Today, we are in the midst of a “quantum race”. Government investments, such the UK, US, China, Japan and the European Commission, are in the 20s of billions of US dollars into developing the technology in the last decade.
Quantum technology (QT) deal with practical applications of quantummechanical properties—especially quantum entanglement, quantum superposition and quantum tunnelling—applied to quantum systems such as atoms, ions, electrons, photons, molecules or various quasiparticles.
Quantum bit is the basic unit of quantum information. Whereas in a classical system, a bit is either in one state or the another. However, Superposition enables quantum qubits to exist in large number of states simultaneously. Quantum entanglement is a phenomenon where entangled particles stay connected in the sense that the actions performed on one of the particles affects the other, no matter the distance between two particles. Another feature is the no-cloning theorem, which says that quantum information (qubit) cannot be copied. Tunneling means that quantum mechanical particles can simply pass-through energy barriers even if they don’t have the energy to surmount them.
Quantum computers by harnessing quantum super-positioning to represent multiple states simultaneously, promise exponential leaps in performance over today’s traditional computers. A qubit is the unit of quantum information that is equivalent to the binary bit in classical computing. What makes qubits so interesting is that the 1s and 0s can be superimposed, which means that it can perform many calculations at the same time in parallel.
So unlike a binary computer, where a bit is either 0 or 1, a quantum computer has three basic states—0, 1, and 0 or 1. In greatly oversimplified terms, that allows operations can be carried out using different values at the same time. Coupled with well-constructed algorithms, quantum computers will be at least as powerful as today’s supercomputers, and in the future they are expected to be orders of magnitude more. Quantum computers shall bring power of massive parallel computing i.e. equivalent of supercomputer to a single chip.
The development of quantum computers is now advancing rapidly and expected to enter mainstream within a decade. The companies such as IBM, Intel, Microsoft, Google and D-Wave Systems, all are looking to commercialize the technology.
Google’s quantum processor- Bristlecone (72 – Qubit gate system) recently secured the top spot for the world’s largest quantum computer processor. Before, IBM’s quantum processor with 50 Qubit gate system acclaimed the position. Google and NASA announced that they have achieved ‘quantum supremacy.’ Their quantum computer solved a problem in 200 seconds, which would take 10,000 years for the world’s fastest supercomputer to solve.
Now scientists in China have tested two different quantum computers on what they say are more challenging tasks than Sycamore faced and showed faster results. They note their work points to “an unambiguous quantum computational advantage.”
In one study, the researchers experimented with Zuchongzi, which used 56 superconducting qubits on a task whose solutions are random instances, or samples, from a given spread of probabilities. They found Zuchongzi completed such a sampling task in 1.2 hours, one they estimated would take Summit at least 8.2 years to finish. They also noted this sampling task was tens to hundreds of times more computationally demanding than what Google used to establish quantum advantage with Sycamore.
In another study, the scientists tested Jiuzhang 2.0, a photonic quantum computer, using Gaussian boson sampling, a task where the machine analyzes random patches of data. Using 113 detected photons, they estimated Jiuzhang 2.0 could solve the problem roughly 1024 faster than classical supercomputers.
The field of Quantum Computing (QC) has seen considerable progress in recent years, both in the number of qubits that can be physically realized, and in the formulation of new quantum search and optimization algorithms. However, numerous challenges remain to be solved to usefully employ QC to solve real world problems. These include challenges of scale, environmental interactions, input/output, qubit connectivity, quantum memory (or lack thereof), quantum state preparation and readout, and numerous other practical and architectural challenges associated with interfacing to the classical world.
The demand for quantum technologies is being driven by large and significant societal challenges, including the need to build in more inhospitable places, for greater security around information and transactions, for better medicines and therapies, and to counter cyber terrorism. Technologies that will allow fire crews to see through smoke and dust, computers to solve previously unsolvable computational problems, construction projects to image unmapped voids like old mine workings, and cameras that will let vehicles ‘see’ around corners are just some of the developments already taking place in the UK.
Researchers have demonstrated proof of concept for transformational quantum technologies including precision sensors, secure communication networks, and quantum computers. Successful commercialisation of these technologies can underpin industry growth for decades to come while driving productivity growth and enhancing security across a range of existing industries. Global investments in quantum technology have rapidly increased in recent years as private investors, businesses and governments grow more confident in the economic potential of quantum technology.
Quantum computing is a rising phenomenon in the Gartner Hype Cycle. The hype cycle is a graphical representation of technologies’ maturity, adoption & applications across industries, and the benefits they bring to the businesses. It is expected to become one of the greatest disruptions of the age. Quantum computing can process huge datasets in a fraction of a second that would have previously taken days and weeks. It can also calculate almost any kind of risks, such as the impact of an approaching hurricane.
