Issler, and Ming Xu

Quantum computing can leverage the states of entangled qubits1 to solve problems that classical computing cannot currently solve and to substantially improve existing solutions. These qubits, which are typically constructed from photons, atoms, or ions, can only be manipulated using specially engineered signals with precisely controlled energy that is barely above that of a vacuum and that changes within nanoseconds. This control system for qubits, referred to as quantum control, is a critical enabler of quantum computing because it ensures quantum algorithms perform with optimal efficiency and effectiveness.

While the performance and scaling limitations of current quantum control systems preclude large-scale quantum computing, several promising technological innovations may soon offer scalable control solutions.

A modern quantum computer comprises various hardware and software components, including quantum control components that require extensive space and span meters. In quantum systems, qubits interact with the environment, causing decoherence and decay of the encoded quantum information. Quantum gates (building blocks of quantum circuits) cannot be implemented perfectly at the physical system level, resulting in accumulated noise. Noise leads to decoherence, which lowers qubits superposition and entanglement properties. Quantum control minimizes the quantum noisefor example, thermal fluctuations and electromagnetic interferencecaused by the interaction between the quantum hardware and its surroundings. Quantum control also addresses noise by improving the physical isolation of qubits, using precise control techniques, and implementing quantum error correction codes. Control electronics use signals from the classical world to provide instructions for qubits, while readout electronics measure qubit states and transmit that information back to the classical world. Thus, the control layer in a quantum technology stack is often referred to as the interface between the quantum and classical worlds.

Components of the control layer include the following:

A superconducting- or spin qubitbased computer, for example, includes physical components such as quantum chips, cryogenics (cooling electronics), and control and readout electronics.

Quantum computing requires precise control of qubits and manipulation of physical systems. This control is achieved via signals generated by microwaves, lasers, and optical fields or other techniques that support the underlying qubit type. A tailored quantum control system is needed to achieve optimal algorithm performance.

In the context of a quantum computing stack, control typically refers to the hardware and software system that connects to the qubits the application software uses to solve real-world problems such as optimization and simulation (Exhibit 1).

At the top of the stack, software layers translate real-world problems into executable instructions for manipulating qubits. The software layer typically includes middleware (such as a quantum transpiler2) and control software comprising low-level system software that provides compilation, instrument control, signal generation, qubit calibration, and dynamical error suppression.3 Below the software layer is the hardware layer, where high-speed electronics and physical components work together to send signals to and read signals from qubits and to protect qubits from noise. This is the layer where quantum control instructions are executed.

Quantum control hardware systems are highly specialized to accommodate the intricacies of qubits. Control hardware interfaces directly with qubits, generating and reading out extremely weak and rapidly changing electromagnetic signals that interact with qubits. To keep qubits functioning for as long as possible, control hardware systems must be capable of adapting in real time to stabilize the qubit state (feedback calibration) and correct qubits from decaying to a completely decoherent state4 (quantum error correction).

Although all based on similar fundamental principles of quantum control, quantum control hardware can differ widely depending on the qubit technology with which it is designed to be used (Exhibit 2).

For example, photonic qubits operate at optical frequencies (similar to fiber internet), while superconducting qubits operate at microwave frequencies (similar to a fifth-generation network). Different types of hardware using laser technology or electronic circuits are needed to generate, manipulate, and transmit signals to and from these different qubit types. Additional hardware may be needed to provide environmental control. Cryostats, for example, cool superconducting qubits to keep them in a working state, and ion trap devices are used in trapped-ion qubit systems to confine ions using electromagnetic fields.

Quantum control is critical to enable fault-tolerant quantum computingquantum computing in which as many errors as possible are prevented or suppressed. But realizing this capability on a large scale will require substantial innovation. Existing control systems are designed for a small number of qubits (1 to 1,000) and rely on customized calibration and dedicated resources for each qubit. A fault-tolerant quantum computer, on the other hand, needs to control 100,000 to 1,000,000 qubits simultaneously. Consequently, a transformative approach to quantum control design is essential.

