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Technology with limitless possibilities looking for tough problems to solve!  

Introduction

 At the turn of the 20th century German physicist Max Planck proposed that a ‘perfect black body’ emitted and absorbed electromagnetic waves as discrete bundles of energy referred to as ‘quanta’. This was the birth of quantum mechanics that gave scientists unprecedented, and unique mathematical tools to understand our world on the atomic scale. Now, in the 21st century, the concept of quantum-mechanical states forms the basis of ‘quantum computers’—a term coined by Richard Feynman in the early 1980s during his call for producing quantum mechanical computer systems [1]. Scientists agree that quantum computers are potentially exponentially faster and much cleverer in cracking codes deemed impossible for classical technology to accomplish.

 Challenges and issues to resolve

There are three broad categories of challenges to realize the true potential of quantum computing. First, and perhaps most fundamental is the quest for the ‘killer application’ to demonstrate the advantages of quantum computers over conventional technology. As a result, there is intense interest in the development of quantum algorithms with emphasis on identifying problems where quantum computers conclusively outperform classical ones.

 Another, perhaps more difficult challenge is design and engineering of devices and hardware. Specifically, thermal and vibrational noise disturbs the quantum entangled state of qubits and leads to erroneous computation. The term Noisy Intermediate Scale Quantum (NISQ) refers to the state of the art generation of quantum processors being used now. Ensuring high fidelity of qubit operations, especially with many qubits, is one of the most critical engineering challenges for the proliferation of quantum computers. Recently, Honeywell reported stabilizing qubits based on ion traps with a fidelity of 99.997% [21].

 Finally, nurturing professionals to work with quantum computers is also a challenge. IBM, D-Wave, and Rigetti offer software libraries enabling users to learn how to program quantum computers without in-depth knowledge of the underlying quantum physics. In academia, MIT has launched a quantum computing course for helping professionals understand the benefits and challenges of quantum computing. In Japan, the IBM Q Network Hub @ Keio University — launched in May 2018 at the Yagami Campus in Yokohama— is the only Asian hub with access to the IBM Q quantum computer; Keio University now offers courses in programming and related topics [https://research-highlights.keio.ac.jp/2018/07/b.html].

Applications and future directions

The logistics industry would benefit if quantum computers could solve the ‘travelling salesman problem’ (TSP) to optimize routes to deliver packages on a global scale. The main problem is that as the number of destinations increases, so does the possible number of routes. Currently, TSP is analyzed using approximate methods that have no theoretical certainty of yielding optimal solutions. In contrast, quantum computing algorithms are expected to offer exponential increases in speed and efficiency for this application [22].

 Materials science and specifically, materials informatics is also expected to benefit from quantum computing, in this case for the design of new materials with tailored structural and chemical properties. Another area is predicting local weather, where for example, the UK’s Met Office is looking at the combinatorial optimization provided by D-Wave systems for more accurate weather forecasting [23].

 In the long term, one of the most important roles of quantum computing is accelerating the industrial applications of artificial intelligence (AI). Implementation of Internet of Things (IoT) is leading to an ever-increasingly connected world that is generating unprecedented amounts of data that AI systems must analyze to “learn” and pick up pertinent trends and correlations. In this context, ‘quantum machine learning’ has the potential to analyze vast amounts of data that currently overpowers classical computers. Quantum machine learning [24] could fundamentally transform a wide range of industries including financial trading, digital marketing, and quantum cryptography equipment. Notably, the financial sector is taking a keen interest in quantum computing to provide new products for their customers. To this end, major banks in Japan have joined the IBM Q network at Keio University. As NISQ devices mature and qubits increase, industries reliant on combinatorial optimization problems will benefit from quantum computation. Examples include chemicals, pharmaceuticals, and materials design industries. 

 Looking beyond the next decade, quantum gate computers are expected to be integrated with classic computers to form so-called quantum accelerators—computers designed to solve a specific set of problems, such as AI engines found in smartphones today that perform pre-defined tasks such as facial recognition in real time.

 Ultimately, the fusion of quantum computers, machine learning, and AI will lead to a new era of applications for quantum mechanics that Max Planck could not have envisaged in the 1900s. The ‘quantum computational supremacy’ of quantum computing will be determined by whether these computers can solve problems that are beyond the power of conventional computers. We are at a stage where quantum computing is a technology with limitless possibilities that is looking for tough problems to solve!