Richard Feynman’s original vision for quantum computing sprang from the insight that there are hard problems, e.g. in quantum physics, quantum chemistry, and materials, that are nearly intractable using classical computation platforms but that might be successfully modeled using a universal quantum computer.
“Quantum computing could be potentially transformative, enabling us to solve problems that are impossible or impractical to solve today,” said Arvind Krishna, senior vice president and director of IBM Research, in a statement. “While quantum computers have traditionally been explored for cryptography, one area we find very compelling is the potential for practical quantum systems to solve problems in physics and quantum chemistry that are unsolvable today. This could have enormous potential in materials or drug design, opening up a new realm of applications.”
While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization.
Researchers are also looking exploit quantum information processing before fully fault-tolerant quantum computers exist. Fault-tolerant means that if one part of the computer stops working properly, it can still continue to function without going completely haywire. The Researchers are working to demonstrate quantitative advantage of Quantum Information Processing (QIP) over the best classical methods for solving combinatorial optimization problems using Noisy Intermediate-Scale Quantum (NISQ) devices.
Quantum Computer Applications
Computational chemistry: The immense potential power in quantum computing could enable machines to successfully map molecules and solve traditionally challenging issues – like removing carbon dioxide from our atmosphere for a better climate or even help to create solid-state batteries to solve many current energy storage problems.
“The first applications will probably be in things like quantum chemistry or quantum simulations,” said Jeff Welser, vice president and lab director at IBM Research Almaden. “People are looking for new materials, simulating molecules such as drug molecules, and to do that you probably only need to be at around 100 qubits. We’re at 50 qubits today. So we’re not that far off. It’s going to happen within the next year or two. The example I give is the caffeine molecule, because it’s a molecule we all love. It’s a fairly small molecule that has 95 electrons. To simulate the molecule, you simulate the electron states. But if you were to exactly simulate the 95 electrons on that to actually figure out the energy state configuration, it would take 10 exp(48) classical bits. There are 10 exp(50) atoms in the planet Earth, so there’s no way you’re ever going to build a system with 10exp(48) classical bits. It’s nuts. It would only require 160 qubits to do those all exactly, because the qubits can take on exactly all the quantum states and have all the right entanglements.”
Logistic optimization problems: Commercial quantum computers could help to optimize real-time dynamism and improve speed and accuracy in operational problems in the logistics industry. It is also likely to improve self-driving car technologies and could be turned to predicting and preventing traffic congestion.
An issue of particular interest is the potential impact of QC on “second wave” AI/ML optimization. ML has shown significant value in a broad range of real world problems, but the training time (due to the size and variety of the data needed for learning) and also network design space (due to a paucity of detailed analysis and theory for ML/deep learning (DL) systems) are large. It has been suggested that QC could significantly decrease training time of currently standard ML approaches by providing quantum speedup on optimization subroutines.
To solve practical problems on a real quantum computer, we must translate the real-world problem into something that can be understood by the physical hardware — a process known as compiling.
There are two dominant computational models for quantum computing:
Circuit (Gate) models, with hardware from Google, IBM, and Rigetti.
Adiabatic Quantum Computing (AQC) with hardware from D-Wave.
Quantum Computing in Insurance
Mitch Wein and Tom Kramer offer various use cases of quantum computing in “Quantum Computing and Insurance: Overview and Potential Players.” However, this technology isn’t yet available for commercial use, unlike AI. The insurance industry is simplifying many of its back and front-office operations through AI, it is still restricted by barriers of binary computing. Quantum computing can unlock and change the entire dynamics of how insurance companies carry out complex calculations. Insurtech companies are creating and testing solutions around this approach and its effects will soon be visible.
Quantum Computing in Retail
In the future, Quantum computing will remold our economic, industrial, academic, and societal landscape. A quantum computer can solve complex problems much faster than a classical computer in a given time frame. The retail sector generates a huge amount of data which is analyzed to study shoppers’ demographics & preferences and manage the supply chains efficiently.
With the introduction of quantum computers, the process of analyzing the data will become much faster and easier- hence making it easy to deliver a highly personalized experience to the customers. Microsoft envisions a future where quantum computing is available to a broad audience, scaling as needed to solve some of the world’s toughest challenges.
The quantum approach in retail begins within a tool we are acquainted with- such as Visual Studio. It provides development resources to build and promote quantum solutions, and it continues with deployment through Azure for a streamlined combination of both quantum and classical processing.
