📈 To Quantum or not To Quantum, Pasqal optimization, and the promise of Quantum Computing
Local Optimum: short, imperfect-yet-useful ideas - Edition #3
Welcome to a new edition of Local Optimum: a short, imperfect-yet-useful collection of ideas related to optimization, decision-making, and applied Operations Research.
Let’s dive in! 🪂
1) 🔬 To Quantum or not To Quantum
There’s a lot of buzz around quantum computing, and much of it touches on solving optimization problems.
One approach stands out: analog quantum computers, like D-Wave’s, which are specifically designed for optimization. Did you read about QUBO (Quadratic Unbouned Binary Optimization) formulations? They exploit that kind of formulations.
And two quantum algorithms keep popping up:
Quantum Approximation Optimization Algorithm (QAOA).
Variational Quantum Eigensolvers (VQE).
Will any of these succeed long-term? It depends on scalability, hardware advances, and whether they truly speed up computations.
(And no, speeding up ≠ proving P = NP.)
Just like NVIDIA uses GPUs to accelerate computing, quantum machines might end up as hardware accelerators. But the real question is:
Can they reduce the complexity of optimization problems themselves?
My take: they will remain as hardware accelerators, and we could see some quantum algorithms that reduce complexity of some problems a bit.
Or qubit 😉
2) 💎 Pasqal’s approach to optimization
Pasqal is a French company that develops quantum computers, and they recently shared a blog post in which they explore the boundaries of current benchmarks:
(click on the image below to go to the post)
They’re also collaborating with NVIDIA to offer hybrid classical-quantum approaches to solve the most prominent problems.
3) 🙏🏻 The promise of Quantum Computing
I’ve noticed a big wave of renewed interest in OR, and it’s coming from two powerful fronts: Quantum Computing and AI.
In this post — the first of a 3-part series — I focus on the Quantum side. Why is quantum computing so relevant to OR? Because we’re approaching the physical limits of classical computers, and quantum offers a way to leap beyond them.
We’re talking about qubits, superposition, and algorithms — quantum tools that can, in theory, solve complex optimization problems far faster than classical ones. Major consulting firms and top researchers (like Amira Abbas and her 44 co-authors!) are already betting big on this.
The field is still young, but the momentum is real.
And what excites me most? People from physics, computer science, and other disciplines are bringing fresh eyes to optimization. OR is getting the spotlight it deserves. Now with a Quantum Computing flavor.
If you want to read the full article, click here 👇
What if we could use AI agents for helping us solve optimization problems?
Next Monday, I’ll share some exciting news about the intersection of AI Agents and Operations Research.
Here’s what I’ll cover:
🤖 What AI Agents might do for OR
💡 How LLMs are shaping the future of optimization
➗ Whether math itself could be automated by AI
If you want to stay ahead of the curve, you won’t want to miss this. See you Monday!
And that’s it for today!
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Have a nice day ahead ☀️
Borja.