Specifically, to achieve fault-tolerant quantum computing on a large scale, there must be advances to address issues with current state-of-the-art quantum control system performance and scalability, as detailed below.

Equipping quantum systems to perform at large scales will require the following:

The limitations that physical space poses and the cost to power current quantum computing systems restrict the number of qubits that can be controlled with existing architecture, thus hindering large-scale computing.

Challenges to overcoming these restrictions include the following:

Several technologies show promise for scaling quantum control, although many are still in early-research or prototyping stages (Exhibit 3).

Multiplexing could help reduce costs and prevent overheating. The cryogenic complementary metal-oxide-semiconductor (cryo-CMOS) approach also helps mitigate overheating; it is the most widely used approach across industries because it is currently the most straightforward way to add control lines, and it works well in a small-scale R&D setup. However, cryo-CMOS is close to reaching the maximum number of control lines, creating form factor and efficiency challenges to scaling. Even with improvements, the number of control lines would only be reduced by a few orders of magnitude, which is not sufficient for scaling to millions of qubits. Another option to address overheating is single-flux quantum technology, while optical links for microwave qubits can increase efficiency in interconnections as well as connect qubits between cryostats.

Whether weighing options to supply quantum controls solutions or to invest in or integrate quantum technologies into companies in other sectors, leaders can better position their organizations for success by starting with a well-informed and strategically focused plan.

The first strategic decision leaders in the quantum control sector must make is whether to buy or build their solutions. While various levels of quantum control solutions can be sourced from vendors, few companies specialize in control, and full-stack solutions for quantum computing are largely unavailable. The prevailing expertise is that vendors can offer considerable advantages in jump-starting quantum computing operations, especially those with complex and large-scale systems. Nevertheless, a lack of industrial standardization means that switching between quantum control vendors could result in additional costs down the road. Consequently, many leading quantum computing players opt to build their own quantum control.

Ideally, business leaders also determine early on which parts of the quantum tech stack to focus their research capacities on and how to benchmark their technology. To develop capabilities and excel in quantum control, it is important to establish KPIs that are tailored to measure how effectively quantum control systems perform to achieve specific goals, such as improved qubit fidelity.5 This allows for the continuous optimization and refinement of quantum control techniques to improve overall system performance and scalability.

Quantum control is key to creating business value. Thus, the maturity and scalability of control solutions are the chief considerations for leaders exploring business development related to quantum computing, quantum solutions integration, and quantum technologies investment. In addition to scalability (the key criterion for control solutions), leaders will need to consider and address the other control technology challenges noted previously. And as control technologies mature from innovations to large-scale solutions, establishing metrics for benchmarking them will be essential to assess, for example, ease of integration, cost effectiveness, error-suppression effectiveness, software offerings, and the possibility of standardizing across qubit technologies.

Finally, given the shortage of quantum talent, recruiting and developing the highly specialized capabilities needed for each layer of the quantum stack is a top priority to ensure quantum control systems are properly developed and maintained.

Henning Soller is a partner in McKinseys Frankfurt office, and Niko Mohr is a partner in the Dsseldorf office. Elisa Becker-Foss is a consultant in the New York office, Kamalika Dutta is a consultant in the Berlin office, Martina Gschwendtner is a consultant in the Munich office, Mena Issler is an associate partner in the Bay Area office, and Ming Xu is a consultant in the Stamford office.

1 Entangled qubits are qubits that remain in a correlated state in which changes to one affect the other, even if they are separated by long distances. This property can enable massive performance boosts in information processing. 2 A quantum transpiler converts code from one quantum language to another while preserving and optimizing functionality to make algorithms and circuits portable between systems and devices. 3 Dynamical error suppression is one approach to suppressing quantum error and involves the periodic application of control pulse sequences to negate noise. 4 A qubit in a decoherent state is losing encoded quantum information (superposition and entanglement properties). 5 Qubit fidelity is a measure of the accuracy of a qubits state or the difference between its current state and the desired state.

