June 2023, A&D India Magazine, Mr. Kedar Warang, Vice President – Solutions R&D, Commtel Networks:
Quantum computing promises to revolutionise traditional manufacturing procedures, supply chain logistics and product design processes, offering a range of economic and performance benefits. Amid the swift evolution of technological innovation, quantum computing has surfaced as a potential game-changer, poised to redefine traditional computing paradigms. To comprehend these potential benefits, we must delve into the genesis and evolution of quantum computing…
Quantum Computing: Roots and Journey
The conceptual foundations of quantum computing date back to the early 1980s, with theoretical physicist Richard Feynman posing a question that would become the cornerstone of this field. Feynman pondered whether physics could be simulated by a universal computer and suggested that the simulation of quantum phenomena necessitated a quantum computer1. During the same period, Paul Benioff, a theoretical physicist, formalised the concept of quantum computing by demonstrating that a computer could function according to the principles of quantum mechanics2. Although these ideas were ground-breaking, they were principally theoretical constructs and did not spur the immediate development of quantum computers.
The first significant leap from theory to practice emerged in the mid-1990s, with Peter Shor, a mathematician at Bell Labs, developing an algorithm using quantum principles to factor large numbers more efficiently than classical computers3. Shor’s algorithm demonstrated the real potential and practicality of quantum computing, propelling the pursuit of developing functional quantum computers. This led to the genesis of quantum hardware. The initial experimental models of quantum computers relied on Nuclear Magnetic Resonance (NMR), using the spins of atomic nuclei as qubits. These early attempts succeeded in proving the feasibility of quantum computing but were constrained by scalability issues.
The 2000s marked several crucial advancements. Quantum error correction codes were developed to address the issues of qubit instability and decoherence. Simultaneously, different approaches to quantum computing began to emerge, including trapped ion quantum computers and superconducting quantum computers.
The mid-2010s witnessed substantial involvement from tech giants. IBM, Google and Microsoft all launched their quantum research programs, fostering rapid advancements. Google’s claim of quantum supremacy in 2019, when their 53-qubit quantum processor named ‘Sycamore’ completed a task in 200 seconds — a task that would take a supercomputer an estimated 10,000 years —represented a significant milestone in the quantum journey4.
Although this demonstration of ‘quantum supremacy’ utilised a task of limited practical use, it effectively highlighted the potential of quantum computation and invigorated the industry. Since then, IBM has revealed its roadmap for scaling quantum technology, with a target of achieving 1,121 qubits by 20235. Meanwhile, China has made significant strides, claiming quantum supremacy with photonic quantum computing and pushing the boundaries of quantum communication with satellite-based quantum encryption6. As of 2023, the journey of quantum computing from a theoretical concept to a practical reality continues, evolving from Feynman’s and Benioff’s theoretical postulations into an industry teeming with promise, burgeoning with researchers, start-ups and major corporations dedicated to uncovering the vast potential of quantum computing.
Potential Applications of Quantum Computing in the Process Industry
Augmented Product Design and Pretesting
One significant application of quantum computing in the process industry lies in product design and pretesting. Traditional simulations, although indispensable, are often hindered by the computational capacity of classical computers. This constraint results in safety margins, leading to product weight variations and, consequently, elevated production costs.
Quantum computers, with their inherent ability to evaluate multiple possibilities concurrently, could considerably augment simulation and modelling processes. They could simulate and analyse each component’s behaviour under various conditions, accounting for factors such as vibrations, noise and system loads. For instance, in the automobile industry, quantum simulations could be employed to design more efficient engines or predict the wear and tear of individual parts with greater precision.
Moreover, in sectors where weight reduction is a crucial concern, such as aerospace and automobile manufacturing, quantum simulations could enable the production of lighter, more fuel-efficient vehicles without compromising safety or performance.
Quantum Machine Learning for Optimising Manufacturing
Another transformative application of quantum computing is its integration with machine learning to boost manufacturing optimisation. Classical computers often grapple with optimisation problems involving large, multidimensional datasets, limiting the analysis of various interactive factors and processes.
Quantum Computing can overcome this limitation. Quantum computers can process vast datasets simultaneously, allowing for an exhaustive analysis of different variables affecting the manufacturing process. Coupled with machine learning algorithms, they could deliver more accurate predictions and facilitate faster, more efficient decision-making processes.
For instance, in the pharmaceutical industry, quantum machine learning could be employed to optimise drug formulation processes. By analysing and optimising multiple parameters like temperature, pH, pressure and ingredient ratios, manufacturers could significantly enhance production yields and minimise waste.
Revolutionising Supply Chain and Logistics
Quantum Computing also holds the potential to transform supply chain management and logistics, fields notorious for their complexity. The requirements for real-time decision-making, coupled with a large number of interacting components, make this an ideal application for Quantum Computing.
Utilising quantum algorithms, companies could optimise routes for delivery vehicles, manage inventories more efficiently, predict demand more accurately and optimise vendor orders. All these improvements could substantially reduce operational costs and lost sales, resulting in improved profit margins.
For example, a company could deploy a quantum computer to solve the ‘travelling salesman’ problem — a classic optimisation problem in the fields of computer science and operations research. This problem involves finding the shortest possible route that visits a set of cities and returns to the origin city. A solution to this problem, enhanced by quantum computing, could lead to considerable cost savings in logistics and distribution.
Material Science and Process Optimisation
Quantum computing could also profoundly impact material science and process optimisation in the manufacturing industry. Material science involves understanding the structure of materials at the atomic and molecular scales — a natural application for quantum computing, given that it operates using the principles of quantum mechanics.
