Modern workloads are changing the dynamics of computing capabilities due to a surge in data from devices, sensors, and wearables beyond the traditional applications. Most of the complexes have been addressed via High-Performance Computing which can be also called the epitome of digital computing at present. Yet the expectation is to further cross the boundaries via different approaches of using quantum-mechanical phenomenon using superposition and entanglement for performing the computations and changing the notion of computation itself.
Quantum computers are different altogether from digital computers which use binary systems and are based on transistors. Digital computing requires data encoded into bits, where a bit can be only in one exclusive state (0 or 1). Quantum computation, on the other hand, uses quantum bits (qubits), which need not be in an exclusive state; rather, the qubits can be in superpositions of states. With quantum computers, information is not held in individual units but rather in the system as a whole.
Let me explain. A quantum computer uses quantum states to represent bits simultaneously to achieve an exponential increase in speed and power, the system can exist in two states at the same time called the superposition principle of quantum mechanics. Qubits can store a “0” and “1” simultaneously. If you build a system comprising two qubits, it can hold four values at once — 00, 01, 10, and 11. Now with this amazing feature available at the quantum level, we will be able to program the atoms to represent all possible input combinations simultaneously to run an algorithm with all possible combinations being tested at once. With a regular computer, we would have to serially cycle through every possible input combination to arrive at the solution. Combining large numbers of qubits means that the number of states they could represent rises exponentially, making it possible to compute millions of possibilities instantaneously. That’s why quantum computing speeds up data processing, making it possible to solve problems far beyond the reach of traditional computers.
A quantum Turing machine is a theoretical model of such a computer, also known as the universal quantum computer. The major groundwork in the field of quantum computing was done by Paul Benioff and Yuri Manin in 1980. Until 1968, a quantum computer with spins as qubits was also formulated for use as a quantum computer. There are several models of quantum computing, including the quantum circuit model, quantum Turing machine, adiabatic quantum computer, one-way quantum computer, and various quantum cellular automata. The most widely used model is the quantum circuit. Since 2017, the development of actual quantum computers is rapidly gaining pace with advances being made in both practical and theoretical research. National governments and military agencies are taking deep interest and funding in quantum computing research. Once developed, quantum computers can be employed in a variety of fields such as civilian, business, trade, environmental, and national security purposes. 'Quantum Computing' was the unexpected highlight of Union Budget 2020 when Finance Minister Nirmala Sitharaman allocated Rs 8,000 crore towards the National Mission on Quantum Technologies and Applications in India that is fuelling the innovations in this space more prominent and formally accepted as a strategic direction for India.
Architectural building blocks
Quantum computing rests on two technologies, one is using trapped ionized atoms (trapped ions) and the other uses miniature superconducting circuits, both have advanced to a point where researchers can build small demonstration quantum computing systems, and some are making these available to the research community due to an explosion of interest in quantum computing worldwide. Logically any quantum computing system has few key components.
The quantum data plane is the core component that includes the physical qubits and associated structures to hold them together. It also contains something needed to measure the qubits’ state and perform gate operations on the physical qubits for a gate-based system or control the Hamiltonian for an analog computer. This is used to describe a dynamic system (such as the motion of a particle) in terms of components of momentum and coordinates of space and time, that is equal to the total energy of the system when time is not explicitly part of the function. The control signals routed to the selected qubit(s) set the Hamiltonian it sees, which control the gate operation for a digital quantum computer.
The control and measurement plane converts the control processor’s digital signals, which indicates what quantum operations are to be performed, to the analog control signals needed to perform the operations on the qubits in the quantum data plane. It also converts the analog output of measurements of qubits in the data plane to classical binary data that the control processor can handle. Since no quantum gate can be faster than the control pulse that implements it, even if the quantum system in principle allows ultrafast operation, the gate speed will be limited by the time required to construct and transmit an exquisitely precise control pulse. Fortunately, the speed of today’s silicon technology is fast enough that gate speed is limited by the quantum data plane, and not the control and measurement plane.
