Quantum computing is here

Is your business ready?

Authors

Prateek Jain

Lead Architect AI@Scale,
Machine Vision and Conv. AI

Srinjoy Ganguly

Senior Data Scientist AI@Scale,
Machine Vision and Conv. AI

Businesses are ranking quantum computing in data science, machine learning, and AI high in priority. By 2023 Gartner, predicts 20% of organizations to budget for quantum computing projects. However, are organizations equipped to identify problems that can leverage quantum algorithms? Are they ready yet to extract maximum business value from quantum computing initiatives?

Quantum computing in brief

Basis quantum mechanics, quantum computing enables computations to be performed much faster and more efficiently than classical computing.

Unlike the logical state and two possible values that classical computing represents, quantum computing represents a two-state quantum mechanical system and has properties like the spin of an electron (up or down); low or high (energy states of trapped ions); horizontal or vertical (polarization of a photon.)

The most basic unit of quantum information, Qubit, can simultaneously exist in a combination of quantum states; and groups of qubits in superposition can create infinite, complex, multidimensional computational spaces. Quantum computing draws the computational speed and power from the combination of its special properties of superposition and entanglement.

Application of quantum computing

An ever-expanding quantum-computing ecosystem promises to generate substantial value and revolutionize industries, especially pharmaceuticals, finance and banking, alternative materials, and energy research. Life sciences are one of the industries where quantum computing is expected to be used extensively.

Quantum computing and its key application areas

Applying quantum computing in life sciences

As Quantum Computing matures in both Qubit quality and quantum volume, pharma companies are exploring the application of quantum computers and software in chemistry and biology.

Understanding the structure of proteins and how they fold is crucial to treat diseases such as Alzheimer’s, Huntington’s, Parkinson’s, Cancer, and even COVID. Alphafold, with its deep-learning-based algorithm, is bound by several approximations of the neural network and takes a huge amount of computing power and time.

Applying quantum computing in life sciences

These are instances where classical computers or supercomputers are not powerful enough to make progress. Only Quantum Computing systems, with its ability to process huge amounts of information and data within a short time, can be the way forward.

Quantum computing is expected to provide higher level of clarity with regarding the workings of biological processes, leading to accurate, fast-tracked cures for diseases.

Fractal contribution toward life sciences

At Fractal, we believe quantum computing can disrupt the life sciences industry through its increasingly accurate protein folding predictions, and breakthrough drug discoveries. Toward that, we have designed frameworks and applications, identified algorithms, and are developing libraries and frameworks.

Frameworks and Applications: We have designed frameworks focused on the study of chemistry – the structure of molecules – and life sciences. Fractal-orchestrated Application Layers dwell on discovering and simulating drug-target interactions, chemical interactions, protein folding, protein interactions, and compound generation. Being co-related, the layers will eventually branch out into several applications performing chemical and biological simulations.

Algorithms: We have identified algorithms applicable to protein folding and chemical interaction issues. Efforts are underway to develop different combinations and better variations of the algorithms with an aim to predict workable molecules and proteins.

Libraries & Frameworks: Leveraging reputed Machine Learning libraries like Pennylane, Qiskit, our Quantum AI team is developing full-stack application-focused frameworks. Fractal has access to matured life sciences and open-source chemistry frameworks by Google, Rosetta Commons. This enables the open-source frameworks to interface with libraries for simulations and chemical approximations.

Hardware Stack: Our hardware stack accessible via Cloud has two models – Adiabatic Model, which is restricted and has hard-to-predict behavior at scale; and the Gate Based Circuit Model, which has predictable behavior at scale. Fractal is more inclined to Gate Model as it is closer to a Universal Quantum computer and gives the flexibility to control each qubit.

Fractal efforts in other areas of quantum computing

Our Fractal Quantum AI team is studying Generative Quantum machine learning for various problems and applications in quantum chemistry and finance, and the study of many body systems. Demonstrating quantum advantage over the classical algorithm, simulated molecular interaction was found between a subset of HIV and a hypothetical antiretroviral drug. You can find our entire work under Quantum Computing in HealthCare — Protein Folding Part-1 and Quantum Computing in HealthCare — Protein Folding Part-2.

Also underway, is the protein fold prediction with simulated annealing & D-Wave’s Adiabatic Quantum annealer.

The challenge

While structural and technical problems are common in quantum computing applications, there are a few other challenges that appear simple but can hamper progress.

Lack of awareness: Companies are aware of quantum computing, but the majority struggle to understand what it means for their industry and business, or whether their classical problems can be graduated to quantum problems. There is a pressing need to enhance awareness among business leaders on the magnitude and depth of quantum computing, and what it can do for them.

Lack of understanding: Not all pharmacists, biologists or industry professionals understand the potential and possibilities of quantum computing and how to reap noteworthy benefits from this technology.

Shortage of talent: While the industry produces quantum computing professionals, those with specialization in physics and chemistry are few. The shortage in talent acts as a tough barrier to the growth of the quantum industry.

Our approach

Fractal Quantum AI research lab is developing ‘Day after tomorrow Research & Solutions.’ For this, we have been investing and executing deep research to develop application-focused frameworks on life sciences, financial services, and optimization solutions. While the research and application outcomes will benefit life sciences, pharmaceuticals, material sciences, and financial services industries, the optimization arenas like supply chain, vehicle routing, workforce scheduling would also stand to gain.

Conclusion

While the possibilities are limitless and the technology is still evolving, the real worth of quantum computing can be felt by businesses in the next five to ten years. Till then, efforts to create awareness of how the technology works and the kind of problems it can solve have to continue. To leverage the power of this technology, a growing network of quantum computing workforce is a must.

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