From quantum computing to machine learning,, among others are some of the new age technologies which have been adopted by businesses and this is believed to have sped up the process thereby solve complex problems. This ability to process information faster opens disruptive possibilities in fundamental research and optimisation for the automotive, aerospace and pharmaceutical industries, among others. The market for quantum-enabled applications is expected to grow by 36.89% from 2023 to 2030 and reach $123 billion, as quantum emerges as the next in-line tech.
Experts believe that 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. Apart from this, the quantum computing market might grow at a CAGR of 56.0%, eventually reaching about $65 billion by 2030. Quantum computing is also expected to give a competitive advantage to 25% of Fortune 500 companies in less than three years, as per insights from Deloitte. In conversation with FE-TransformX, Aditya Singh, founder and head of business, BosonQ Psi., a quantum-powered advanced computing platform, on unlocking the potential of quantum computing for industrial applications. (Edited Excerpts)
How can quantum computing help businesses profit?
Quantum computing has the potential to solve complex problems by handling larger datasets and variables for calculations delivering computational efficiency specifically for optimisation and search problems. When equipped with quantum algorithms, businesses can explore large solution landscapes. This eventually, results in faster convergence to optimal solutions with higher accuracy when compared to traditional computers.
Which are the sectors which will benefit more from the use of quantum computing?
Industries such as aerospace, automotive and pharma, among others, require high-quality results for applications such as predictive analytics and optimisation. 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.
With quantum computing, the healthcare sector can cater to personalised medicine by allowing faster genomic analysis to inform tailored treatment plans specific to every patient. Use cases include ProteinQure, a health care startup, which was featured by CB Insights in the 2020 cohorts for the AI 100 and Digital Health 150 are tapping into current quantum computers to predict how proteins will fold in the body.
What are the loopholes in quantum computing which need to be looked at?
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 minimise 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 optimisation, design optimisation, topology optimisation, drug discovery, and predictive analysis. Moreover, with these advances, companies can achieve significant progress in their respective fields and provide better outcomes.
Can you provide some use cases of quantum computing?
I believe we are in the NISQ (Noisy Intermediate-Scale Quantum) era of quantum computing, which includes intermediate-scale quantum devices with limited qubits (tens to hundreds), low qubit connectivity and relatively high error rates. Companies by using Quantum-Inspired Algorithms (QIEA) on HPC 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 advantages over classical computers for optimisation problems like design optimization, topology optimization, and thermal analysis. This provides many new opportunities eliminating the need for additional capex, hardware and a team of experts.
What is the future of quantum computing?
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 of quantum-inspired, hybrid and full-scale- each have their unique strengths. 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.
