As companies race to find practical applications for quantum computers, the short term focus is on boosting machine learning
Companies are jostling to be the first to find a practical application for quantum computers — The race is on to find the first practical use for quantum computing. Tweets: @dandoll , @lilaphasia , @paullecoque , @unhedgedchatter , and @lilaphasia Tweets: Daniel Doll-Steinberg / @dandoll : The world is about to experience a jump in technology like no other Quantum is one of the foundations Quantum advantage is the next goal in the race for a new computer age | Financial Times https://www.ft.com/... InfraHopeHard / @lilaphasia : #quantum whether it was getting close to producing “commercially relevant algorithms”. https://www.ft.com/... ..few doubt that the race is now on. REALLY?COMMERCIALLY not scientific relevant? WWW/Internet/GPS was created as “commercially relevant”🤔 @MazzucatoM @francesca_bria @paullecoque : The first practical application based on quantum advantage will officially launch the quantum age, predicted Peter Chapman, chief executive of $IONQ. https://www.ft.com/... @unhedgedchatter : “The hardware has seen a 10-fold improvement over the past two years, according to Will Oliver, a professor at the Massachusetts Institute of Technology.” $GOOG $GOOGL https://www.ft.com/... InfraHopeHard / @lilaphasia : https://www.ft.com/... quantum hardware system has seen a 10-fold improvement over the past two years..in the short term had shifted to trying to use quantum systems in tandem with classical computers to improve ML @QuantumDom
Context & Ripple Effects
This FT report lands three years after IBM, Google, and Microsoft laid out rival hardware approaches to building quantum computers, and nearly two years into the argument over what even counts as progress — Google chasing a supremacy milestone while IBM insists the real bar is "quantum advantage" on useful problems. Until now, the race has been measured in physics benchmarks; this piece marks the pivot to hunting for a paying workload.
Two data points give the shift substance: MIT professor Will Oliver counts a ten-fold hardware performance improvement over just the past two years, and IonQ CEO Peter Chapman frames the first practical quantum-advantage application as the event that launches the entire quantum age. The short-term focus on boosting machine learning is the industry's answer to a hard question — what can noisy, early-stage quantum hardware actually be sold for today?
First-order effects
- IonQ under Peter Chapman is staking its commercial narrative on the first quantum-advantage application kicking off the quantum age, which pushes machine-learning workloads to the front of its roadmap instead of fault-tolerant machines.
- Will Oliver's ten-fold hardware gain gives enterprise buyers a concrete reason to pilot hybrid quantum-classical setups now, converting quantum computing from a research curiosity into something tested against real ML budgets.
Second-order effects
- IBM and Google, whose rivalry was long scored as a supremacy-versus-advantage milestone race in the drive toward a practical quantum computer, now have to compete on which ecosystem delivers usable ML acceleration first — moving the scoreboard from lab demos to customer workloads.
- Providers offering both classical and quantum compute gain a bundling lever: customers buying ML capacity can be cross-sold quantum access, tying quantum adoption to existing compute contracts rather than forcing standalone purchases.
Third-order effects
- If hybrid ML proves out before fully fault-tolerant hardware exists, quantum vendors structurally become accelerator suppliers attached to classical AI stacks — coprocessors in someone else's data center rather than the makers of a new computing era.
- The counterweight is expert caution about fault-tolerance difficulty and slow qubit speeds: the quantum age Chapman predicts may arrive through accumulated hybrid wins rather than one breakthrough moment, keeping hype risk elevated for anyone marketing "quantum advantage" prematurely.
The trend: Quantum computing's near-term value proposition is being repositioned as an accelerator for machine learning, with vendors racing to claim quantum advantage through applied workloads instead of raw hardware milestones.