By Yuval Boger, Chief Marketing Officer, Classiq Technologies on 15 Jan 2022
Recent advancements in quantum computing — from Google’s quantum supremacy experiment to public market activities and product roadmap announcements — have generated both justified excitement and overblown hype in the machine learning community. Will quantum computers usher in a QML (quantum machine learning) revolution? How soon will this happen? What should forward-thinking executives do to responsibly plan for that day?
Quantum computers use qubits (quantum bits) instead of regular bits. When data scientists deploy algorithms that utilize two intrinsically unique capabilities of qubits — superposition and entanglement — they can potentially create dramatic speed improvements and find new insights in AI/ML algorithms.
There are four key motivations for using quantum computing in machine learning:
Quantum heuristics behave differently from classical heuristics. Since many classical machine learning algorithms work empirically but have no theoretical proof, it is tempting to try quantum heuristics in those instances where classical heuristics do not meet expectations.
Quantum computers can load data in an exponentially more compact way. The superposition attribute of quantum computers allows them to hold multiple values simultaneously while providing a different weight to each value. For instance, a 50-qubit quantum computer can hold and process over a quadrillion value at the same time, whereas a classical computer can process only one such value at any given time. For context, a quadrillion is about 50 times the number of red blood cells in the body. Since many machine learning problems utilize multi-dimensional spaces, it could be easier to model these large spaces on quantum computers, or indeed possible to model some problems on quantum computers that could not be modeled on classical computers.
There is proven exponential acceleration in solving linear systems of equations. Through the Harrow, Hassidim, Lloyd (HHL) algorithm, it was proven that quantum computers can solve such systems (used extensively in least-squares linear regression and Gaussian processes) exponentially faster than classical computers. The exact speedup is different from one quantum algorithm to another. But as an example, the Grover algorithm allows efficient searches in unstructured data sets. If a classical search algorithm completes the search on a given data set in a certain amount of time, that time would increase four-fold if the size of the data set increased four-fold. In contrast, the time to perform a Grover search would double – instead of a four-fold increase – with every four-fold increase in the size of the data set.
Quantum computers produce new types of data patterns. This leads to the hope of identifying these patterns by using reverse algorithms.
But alongside these exciting promises, managers must be aware of the realities and current limitations of quantum computing.
Today’s computers have only a limited number of qubits. While the number of qubits is not the only measure of the capability of quantum computers (other important measures include coherence time, qubit fidelity, and connectivity), it is a good first approximation. This is akin to using horsepower as an approximation of the acceleration or towing capability of a vehicle. The largest announced quantum computer as of November ’21 has 127 qubits, and as a result, practically any algorithm that can execute on quantum computers today can also be executed on classical computers. However, product roadmaps from companies like IBM, Honeywell and many others, predict hundreds or even thousands of qubits in the coming years, leading to a growing performance chasm in favor of quantum computers.
Programming is hard. Quantum programming requires a different way of thinking than classical programming. Most quantum development environments today operate at the gate level, meaning that programmers have to almost manually specify the “wiring” between the qubits and the quantum gates that perform actions on them. While this might be a practical approach for a handful of qubits, it does not scale to hundreds of thousands of them. Fortunately, programming platforms that provide higher-level abstractions are becoming available, allowing machine learning engineers to specify the desired functionality at a high level and then let a computer program synthesize a quantum circuit from it.
Talent is scarce. Many of today’s quantum programming environments practically assume Ph.D.-level knowledge of quantum information science. While universities are ramping up quantum education curricula, qualified and experienced quantum software engineers are hard to come by. While outsourcing quantum development is an option, many organizations feel that quantum is a strategic technology and that it is important to develop internal competencies. This is also an area where new development platforms make quantum more accessible to domain-specific experts (such as those in finance, logistics, material sciences, and of course machine learning) without requiring a deep understanding of quantum physics.
Quantum computers from different manufacturers are not compatible. While IT managers can safely assume that code running on HP servers will run on Dell servers, such is not the case for quantum. Computers from different manufacturers have different architectures, different quantum gates (akin to different instruction sets), and other differences. Customers regularly tell us that they are not yet ready to commit to one hardware vendor because they are unsure who will emerge as the winner of the hardware race. Thus, organizations often look for development platforms that make it easy to port algorithms from one computer to another, as well as to estimate ahead of time what computers might do the job. Additionally, enterprises often prefer to use quantum cloud providers (such as Amazon Braket or Azure Quantum) because these providers carry several types of quantum computers and make experimentation much easier.
When we speak with customers, a common theme emerges: they understand that quantum computing can deliver a strategic impact on their business, but they realize that it might take a couple of years for that impact to materialize. Having said that, enterprises understand that now is the time to take these first steps into the quantum world, build internal expertise, identify early use cases, and perform short proof of concepts, so that they do not lag behind those that mastered quantum machine learning when the hardware and software live up to the immense promise of quantum.
Collected at: https://quantumcomputingreport.com/the-promise-and-challenges-of-quantum-machine-learning/