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Big boys and their quantum toys
An old saying is that "Boys (and girls!) never grow up...their toys just get bigger and more expensive". That saying rings quite true for quantum computing in 2021 because, for the most part we saw toy computer running toy circuits to solve toy problems.
Of course, these "toy computers" are exceptionally sophisticated. They require countless hours of talented and brilliant scientists and engineers, devising ingenious solutions for many problems. But at the end - with a few notable exceptions - anything that you could run on a quantum computer in 2021 could be simulated on a high-performance classical computers. And classical computers are, at least for now, much simpler to use and are much more prevalent.
The "toy problems" were also not solved in vain. Organizations large and small wanted to develop their internal quantum expertise, and determine if quantum computers can solve certain problems and deliver similar results to classical computers. This is in anticipation to a future generation of quantum computers that would outperform their classical counterparts.
As we head into 2022, what needs to happen so that we can solve more real problems with quantum computers?
- Computers need to get better. Several companies have announced quantum models with 100+ qubits. Algorithms that use so many qubits cannot realistically be simulated on classical computers, thus starting to open a chasm between what can be done with quantum and what can be done with classical. Of course, qubit count is not the only measure of a computer's power (coherence and connectivity are a few other measures) but these hardware developments are promising.
- More people. It is hard to hire quantum-literate scientists and engineers to develop and implement quantum algorithms. While universities and corporate training programs are doing their best to increase the supply, the demand for the quantum workforce is large. One problem is that writing quantum software is quite difficult today, often requiring PhD-level understanding of quantum information science, in addition to an understanding of the business problem that needs to be solved.
- Better development platforms. 100- or 1000-qubit computers simply cannot be programmed using the same methods that were used for 5- or 10-qubit machines. One can no longer expect to successfully work at the gate-level or use rigid pre-built code blocks. A new way to specify algorithmic behavior and convert that spec into a working circuit is required. Classiq is addressing this problem, hoping also to help solve the "more people" problem by making quantum programming more accessible to a wider audience.
We love toys, but we hope that in 2022, more and more real-life problems can be addressed with upcoming quantum computers and the software platforms that drive them.
An old saying is that "Boys (and girls!) never grow up...their toys just get bigger and more expensive". That saying rings quite true for quantum computing in 2021 because, for the most part we saw toy computer running toy circuits to solve toy problems.
Of course, these "toy computers" are exceptionally sophisticated. They require countless hours of talented and brilliant scientists and engineers, devising ingenious solutions for many problems. But at the end - with a few notable exceptions - anything that you could run on a quantum computer in 2021 could be simulated on a high-performance classical computers. And classical computers are, at least for now, much simpler to use and are much more prevalent.
The "toy problems" were also not solved in vain. Organizations large and small wanted to develop their internal quantum expertise, and determine if quantum computers can solve certain problems and deliver similar results to classical computers. This is in anticipation to a future generation of quantum computers that would outperform their classical counterparts.
As we head into 2022, what needs to happen so that we can solve more real problems with quantum computers?
- Computers need to get better. Several companies have announced quantum models with 100+ qubits. Algorithms that use so many qubits cannot realistically be simulated on classical computers, thus starting to open a chasm between what can be done with quantum and what can be done with classical. Of course, qubit count is not the only measure of a computer's power (coherence and connectivity are a few other measures) but these hardware developments are promising.
- More people. It is hard to hire quantum-literate scientists and engineers to develop and implement quantum algorithms. While universities and corporate training programs are doing their best to increase the supply, the demand for the quantum workforce is large. One problem is that writing quantum software is quite difficult today, often requiring PhD-level understanding of quantum information science, in addition to an understanding of the business problem that needs to be solved.
- Better development platforms. 100- or 1000-qubit computers simply cannot be programmed using the same methods that were used for 5- or 10-qubit machines. One can no longer expect to successfully work at the gate-level or use rigid pre-built code blocks. A new way to specify algorithmic behavior and convert that spec into a working circuit is required. Classiq is addressing this problem, hoping also to help solve the "more people" problem by making quantum programming more accessible to a wider audience.
We love toys, but we hope that in 2022, more and more real-life problems can be addressed with upcoming quantum computers and the software platforms that drive them.
