Insider Brief

As US legislation to fund quantum hangs in the balance, research advances may boost chances of quantums funding in government, insiders are telling Politico.

Earlier this month, researchers from Harvard University, along with QuEra Computing, MIT, and NIST/UMD, tackled one of the thorniest challenges in quantum information processing errors when they executed large-scale algorithms on an error-corrected quantum computer with 48 logical qubits and hundreds of entangling logical operations. For extremely sensitive quantum calculations that can be thrown off by a range of environmental interferences, this was a huge leap in error correction and a step toward creating quantum devices that can handle meaningful tasks that bog down classical computers.

IBM also reported on a quantum processor that improves managing of errors and touted a big push to designing and building quantum computers with more than 1,000 qubits.

Politico reports the news has not gone unnoticed in Washington D.C.

For that legislation to pass, the goal is for it to be as uncontroversial and as proven as possible, and a recent breakthrough certainly provides a useful talking point, Adam Kovacevich, founder and CEO of the Chamber of Progress, told Politico.

These reports may be critical because the federal government is debating the extension of the reauthorization of the National Quantum Initiative Act, which is currently up for its first five-year extension since the original bill was signed into law in 2018, Politico reports. The legislation partially funded the Harvard study, the political news site added.

Despite these advances, theres no guarantee the legislation to fund quantum will navigate the perilous journey through Congress. While the House Committee on Science, Space and Technology passed the bill in November the House of Representatives did not attach it to the National Defense Authorization Act. Politico reports that representatives could attach quantum spending to another spending bill next month.

A couple of hurdles seem to be blocking the passing of the reauthorization, according to Politico: partisan disagreements and the growing obsession with artificial intelligence.

Garnering attention for quantum in the current tech policy landscape dominated by AI remains an uphill battle, and navigating this environment to secure sufficient recognition and resources for quantum is proving a difficult task, Hodan Omaar, a senior policy analyst at the Information Technology and Innovation Foundation, told Politico, but added, These sorts of breakthroughs dont hurt.

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Recent Research Advances Likely to Boost Government Support of Quantum - The Quantum Insider

Quantum computing is on the cusp of reshaping the landscape of artificial intelligence (AI) in profound ways, ushering in a new era of capabilities and possibilities. Over the next decade, we can expect to witness a transformation in AI applications fueled by the quantum revolution. If youre looking to know how quantum computing will affect artificial intelligence applications, stay tuned.

In this brief exploration, we will delve into three key ways in which quantum computing is poised to reshape AI, offering a glimpse into the future of these groundbreaking technologies.

We have listed three ways in which quantum computing affects artificial intelligence applications. Take a look:

Quantum computing holds immense potential to revolutionize ML and AI algorithms in several profound ways. In this extended examination, we will delve deeper into these enhancements, providing additional case studies and examples of potential applications, and also discuss some of the challenges and limitations of quantum machine learning (QML).

To delve deeper into advanced AI concepts, explore our complete guide on quantum artificial intelligence.

Quantum computers leverage the principles of superposition and entanglement to process data exponentially faster than classical computers. This speed is especially critical in ML, where tasks such as training complex models and performing large-scale simulations can be highly time-consuming.

Grovers algorithm, for instance, showcases quantum computings potential by searching unsorted databases quadratically faster than classical algorithms. This capability could significantly speed up data retrieval tasks in AI systems.

Quantum computing has the potential to efficiently manage models that are currently too complex for classical computers to handle. This includes deep learning networks with an unprecedented number of layers and nodes.

This breakthrough could lead to the development of more advanced AI models capable of learning from even larger datasets and making highly accurate predictions. These models might be particularly advantageous in areas like image recognition, natural language understanding, and autonomous decision-making systems.

Two critical aspects of quantum physics, superposition, and entanglement, contribute significantly to the enhanced computing power of quantum computers. Superposition allows a quantum bit (qubit) to be in a combination of states at once, while entanglement enables particles to remain interconnected regardless of the distance between them, allowing instantaneous interactions.

