The Quantum Race: How Google, Microsoft, and Amazon Are Betting on Physics Einstein Called “Spooky”

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Although I heard about quantum physics early on, thanks to my dad — a physicist — my first real exposure to quantum computing came about 11 years ago, when Google partnered with D-Wave. I remember watching this video and being completely hooked. The idea that computers could one day perform calculations faster than even the most powerful supercomputers was mind-blowing.

But as fascinated as I was, I never really understood it. I’d read articles, watched videos, and asked friends who knew more than I did, but all I retained was this vague idea that quantum computers were “faster.” I couldn’t explain why, and I definitely didn’t know how. So in writing this blog, I finally set out to understand it properly. And as I peeled back the layers, what I found was both bizarre and beautiful—something that defies classical logic but makes perfect sense in its own world. Now, with tech giants like Google, Microsoft, and Amazon building real quantum chips, the future of this strange technology is becoming very real, one breakthrough at a time.

What exactly is quantum computing and why it will never replace your phone

For the longest time, I thought of quantum computers as just insanely fast supercomputers. The kind that would crunch through data in seconds that regular machines would take years to process. But that’s not what they are—not really.

The more I read, the more I realized that quantum computers aren’t just faster versions of what we already have, they’re built on completely different rules. They don’t speak the language of 1s and 0s the way classical computers do. They operate in probabilities, not certainties.

A quantum computer isn’t here to replace your laptop or your phone. What it is built for is solving problems that are too complex, too multidimensional, or too massive for regular computers to even attempt. To understand why, we need to talk about how computers actually work. A regular computer whether it's your phone, a laptop, or a data center works using bits. These are tiny electrical switches that can be either off (0) or on (1). Quantum computers, on the other hand, use something called qubits (quantum bits). And here’s where it gets weird. Unlike a regular bit that’s either 0 or 1, a qubit can be both at the same time - a state called superposition. Imagine flipping a coin into the air. While it’s spinning, it’s not just heads or tails, it’s sort of both. Only when it lands do you get one result. That’s how qubits behave. They're in a blend of possibilities until you “look” at them, or in computing terms, measure them.

This weirdness is what gives quantum computers their potential. While a classical computer looks at one possibility at a time, a quantum computer can explore many possibilities in parallel. And when you string multiple qubits together, especially when they interact in just the right way, that's when the magic really kicks in.

Superposition

In classical computing, each bit can represent one value at a time—either 0 or 1. But a qubit, thanks to a quantum property called superposition, can represent both 0 and 1 at once. This means a quantum system with multiple qubits can hold a massive number of possible combinations simultaneously. For example, two classical bits can store one of four combinations: 00, 01, 10, or 11. But two qubits in superposition represent all four at the same time. Add more qubits, and the number of possibilities grows exponentially.

Now, here’s the catch: quantum computers are terrible at basic math. If you just want to add two numbers or run a spreadsheet, a classical computer will always be faster and more reliable. That’s because when you measure a qubit, it collapses into a single value—either 0 or 1. It’s like catching a spinning coin mid-air: you see one side, but the moment you check, all the other possibilities vanish. Quantum systems aren't built to be precise calculators; they're built to guide probabilities and extract meaningful outcomes from complex systems.

In Avengers: Infinity War, when Dr. Strange goes through millions of futures to find the one path that leads to victory, he doesn’t live through them—he just sifts through possibilities in parallel and locks in the best one. That’s the role superposition plays in quantum computing.

Entanglement

If superposition is what allows quantum computers to hold multiple possibilities in a single moment, entanglement is what allows those possibilities to work together. Entanglement is a strange but powerful quantum property where two or more qubits become linked in such a way that the state of one instantly reveals something about the other even if they’re far apart. You can think of them as being part of the same system, no matter how much physical distance separates them.

This isn't just a trick, it’s how quantum computers scale. When qubits are entangled, they’re no longer just individual units doing their own thing. They start to behave like a coordinated group, sharing information in ways classical bits simply can’t. This lets a quantum system model relationships between variables all at once, instead of calculating each combination one by one.

And this is where the power of quantum computing really kicks in. It’s not that it can just do “more” than a classical computer, it can do different kinds of things entirely. Entanglement makes the system smarter, not just faster. It's what allows a quantum algorithm to navigate through complex solution spaces, prune bad paths early, and zero in on the right answer without brute force.

It's a weird concept, and it sounds like it breaks all the rules. That’s probably why so many physicists, including the guy in our blog title, found it hard to believe. Einstein famously said, “God doesn’t play dice with the universe” pushing back against the randomness and non-locality that entanglement introduced. But it’s real. It’s been tested. And it's one of the reasons quantum chips from companies like Google, Microsoft, and Amazon are worth paying attention to.

So, how does all of this actually work in practice?

Let’s say you’re trying to search a massive database, a digital phonebook with a million names. You’re looking for one person: Alice from Santa Clara. A classical computer would scan through each entry one at a time, or optimize the search using sorting algorithms and indexing. Still, it's checking one by one, just faster.

A quantum computer approaches this very differently. It doesn't check entries one after another it creates a superposition of all possibilities and then uses an algorithm (like Grover’s) to gradually amplify the correct answer and cancel out the wrong ones. The result? It can find Alice significantly faster, especially as the dataset grows.

