Beyond the Hype: Understanding Google's Quantum Computing Breakthrough

Google recently announced that it has demonstrated a useful quantum algorithm that outperforms a supercomputer. In this article we aim to help our readers, particularly those with an interest in computing, business, and innovation, understand the announcement and its implications.

We will also address some of the common questions we have been asked, such as:

- How sound is the result?

- What could its transformational impact be on businesses and the quantum computing ecosystem?

- How is it different from previous “quantum supremacy” claims?

You are welcome to review the original Google article by Vadim Smelyanskiy and Hartmut Neven.

We will assume, however, that most readers may not have gone through it in detail.

This article is not intended for quantum physicists or readers familiar with technical quantum papers. Rather, it is written for those approaching the topic from a business or strategic perspective, seeking to assess whether and when this advancement might become impactful today, in the mid-term, or in the long-term.

For readers wishing to dive deeper:

1. The Nature paper

2. Technical article on verifiable quantum advantage by Xiao Mi and Kostyantyn Kechedzhi

3. Quantum computation of molecular geometry via many-body nuclear spin echoes

To make this more accessible, the analysis begins from the business implications and works backward toward the scientific result. Readers interested only in certain sections can skip those that are too technical.

Before diving in, it is important to say that the publication is balanced and transparent about its claims. When reading the detailed papers, one can clearly see what is known and true today and what is a future extrapolation or speculation. Still, general news outlets sometimes take such results out of context and use news titles that may suggest that the future already is here. In reality, while the technical publication is transparent and credible, understanding its real-world implications requires nuance.

What does it mean for businesses?

Google’s latest result demonstrates an algorithm/method that can leverage quantum computers to solve problems that can not be solved on classical computers. Like most quantum methods, it does not solve every problem; rather, it targets a specific type of challenge.

We know/assume for a while that Quantum computers will be instrumental in modeling quantum mechanical phenomena, such as the interactions of atoms, particles, and the structures (or shapes) of molecules. If you possess a strong “solver” / “simulator” / “model”, you can uncover new material or identify new drugs, etc.

Just as the telescope and the microscope opened unseen worlds to human understanding, this experiment represents a step toward a ‘quantum-scope’ capable of measuring a previously unobservable natural phenomena. This could make quantum computing a powerful enabler in drug discovery, material science, and helping determine how potential medicines bind to their targets, or in materials science for characterizing the molecular structure of new materials like polymers, battery components or even the materials that are needed to build quantum processors (qubits).

The assumption that Quantum computers will be instrumental in modeling quantum mechanical phenomenas, arises from their shared foundations in physics. They can simulate the behavior of physical elements in such an environment more naturally than classical computers. Why simulation? Imagine that you have an enormous space of possible answers and you must find the correct one by evaluating and scoring multiple physical simulations. A highly capable quantum simulator becomes an invaluable tool for exploring such possibilities efficiently.

In partnership with the University of California, Berkeley, Google conducted a proof-of-principle experiment running the new method (Quantum Echoes algorithm) on its quantum computer to study two molecules, one with 15 atoms and another with 28 atoms, to validate this approach. The results on Google’s quantum computer matched those obtained through traditional methods (verified), and revealed information not usually available from the traditional NMR, offering critical validation of the new claimed approach (i.e., quantum-scope).

Key Questions Addressed

What types of challenges can benefit from this new breakthrough?

This experiment represents a significant and rounded step toward developing a ‘quantum-scope’ capable of measuring previously unobservable natural phenomena. It can become a powerful tool for drug discovery, helping determine how potential medicines bind to their targets, in materials science for characterizing the molecular structure of new materials like polymers, battery components, or even the materials that are needed to build quantum processors (qubits).

Is this already solving an unsolvable problem today?

Not yet. The experiment tackled a small and controlled problem that can still be simulated on a classical computer. The work involved synthesizing the molecule with a specific isotope of carbon (carbon-13) in a known location in the molecule, so it is a real utility.

Google demonstrated Quantum advantage (performance surpassing classical computers) and Quantum utility (real-world usefulness), but not both at the same time.

The Quantum Echoes ’ run demonstrated real advantage (x13,000 faster than a classical computer), while the experiment with the molecule demonstrated quantum utility (which is not yet better than what can be simulated on a classical computer).

The significance of the result is that it demonstrates a method that could actually unlock real use cases in the future.

Why did Google focus on a small problem rather than unlocking a really useful problem?

