As many who have been following our blog know, AI, Machine Learning (ML) and Deep Learning (DL) (e.g. see our Learning machine learning – part 3, & Industrial revolution deep learning & NVIDIA’s 3U supercomputer, AI reaches a crossroads posts), have become much more mainstream and AI has anointed DL as the best approach for pattern recognition, classification, and prediction, but has applicability beyond that.
One problem with DL has been it’s energy costs. There have been some approaches to address this, but none have been entirely successful (e.g. see Intel new DL Boost, New GraphCore GC2 chips, AI processing at the edge posts) just yet. At one time neuromorphic hardware was the answer but I’ve become disillusioned with that technology over time (see Are neuromorphic chips a dead end post).
This past week we learned of a whole new approach, something called a Quantum Convolutional NN or QCNN (see PhysOrg Introducing QCNN, pre-print of Quantum CNNs, presentation deck on QCNNs, Nature QCNN paper paywall).
Some of you may not know that convolutional neural networks (ConvNets) are the latest in a long line of DL architectures focused on recognizing patterns and classification of data in sequence. DL ConvNets can be used to recognize speech, classify photo segments, analyze ticker tapes, etc.
But why quantum computing
First off, quantum computing (QC) is a new leading edge technology targeted to solving very hard (NP Complete, wikipedia) problems, like cracking Public Key encryption keys, solving the traveling salesperson problem and assembling an optimum Bitcoin block problem (see List of NP complete problems, wikipedia).
QC utilizes quantum mechanical properties of the universe to solve these problems without resorting to brute force searches, such as, going down every path in the traveling salesmen problem (see our QC programming and QC at our doorsteps posts).
At the moment, IBM, Google, Intel and others are all working on the QC and trying to scale it up, by increasing the number of Qubits (quantum bits) their systems support. The more qubits, the more quantum storage you have, and the more sophisticated NP complete problems one can solve. Current qubit counts include: 72 qubits for Google, 42 for Intel, and 50 for IBM. Apparently not all qubits are alike, and they don’t last very long, ~100 microseconds (see Timeline of QC, wikipedia).
What’s a QCNN?
What’s new is the use of quantum computing circuits to create ConvNets. Essentially the researchers have created a way to apply AI DL (ConvNet) techniques to quantum computing data (qubits).
Apparently there are QC [qubit] phases that need to be recognized and what better way to do that than use DL ConvNets. The only problem is that performing DL on QC data with today’s tools, would require reading out the phase into a digital (a pattern recognition problem), converting to digital data, and then processing it via CPU/GPU DL ConvNets, a classic chicken or egg problem. But with QCNNs, one has a DL ConvNet entirely implemented in QC.
DL ConvNets are typically optimized for a specific problem, varying layer counts, nodes/layer, node connectivity, etc. QCNNs match this and also come in various sizes. Above is a QCNN circuit, optimized to recognize the phase (joining?) of two sets of symmetrically-protected topology numbers (SPT, see pre-print article).
I won’t go into the QC technology used in any detail (as I barely understand it), but the researchers have come up with a way to map DL ConvNets into QC circuitry. Assuming this all works, one can then use QC to perform DL pattern recognition on qubit data.