This is your Quantum Research Now podcast.

Welcome, everyone, to Quantum Research Now. I’m Leo, your resident quantum whisperer. If you’ve been following the quantum headlines this week, you’ll know we’re living through a tectonic shift—a moment when the abstract dreams of quantum physics are colliding with the tangible realities of computing. Let’s jump straight into what happened just two days ago, on October 24, when MicroCloud Hologram Inc. announced something bold: a hybrid quantum-classical convolutional neural network, or QCNN, that’s been tested on the storied MNIST dataset.

Now, picture a classical neural network as a bustling city street, each neuron a shopkeeper shouting predictions about handwritten digits—0 through 9—on the MNIST dataset. But now, MicroCloud is turbocharging this street with a quantum shortcut, a back alley where light bends and information travels both ways at once. Their QCNN isn’t just faster; it’s fundamentally different, blending quantum circuits with classical deep learning in a multi-class classification experiment—a first for a commercial quantum company, if you can believe it.

But before we get lost in the maze of qubits and CNNs, let me zoom out and connect this to the broader landscape. Quantum computing has had a blockbuster week. Over at Google Quantum AI, researchers published a Nature paper demonstrating a 13,000-fold speedup over the world’s fastest supercomputer—Frontier—using their new Quantum Echoes algorithm. The analogy here? Imagine you need to solve a million-piece jigsaw puzzle, and classical computers are painstakingly sorting each piece while the quantum processor snaps them into place, not just quickly, but in ways that classic logic cannot even follow. This isn’t just a technical stunt—it’s a glimpse into a world where quantum machines begin to answer scientific questions that are, quite literally, out of reach for any silicon-based brain.

What makes Quantum Echoes so dramatic is that, for the first time, the results are independently verifiable—a quantum computer in Tokyo could, in principle, reproduce the same computation as one in Mountain View, and you’d get the same answer. That’s the dream Richard Feynman scribbled in his notebooks decades ago: quantum systems that not only simulate nature, but allow us to check that simulation against reality. The team at Google, led by Nobel laureate Michel Devoret, didn’t stop at quantum supremacy; they tied their breakthrough to real-world chemistry, showing how this algorithm could extend the reach of nuclear magnetic resonance (NMR) spectroscopy—a tool every chemist uses to peer into the heart of molecules.

This week’s news isn’t just about speed, though. Over at IonQ, engineers have smashed another record, achieving 99.99% fidelity in two-qubit gates. Think of qubit fidelity as the purity of a musical note in a symphony—every imperfect note muddles the melody. IonQ’s achievement means the orchestra sounds clearer than ever, a critical step toward scaling up to millions of qubits and unlocking error-corrected, fault-tolerant quantum computers.

Now, back to MicroCloud Hologram’s QCNN. What does this mean for you, for science, for the future? It’s a bit like upgrading from Morse code to the internet. Classical AI is already changing how we live, but the fusion with quantum logic could let us recognize patterns and make predictions that are simply unthinkable today, from drug discovery to climate modeling.

Let’s ground this with a sensory snapshot. Step inside a quantum lab: The hum of cryogenic coolers, the ethereal glow of control lasers, the faint odor of liquid helium—this is where the weirdness happens. In this cathedral of superposition, you can almost feel the simultaneous futures branching out, the “what-ifs” of computation becoming real.

To close, I want to thank you for joining me on this journey through the quantum...