Depending on who you ask, quantum computing is here, not here, and both. A couple things this week reminded me that it doesn’t really matter whether you believe quantum-mechanical phenomena is going to change everything. The mere research into the field is already impacting technology across the board.
Binary digits (bits) are the basic units of information in classical computing, while quantum bits (qubits) make up quantum computing. Bits are always in a state of 0 or 1, while qubits can be in a state of 0, 1, or a superposition of the two. Quantum computing leverages qubits to perform computations that would be much more difficult for a classical computer. But today’s physical quantum computers are very noisy and there are still no commercially useful algorithms published for them.
AI and quantum information science
In short, a true quantum computer is still years, if not decades, away. When has that ever stopped researchers?
Last month, Mobileye cofounder Amnon Shashua and a team from Hebrew University in Israel published a paper in Physical Review Letters titled “Quantum Entanglement in Deep Learning Architectures.” (Intel acquired the computer vision firm Mobileye for $15.3 billion in March 2017.)
The paper argues that the latest advancements in deep neural networks could help physicists better understand the quantum behavior of nature. This week, Shashua discussed his computer science research group’s findings at the Science of Deep Learning conference in Washington, DC. He declared that they had mathematically proven that AI can help us understand quantum physics phenomena. It’s a question of when, not if.
That’s the argument for AI helping quantum physics. Now let’s go the other way.
Also this week, IBM Research, MIT, and Oxford scientists published a paper in Nature titled “Supervised learning with quantum enhanced feature spaces.” The paper describes that as quantum computers become more powerful, they will be able to perform feature mapping on highly complex data structures that classical computers cannot.
Feature mapping is a component of machine learning that disassembles data into non-redundant “features.” The authors argue they can use quantum computers to create new classifiers that generate more sophisticated data maps. Researchers would then be able to develop more effective AI that can, for example, identify patterns in data that are invisible to classical computers.
IBM did more than just publish a paper, though. The company offered the feature-mapping algorithms to IBM Q Experience users and IBM Q Network organizations through Qiskit Aqua, its quantum information science kit. The company even provided an online demo.
Neither of these papers necessarily means that AI will solve our quantum problems or that machine learning will benefit from quantum advancements. The point at which quantum computers surpass classical computers is still out of reach.
What did become increasingly clear this week, however, is that the two fields are on a collision course.
ProBeat is a column in which Emil rants about whatever crosses him that week.
Content sourced fromTNW
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