Unlocking the Potential of Quantum Computing for Industrial Applications

By Aditya Singh, Founding Member & Head Of Business, BosonQ Psi

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 optimisation 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 commercialisation to capitalise 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 percent from 2023 to 2030 and reach $123 billion, as quantum clearly emerges as the next-in-line-tech.

Upsides of Quantum computing and commercial use cases 

Quantum computing can handle larger datasets and variables for calculations delivering computational efficiency. With quantum algorithms, one can explore large solution landscapes. This results in faster convergence to optimal solutions with higher accuracy when compared to traditional computing.   

Industries such as aerospace, automotive, and pharma require high-quality results for applications like 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.  

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 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.

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, characterised 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 traditional computing. 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 traditional hardware, alleviating concerns about Quantum and traditional hardware limitations for industry players.

For industrial use cases, QIEA provides significant advantages over classical computers for optimisation problems like design optimisation, topology optimisation, 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 and 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 its 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.

Advanced Computinghardwarequantum algorithmsQuantum Computing
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