Quantum Computing in Gaming
Being a very new technology in itself, quantum computing can take game development experience to a whole new level. In the future, one can implement it into game development in interesting ways such as procedural generations, random generation of content, and many more. But today it’s hampered by the fact that you need to make a game decisively solvable and this is going to be a lot easier with a quantum computer.
Quantum Computing in Healthcare
Quantum computing could provide unprecedented power and speed of processing as well as novel and fundamentally different algorithmic search and data homogenization strategies. From the healthcare perspective, quantum computing technology can lead to “dramatic” accelerations in speed and performance. “MRIs were basically invented because of our acquired understanding of quantum physics, and getting the true quantum computer will allow us to truly understand the nature of all matter, which means everything from better medicine with fewer side effects to better diagnostics,” – Roger Grimes, data-driven defence evangelist at KnowBe4, told HealthcareITNews.
With increased computing available, clinicians could easily review CT scans over time and quickly identify changes and anomalies. Similarly, it can accelerate precision medicine. With quantum computing’s enhanced data processing abilities, medical practitioners can quickly identify targeted chemotherapy protocols and with more customization.
Drug discovery: Developing new drugs still requires a lot of trial and error, which can be both expensive and risky. Quantum technologies could remove this by helping us to understand more about drugs and their reactions in humans. It could also assist with extracting more information on chemical structures, expediting the drug discovery process.
Entos’ OrbNet platform, which incorporates features of quantum mechanics into its drug-designing algorithms. The company aims to leap-frog the traditional trade-offs seen between accuracy and computational requirements, by predicting the molecular energies at play within a compound’s nuclear structure. According to a paper published last year on the preprint server arXiv, Entos’ OrbNet AI aims to simulate molecular models with accuracy on par with certain quantum mechanics methods, but with computational costs reduced by a hundred- to a thousand-fold or more—with no custom supercomputers necessary. The new funding will help accelerate OrbNet’s development and help integrate the system with robotic hardware for synthesizing the molecules generated, to quickly advance them into preclinical testing.
Quantum Computing in Agriculture
A Quantum computer in agriculture could help in detecting weed through an invasive weed optimization algorithm. Farmers can hence, effectively craft fertilizers. Almost all the fertilizers contain ammonia. Therefore efficient manufacturing of ammonia or its substitute will result in cheaper and less energy-intensive fertilizer generation. However, there hasn’t been substantial progress because the number of possible catalyst combinations to do so is infinite. A Quantum computer can quickly analyze and come up with a catalytic combination, which is beyond the abilities of our largest supercomputers.
Quantum Computing in Financial Services
While the promise of quantum computing and its abilities to parse enormous volumes of data has long-term implications in medicine for applications like protein folding or drug synthesis, the challenges of financial services are comparatively much less complex. Some of the world’s leading banks, however, are already looking beyond AI to quantum computing. Most of the major banks — Barclays, Wells Fargo and Bank of America, in addition to JPMorgan and Goldman Sachs — have added talent in-house and are partnering with quantum innovators like Toshiba and IBM. JPMorgan and Goldman Sachs both have quantum teams, conducting research and evaluating practical uses of quantum computers and quantum-based technologies.
Financial modeling: Here it is being investigated in order to optimize risk management and compliance, enhance trading models, and improve targeting and prediction. Quantum technologies could also have wide-reaching impacts for consumers by helping to shorten and transform credit scoring processes and customer onboarding for banks.
At the Q2B Practical Quantum Computing conference in December 2020, Pistoia of JPMorgan said evidence pointed to the potential for speeding up asset-pricing models as well as cultivating performance improvements. Anything with exponential complexity like option pricing or risk analysis or determining predictive trading patterns which require large data crunching is ripe for quantum computing. further study and analysis could then be applied toward individual portfolios, across trading desks and for specific high net-worth individuals. Quantum, then, has the potential to give an investment bank a leg up on competition and skills to further retain clients and attract and create new opportunities.
The data in financial services applications is highly sensitive, highly personal and it has a long shelf life. Breaches threaten trust between the firms and their clients. Japanese tech giant Toshiba (alongside Nomura HD, Nomura Securities, NICT and NEC) recently announced a first-of-its-kind initiative to test quantum cryptography in a real-world financial services setting, citing what it calls “an urgent need for new safety measures in preparation for such future threats.”
While widespread fraud in banking is relatively rare, it is on the rise along with most other cybercrime. But one of the challenges with it is that a small data set of fraudulent activity can make it very difficult to analyze. When you’re analyzing data and looking for patterns, there aren’t that many tangible data results that one can analyze when it comes to a specific case of fraud. That’s where the speed of quantum computing can help — you can conduct multiple analysis in a short timeframe.