Current quantum computers are still limited in qubit count, error levels, and depth of calculation, and therefore cannot yet handle large-scale simulations. As hardware scales and error correction improves, these same methods will extend to problems beyond classical reach.

What exactly was studied?

NMR stands for Nuclear Magnetic Resonance. It’s a physical phenomenon and a powerful tool that enables scientists to observe inside molecules by watching how atomic nuclei behave in a magnetic field.

When nuclei are close together in a molecule, their spins can interact; they slightly influence each other’s magnetic fields. By measuring how much and in what way they interact, we can infer: Which atoms are neighbors & what the molecular structure looks like. This is how we get detailed 3D models of molecules, including proteins and drugs, without physically seeing them.

As molecules grow larger, there are many more spins, each interacting with many others & the number of interactions grows roughly exponentially. These overlapping spin networks create very complex signals that are hard to untangle and simulate on classical computers. So, traditional NMR focuses mainly on nearby nuclei, where the interactions are strong and interpretable.

In this study, the researchers used an NMR machine to create the physical equivalent of a “quantum echo” in a molecule and then used the “quantum echoes” method to measure it (the new result presented by Google).

The influence of what’s going on in these distant spins could allow us to use quantum echoes to tease out structural information at greater distances than we currently do with NMR (we still need an accurate model of how the echoes will propagate through the molecule).

This demonstrated that such highly complex systems are indeed within the computational reach of quantum computing, while we know that it is difficult to do with classical computations.

What This Means for the Quantum Ecosystem?

The broader quantum ecosystem is progressing along two fronts:

• Hardware scaling -building larger, more stable quantum computers;

• Application development - designing meaningful, verifiable, real use cases for these machines.

Google’s work delivers a credible & verifiable method, marking a major step forward in proving that quantum computers can be genuinely useful. This is very different from quantum supremacy results, where the advantage over classical computing was not clearly explained.

It is likely that in the short term, this result will be expanded significantly, and we will see progress on the specific outcome as well as extended results (one can expect extensive research around the next research questions suggested in the articles). As in most scientific domains, one successful demonstration tends to accelerate many others, leading to faster progress across the field.

Is the outcome specific to the Google QPU and the Willow chip?

Not necessarily. Although the application ran on Google’s Willow chip, it could run on different quantum computers, provided that they meet the right specs and scale. Google claimed that they are the only ones capable of running it today, but even if true, it’s a short-term limitation.

Overall, this is positive news for the entire industry, and we expect this new application to be able to run on multiple modules in the future.

The Technical Breakthrough (Optional Deep Dive)

Google's new result is based on the OTOC (Out-of-Time-Ordered Correlator) concept, which is a mathematical object used in quantum physics to measure how information spreads and how quickly a quantum system becomes “chaotic”.

Google designed “quantum echoes”, performing a set of quantum calculations, altering the state of the system, and later performing the reverse set of calculations. On its own, this would return the system to its original state, but for quantum echoes, Google inserts a randomized parameter that alters the state of the system before the reverse takes place, ensuring that the system won’t return to exactly where it started. That explains the “echoes” portion of the name.

On a classical computer, simulating this evolution scales exponentially, and the computational cost explodes. On a quantum system, however, strange things happen, and these forward and backward evolutions interfere with each other. Hence, a quantum processor can directly implement the time evolution and measure it experimentally. If you repeat the operations multiple times, you can begin to understand the details of this quantum interference.

In the proof-of-work-experiment, the team employed TARDIS, or Time-Accurate Reversal of Dipolar Interactions. Using the “out of time order” aspect of OTOC, a set of control pulses is sent to the NMR sample that starts a perturbation of the molecule’s network of nuclear spins. A second set of pulses then reflects an echo to the source.

The demonstrations were done on very simple molecules, making this work mostly a proof of concept.

The researchers are optimistic that there are many ways the system could be used to extract structural information from molecules at distances that are currently unattainable using NMR. The articles list a lot of potential upsides that should be explored.

Conclusions and Outlook 

We are confident that in the near future, we will witness real-world demonstrations in this field, leading to entirely new ways of utilizing NMR machines. We expect rapid progress and a clearer understanding of which of the proposed approaches will prove practically valuable.

This recent result from Google represents a significant milestone toward achieving quantum advantage and quantum utility, that is, demonstrating a genuine performance advantage in a useful, real-world scenario. However, it is not sufficient on its own; further advancements are still needed.

Overall, this is highly encouraging news for the entire industry, and we anticipate that this and other emerging applications will soon operate across multiple modalities, broadening the scope and impact of quantum technologies.

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Also published on Medium