Quantum Computing: How Advances May Reshape Our Understanding of the World

Left to right: Dr. Bob Sutor (moderator), Jamie Garcia (IBM Quantum), Ravi Pillarisetty (Intel), John Martinis (QoLab), Igor Markov (Synopsys), and Norbert Lütkenhaus (University of Waterloo) at Synopsys Executive Forum

One barrier to predicting the future uses of quantum computing is that it’s nothing like what’s come before it. While it’s true that quantum computers solve problems much faster than classical computers, that’s like saying a teleporter is faster than a horse — both transport you, but in ways that are entirely unlike each other.

“A quantum computer is a very different beast,” said Norbert Lütkenhaus, professor at the University of Waterloo’s Institute for Quantum Computing. “It can do things you can't really do otherwise. And we have not yet fully seen what a quantum computer can do.”

Quantum computers process information using the mind-bending laws of quantum mechanics. That means they work in fundamentally different ways than any form of classical computing — including supercomputers — and will be useful for solving unique kinds of problems.

Garcia highlighted three broad categories of probable applications:

1. Simulation — related to nature at a molecular level, such as quantum chemistry, quantum materials, and molecular dynamics.
2. Structure and search — exploiting patterns in data as well as solving mathematical problems, such as cryptography.
3. Optimization — tackling combinatorial problems like protein folding or logistics routing by iteratively refining solutions.

“Those applications start to solve intractable problems across almost every segment of the economy,” said Ravi Pillarisetty, a senior quantum hardware researcher at Intel. “That’s why there’s so much interest in this technology.”

Experts anticipate that prime initial use cases will be simulating molecular reactions for drug discovery and materials.

“Fundamentally, nature is quantum mechanical,” said Pillarisetty. “You need a quantum mechanical system to simulate another quantum mechanical system.”

When and how quantum computing reaches utilitarian use is hard to say. Researchers are evaluating as many as 10 different technological methods for building “qubits” (quantum bits) and performing operations — everything from superconducting circuits and silicon spins to trapped ions and photonic systems. Each of these so-called modalities creates qubits with varying characteristics that offer unique advantages and trade-offs, including coherence times, scalability, and sensitivity to noise.

“It’s a very unusual situation where there’s no clear single technology that dominates everything else,” said Igor Markov, a Synopsys distinguished architect specializing in quantum computing. “It’s basically a horse race, and it’s very interesting to see how it will turn out.”

“We have a lot of hard work to do to make the qubits better and get the architecture all set up right,” added Martinis. “I'm optimistic, but we have hard things to still solve.”

Among those additional challenges: error correction, hardware development, software abstraction, and optimizing application-specific algorithms.

“It’s a big system engineering problem, lots of variables here,” said Martinis. “This is why it’s hard to say what’s the winner right now.”