The control processor plane identifies and triggers the proper Hamiltonian or sequence of quantum gate operations and measurements (which are subsequently carried out by the control and measurement plane on the quantum data plane). These sequences execute the program, provided by the host processor, for implementing a quantum algorithm. Programs must be customized for the specific capabilities of the quantum layer by the software tool stack.
The control processor plane operates at a lower level of abstraction - it converts compiled code to commands for the control and measurement layer. Significant classical information processing is required to compute the quantum operations needed to correct errors based upon the measured syndrome results, and the time required for this processing may slow the operation of the quantum computer. Building a control processor plane for large quantum machines is thus challenging and remains an active area of research.
As a part of Qubits interconnect technology, is the strategy for connecting multiple qubit subsystems into a much larger system to use quantum communication channels. In quantum information theory, a quantum channel is a communication channel that can transmit quantum information, as well as classical information. Quantum interconnects (QuICs) are devices or processes that allow the transfer of quantum states between two specified physical degrees of media (Read Material, electromagnetics, etc.) or a mechanism to connect a quantum system with a classical one.
Many Qubit technologies have significantly improved over the past decade, leading to the small gate-based quantum computers available today. For all qubit technologies, the first major challenge is to lower qubit error rates in large systems while enabling measurements to be interspersed with qubit operations. Photons have several properties that make them an attractive technology for quantum computers and are the best way to transmit information since they move at the speed of light and do not strongly interact with their environment or each other.
This natural isolation from the environment makes them an obvious approach to quantum communication. In another approach, neutral atoms are used for qubits that are very similar to trapped ions, but instead of using ionized atoms and exploiting their charge to hold the qubits in place, neutral atoms and laser tweezers are used.
Worth a mention, about Quantum Error Correction, (Read, QEC) that essential technique to achieve fault-tolerant quantum computations while not only dealing with noise on stored quantum information, but also managing with faulty quantum gates, faulty quantum preparation, and faulty measurements, etc. The QEC performs a multi-qubit measurement that does not disturb the quantum information in the encoded state but retrieves information about the error.
In 1997, IBM’s computer Deep Blue defeated chess champion, Garry Kasparov, using used custom VLSI chips to execute the alpha-beta search algorithm in parallel, an example of GOFAI (Good Old-Fashioned Artificial Intelligence) rather than modern deep learning which came into existence after a decade, It was able to gain a competitive advantage because it examined 200 million possible moves each second. A quantum machine if used would be able to calculate 1 trillion moves per second, Yes! That's what we are talking about.
Quantum computers are very fragile, any kind of vibration impacts the atoms and causes decoherence, As a result of this process, quantum behavior is lost, just as energy appears to be lost by friction in classical mechanics and so is the Qubit state that matters the most in quantum computing.
Yes, there are several other challenges in building a large-scale quantum computer, namely fabrication, verification, and its architecture. Since the power of quantum computing comes from the ability to store a complex state in a single qubit it is indeed difficult to build, verify and operate. The quantum states are always fragile thus fabrication must be precise, and qubits often operate at very low temperatures so the exact state may not be measured precisely, or the verification is difficult. Imagine verifying an operation that is expected to not always get the same answer, but only an answer with a particular probability making way for errors to occur more often than with what we see in classical computing.
Making error correction the dominant task that quantum architectures need to address well. The hidden advantage of error correction taking so many more resources than that of the actual computation itself regardless of how future quantum algorithms behave, thus the architecture optimized for error correction will be the most efficient design to maintain qubit quality and measurability, so to say.
The root cause of this is related to quantum interference which is the manifestation of a coherent superposition of quantum states, which is the key aspect behind all quantum information tasks such as quantum computation and quantum communication. A major source of problems is the inability to prevent our quantum system of interest from interacting with the surrounding environment. This interaction results in the entanglement between the quantum system and the environment, leading to decoherence i.e. viewed as the loss of information from a system into the environment. Simply put, a balance is required so that the coherence of states be preserved and that decoherence is managed, to perform quantum computations.