Quantum computing and ML can significantly boost each other. It is not just about speed; its also about the ability to create models that reflect complex conditions far better than current models. This is especially beneficial in areas like financial portfolio optimization, fluid dynamics simulations, and material design.

Quantum computing holds the potential to revolutionize the way complex optimization problems are solved across various industries. This is primarily due to its ability to process and analyze large datasets much faster than classical computers, as well as its unique approach to handling data through quantum mechanics principles.

Quantum entanglement is a phenomenon where pairs or groups of qubits interact in such a way that the state of one qubit is directly related to the state of another, regardless of the distance between them. This interconnectedness enables high-level correlation and parallelism in computations.

In optimization problems, entanglement allows for more efficient coordination of information across different parts of a system, enabling the quantum computer to find correlations and patterns that are not easily discernible with classical computing methods.

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Quantum computers can perform certain types of search operations much faster than classical computers, thanks to algorithms like Grovers algorithm. This is particularly beneficial in optimization problems, where the solution involves searching through a vast number of possibilities.

Quantum computers are well-suited for dealing with problems involving a large number of variables and high-dimensional spaces, which are common in complex optimization tasks. Their ability to handle these types of problems can lead to more efficient solutions than those achievable with classical techniques.

Quantum algorithms, in some cases, can provide exponential speedups over their classical counterparts. This is particularly relevant in optimization problems where traditional algorithms may struggle with the complexity or size of the data.

This quantum computing technique is used specifically for solving optimization problems. It works by encoding the problem into a quantum system and then gradually evolving this system to its lowest energy state, which corresponds to the optimal solution.

Quantum computing significantly impacts artificial intelligence applications in RL by enhancing the learning processs speed and efficiency. The unique capabilities of quantum computing, such as superposition and entanglement, allow RL algorithms to explore multiple solutions simultaneously, leading to faster learning compared to traditional methods.

A significant advancement in quantum computing for reinforcement learning is the development of hybrid systems that combine quantum and classical computing. These systems use quantum computing for trial-and-error exploration in RL, while a classical computer provides feedback or rewards based on the AI agents performance. This approach has been shown to speed up the learning process of an AI agent by over 60% in certain scenarios.

Quantum reinforcement learning (QRL) can potentially require fewer training steps to reach convergence compared to traditional RL methods. This efficiency is particularly beneficial in applications where data collection is resource-intensive or difficult, such as in autonomous systems and healthcare.

Research has focused on finding the most suitable quantum architectures and learning algorithms for RL tasks. For instance, selecting a hardware-efficient parametrized quantum circuit (PQC) and refining classical algorithms like REINFORCE to work with quantum platforms have been crucial steps. These advancements contribute to successfully training RL agents on quantum devices, albeit with some adaptations to fit the constraints of current quantum hardware.

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Lets delve into each of these eight real-life applications in greater depth to appreciate the profound impact quantum computing is set to exert:

Quantum computing offers a quantum leap in the field of pharmaceuticals. By leveraging quantum algorithms, it can significantly accelerate the complex process of molecular simulation, expediting the analysis of intricate molecular structures and interactions essential for drug discovery.

This revolutionary speed-up enables researchers to explore a vast chemical space, leading to the rapid identification and development of new drugs and treatments for a multitude of diseases, ultimately improving global healthcare.

Financial institutions worldwide stand to gain immensely from quantum computings computational prowess. Its capacity to process vast volumes of financial data with unprecedented speed and precision can transform asset allocation strategies, revolutionize risk assessment models, and fortify fraud detection mechanisms.

This quantum advantage empowers financial professionals with more accurate insights into market trends, enhancing decision-making and risk management in the financial sector.

The intricacies of modern transportation systems and logistics networks demand sophisticated optimization solutions. Classical computers often struggle to tackle the complexity of these problems efficiently.

Quantum algorithms, however, excel in handling numerous variables simultaneously, making them indispensable for solving intricate route optimization challenges. This, in turn, leads to reduced congestion, improved urban mobility, and enhanced supply chain efficiency, benefitting both cities and businesses.

