By Rolando Somma
Google achieved this milestone against the backdrop of a more sobering reality: Even the best gate-based quantum computers today can only muster around 50 qubits. A qubit, or quantum bit, is the basic piece of information in quantum computing, analogous to a bit in classical computing but so much more.
Gate-based quantum computers operate using logic gates but, in contrast with classical computers, they exploit inherent properties of quantum mechanics such as superposition, interference and entanglement. Current quantum computers are so noisy and error-prone that the information in its quantum state is lost within tens of microseconds through a mechanism called decoherence and through faulty gates.
Still, researchers are making demonstrable, if slow, progress toward more usable qubits. Perhaps in 10 years, or 20, we’ll reach the goal of reliable, large-scale, error-tolerant quantum computers that can solve a wide range of useful problems.
When that day comes, what should we do with them?
We’ve had decades to prepare. In the early 1980s, the American physicist Paul Benioff published a paper demonstrating that a quantum-mechanical model of a Turing machine—a computer—was theoretically possible. Around the same time, Richard Feynman argued that simulating quantum systems at any useful scale on classical computers would always be impossible because the problem would get far, far too big: the required memory and time would increase exponentially with the volume of the quantum system. On a quantum computer, the required resources would scale up far less radically.
Feynman really launched the field of quantum computing when he suggested that the best way to study quantum systems was to simulate them on quantum computers. Simulating quantum physics is the app for quantum computers. They’re not going to be helping you stream video on your smartphone. If large, fault-tolerant quantum computers can be built, they will enable us to probe the strange world of quantum mechanics to unprecedented depths. It follows different rules than the world we observe in our everyday lives and yet underpins everything.
On a big enough quantum computer, we could simulate quantum field theories to study the most fundamental nature of the universe. In chemistry and nanoscale research, where quantum effects dominate, we could investigate the basic properties of materials and design new ones to understand mechanisms such as unconventional superconductivity. We could simulate and understand new chemical reactions and new compounds, which could aid in drug discovery.
By diving deep into mathematics and information theory, we already have developed many theoretical tools to do these things, and the algorithms are farther along than the technology to build the actual machines. It all starts with a theoretical model of the quantum computer, which establishes how it will harness quantum mechanics to perform a useful computation. Researchers write quantum algorithms to perform a task or solve a problem using that model. These are basically a sequence of quantum gates together with a measurement of the quantum state that provides the desired classical information.
So, for instance, Grover’s algorithm shows a way to perform faster searches. Shor’s algorithm has proved that large quantum computers will one day be able to break computer security systems based on RSA, a method widely used to protect, for instance, e-mail and financial websites worldwide.
In my research, my colleagues and Ihave demonstrated very efficient algorithms to perform useful computations and study physical systems. We have also demonstrated one of the methods in one of the first small-scale quantum simulations ever done of a system of electrons, in a nuclear magnetic resonance quantum information processor. Others have also followed up on our work and recently simulated simple quantum field theories on the noisy intermediate scale quantum computers available today and in laboratory experiments.
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