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Quantum control's role in scaling quantum computing - McKinsey

It's a marriage that could only happen in cyberspace -- quantum computing and artificial intelligence.

Quantum AI is a burgeoning computer science sector, dedicated to exploring the potential synergy that exists between quantum computing and AI, says Gushu Li, a professor at the University of Pennsylvania School of Engineering and Applied Science, in an email interview. "It seeks to apply principles from quantum mechanics to enhance AI algorithms." A growing number of researchers now believe that AI models developed with quantum computing will soon outpace classical computing AI development.

Quantum AI creates an intersection between quantum computing and artificial intelligence, observes Romn Ors, chief scientific officer at quantum computing software development firm Multiverse Computing, via email. He notes that quantum computing has the potential to take AI to entirely new levels of performance. "For instance, it's possible to develop quantum neural networks that teach a quantum computer to detect anomalies, do image recognition, and other tasks." Ors adds that it's also possible to improve traditional AI methods by using quantum-inspired approaches to dramatically reduce the development and training costs of large language models (LLMs).

Related:Demystifying Quantum Computing: Separating Fact from Fiction

Combining the quantum physics properties of superposition and entanglement, which can perform limitless processes simultaneously with machine learning and AI, and suddenly it's possible to do more than ever imagined, says Tom Patterson, emerging technology security lead at business advisory firm Accenture, via email. "Unfortunately, that includes being used by adversaries to crack our encryption and develop new and insidious ways to separate us from our information, valuables, and anything else we hold dear."

Still, Patterson is generally optimistic. Like ChatGPT, he expects quantum AI to arrive gradually, and then all at once. "While full use of an AI-relevant quantum computer remains years away, the benefits of thinking about AI with quantum information science capabilities are exciting and important today," he states. "The opportunities are here and now, and the future is brighter than ever with quantum AI."

For his part, Li believes that quantum AI's biggest initial impact will be in four specific areas:

Drug Discovery: Simulating molecules to design new drugs and materials with superior properties.

Financial Modeling: Optimizing complex financial portfolios and uncovering hidden trends in the market.

Related:Cybersecurity's Future: Facing Post-Quantum Cryptography Peril

Materials Science: Developing new materials with specific properties for applications like superconductors or ultra-efficient solar cells.

Logistics and Optimization: Finding the most efficient routes for transportation and optimizing complex supply chains.

Quantum AI is already here, but it's a silent revolution, Ors says. "The first applications of quantum AI are finding commercial value, such as those related to LLMs, as well as in image recognition and prediction systems," he states. More quantum AI applications will become available as quantum computers grow more powerful. "It's expected that in two-to-three years there will be a broad range of industrial applications of quantum AI."

Yet the road ahead may be rocky, Li warns. "It's well known that quantum hardware suffers from noise that can destroy computation," he says. "Quantum error correction promises a potential solution, but that technology isn't yet available."

Meanwhile, while quantum AI algorithms are being developed, classical computing competitors are achieving new AI successes. "While progress is being made, it's prudent to acknowledge that the integration of quantum computing with AI is a complex endeavor that will unfold gradually," Li says.

Related:What Is the Future of AI-Driven Employee Monitoring?

Patterson notes that many of the most promising quantum AI breakthroughs aren't arriving from university and corporate research teams, but from various regional developer and support communities that closely mirror natural ecosystems. "Regions that have decided that quantum and AI are too big and too important to leave to one group or another have organized around providing everything progress demands -- from investment to science to academics to entrepreneurs, growth engines, and tier-one buyers," he says. "These regional ecosystems are where the magic happens with quantum AI."

GenAI and quantum computing are mind-blowing advances in computing technology, says Guy Harrison, enterprise architect at cybersecurity technology company OneSpan, in a recent email interview. "AI is a sophisticated software layer that emulates the very capabilities of human intelligence, while quantum computing is assembling the very building blocks of the universe to create a computing substrate," he explains. "We're pushing computing both into the realm of the mind and the realm of the sub-atomic."