Quantum computing offers a ray of hope in addressing the pressing issue of climate change. Its capacity to simulate the complexities of environmental systems with high precision promises the development of more accurate climate models.

These advanced models provide invaluable insights into climate dynamics, empowering policymakers to formulate more effective strategies for mitigating the effects of climate change and preserving the environment for future generations.

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Quantum computings prowess extends to the realm of energy optimization. It can simulate and optimize energy distribution networks, paving the way for increased energy efficiency and reduced operational costs across various industries and power grids.

This transformative application has far-reaching implications for sustainable energy practices, contributing to a greener and more environmentally conscious future.

Quantum computing plays a pivotal role in the discovery of groundbreaking materials with tailor-made properties. Through precise atomic-level simulations, it facilitates the development of materials optimized for specific applications.

These include high-performance batteries for renewable energy storage, advanced materials for cutting-edge electronics, and innovative materials for aerospace and engineering, driving innovation across a wide array of industries.

Quantum computing presents both challenges and opportunities in the realm of cybersecurity. While its immense processing power threatens existing encryption methods, it also necessitates the development of quantum-resistant encryption techniques.

Quantum computing can also contribute to the creation of more robust cryptographic protocols, bolstering cybersecurity across industries, protecting sensitive data, and ensuring the integrity of digital systems.

In essence, these applications underscore the extraordinary potential of quantum computing to catalyze innovation and transformation across diverse sectors of our global economy.

Although quantum computing is affecting artificial intelligence applications in positive ways, it also faces several limitations that are important to consider:

One of the major challenges of quantum computing is the current limitations in hardware. Quantum computers are limited in terms of their size and the number of qubits they can support.

This constrains the complexity of the problems that can be solved and makes it difficult to scale the technology. As quantum computing evolves, addressing these hardware limitations will be crucial for its broader application in AI and other fields.

Quantum computing is more prone to errors compared to classical computing due to the delicate nature of qubits. Moreover, developing robust error correction techniques is critical for creating reliable quantum computing systems.

This is a significant area of research in quantum computing, as the effectiveness of quantum algorithms depends heavily on the accuracy of quantum operations.

Quantum computing requires a completely different set of algorithms and programming techniques compared to classical computing. This complexity makes it challenging for developers to adapt and limits the number of professionals who can effectively work with quantum technology.

The distinct nature of quantum programming, involving quantum gates and the probabilistic nature of qubit states, requires a new mindset and understanding.

Quantum computing poses potential risks to cybersecurity. Furthermore, its ability to process complex algorithms at high speed could potentially break many of the cryptographic algorithms currently used to secure data and communications.

Therefore, the development of new quantum-resistant cryptographic algorithms is essential.

The absence of universally accepted standards in quantum computing hinders collaboration and idea-sharing among researchers and developers. Moreover, establishing such standards will be essential for the development of a robust and scalable quantum computing industry.

Despite these challenges, quantum computing holds the promise of transformative outcomes. Furthermore, it includes increased computational power, improved efficiency, new scientific discoveries, enhanced cybersecurity, and increased innovation.

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The future developments in quantum computing are expected to focus on several key areas, each addressing current limitations and expanding the potential applications of this technology:

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As quantum computing continues to advance at a rapid pace, its impact on artificial intelligence applications in the coming decade cannot be overstated. The synergy between quantum computing and AI is set to drive monumental changes. Additionally, this ranges from supercharged data analysis and optimization to the development of more sophisticated AI models.

With each passing year, we edge closer to realizing the full potential of this transformative partnership. Moreover, it promises breakthroughs that will not only reshape industries but also broaden the horizons of what AI can achieve. The next decade promises to be an exciting journey as we witness the evolution of quantum-powered AI. Finally, this opens doors to solutions for some of humanitys most complex challenges.

