May 16, 2023, by Aditya Singh:
Quantum Computing uses the laws of quantum mechanics to solve complex problems efficiently due to its phenomenal processing power. This ability to process information faster opens disruptive possibilities in fundamental research and optimization for automotive, aerospace, and pharmaceutical industries, among others. As the quantum industry continues to grow at an unprecedented pace, businesses must understand the roadmap to investment and commercialization to capitalize on this emerging technology.
Advancements in quantum error correction techniques and fault-tolerant quantum computing pave the way for more reliable and scalable quantum processors with higher qubits and quantum gate fidelity. Such innovations have resulted in investments of over $40 billion from corporations and governments into Quantum. The market for quantum-enabled applications is projected to grow by 36.89% from 2023 to 2030 and reach $123bn, as quantum clearly emerges as the next in-line-tech.
Advantages in commercialized use cases
Quantum computing has the potential to solve complex problems that are practically impossible to solve with classical computers. They can handle larger datasets and variables for calculations delivering computational efficiency specifically for optimization and search problems. When equipped with quantum algorithms, it can explore large solution landscapes. This results in faster convergence to optimal solutions with higher accuracy when compared to traditional Classical computers.
Industries such as aerospace, automotive, and pharma require high-quality results for applications like predictive analytics and optimization. In addition, given the safety and compliance requirements of these industries, it is essential to have accurate results for use cases, including better vehicle design, airline flight trajectory planning, and drug discovery.
Overcoming the Challenges of Quantum Computing
In quantum computing, errors can significantly impact the quality of solutions. Errors are often quantified as a percentage of fidelity. Lower fidelity qubits generate more errors, while high-quality qubits improve quantum volume and performance, significantly boosting the adoption of quantum for industrial use cases.
While research is focused on developing high-fidelity qubits, quantum algorithms can mitigate some of the errors affecting qubit fidelity in quantum computing systems. Quantum algorithms can be designed to minimize the number of quantum gates needed to perform a given computation, reducing the opportunities for errors to accumulate. Additionally, some quantum algorithms are designed to be error corrected, meaning they can continue to reduce the mistakes even when there are noises in the system. This approach can provide higher accuracy of results for tasks like Route Optimization, Design Optimization, Topology Optimization, Drug Discovery, and Predictive Analysis.
It can reduce computational costs and time for industry players, leading to a better competitive advantage. With these advances, companies can achieve significant progress in their respective fields and provide better business outcomes
Experience the power of Quantum
As experts point out, we are in the NISQ (Noisy Intermediate-Scale Quantum) era of quantum computing, characterized by intermediate-scale quantum devices with limited qubits (tens to hundreds), low qubit connectivity, and relatively high error rates. Quantum computers need to be more powerful to solve practical problems beyond the reach of classical computers. Still, they have demonstrated the potential to perform certain quantum computations that are difficult or impossible for classical computers.
Companies can leverage the potential of quantum by using Quantum-Inspired Algorithms (QIEA) on HPC. QIEA can emulate qubits and deliver Quantum computing benefits on classical hardware, alleviating concerns about Quantum and classical hardware limitations for industry players.
For industrial use cases, QIEA provides significant advantages over classical computers for optimization problems like design optimization, topology optimization, and thermal analysis.
For example, BQP’s QIEA converged faster and used fewer computational resources than classical computing approaches. This provides many new opportunities eliminating the need for additional capex, hardware, or a team of experts.
The Future of Quantum
The Road ahead for quantum computing involves several critical areas of development, including developing more efficient quantum algorithms, hybrid classical (HPC) -quantum computing approaches, and fault-tolerant quantum computing systems.
In the immediate term, quantum algorithms with HPCs, using CPUs and GPUs, can solve practical problems more efficiently than classical computers with incremental speedups.
The second approach is to use a hybrid environment, where HPC is used to solve some aspects of the problem, and quantum computers are used for the computationally heavy aspects. At this stage, the potential speedup and industrial advantage could be huge, potentially in the order of 10x or 100x.
The third approach is full-scale quantum computing, which aims to build fault-tolerant, error-corrected quantum computers with large numbers of qubits that can perform calculations beyond the capabilities of classical computers where the differential could be 500x to 1000x
These approaches to quantum computing i.e. quantum-inspired, hybrid, and full-scale- each has their unique strengths. As the field of quantum computing continues to evolve, we will likely see different industries and applications benefitting from different approaches based on their specific needs and requirements.