For instance, in the semiconductor industry, quantum computers could be used to simulate and analyse different material properties at the quantum level, aiding in development of more efficient and powerful semiconductors. Similarly, in the chemical industry, quantum computers could simulate and optimise chemical reactions, reducing the time and cost of developing new chemicals and materials.
Quality Control and Maintenance
Lastly, quantum computing could revolutionise quality control and maintenance procedures in the process industry. Quantum machine learning algorithms can analyse extensive volumes of production data to predict potential failures or quality issues, enabling preventative maintenance and minimising downtime. This capability would improve the overall efficiency and productivity of manufacturing.
Despite the potential of quantum computing, the path to practical applications is fraught with significant challenges that researchers are currently striving to overcome. Below, we discuss some of the most critical roadblocks and the near-term and long-term goals in the quest to build practical quantum computers.
Quantum Coherence and Stability
One of the key challenges in constructing a quantum computer is maintaining quantum coherence — the delicate state in which qubits exist and interact to perform computations. Qubits can exist in a superposition of states, allowing them to perform multiple computations simultaneously, which is the fundamental advantage of quantum computing. However, these states are extremely fragile and susceptible to environmental ‘noise’ caused by heat, electromagnetic radiation and material defects, leading to qubits losing their coherence — a process known as decoherence.
Overcoming decoherence is a substantial challenge. Current quantum systems can maintain coherence for only a short duration, after which errors occur, limiting the complexity of computations that can be performed. Researchers are investigating various methods to extend coherence time like error correction codes and improving the quality of the qubits.
Quantum Error Correction
Even with high-quality qubits, quantum computers are prone to errors due to their sensitive nature. The development and implementation of quantum error correction codes, therefore, pose another significant challenge. Quantum error correction codes work by distributing information across multiple physical qubits. Hence, if one qubit fails, the information it carries can be reconstructed from other qubits.
However, implementing these codes requires more qubits, which makes the quantum computer more complex and harder to build. Scaling up the number of qubits while maintaining their quality and coherence is a significant challenge that researchers and engineers are striving to overcome.
Quantum Software and Algorithms
Quantum hardware is just one aspect of the equation. Developing software and algorithms that can exploit the power of quantum computing is another significant challenge. Quantum algorithms must be designed to operate within the constraints of quantum mechanics, which sets them apart from classical algorithms.
Furthermore, developing software that can translate real-world problems into a form that a quantum computer can solve is also a major challenge. Today, only a few quantum algorithms have been developed, such as Shor’s factoring algorithm and Grover’s search algorithm, which underscores the field’s early stage.
Scalability and Resource Requirements
Quantum computing also faces significant scalability and resource challenges. Building a quantum computer requires maintaining a controlled environment with extremely low temperatures and isolating the system from external ‘noise’. These requirements increase with size of the quantum computer, making the construction of largescale, practical quantum computers a formidable task.
Despite these challenges, the field of quantum computing is progressing rapidly, with breakthroughs and developments announced regularly. The promise of quantum computing’s transformative power keeps researchers, companies and governments invested in overcoming these hurdles and pushing the boundaries of what is currently possible.
Where We Stand Today and Future Goals
As of 2023, several tech giants and start-ups are vying to build scalable and fault-tolerant quantum computers. Currently, we have quantum computers with over 100 qubits; however, these are noisy and prone to errors.
In the near term, the goal is to enhance the quality of these qubits, reduce errors and extend the coherence time. Companies like IBM and Google are aiming to achieve ‘quantum advantage’ or ‘quantum practicality’ — the point where quantum computers can solve practical problems faster or more accurately than classical computers — within the next decade.
In the long term, the objective is to build a fault-tolerant quantum computer — a quantum computer that can correct any errors that occur during computation. This would require hundreds of thousands, if not millions, of high-quality qubits and is likely to be a few decades away.
A Quantum Leap Forward
In conclusion, the potential applications of quantum computing in the process industry are vast and transformative. However, it is vital to note that many of these applications are currently in their nascent stages and require further development and refinement. It will necessitate collaborative efforts among researchers, manufacturers, technology providers and governments to address current challenges and accelerate the maturation of quantum computing technologies.
Moreover, although quantum computers hold vast potential, they are not intended to replace classical computers. Instead, they are expected to operate alongside classical computers while addressing particular, specific problems that are currently beyond the reach of classical computation.
Quantum computing is a disruptive technology that can redefine the future of the process industry. It can revolutionise how we design products, optimise manufacturing processes, manage supply chains and ensure the safety and security of our critical national infrastructure. However, to fully harness this potential, industry leaders must commit to understanding this technology, investing in research and development and preparing their workforce for a quantum future.
The journey to a fully quantum-enabled process industry will be a marathon, not a sprint. It will demand patience, persistence and a continued emphasis on innovation and learning. The potential rewards — increased efficiency, reduced costs and enhanced product quality — make it a journey worth undertaking. Therefore, it is time we embrace this quantum leap forward, delve deeper into this technology and keep abreast of the latest knowledge and trends, prepared for the quantum revolution in the process industry.
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2. Benioff, P. (1980). The computer as a physical system: A microscopic quantum mechanical Hamiltonian model of computers as represented by Turing machines. Journal of Statistical Physics, 22(5), 563–591.
3. Shor, P.W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. In Proceedings 35th Annual Symposium on Foundations of Computer Science (pp. 124–134). IEEE.
4. Arute, F., et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574(7779), 505–510.
5. IBM Quantum Team (2020). IBM Quantum Development Road Map.
6. Quantum Experiments at Space Scale (2020).