Quantum computing uses a new way to store, process, and measure information in computer systems to record the results. This new way depends on the quantum-mechanic state of subatomic particles such as electrons. Quantum states are represented by quantum bits (qubits) to represent computer information. In classical computing architecture, bits represent information in either 0s or 1s. However, in quantum-computing architecture, qubits could represent information in 60% 1 and 40% 0 state, for example, or 85% 1 and 15% 0, and so on. These multiple qubit states give this technology the power to make massive parallel execution of computational processing. Complex problems for classical computation methods, including with high-performance computers (HPCs), become solvable, more efficient, and much faster with quantum computing. For example, classical computers would read each book in a library sequentially; however, quantum computers would read all books simultaneously. An example of a quantum computer is shown in Fig. 1.
Quantum Computing Models
Several architecture models of quantum computing exist, and each has its strengths and weaknesses. Companies and universities are in the experimental phase with these different models. As a result, each vendor has a unique approach to building quantum computers and standards bodies such as the Institute of Electrical and Electronics Engineers have yet to develop industry-standard approach for vendors to follow. Topological quantum computation is an architecture model that has been adopted by Microsoft and is expected to produce robust results and overcome coherence problems. Another architecture is the universal gate model, which performs computation on qubits in a similar manner as classical computers’ logic circuits. IBM and Rigetti are among vendors that deploy quantum circuits. Another approach to quantum computing is the quantum annealer, which is ideal for optimization use cases. D-Wave has adopted this model type.
Market Adoption and Outlook
Quantum computing is a nascent technology, and it is expected to introduce drastic changes in technology developments, algorithmic discoveries, and architectural advancements by 2028. According to research conducted by Gartner in 2018, by 2023, 20% of organizations will be budgeting for quantum computing projects compared with less than 1% now.
The International Data Corporation (IDC) has said that, by 2027, it expects opportunities in quantum computing could exceed $10 billion. Market opportunities in the coming 10 years will be divided between current workloads of classical computing and expected upcoming quantum-native workloads. The existing workload will shift eventually toward hybrid quantum/classical computing with a market estimate of $6 billion, and, by 2027, quantum-native workloads running only on quantum computers will represent a predicted $4 billion market opportunity (Fig. 2).
Potential Applications
The IDC has said that quantum computing uses “a completely different way to do computation, which potentially offers the opportunity to solve specific types of problems.” The greatest business effects from quantum computing will come in the areas of optimization, chemistry, materials science, image analysis, drug discovery, machine learning, and code breaking.
Optimization. Quantum computing offers an optimal solution for a problem among other options. Examples include extracting resources from mines, finding cost-effective methodology for financial services and shipping goods, and optimizing asset pricing and capital project budgeting.
Chemistry. Quantum computing will play a key role in enabling atomic quantum simulation in order to generate new chemical and industrial processes that could lead to new sources of profit for the petrochemical industry.
Material Science. Complex atomic interactions can be analyzed through quantum computing. This has the potential to lead to the discovery of new patentable materials that could provide opportunities for economic growth for early adopters in the oil and gas industry.
Machine Learning. Quantum computing has the potential to go through complex and vast amounts of data to feed machine-learning and deep-learning models.
Personalized Medicine. Molecular interactions at the atomic level can be modeled to find new cancer-treating drugs. Quantum computing can predict precisely the interactions of proteins that could lead to new methodologies in medicine making.
Information Security. One of the more important applications of quantum computers is their ability to compromise today’s cryptographic key-exchange protocol such as Rivest, Shamir, and Adelman (RSA) and elliptic-curve cryptography (ECC) (Fig. 3). As a result, the US National Security Agency has instructed its employees and vendors to begin checking their encryption because of potential quantum-computer threats. The National Institute of Standards and Technology (NIST) is looking for algorithms to replace RSA and ECC. The NIST has said it expects to make recommendations for quantum encryption standardization algorithms.
Challenges
The dawn of quantum computing has shed light on several challenges related to yielding meaningful computational power, and great effort will be necessary to overcome these hurdles.
Decoherence. Qubits are extremely fragile and sensitive to the surrounding environment. External noises can affect the properties of qubits; therefore, qubit coherence length before the quantum properties collapse is important to determine.
Quantum Errors. Quantum computers require low error rates to achieve meaningful computation. Scaling quantum computer models requires an error-correction scheme to make practical use of this technology.
Lack of a Standard Development Language. Quantum computer programming languages are different from those of classical computers. The race is on to develop a standard language as quantum computers move in to the hardware sector.
Recommendations
Quantum computing is a promising technology for solving complex problems and discovering new materials, chemicals, and industrial applications and processes in the oil and gas industry. A 10-year roadmap for adopting quantum-computing technology will play a significant and pivotal role for future economic growth.
Gartner made the following recommendations for major oil and gas companies to be early adopters of this technology.
Create Local Quantum Competency. Hire or nurture a quantum physics skillset. Many geophysicists have a background in quantum mechanics, and leveraging those backgrounds is crucial. Companies can also collaborate with local universities to nurture graduate students with a quantum computing skillset.
Partner With Algorithm-Development Companies. Companies such as Zapata Computing and 1Qbit are working to develop quantum algorithms for use by oil and gas companies.
Consider Quantum Computing as a Service. A few vendors, such as IBM Q, Rigetti, and D-Wave, provide cloud-based capabilities and recommend not using in-house solutions because hardware techniques are not mature and the scale of quantum systems and business benefits is small compared with the huge investment would be needed to buy a single quantum computer.