The transition to quantum AI won't be optional, Ors warns, since current AI is fundamentally flawed due to excessive energy costs. New models and methods will be needed to lower energy demands and to make AI feasible in the long term. "Early adopters of quantum AI will get a competitive advantage and will survive, as opposed to those that do not adopt or adopt it too late."

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Quantum Computing and AI: A Perfect Match? - InformationWeek

By U.S. Department of Energy June 14, 2024

Artists representation of the formation pathway of vacancy complexes for spin-based qubits in the silicon carbide host lattice and to the right the associated energy landscape. Credit: University of Chicago

Quantum computers, leveraging the unique properties of qubits, outperform classical systems by simultaneously existing in multiple states. Focused research on silicon carbide aims to optimize qubits for scalable application, with studies revealing new methods to control and enhance their performance. This could lead to breakthroughs in large-scale quantum computing and sensor technologies.

While conventional computers use classical bits for calculations, quantum computers use quantum bits, or qubits, instead. While classical bits can have the values 0 or 1, qubits can exist in a mix of probabilities of both values at the same time. This makes quantum computing extremely powerful for problems conventional computers arent good at solving. To build large-scale quantum computers, researchers need to understand how to create and control materials that are suitable for industrial-scale manufacturing.

Semiconductors are very promising qubit materials. Semiconductors already make up the computer chips in cell phones, computers, medical equipment, and other applications. Certain types of atomic-scale defects, called vacancies, in the semiconductor silicon carbide (SiC) show promise as qubits. However, scientists have a limited understanding of how to generate and control these defects. By using a combination of atomic-level simulations, researchers were able to track how these vacancies form and behave.

Quantum computing could revolutionize our ability to answer challenging questions. Existing small scale quantum computers have given a glimpse of the technologys power. To build and deploy large-scale quantum computers, researchers need to know how to control qubits made of materials that make technical and economic sense for industry.

The research identified the stability and molecular pathways to create the desired vacancies for qubits and determine their electronic properties.

These advances will help the design and fabrication of spin-based qubits with atomic precision in semiconductor materials, ultimately accelerating the development of next-generation large-scale quantum computers and quantum sensors.

The next technological revolution in quantum information science requires researchers to deploy large-scale quantum computers that ideally can operate at room temperature. The realization and control of qubits in industrially relevant materials is key to achieving this goal.

In the work reported here, researchers studied qubits built from vacancies in silicon carbide (SiC) using various theoretical methods. Until now, researchers knew little about how to control and engineer the selective formation process for the vacancies. The involved barrier energies for vacancy migration and combination pose the most difficult challenges for theory and simulations.

In this study, a combination of state-of-the-art materials simulations and neural-network-based sampling technique led researchers at the Department of Energys (DOE) Midwest Center for Computational Materials (MICCoM) to discover the atomistic generation mechanism of qubits from spin defects in a wide-bandgap semiconductor.

The team showed the generation mechanism of qubits in SiC, a promising semiconductor with long qubit coherence times and all-optical spin initialization and read-out capabilities.

MICCoM is one of the DOE Computational Materials Sciences centers across the country that develops open-source, advanced software tools to help the scientific community model, simulate, and predict the fundamental properties and behavior of functional materials. The researchers involved in this study are from Argonne National Laboratory and the University of Chicago.

Reference: Stability and molecular pathways to the formation of spin defects in silicon carbide by Elizabeth M. Y. Lee, Alvin Yu, Juan J. de Pablo and Giulia Galli, 3 November 2021,Nature Communications. DOI: 10.1038/s41467-021-26419-0

This work was supported by the Department of Energy (DOE) Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division and is part of the Basic Energy Sciences Computational Materials Sciences Program in Theoretical Condensed Matter Physics. The computationally demanding simulations used several high-performance computing resources: Bebop in Argonne National Laboratorys Laboratory Computing Resource Center; the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility; and the University of Chicagos Research Computing Center. The team was awarded access to ALCF computing resources through DOEs Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. Additional support was provided by NIH.

