Alex K. Jones
The introduction of , Willow, has sparked excitement both for its performance milestones and the ways it might help advance the commercial viability of quantum computing.
, chair of the Electrical Engineering and Computer Science Department at ϲ, shared his insights on the announcement and its implications for the future of the field.
Q: How significant is this announcement compared to previous quantum chips?
A: The biggest challenge around superconducting quantum systems has been noise, which limits the size of problems that can be computed. If a quantum problem is too large, the noise will overcome the final calculation. A popular approach to address the noise problem is to apply an error correction algorithm called the surface code. However, noise can also cause problems during error correction, itself. For error correction to be useful, the result after error correction must not be worse than if no error correction was attempted.
The excitement from the Google team centers on a . The Google team was able to demonstrate using surface code error correction they could decrease the error on their system. This capability has been very challenging to demonstrate previously. Successful error correction is an important enabler to make more practical, scalable quantum computers.
The actual hardware in the Google Willow chip does not advance superconducting hardware dramatically over other systems. For instance, their qubit device lifetimes are not as good as IBM systems. It seems Google has found a “sweet spot” to provide the right parameters to allow error correction to be a net positive.
If the past is prologue, then the advancements in classical computing technologies warrant excitement for commercial quantum computing.
Alex K. Jones
Q: What are some potential implications of quantum computing/real world applications?
A: Quantum computing has the potential to solve certain classes of problems much more efficiently than classical computing. This has to do with the exponentially increasing state space possible through entanglement that is not possible using classical approaches. An example is determining the prime factorization of extremely large numbers, which is accomplished with Shor’s algorithm and has applications in cryptography.
Quantum computing also has the potential to simulate large physical systems with much higher fidelity than classical approaches, which has applications to better understand our physical world as well as improve our knowledge of chemistry and material science.
There is also a significant thinking that quantum computing will enable further improvement in artificial intelligence (AI) due to the larger datasets involved, but that is much less certain. For small to moderate size computing problems, classical computing remains the winner.
Q: Does this push us closer to commercially viable quantum computing?
A: This datapoint in useful error correction is a boon to the multiple companies that offer commercial quantum solutions. This is an important milestone toward advancing problem scale, but it is only one of many steps along the way. Google has noted they are limited by the same fidelity improvement floor (factor of 2) no matter how many bits of correction they employ with the surface code. They are now promising to join the already substantial amount of research into other error correction algorithms that could be simpler than surface codes and unlock better error correction.
However, the promise of this technology remains quite exciting and having large-scale commercial investment in the area is part of a rich ecosystem that is leading to substantial advancements. If the past is prologue, then the advancements in classical computing technologies warrant excitement for commercial quantum computing.
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Chris Munoz
Media Relations Specialist
Division of Communications
cjmunoz@syr.edu
