Quantum Computing: What Next?

Quantum computing was back in the headlines Thursday. The U.S. is backing nine quantum‑computing firms, including IBM with $2 billion to scale the hardware and manufacturing. The long bet is on breakthroughs in things like drug discovery, battery chemistry, and new materials.

To see why that matters, start with the one idea everything else rests on.

A regular computer bit is a switch: off (0) or on (1). A quantum bit, or "qubit", can be both at once. Like a spinning coin that's neither heads nor tails until it lands. That's superposition. 

And qubits can be linked so what happens to one instantly tells you about another. That's entanglement. 

Together, they let the machine explore a vast number of possibilities at once instead of one after another.

That doesn't make quantum "faster at everything," BUT potentially transformative at a few specific things: simulating molecules, finding the best option among astronomically many, and untangling messy data. Think drug discovery, better batteries, new materials (not your spreadsheet).

The deepest reason traces to a 1981 insight from Richard Feynman: "Nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical." 

Chemistry is quantum at its core. 

Classical computers approximate it and choke as molecules get bigger; a quantum computer models it natively. That's why the credible excitement is about designing drugs, catalysts, and batteries from first principles.

The catch is that qubits are fragile. Heat, vibration, even cosmic rays can wreck a calculation, which is why these machines run colder than outer space. 

Today's best systems have a few thousand qubits, FAR short of the millions the big payoffs need. It can take hundreds to thousands of physical qubits to make one reliable ‘logical’ qubit, depending on how good the hardware is.

At the same time classical computers keep improving, so the bar for proving real "quantum" advantage keeps rising.

Three interesting developments worth tracking: 

1\ Google's Willow chip ran an algorithm thousands of times faster than the top supercomputers. This was a physics problem quantum machines are suited to, not a consumer app, but real. 

2\ Microsoft's is the first chip built on so‑called "topological qubits". This is new kind of qubit that stores information in a stable pattern, like a rubber band that stays a loop no matter how you stretch it. The bet is that this makes the qubits naturally resistant to noise. If the underlying topological behavior holds up, it’s a genuine leap.

3\ HSBC and IBM tested quantum on bond trading, getting roughly a 34% improvement in predicting which trades would fill. This was a messy, real-world dataset, not a lab demo.

Feynman pointed at where to dig. 

We're early, the hurdles are real, and the results are starting to show up, slowly. That's the honest case for optimism: not what quantum does today, but the direction the evidence points to. Until next time,

Ram — 

Ram Srinivasan

MIT Alum | Author, The Conscious Machine | Global Future of Work and AI Adoption Leader published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

—

A Message From Ram:

My mission is to illuminate the path toward humanity's exponential future. If you're a leader, innovator, or changemaker passionate about leveraging breakthrough technologies to create unprecedented positive impact, you're in the right place. If you know others who share this vision, please share these insights. Together, we can accelerate the trajectory of human progress.

Disclaimer:

Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Exponential Human."

All views expressed on "Substrate" and across all digital channels and social media platforms are strictly personal opinions and do not represent the official positions of any organizations or entities I am affiliated with, past or present. The content shared is for informational and inspirational purposes only. These perspectives are my own and should not be construed as professional, legal, financial, technical, or strategic advice. Any decisions made based on this information are solely the responsibility of the reader.

While I strive to ensure accuracy and timeliness in all communications, the rapid pace of technological change means that some information may become outdated. I encourage readers to conduct their own due diligence and seek appropriate professional advice for their specific circumstances.