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Better Qubits: Quantum Breakthroughs Powered by Silicon Carbide - SciTechDaily

The electrical grid is very complicated. Nobody thinks about it ever until it doesn't work. But it is critical infrastructure that runs minute-to-minute energy being consumed now was generated milliseconds ago, somewhere far away, instantaneously shot through power lines and delivered.

This gets more complicated when locally generated sustainable energy joins the mix, pushing it beyond the capabilities of classical computing solutions. Home energy supplier E.ON is trialing quantum computer solutions to manage this future grid.

Speaking at the AI Summit London, E.ON chief quantum scientist Corey OMeara explained the challenges presented by future decentralized grids.

The way grids are changing now is, if buildings have solar panels on the roofs, you want to use that renewable energy yourself, or you might want to inject that back into the grid to power your neighbor's house, he said.

This decentralized energy production and peer-to-peer energy-sharing model presents a massive overhead for an aging grid that was never meant to be digital. E.ON is working on solving this renewable energy integration optimization problem using quantum computing.

E.ON also uses AI extensively and some functions could in the future be enhanced using quantum computing. An important example is AI-driven predictive maintenance for power plants.

Related:Unilever's Alberto Prado on Quantum Computing's Future, Impact on Emerging Tech

Power plants are complex objects that have thousands of sensors that measure and monitor factors such as temperatures and pressures and store the data in the cloud. We have AI solutions to analyze them to make sure that they're functioning correctly, said OMeara.

We published a paper where we invented a novel anomaly detection algorithm using quantum computing as a subroutine. We used it with our gas turbine data as well as academic benchmark data sets from the computer science field and found that the quantum-augmented solution did perform better but only for certain metrics.

E.ON plans to develop this trial into an integrated quantum software solution that could run on today's noisy, intermediate-scale quantum computers rather than waiting for next-generation fully error-corrected devices.

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Quantum, AI Combine to Transform Energy Generation, AI Summit London - AI Business

Diraq is Partnering with GlobalFoundries for Integrated CMOS/Qubits Logic on the Same Chip and Achieves Record Spin Qubit Gate Fidelities at 1 Kelvin for Test Chips Fabricated at Imec.  Quantum Computing Report

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Diraq is Partnering with GlobalFoundries for Integrated CMOS/Qubits Logic on the Same Chip and Achieves Record Spin Qubit Gate Fidelities at 1 Kelvin...

image:

Kaushalya Jhuria in the lab testing the electronics from the experimental setup used to make qubits in silicon.

Credit: Thor Swift/Berkeley Lab

Quantum computers have the potential to solve complex problems in human health, drug discovery, and artificial intelligence millions of times faster than some of the worlds fastest supercomputers. A network of quantum computers could advance these discoveries even faster. But before that can happen, the computer industry will need a reliable way to string together billions of qubits or quantum bits with atomic precision.

Connecting qubits, however, has been challenging for the research community. Some methods form qubits by placing an entire silicon wafer in a rapid annealing oven at very high temperatures. With these methods, qubits randomly form from defects (also known as color centers or quantum emitters) in silicons crystal lattice. And without knowing exactly where qubits are located in a material, a quantum computer of connected qubits will be difficult to realize.

But now, getting qubits to connect may soon be possible. A research team led by Lawrence Berkeley National Laboratory (Berkeley Lab) says that they are the first to use a femtosecond laser to create and annihilate qubits on demand, and with precision, by doping silicon with hydrogen.

The advance could enable quantum computers that use programmable optical qubits or spin-photon qubits to connect quantum nodes across a remote network. It could also advance a quantum internet that is not only more secure but could also transmit more data than current optical-fiber information technologies.

