Alex Krasnok argues that the value of a university quantum computer lies in what students learn from it
The best moment in my quantum-computing class often comes when something fails. A student writes a short quantum circuit – a program that tells qubits what operations to perform – then runs it on an ideal simulator and gets the clean answer theory predicts. Yet when the same circuit runs on real quantum hardware, unexpected results appear.
What once looked certain now becomes messy. The student may think the machine has failed but this is, in fact, when the real lesson begins. A run on a quantum processor is a physical experiment that produces a computational result.
Quantum education is expanding rapidly. A recent analysis of course catalogues from 1456 US institutions identified more than 8000 courses the titles or descriptions of w hich mentioned “quantum”. That growth is welcome, but it also carries a risk. Many students on such courses first meet quantum computing as a perfect cartoon: ideal qubits, flawless gates and clean probabilities.
Such idealization can be useful with computer simulators – ordinary software that imitates a quantum computer without real qubits. Simulators make excellent first classroom tools because they let students grasp three basic quantum ideas: superposition, where several outcomes remain possible before measurement; interference, where those possibilities strengthen or cancel one another; and entanglement, where two qubits become linked so measuring one helps predict the other.
Students should feel the shock of an imperfect result
Simulators also let students debug code and see the mathematics clearly. That matters in my classroom, because many students arrive with more confidence in Python than in quantum mechanics.
Useful as those abstractions are, they are not enough: students should learn not only the algorithm but also the machine that runs it. Real hardware exposes the gap between an ideal circuit and a physical device. A qubit is not a tiny coin showing heads and tails at once. It is a fragile physical system that gives different answers with varying probabilities when measured. Qubits lose information, gates make small mistakes, measurements misread states and nearby qubits can disturb one another.
The educational goal is to connect a quantum circuit to a real device – one in the lab, or one reached through the cloud. Yet that same circuit can behave differently after a recalibration, or after the device has “drifted”.
Students should see this as soon as possible. Teaching noise early shows students that quantum computing is an experimental science. If universities buy or rent quantum computers merely so that students can press “run”, they miss the best part of the lesson.
Intel inside
At Florida International University, this view shapes how I teach quantum computing. My students use Python, Qiskit, Jupyter notebooks, simulations and cloud quantum systems. My students have cloud access to an IonQ quantum computer that uses trapped-ion qubits, charged atoms confined by electromagnetic fields and manipulated with lasers. For now, our teaching uses cloud access to a quantum processing unit (QPU), raising a useful question: what does that access teach well, and what does it hide?
What cloud access teaches best is comparison. Students can run the same circuit on a simulator and a real QPU, then ask why the histograms – bar charts of measured output strings – differ. On platforms that expose device details, students can compare connectivity maps and calibration data such as coherence times, readout error rates and gate error rates.
Cloud systems, however, can still hide too much. A polished interface can make a QPU look like a web service rather than a delicate machine. Students may not see the lasers that control ions or atoms, the cryostat that cools a superconducting chip, the microwave electronics that drive gates, or the human work behind calibration, where engineers tune the machine to keep it reliable. Cloud access teaches students to submit and analyse circuits. Access to hardware, control settings and error data helps them understand how the processor operates.
On-campus systems matter when students can inspect what sits behind the screen. In 2024 Rensselaer Polytechnic Institute in New York became the first university campus to host an IBM Quantum System One, initially powered by a 127-qubit Eagle processor. It is a striking example of a university putting a major machine within students’ reach.
Yet the educational value of any system depends less on qubit count than on what students can inspect, control and measure. A modest, accessible teaching platform – such as a photonic testbed that uses light to process information, a cryogenic set-up for testing devices at extremely low temperatures or a control-electronics lab that shows how signals drive qubits – can often teach more than a larger system that students cannot inspect.
Universities then should judge quantum-computing investments by learning outcomes, not headlines. Can students compare simulation and hardware? Can they test how a quantum computer changes over time by running the same circuit in the morning, afternoon and again the next day? Can they keep a notebook of circuits that worked in simulation but failed on hardware? Can they explain why one qubit pair works better than another? Can they connect algorithms to measurement, control, optics, microwaves, cryogenics and error reduction? Quantum computing on the verge: a look at the quantum marketplace of today
The experience has an emotional dimension too: a beautiful circuit on the whiteboard can become a difficult experiment on the chip. Students need to ask, “What algorithm did I write, and what physical experiment did the machine actually run?”
Students should feel the shock of an imperfect result, the frustration of a circuit that should have worked and the satisfaction of tracking down the physical cause. That experience turns quantum computing from a diagram into a craft.
A university quantum computer should be a teaching instrument that reveals the machine behind the calculation. Its purpose is to train people who can question claims, understand the hardware, and build the technology with sound judgement. The field needs graduates who understand the algorithm, the machine and the gap between them.