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3 Ways Quantum Computing Will Affect Artificial Intelligence Applications in the Next Decade - AMBCrypto Blog

Dec. 20, 2023 As industrial computing needs grow, the size and energy consumption of the hardware needed to keep up with those needs grows as well. A possible solution to this dilemma could be found in superconducting materials, which can reduce that energy consumption exponentially. Imagine cooling a giant data center full of constantly running servers down to nearly absolute zero, enabling large-scale computation with incredible energy efficiency.

Physicists at the University of Washington and the U.S. Department of Energys (DOE) Argonne National Laboratory have made a discovery that could help enable this more efficient future. Researchers have found a superconducting material that is uniquely sensitive to outside stimuli, enabling the superconducting properties to be enhanced or suppressed at will. This enables new opportunities for energy-efficient switchable superconducting circuits. The paper was published in Science Advances.

Superconductivity is a quantum mechanical phase of matter in which an electrical current can flow through a material with zero resistance. This leads to perfect electronic transport efficiency. Superconductors are used in the most powerful electromagnets for advanced technologies such as magnetic resonance imaging, particle accelerators, fusion reactors and even levitating trains. Superconductors have also found uses in quantum computing.

Todays electronics use semiconducting transistors to quickly switch electric currents on and off, creating the binary ones and zeroes used in information processing. As these currents must flow through materials with finite electrical resistance, some of the energy is wasted as heat. This is why your computer heats up over time. The low temperatures needed for superconductivity, usually more than 200 degrees Fahrenheit below freezing, makes those materials impractical for hand-held devices. However, they could conceivably be useful on an industrial scale.

The research team, led by Shua Sanchez of the University of Washington (now at the Massachusetts Institute of Technology), examined an unusual superconducting material with exceptional tunability. This crystal is made of flat sheets of ferromagnetic europium atoms sandwiched between superconducting layers of iron, cobalt and arsenic atoms. Finding ferromagnetism and superconductivity together in nature is extremely rare, according to Sanchez, as one phase usually overpowers the other.

It is actually a very uncomfortable situation for the superconducting layers, as they are pierced by the magnetic fields from the surrounding europium atoms, Sanchez said. This weakens the superconductivity and results in a finite electrical resistance.

To understand the interaction of these phases, Sanchez spent a year as a resident at one of the nations leading X-ray light sources, the Advanced Photon Source (APS), a DOE Office of Science user facility at Argonne. While there he was supported by DOEs Science Graduate Student Research program. Working with physicists at APS beamlines 4-ID and 6-ID, Sanchez developed a comprehensive characterization platform capable of probing microscopic details of complex materials.

Using a combination of X-ray techniques, Sanchez and his collaborators were able to show that applying a magnetic field to the crystal can reorient the europium magnetic field lines to run parallel to the superconducting layers. This removes their antagonistic effects and causes a zero-resistance state to emerge. Using electrical measurements and X-ray scattering techniques, scientists were able to confirm that they could control the behavior of the material.

The nature of independent parameters controlling superconductivity is quite fascinating, as one could map out a complete method of controlling this effect, said Argonnes Philip Ryan, a co-author on the paper. This potential posits several fascinating ideas including the ability to regulate field sensitivity for quantum devices.

The team then applied stresses to the crystal with interesting results. They found the superconductivity could be either boosted enough to overcome the magnetism even without re-orienting the field or weakened enough that the magnetic reorientation could no longer produce the zero-resistance state. This additional parameter allows for the materials sensitivity to magnetism to be controlled and customized.

This material is exciting because you have a close competition between multiple phases, and by applying a small stress or magnetic field, you can boost one phase over the other to turn the superconductivity on and off, Sanchez said. The vast majority of superconductors arent nearly as easily switchable.