D-Wave, NASA and DOD explore massive potential of Quantum Computers
From improving the logistics of retail supply chains to simulating new proteins for therapeutic drugs, through optimizing vehicles’ routes through busy city streets, D-Wave is currently counting 250 early quantum annealing applications from various different customers.
The D-Wave quantum annealer has been employed in solving the coloring problem, analyzing optimization of traffic flow, computing small molecules, and simulating real materials, among several other relevant problems. And Brownell described how Volkswagen is using the $15-million D-Wave 2000Q computer to route taxis in Beijing. “They started with data from Beijing and they developed technology that will basically send a command to a particular taxicab to take an alternate route with the goal of sort of smoothing out the traffic,” Brownell said.
Some applications for D-Wave’s quantum computer include machine learning, financial simulations, and coding optimization. For example, the quantum computers could be used to build classifiers for better speech recognition or labeling of images, Vern Brownell, D-Wave’s CEO said. Algorithms play a big role in making D-Wave’s quantum computers effective. “Our belief is that machine learning is the killer app for quantum computing,” Brownell said.
Protein design pioneer Menten AI has developed the first process using hybrid quantum programs to determine protein structure for de novo protein design with very encouraging results often outperforming classical solvers. Menten AI’s unique protein designs have been computationally validated, chemically synthesized, and are being advanced to live-virus testing against COVID-19.
Western Canadian grocery retailer Save-On-Foods is using hybrid quantum algorithms to bring grocery optimization solutions to their business, with pilot tests underway in-store. The company has been able to reduce the time an important optimization task takes from 25 hours to a mere 2 minutes of calculations each week. Even more important than the reduction in time is the ability to optimize performance across and between a significant number of business parameters in a way that is challenging using traditional methods.
Accenture, a leading global professional services company, is exploring quantum, quantum-inspired, and hybrid solutions to develop applications across industries. Accenture recently conducted a series of business experiments with a banking client to pilot quantum applications for currency arbitrage, credit scoring, and trading optimization, successfully mapping computationally challenging business problems to quantum formulations, enabling quantum readiness.
Volkswagen, an early adopter of D-Wave’s annealing quantum computer, has expanded its quantum use cases with the hybrid solver service to build a paint shop scheduling application. The algorithm is designed to optimize the order in which cars are being painted. By using the hybrid solver service, the number of color switches will be reduced significantly, leading to performance improvements.
In Nov 2019, at the WebSummit conference in Lisbon, D-Wave and Volkswagen teamed up to manage a fleet of buses using a new system that, among other things, used D-Wave’s quantum technology to help generate the most efficient routes. Unlike other players in the quantum computing market, D-Wave always bet on quantum annealing as its core technology. This technology lends itself perfectly to optimization problems like the kind of routing problem the company tackled with VW, as well as sampling problems, which, in the context of quantum computing, are useful for improving machine learning models, for example.
D-Wave Systems Inc. says their quantum computers can help solve climate change, too. In Dec 2018, the British Columbia government invested $2 million in Burnaby-based D-Wave, through the province’s Innovative Clean Energy fund, and Sustainable Technologies Development Canada gave the company $10 million to continue developing quantum computers, on the basis that they could save energy.
One of the toughest problems in mathematics is known as the traveling salesperson problem, which asks to find the shortest route between a list of cities.The traveling salesperson problem is also pervasive. Practically anytime you want to make a complex process more efficient, you need to do this kind of combinatorial optimization. Logistics businesses need to solve a version of it every time they plan a route. Semiconductor manufacturers encounter similar issues when they design and manufacture chips.
“D-Wave has begun to work with investment managers on the related problem of designing portfolios. In order to generate the maximum returns for a given risk profile, a fund manager needs to not only choose among the thousands of available securities, but also minimize transaction costs by achieving the most optimal portfolio in the minimum number of trades,” writes Greg Satell in Forbes.
In each case, D-Wave’s quantum systems allow us to swallow complexity whole, rather than using shortcuts that reduce efficiency. Jeremy Hilton, Senior Vice President, Systems, at D-Wave says “Complex processes are all around us. By using quantum computing to operate them more effectively, we can make just about everything we do run more smoothly.”
“Scientists at Harvard have found that quantum computers will allow us to map proteins much as we do genes today. D-Wave has also formed a partnership with DNA-SEQ to use its quantum computers to explore how to analyze entire genomes to create more effective therapies,” writes Greg Satell.
