## Electronic and Opto-Electronic Sub-Systems, Software Tools and complete Environments for Design and Operation of Hybrid - Quantum and Classical - Computing enabled reference products

## Who we are

The scaling of computing and communication has led to a large wealth creation and lifted large populations from poverty to middle class but can no longer be continued due to our running into physical limits. The use of multi-processing and of many accelerators produces diminishing returns. In particular the use of multi-processing lowers the compute time by computing in parallel but incurs communication time cost to communicate an increasing number of intermediate results from producers to consumers. Quantum computing, by making available new resources, is seen as able to provide an exponential advantage in complexity of computation performed and/or amount of computational resources required.

With COVID in recent past, likes of Moderna and Pfizer have become, if they were not already, household names. The amount of good they do and their market valuation is often in direct proportion to the number of 2, 5 and now even 10 billion dollars a year revenue drugs in their portfolio. As broad cures for diseases such as Cancer are yet to be found and as just as SAARS was followed by COVID, new pandemics will follow COVID and will need vaccines and therapies, there is room for many block-buster drugs.

While medicines are only used by unwell people, materials are used by unwell and well people and also industries and governments and can potentially produce revenues of 20, 50 or even 100 billion dollars a year!

Some example materials are those used in making of batteries. The materials give a battery a certain capacity, a certain weight and a certain probability of explosion. Can materials be found that quadruple the capacity and reduce the weight and probability of explosion to a quarter?

Today with help of computing, including learning-aided-computing (‘AI’), we can already map properties desired in a drug or a material to candidate molecular structures and even find paths in a reaction network from nodes corresponding to readily found molecules to the desired molecule. However the difficulty lies in the fact that the number of candidate molecules can be very large and making and testing them in the laboratory can takes 1, 2, 5 or even 10 years!

The solution to the basic problem as famously prescribed by Feynman is simulation of Quantum Chemistry using Quantum Computers and, in this particular case, to replace making-and-testing in the laboratory with such simulations. However efficiently performing using a Quantum Computer a task hard for classical computers requires an appropriate quantum algorithm.

While quantum algorithms to efficiently simulate the evolution of a system of many parti- cles exist, those for predicting the ground state of the Hamiltonian describing a system of many particles has to resort to heuristics as the latter problem belongs in the complexity class QMA, the quantum analog of NP. It further turns out that the latter problem can be solved by learning from a polynomial number of example problem-solution pairs.

It is now well-established that learning for performing and performing learning aided tasks employing classical computing are done cost-performance efficiently if done employing hardware acceleration. The plentiful availability of hardware acceleration for Deep Neural Networks (DNNs) (1) has encouraged their use as sub-algorithms in larger - Reinforcement Learning (RL) (2), Generative-Adversarial Networks - algorithms. RL and GAN (and use of DNN by the RL and GANs for policy (or actor) and value (or critic) function approximation) have established themselves as advantageous means of performing tasks autonomously and of performing synthesis tasks. DNN learning is susceptible to inefficiencies and failures including vanishing/exploding gradients and local minima.

The two primary means of achieving efficiency in DNN inference - sparsity and parallelism - conflict. The simplest Quantum Algorithm - the Deutsch Jozsa - exploits the quantum resources of Superposition to evaluate all alternatives in parallel (referred to as in superposition) and the resource Interference to (interfere away all-but and) be left with the optimal altaernative. Most sophisticated recent algorithm, that for solving unstructured NP search problem, takes a problem - decoding a list- recoverable code - known to be classically hard and presents a polynomial quantum algorithm for the problem.

This implies that given a drug or material design or discovery job, the job needs to be divided into tasks such that some tasks are best done on a quantum computer, some on a classical computer employing learning and rest on classical computers and a framework is required to manage the division, identification of the mode with highest performance for each task, the mode that is optimal considering both the required performance and available resources at run-time must be employed.

Netway Inc has significant intellectual properties in all of the areas of - quantum algorithms and/or efficient realization of quantum algorithms with demonstrable exponentail advantage over classical computing, DNN hardware acceleration and learning aided task-data placement, task scheduling, data routing and congestion management.

Soon to be announced solutions in the Netway Inc portfolio are attanged into three groups and are driven by a corresponing Buisiness Unit. Solution groups are - Custom Materials and Medicinal Compounds, Accelerator-Rich many-core Application Acceleration Processors and Virtual-Private Hybrid Computing Clouds.