About the Advanced Photon Source

The U. S. Department of Energy Office of Sciences Advanced Photon Source (APS) at Argonne National Laboratory is one of the worlds most productive X-ray light source facilities. The APS provides high-brightness X-ray beams to a diverse community of researchers in materials science, chemistry, condensed matter physics, the life and environmental sciences, and applied research. These X-rays are ideally suited for explorations of materials and biological structures; elemental distribution; chemical, magnetic, electronic states; and a wide range of technologically important engineering systems from batteries to fuel injector sprays, all of which are the foundations of our nations economic, technological, and physical well-being. Each year, more than 5,000 researchers use the APS to produce over 2,000 publications detailing impactful discoveries, and solve more vital biological protein structures than users of any other X-ray light source research facility. APS scientists and engineers innovate technology that is at the heart of advancing accelerator and light-source operations. This includes the insertion devices that produce extreme-brightness X-rays prized by researchers, lenses that focus the X-rays down to a few nanometers, instrumentation that maximizes the way the X-rays interact with samples being studied, and software that gathers and manages the massive quantity of data resulting from discovery research at the APS.

This research used resources of the Advanced Photon Source, a U.S. DOE Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

About Argonne National Laboratory

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nations first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance Americas scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energys Office of Science.

Source: Shua Sanchez And Andre Salles, Argonne

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Argonne's New Research Unlocks Potential of Switchable Superconducting Circuits in Industrial Computing - HPCwire

As your credit card is scanned one final time this holiday season, say thanks to prime numbers for keeping the checkout queues short and your money safe. Well, most of the time anyway. Much of the cryptography that goes into beating credit-card fraud comes down to 3,5,7, etc, or integers that can only be divided into themselves and 1. Banks randomly generate two huge primessay, 150 digits longand multiply them to encrypt payment authorization from the microchip of your card to the point-of-sale terminal.

As your credit card is scanned one final time this holiday season, say thanks to prime numbers for keeping the checkout queues short and your money safe. Well, most of the time anyway. Much of the cryptography that goes into beating credit-card fraud comes down to 3,5,7, etc, or integers that can only be divided into themselves and 1. Banks randomly generate two huge primessay, 150 digits longand multiply them to encrypt payment authorization from the microchip of your card to the point-of-sale terminal.

Even supercomputers cant easily decipher the original numbers because the time required to run any of the known algorithms increases exponentially with their length. A 250-digit number that was part of a 1991 factorization challenge was finally broken down into a product of two primes in 2020. On a single advanced computer running non-stop, the calculations would take 2,700 years.

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Even supercomputers cant easily decipher the original numbers because the time required to run any of the known algorithms increases exponentially with their length. A 250-digit number that was part of a 1991 factorization challenge was finally broken down into a product of two primes in 2020. On a single advanced computer running non-stop, the calculations would take 2,700 years.

Rivest-Shamir-Adleman (RSA) private keys generated with the help of large numbers let bank affix a unique, tamper-proof digital signature via microchips embedded in cards. With these replacing magnetic strips, the menace of counterfeiting has gone down. Payment scams are now more likely in e-commerce deals. However, a new challenge is emerging that could begin to erode the protection offered by prime numbers, perhaps by decade-end.

The first hint of trouble came at the start of the millennium. A team of scientists at IBM exploited the mysterious interplay of subatomic matter and energy to run calculations that would be impossible on a classical computer. Their primitive quantum computer figured out that 15 was a product of 3 and 5. A subsequent experiment in 2012 split 21 into 3 and 7. While every middle-schooler knows how to break down small integers like 15 and 21, these were the first demonstrations of Shors algorithm, a method of quantum factorization that does not get exponentially more time-consuming as the number gets bigger. In August, Oded Regev of New York University proposed what many consider the first big improvement to Peter Shors 1994 technique. If it works, the time taken to decode complex ciphers may shorten. By 2030, a $1 billion quantum computer may be able to break RSA Laboratories widely used 2048-bit encryption by factoring a 617-digit number in a few hours, according to a 2016 estimate by the National Institute of Standards and Technology in Maryland. NIST has come up with new protocols that will be resistant to quantum computers, but what if the threat arrives before the weaponry to ward it off has been adopted?

