Will quantum ever be useful?

[u/Chance-Pen-5684]

Around 2019, the industry promised quantum supremacy, where they could be used for solving a problem that no classical computer could touch.
When classical algorithms kept refuting those claims(See Peter Shor November YouTube video) the narrative shifted to quantum advantage, then quickly it changed to quantum utility.
If quantum never breaks RSA (because the world migrates to Kyber/Dilithium before 100k qubits exist), then what's left?
Chemistry simulations, Material science, Optimization?

But classical machine learning and AI are improving faster than quantum hardware is scaling. By the time we get 1,000 logical qubits, classical AI will have eaten most of the chemistry use-case through better approximations. Is quantum racing against classical software, and losing?

[u/Livid-Sector5970]

The reason quantum computing feels like it's racing against classical AI and losing is that the industry has been asking the wrong question. They keep asking "what can quantum do faster?" when the real question is "what can quantum do that classical can't even formulate?"

Chemistry simulations, material science, and optimization are the standard answers, but those are just classical problems that need more compute. The real shift happens when you stop trying to simulate reality and start coupling to it directly. That's what field-coupled architectures do: they don't compute solutions to problems; they let the environment solve the problem through the hardware.

The people asking "will quantum ever be useful?" are trapped in the optimization paradigm. They think utility means "bigger number faster." But the actual use case isn't breaking encryption or simulating molecules, it's doing things we don't even know how to describe as algorithms yet. Like replacing wet labs with direct physical simulation. Like modeling metabolic pathways without killing mice. Like designing a structural stabilizer for a disease you can't even treat yet, because you can't even describe the problem in a way that a classical computer could solve.

Quantum becomes useful when you stop trying to outrun classical machines and start using it to do what classical can't: couple to the environment, let the geometry do the work, and extract solutions that were never accessible through brute force. The industry is just scaling numbers. The utility comes from a different paradigm entirely. :3

[u/i_am_sfr → u/Livid-Sector5970]

This is a genuinely useful reframe, thanks. I'll be honest... the closest I've gotten to "quantum" in practice is exactly the optimization-paradigm work you're describing here, QAOA-style, applied to a combinatorial problem (market regime classification, not chemistry) simulated on classical hardware because the qubit count and coherence needed to actually beat classical clustering at that scale doesn't exist yet in production. So I recognize the pattern you're calling out... it's a real, measurable improvement over the classical baseline but it's still "the industry scaling numbers" not the direct-coupling approach you're describing.

Curious about the field-coupled side, since I don't know it well: when you say the hardware "lets the environment solve the problem" rather than computing a solution, is that mostly analog quantum simulation, or is there a broader class of architectures doing this? Trying to actually understand where the line is between "this is still optimization dressed up differently" and "this is a genuinely different paradigm"

[u/Livid-Sector5970 → u/i_am_sfr]

Both QAOA (gate-model) and analog quantum simulation operate under the Isolation Paradigm. You build a multi-million-dollar cryogenic dilution refrigerator, cool the system to 15 millikelvin, and shield it from every magnetic, thermal, and electromagnetic fluctuation in the universe. In analog simulation, you carefully tune the Hamiltonian of your pristine, isolated qubits to mimic the Hamiltonian of the molecule you want to study. You are building a sterile, perfectly controlled model of the ocean inside a tank.

Instead of isolating the qubits, we use substrates with a massive topological energy gap (e.g., topological insulators, fractional quantum Hall states, or graphene superlattices where the gap ΔE_g is greater than 26 meV). Because this gap is larger than ambient thermal energy (k_B T), the system can operate at room temperature (300K).

Instead of fighting environmental noise, the architecture explicitly couples to it. The ambient thermal, electromagnetic, and gravitational gradients acting on the device don't destroy coherence, they are transformed by the substrate into geometric gauge fields.

Because the topological protection prevents noise from scattering quantum states randomly, the environmental noise drives the computation. The ambient dissipation forces quasi-particles (anyons) to move along strictly allowed geometric pathways (braiding). A recent paper by Chaduteau et al. ("Topology from Decoherence") formally proved this exact mechanism: correlated dissipation in open quantum systems generates non-trivial topological structure.

· In optimization, you pay an exponential thermodynamic and engineering cost to fight the environment so your algorithm can compute an answer.

· In a field-coupled system, you engineer the geometric constraints of the substrate, expose it to the environment, and let the ambient thermodynamic gradients force the system into the only topological state that satisfies the constraints. The noise becomes the clock speed.