To make a scalable quantum architecture or network, we need qubits that can reliably form on-demand, at desired locations, so that we know where the qubit is located in a material. And that's why our approach is critical, said Kaushalya Jhuria, a postdoctoral scholar in Berkeley Labs Accelerator Technology & Applied Physics (ATAP) Division. She is the first author on a new study that describes the technique in the journal Nature Communications. Because once we know where a specific qubit is sitting, we can determine how to connect this qubit with other components in the system and make a quantum network.

This could carve out a potential new pathway for industry to overcome challenges in qubit fabrication and quality control, said principal investigator Thomas Schenkel, head of the Fusion Science & Ion Beam Technology Program in Berkeley Labs ATAP Division. His group will host the first cohort of students from the University of Hawaii in June as part of a DOE Fusion Energy Sciences-funded RENEW project on workforce development where students will be immersed in color center/qubit science and technology.

Forming qubits in silicon with programmable control

The new method uses a gas environment to form programmable defects called color centers in silicon. These color centers are candidates for special telecommunications qubits or spin photon qubits. The method also uses an ultrafast femtosecond laser to anneal silicon with pinpoint precision where those qubits should precisely form. A femtosecond laser delivers very short pulses of energy within a quadrillionth of a second to a focused target the size of a speck of dust.

Spin photon qubits emit photons that can carry information encoded in electron spin across long distances ideal properties to support a secure quantum network. Qubits are the smallest components of a quantum information system that encodes data in three different states: 1, 0, or a superposition that is everything between 1 and 0.

With help from Boubacar Kant, a faculty scientist in Berkeley Labs Materials Sciences Division and professor of electrical engineering and computer sciences (EECS) at UC Berkeley, the team used a near-infrared detector to characterize the resulting color centers by probing their optical (photoluminescence) signals.

What they uncovered surprised them: a quantum emitter called the Ci center. Owing to its simple structure, stability at room temperature, and promising spin properties, the Ci center is an interesting spin photon qubit candidate that emits photons in the telecom band. We knew from the literature that Ci can be formed in silicon, but we didnt expect to actually make this new spin photon qubit candidate with our approach, Jhuria said.

The researchers learned that processing silicon with a low femtosecond laser intensity in the presence of hydrogen helped to create the Ci color centers. Further experiments showed that increasing the laser intensity can increase the mobility of hydrogen, which passivates undesirable color centers without damaging the silicon lattice, Schenkel explained.

A theoretical analysis performed by Liang Tan, staff scientist in Berkeley Labs Molecular Foundry, shows that the brightness of the Ci color center is boosted by several orders of magnitude in the presence of hydrogen, confirming their observations from laboratory experiments.

The femtosecond laser pulses can kick out hydrogen atoms or bring them back, allowing the programmable formation of desired optical qubits in precise locations, Jhuria said.

The team plans to use the technique to integrate optical qubits in quantum devices such as reflective cavities and waveguides, and to discover new spin photon qubit candidates with properties optimized for selected applications.

Now that we can reliably make color centers, we want to get different qubits to talk to each other which is an embodiment of quantum entanglement and see which ones perform the best. This is just the beginning, said Jhuria.

The ability to form qubits at programmable locations in a material like silicon that is available at scale is an exciting step towards practical quantum networking and computing, said Cameron Geddes, Director of the ATAP Division.

Theoretical analysis for the study was performed at the Department of EnergysNational Energy Research Scientific Computing Center (NERSC) at Berkeley Lab with support from the NERSC QIS@Perlmutterprogram.

The Molecular Foundry and NERSC are DOE Office of Science user facilities at Berkeley Lab.

This work was supported by the DOE Office of Fusion Energy Sciences.

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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to delivering solutions for humankind through research in clean energy, a healthy planet, and discovery science. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Researchers from around the world rely on the Labs world-class scientific facilities for their own pioneering research. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energys Office of Science.

DOEs Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visitenergy.gov/science.

Nature Communications

Experimental study

Not applicable

Programmable quantum emitter formation in silicon

27-May-2024

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New technique could help build quantum computers of the future - EurekAlert