Retail losses may be kept to manageable levels, at least for a while. But a wholesale quantum heist would be catastrophic. The security of worldwide interbank payments may be compromised if the digital signatures authorizing release of funds lose their sanctity. Scammers already have a blueprint in the $81 million theft from Bangladeshs accounts with the Federal Reserve Bank of New York in 2016.

It isnt just the security of existing products thats at stake; innovative new payment instruments will be affected, too. Monetary authorities are experimenting with paperless cash. The Bank for International Settlements estimates that by 2030, there may be 15 central bank digital currencies (CBDCs) in retail circulation.

Tourbillon, a recent BIS project, has shown that the cash-like privacy that users will expect of these instruments may be realizable. The so-called blind signature protocol, invented by privacy pioneer David Chaum in 1982, may be enough to ensure that payers dont reveal their identities to anyone. Even the central bank will only check that the eCash that comes to it for verification has its signature and not been spent before. Payments made using such highly private CBDCs may also be fast and able to handle peak demand. However, this is only true as long as the RSA encryption technology is strong enough to keep malicious actors at bay. Introduce quantum-safe cryptography into the equation, and the cost to users increases: A one-second payment cycle stretches to five; transactions per second drop by a factor of 200. More experimentation is needed. If it turns out that a central banks virtual signature on CBDC tokens is not impervious to quantum counterfeitingor can only be foolproofed by slowing payments downthen ordinary people are going to reject them.

As the ChatGPT-induced revolution in generative AI has shown, meaningful breakthroughs can elude a field for decades. But once they do occur, they can multiply at an overwhelming speed. Quantum computing may be no different. Prime numbers have served the internet age well, but the private sector and public authorities cannot take their continued guardianship for granted. bloomberg

See the rest here:
Quantum computing will make online heists likelier than we can fathom | Mint - Mint

IBM physicist Olivia Lanes says quantum tech needs workers from various educational levels.Credit: IBM

The first year of university is always an opportunity to explore, but William Papantoniou really took the plunge. From the start of his studies in 2021 at the University of New South Wales (UNSW) in Sydney, Australia, he signed up for the universitys latest offering: an undergraduate degree in quantum engineering.

Now a third-year student, Papantoniou chose the programme because he wanted to learn more about quantum computers and the physics that makes them run. He first heard of the devices in a programming class during secondary school. It was presented as the future of computing, he says. They described how quantum computing makes complex problems simpler.

The programme prepares students to enter the emerging quantum-technology industry, which has begun to develop devices that use individual atoms, electrons, photons and other components exhibiting quantum properties. These distinctive properties allow quantum computers to execute types of algorithm that are not easily accessed by conventional computers.

Quantum technology includes magnetic sensors and atomic clocks, as well as quantum computers, the development of which some specialists project will take at least a decade to be commercially useful. Proponents tout these devices as a technological paradigm shift, in which quantum mechanics enables extremely precise measurements and a fresh way for computers to crunch numbers.

William Papantoniou explores quantum devices in a practical class as part of his degree.Credit: William Papantoniou/The UNSW Quantum Engineering Society

Many industries are betting that they will benefit from the anticipated quantum-computing revolution. Pharmaceutical companies and electric-vehicle manufacturers have begun to explore the use of quantum computers in chemistry simulations for drug discovery or battery development. Compared with state-of-the-art supercomputers, quantum computers are thought to more efficiently and accurately simulate molecules, which are inherently quantum mechanical in nature.

From software developers to biologists and chemists, users are now investigating whether quantum technology can bolster their fields. But there is still lively debate about how the technology will pan out, says physicist Olivia Lanes, a researcher at IBM in Yorktown Heights, New York. A lot of people dont want to enter the industry until they see the technology is robust, but can we make it robust without them?

The UNSWs undergraduate degree begins to fill a void in quantum education outside PhD programmes. The study of quantum mechanics has fallen largely under basic research since its discovery in the early twentieth century, falling in the purview of graduate studies. When quantum technologies began to be commercialized in the 2010s, the industry predominantly hired researchers with physics PhDs.