In a way, it’s like being at a Coldplay concert. The entire stadium is dark except the stage. Chris Martin shouts, “Is Alice from Santa Clara here?” A few nearby fans point their flashlights toward her, and over a few moments, the lighting team locks a spotlight onto the right person. That’s what a quantum computer does, it amplifies the right answer through interference and shines a spotlight on it.

But, and this is important, this doesn’t mean quantum computers are good at everything. If instead of finding Alice, you were asked to sort all one million names alphabetically, the output is now almost as large as the input. Suddenly, you run into a bottleneck: all those results have to be read out from the quantum system and passed back into a classical computer. That transition—the interface between the quantum and classical world is slow and messy. It’s like using the world’s fastest car but getting stuck in traffic the moment you reach the city.

That’s why quantum computers won’t replace classical ones, they’ll work alongside them, like a co-processor or a specialized agent that’s summoned for very specific tasks: search, optimization, simulation, and more. You still need a classical computer to hand off the problem, interpret the result, and do everything else around it. And even then, quantum computers don’t give you exact answers, they give you probabilities. They’re designed to tilt the odds in your favor, not deliver a guaranteed outcome every time. That’s a big shift from how we’re used to computing today.

So with all these challenges, why are companies like Google, Microsoft, and Amazon pouring resources into this space? Because if they get it right, even partially, it unlocks a new class of problems that today’s machines simply can’t solve. Problems in drug discovery, material design, logistics, cryptography, and artificial intelligence. Problems where you don’t need to sort the whole haystack, you just need to find the needle faster than anyone else.

Google’s Willow: Scaling Through Error Correction

In December 2024, Google unveiled its latest quantum processor: Willow. Building on its earlier breakthroughs with Sycamore, Willow pushes the boundaries of scalability and error correction, two of the biggest hurdles in quantum computing today. The chip features 105 superconducting qubits arranged in a grid-like architecture designed for error correction using a method called surface code. What makes Willow a leap forward is not just the number of qubits, but the fact that Google demonstrated exponential error suppression as more qubits were added. In other words, the chip doesn’t just get bigger—it gets smarter as it grows.

One of the most jaw-dropping benchmarks came from a simulation that would have taken a classical supercomputer an estimated 10 septillion years to complete. Willow did it in under five minutes. But that doesn’t mean it can replace a supercomputer. It simply shows how effective it is for very specific types of problems, especially those grounded in quantum physics. Willow’s qubits also show improved coherence times, increasing from 20 microseconds in Sycamore to around 100 microseconds. That may not sound like much, but in quantum computing, even tiny gains give you more time to perform meaningful calculations before the system collapses.

With Willow, Google is focusing not just on quantum speed, but on building a foundation that can scale into the millions of qubits needed for real-world applications. It's a bet on depth, discipline, and data integrity—one that positions Google at the front of the quantum hardware race.

Microsoft’s Majorana 1: Betting on a Different Kind of Qubit

While Google is scaling up with traditional superconducting qubits, Microsoft is taking a different path entirely. In early 2025, they announced Majorana 1, a quantum chip built on what they call topological qubits, a type of qubit that in theory, is far more stable and less error-prone than anything built so far.

Topological qubits aren’t just a different flavor of the same thing; they’re based on exotic physics involving something called Majorana zero modes. These are quasi-particles that, instead of storing information in a single place, braid that information into the space between particles. That "braiding" gives the qubit natural protection from many sources of noise and disturbance. Instead of painting data onto a surface where it can be easily smudged, topological qubits engrave that information into the structure itself, making it far more resistant to errors.

But this approach is still experimental. While Microsoft claims to have demonstrated the first topological qubit, some physicists are still waiting for stronger proof. That said, if they’re right, it would solve one of quantum computing’s biggest problems: error correction at scale. Where others add more physical qubits to fix errors, Microsoft is trying to build qubits that don’t need as much fixing in the first place.

Majorana 1 might not be the most powerful chip today, but if topological qubits work as hoped, it could be a long-term game-changer—one that trades short-term benchmarks for long-term stability.

Amazon’s Ocelot: Fighting Errors with Schrödinger’s Cat

In February 2025, Amazon joined the quantum hardware race with the announcement of Ocelot, a new chip from its AWS Center for Quantum Computing. But instead of trying to build more qubits or reinvent them entirely, Amazon focused on a different problem: how to handle errors more efficiently, and their solution leans on a clever twist inspired by Schrödinger’s cat.

Ocelot uses something called “cat qubits,” a special type of qubit that naturally resists certain types of errors. Rather than correcting mistakes after they happen, these qubits are designed to avoid them in the first place, reducing the amount of extra hardware needed to keep calculations on track. In early tests, Amazon claims this could cut error correction costs by as much as 90%.

That’s the real headline here. While others are chasing power, Amazon is chasing practicality. Ocelot isn’t the flashiest chip, but it’s built to be scalable, sustainable, and manufacturable, a long-term play for a future where quantum computers need to do more than just prove a point. It may still be early days for Ocelot, but it shows that Amazon is thinking not just about what quantum computers can do, but how to actually build them at scale.

A Race That’s Just Getting Started

For something I didn’t fully understand for over a decade, quantum computing now feels more real than ever. Google, Microsoft, and Amazon are all chasing it from different angles: scaling, stability, and sustainability, but they’re all betting on the same future.

Quantum computers won’t replace your phone, but they might solve problems even today’s supercomputers can’t. From drug discovery to AI, the impact could be massive.

If this post made quantum computing feel a little less mysterious, I’d love to hear your thoughts. Let’s connect on LinkedIn and keep the curiosity going.