Once a stable quantum computer gets developed, expect that machine learning will exponentially accelerate even reducing the time to solve a problem from hundreds of thousands of years to seconds.
Areas of Interests for Quantum Computing
Although there are many use cases that Quantum computing can help solve, it is indeed left to our imagination since the capabilities that Quantum computing can bring to the table opens up many frontiers that we thought were not possible at all. Let us look at few domains that can get immense benefit
Since Artificial Intelligence is based on the principle of learning from experience, becoming more accurate as feedback is given, until the computer program appears to exhibit the expected culmination of the “intelligence”, machine learning is the area that will have a major boost if aided by quantum computing. Quantum-enhanced machine learning will enable quantum algorithms to improve and often expediting classical machine learning techniques at an astronomical proportion.
Another area is precision modeling of molecular interactions, finding the optimum configurations for chemical reactions. Such “quantum chemistry” is so complex that only the simplest molecules can be analyzed by today’s fasted digital computers. But fully-developed quantum computers would not have any difficulty evaluating even the most complex processes.
Most online security currently depends on the difficulty of factoring large numbers into primes. This is presently being accomplished by using digital computers to search through every possible factor, the immense time required makes “cracking the code” expensive and lengthy thus impractical. Quantum computing can help perform such factoring exponentially more efficiently than digital computers, meaning such security methods will soon become obsolete and a new era in the field of Cryptography will cherish.
Stock markets have some of the most complicated and real-time systems, although we have developed scientific and mathematical tools to handle the transaction volumes and load, still, there’s no controlled environment available to run experiments or simulations of few lacks of trades and variations thereof. Quantum computing can provide an immediate advantage since the randomness inherent to quantum computers is harmonic to the stochastic nature of financial markets. The test cases investors often wish to evaluate for the distribution of outcomes under an extremely large number of scenarios generated at random will be possible to replicate and execute on the go.
Globally most of the countries’ GDPs are directly or indirectly affected by weather, impacting food production, transportation, and retail trade, among others. The ability to better predict the weather would thus have enormous benefit to many fields, not to mention more time to take cover from disasters. Weather forecasting requires analyzing huge amounts of data containing several dynamic variables, such as air temperature, pressure, and density that interact in a non-trivial way. Quantum computing has the potential to improve conventional numerical methods to boost tracking and predictions of meteorological conditions by handling huge amounts of data containing many variables efficiently and quickly, by harnessing the computing power of qubits, and by using quantum-inspired optimization algorithms. Moreover, pattern recognition, crucial for understanding the weather, can be enhanced using quantum machine learning easily.
In academia, models of particle physics are often extraordinarily complex, confounding pen-and-paper solutions and requiring vast amounts of computing time for numerical simulation. This makes them ideal for quantum computation, and researchers have already been taking advantage of this. Researchers at the University of Innsbruck and the Institute for Quantum Optics and Quantum Information (IQOQI) recently used a programmable quantum system to perform such a simulation. Published in Nature, the team used a simple version of a quantum computer in which ions perform logical operations, the basic steps in any computer calculation. This simulation showed excellent agreement compared to actual experiments of the physics described.
Quantum computing, a topic unknown to most of the population a decade ago, has developed into a distinctive field over the past few years. Part of this interest can be attributed to concerns about the slowing of technology scaling, also known as Moore’s law, which has driven computing performance for over half a century, increasing interest in alternative computing technology. But most of the excitement comes from the unique computational power of a quantum computer and recent progress in creating the underlying hardware, software, and algorithms necessary to make it work. Of course, time will tell the truth, for sure.***
Sept 2020. Compilation from various publicly available internet sources, authors' views are personal.Suggested Reading