But in the past decade, governments including those in Australia, the United States, the United Kingdom, China and the European Union have collectively pledged billions of dollars to develop the quantum-technology industry. Thats aside from the commercial investment by technology companies such as Google, Microsoft, IBM and smaller start-ups. As the industry grows, experts have already started to bemoan a lack of qualified job candidates, and the shortfall looks likely to expand.

Andrea Morello instructs students in the quantum-engineering teaching laboratory at the University of New South Wales in Sydney, Australia.Credit: UNSW Sydney

For example, one estimate suggests that Australias quantum-technology industry could provide 19,400 jobs by 2045 (see go.nature.com/3ubxvac), yet a 2016 survey tallied only about 5,000 PhD physicists in the entire country (see go.nature.com/46rgpuu). With a physics graduate degree often taking five years or longer, we simply cannot produce PhDs fast enough to satisfy the needs of this booming industry, says physicist Andrea Morello, who helped to start the UNSWs undergraduate programme in quantum engineering. Instead, the industry will predominantly need engineers with undergraduate training in relevant quantum topics, such as how the hardware components work and how to write relevant software. The evolution of the quantum industry parallels that of the computer-science industry over the past 50 years. Jobs in computing in the United States grew by more than tenfold between 1970 and 2014, according to the US Census Bureau. In the early 1970s, many universities established and expanded their computer-science undergraduate programmes in anticipation.

The quantum-tech industry will need workers with various educational backgrounds to benefit society. A technology cant succeed if the only people who know how to use it are PhDs, says Lanes.

In response to this demand, some universities are starting quantum-training programmes at both the bachelors and masters levels. In 2019, Saarland University in Saarbrcken, Germany, introduced an undergraduate quantum-engineering degree similar to the UNSWs and launched a masters programme a year later. Bachelors students at Virginia Tech in Blacksburg can opt for quantum and information science as a secondary specialization, which was introduced in 2022. Pretty much every week, Ill learn about a new programme somewhere, says quantum experimentalist Abraham Asfaw, who leads education and outreach efforts for Googles quantum team in Santa Barbara, California.

Google quantum experimentalist Abraham Asfaw works on a dilution refrigerator in his laboratory in Santa Barbara, California.Credit: Erik Lucero, Google Quantum AI

Undergraduate degree programmes aim to train engineers who work directly with quantum devices and require a relatively deep understanding of quantum mechanics. The industry also needs engineers to work with conventional technology, such as the cryogenics systems that keep quantum computers cold enough to operate, or the optical fibres that link multiple quantum devices. These engineers could perhaps learn the necessary quantum mechanics in an undergraduate course or two that are incorporated into a conventional engineering degree or vocational programme, says Asfaw.

Morello and his colleagues built the UNSWs quantum-engineering programme on the framework of a conventional electrical-engineering degree. Students take largely the same course as do non-quantum engineers, but with extra, quantum-specific classes. Morello says they designed the programme so that its graduates could still choose to work as conventional electrical engineers. Its really important to choose a degree that gives you a solid basis while providing you options, says Morello.

UNSWs quantum courses originate from masters classes that Morello and his colleagues deliver. These have required academics to rethink how they teach quantum mechanics. The conventional approach comes from a theoretical physics perspective, which centres on understanding the behaviour of idealized quantum objects, such as a single confined particle. In traditional quantum mechanics courses, you [might] spend a day talking about applications, but its not the focus of the course, says physicist Lex Kemper, who is developing an undergraduate quantum engineering course at North Carolina State University in Raleigh.

How to get started in quantum computing

For example, undergraduate physics students typically learn about quantized energy levels, in which quantum objects can lose or gain energy only in discrete amounts, or quanta, through physicist Niels Bohrs quantum model of hydrogen, the simplest atom. Bohrs model depicts hydrogen as a positively charged nucleus with orbiting negatively charged electrons, and the atom can lose or gain a quantum of energy by emitting or absorbing a photon, a particle of light. Instead, Morello uses a real-world example in his teaching a material called a quantum dot, which is used in some LEDs and in some television screens. I can now teach quantum mechanics in a way that is far more engaging than the way I was taught quantum mechanics when I was an undergrad in the 1990s, he says.