Current machine learning algorithms generate misclassification errors because of the limited capacity of conventional computers, data is lost in the training process. D-Wave is working with a number of partners, such as NASA, to help train artificial intelligence systems to reflect human thought processes far more completely than is possible with conventional computers, which will help to minimize mistakes.
Scientists at Google, NASA and USRA have been using it to explore the potential for quantum computing and its applicability to a broad range of complex problems such as web search, speech recognition, planning and scheduling, air-traffic management and robotic missions to other planets.
Computing giants believe quantum computers could make their artificial-intelligence software much more powerful and unlock scientific leaps in areas like materials science, according to MIT Technology Review. NASA hopes quantum computers could help schedule rocket launches and simulate future missions and spacecraft. NASA’s QuAIL team aims to demonstrate that quantum computing and quantum algorithms may someday dramatically improve the agency’s ability to solve difficult optimization problems for missions in aeronautics, Earth and space sciences, and space exploration.
D-Wave Systems Inc., the leader in quantum computing systems, software, and services, announced in Mar 2019 that Los Alamos National Laboratory has upgraded their D-Wave quantum computer to the D-Wave 2000Q system. Los Alamos and its research collaborators have built over 60 early quantum applications and conducted essential research in domains ranging from quantum mechanics, linear algebra, computer science, and machine learning, to earth science, biochemistry, sociology, and more. “Quantum computers enable us to use the laws of physics to solve intractable mathematical problems,” said Marcos de López de Prado, Senior Managing Director at Guggenheim Partners and a Research Fellow at Lawrence Berkeley National Laboratory’s Computational Research Division. “This is the beginning of a new era, and it will change the job of the mathematician and computer scientist in the years to come.”
The Space and Naval Warfare Systems Center Pacific in San Diego is working with one of the few quantum computers in existence to assess its applicability to military computing problems. “Some of those problems would be cooperative communication and ad hoc networks, time division multiple access message scheduling, or algorithms for data storage and energy data retrieval with underwater autonomous robots—optimization-type problems,”said Dr. Joanna Ptasinski, an electronics engineer at SPAWAR. SPAWAR provides the Navy and other military branches with essential capabilities in the areas of command and control, communications, computers, intelligence, surveillance, and reconnaissance.
Worldwide Quantum Computing Market
International Data Corporation (IDC) published in Nov 2021 its first forecast for the worldwide quantum computing market, projecting customer spend for quantum computing to grow from $412 million in 2020 to $8.6 billion in 2027. This represents a 6-year compound annual growth rate (CAGR) of 50.9% over the 2021-2027 forecast period. The forecast includes core quantum computing as a service as well as enabling and adjacent quantum computing as a service.
IDC states that major breakthroughs in quantum computing technology, a maturing quantum computing as a service infrastructure and platform market, and the growth of performance intensive computing workloads suitable for quantum technology will drive the majority of the market growth over the forecast period.
IDC also expects investments in the quantum computing market will grow at a 6-year CAGR (2021-2027) of 11.3% and reach nearly $16.4 billion by the end of 2027. This includes investments made by public and privately funded institutions, government spending worldwide, internal allocation (R&D spend) from technology and services vendors, and external funding from venture capitalists and private equity firms.
Like any breakthrough technology of the last few decades, the industry will pour billions of dollars into making the technology common place and ready for mass adoption. The closest comparison is classical computing, the very technology that quantum computing is setting out to disrupt.
IDC anticipates that these investments will cause current limited quantum computing capabilities to be replaced by a new generation of quantum computing solutions, leading to the development of new use cases and market segments that will accelerate the adoption of quantum computing to gain a competitive advantage. As a result, the quantum computing market will see a surge in customer spend toward the end of the forecast period.
IDC sees 2021 as a pivotal year in the quantum computing industry. Strategic approaches implemented to reach quantum advantage became more defined as vendors published quantum computing roadmaps emphasizing methods for improving qubit scaling and error correction, sought new funding opportunities by going public or partnering with government, educational, or private entities, or merged in anticipation of offering a more full-stack approach. For most vendors, these approaches included the further development of the quantum ecosystem. This trend promises to continue into 2022 and beyond as quantum computing vendors progress towards quantum advantage and enterprise businesses seek a competitive advantage using current and emerging quantum technologies.
“For many critical problems, classical computing will run out of steam in the next decade and we will see quantum computing take over as the next generation of performance-intensive computing.”, said Peter Rutten, global research lead for performance intensive computing at IDC.
“Advances in quantum computing will be a drumbeat over time with the most distant advances being most relevant to the most complex problems. Organizations should start experimenting now using quantum road maps to guide their quantum journey,” added Heather West, senior research analyst, Infrastructure Systems, Platforms and Technologies Group at IDC.