Morello also teaches the mathematics behind quantum mechanics in a more computer-friendly way. His students learn to solve problems using matrices that they can represent using code written for the Python programming language, rather than conventional differential equations on paper.

His colleagues at the UNSW are also developing laboratory courses to give students hands-on experience with the hardware in quantum technologies. For example, they designed a teaching lab to convey the fundamental concept of quantum spin, a property of electrons and some other quantum particles, using commercially available synthetic diamonds known as nitrogen vacancy centres (V. K. Sewani et al. Preprint at https://arxiv.org/abs/2004.02643; 2020). Students can use magnets and a laser to observe and measure effects resulting from the diamonds quantum spin.

Quantum computers: what are they good for?

During his second trimester, Papantoniou started the Quantum Engineering Student Society. Its a difficult degree. Theres a lot of physics, a lot of maths and a lot of engineering, all of it combined together, he says. I realized straight away that there would be a need for study groups and social events to bring us together. The group invites people working at quantum-technology companies to give talks, and organizes tours of academic labs.

Asfaw thinks of these academic programmes as experiments. The quantum-technology community still needs to work out how to evaluate their success, and how various programmes can share their experiences, says Asfaw, who has helped to organize the quantum-education community. In 2020, he worked with a group of academics to identify the key concepts needed to prepare students for entering the quantum industry. These include the idea of a quantum bit, or qubit, which is the fundamental unit of information; and of a quantum state, which is a mathematical representation of a quantum object. In 2021, Asfaw worked with academics and quantum-industry specialists to publish an undergraduate curriculum in quantum engineering (A. Asfaw et al. Preprint at https://arxiv.org/abs/2108.01311; 2021).

Quantum-computing companies are helping to develop quantum education directly. The industrys overall objectives are to build quantum computers and work out how to use them, says Asfaw. It will require a large and diverse workforce to achieve those objectives, so it is in the companies interests to help to train that workforce.

IBM quantum researcher Abby Mitchell.Credit: IBM

Companies are offering teaching resources for undergraduate educators. Kemper has logged into IBMs small prototype quantum computers through the cloud to teach his undergraduates the basics. Both IBMs Qiskit and Googles Cirq are open-source software packages that anyone can use and build on. For those who have left university, contributing to this software offers a path into a quantum-computing-related job, if theyre willing to put in the time. Abby Mitchell, who works for IBMs quantum team in Yorktown Heights and who studied arts and sciences as an undergraduate, learnt quantum computing on the job by writing and debugging code for Qiskit. I managed to transfer from my old job at IBM doing web development into a full-time member of the Qiskit community team, she says.

Its still unclear how quantum technology will bring commercial value. In many ways, it is a solution looking for a problem. Quantum communications, such as creating and delivering encryption keys encoded in single photons, is theoretically more secure than current cryptography techniques. But these technologies have delivered mixed results in practice, and require buy-in from institutions such as banks and governments. Existing quantum computers still make too many errors to be able to execute commercially valuable algorithms, and researchers have not worked out whether they can do anything useful with these adolescent machines. Its a chicken-and-egg problem in some ways, says Lanes.

But Papantoniou sees the uncertain future of quantum technologies as an opportunity. Even if quantum computing doesnt become commercially successful in the next few years, he says he can use the skills in short-term technologies, such as quantum sensing.

He has two more years before he graduates with two bachelors degrees, in quantum engineering and computer science. He plans to enter the quantum-technology industry after graduation, and is particularly interested in the development of algorithms for quantum computers. I have to do a lot of explaining to my parents [about] what I study, says Papantoniou. At this point, nobody really knows what a quantum engineer is. But in ten years time, they will.

Link:
The future is quantum: universities look to train engineers for an emerging industry - Nature.com