Quantum computing headlines are full of bold claims: one machine achieves “supremacy,” another boasts “millisecond coherence,” a third declares itself “fault-tolerant.”
While these phrases sound impressive, they often mask the real engineering and scientific challenges that determine how useful a device actually is.
This post unpacks the key metrics and concepts that experts use to judge the quality of a quantum computer and explains why each one matters.
The Qubit: Small Piece, Big Deal
Classical computers use bits (0 or 1); quantum computers use qubits, which can exist in a superposition of both states.
A “good” qubit must satisfy the DiVincenzo criteria: it should be well-defined, controllable, readable, and scalable.
Today’s qubits come in many flavors—superconducting circuits, trapped ions, neutral atoms, photonic modes, semiconductor spins—each with strengths and weaknesses.
Coherence Time: How Long Does Quantum Information Survive?
Two numbers dominate this discussion:
T1 (energy relaxation) and T2 (dephasing).
• Longer T1 means less chance the qubit decays to its ground state.
• Longer T2 means superpositions maintain phase relationships.
Practical quantum circuits must finish computations before decoherence ruins the qubits, so higher coherence times directly translate to deeper circuits—or equivalently, more complex algorithms.
Gate Fidelity and Error Rates
A coherent qubit is useless if operations on it are sloppy. Gate fidelity measures how closely a real quantum gate matches its mathematical ideal.
Industry benchmarks often quote single-qubit fidelities (>99.9 %) and two-qubit fidelities (>99 %).
Lower error rates reduce the overhead required for error correction and expand the size of useful algorithms.
Connectivity & Crosstalk
Which qubits can interact directly? In superconducting devices, couplers are local; trapped-ion chains allow all-to-all connectivity; photonics offers long-distance links.
Poor connectivity forces extra “swap” gates, increasing depth and errors.
Crosstalk—unintentional interaction between qubits—can further inflate error budgets.
Fault Tolerance and Quantum Error Correction (QEC)
Because physical error rates are still high, logical qubits are built from many physical qubits through QEC codes (e.g., surface code).
The concept of a fault-tolerant threshold defines the maximum tolerable error rate per gate; stay below it and errors can, in principle, be suppressed arbitrarily by adding redundancy.
Doing so, however, may require thousands of physical qubits per logical qubit—hence the race to lower native error rates.
Quantum Advantage vs. Quantum Supremacy
• Quantum supremacy: demonstrated when a quantum device performs any computation infeasible for a classical computer, even if the task has no practical value (Google’s 2019 random-circuit sampling claim).
• Quantum advantage: a more pragmatic milestone—outperforming the best classical algorithm on a useful problem (e.g., chemistry simulation, optimization).
Moving from supremacy to advantage usually requires higher fidelity, better error mitigation, and algorithms tailored to hardware constraints.
Benchmarks and Metrics
• Quantum Volume (IBM): combines qubit count, gate depth, and error rates into a single number.
• Circuit Layer Operations per Second (CLOPS): measures how fast full layers of gates can be executed.
• Randomized Benchmarking: statistically estimates average gate error.
• XEB (Cross-Entropy Benchmarking): used in Google’s supremacy experiment to quantify fidelity on random circuits.
No metric is perfect—each highlights different aspects of performance.
Algorithmic Suitability
A device tuned for chemistry (dense entanglement, high two-qubit fidelity) may not excel at optimization (need for many qubits but shallow depth).
Evaluating “goodness” therefore depends on the target workload.
Users increasingly seek application-specific benchmarks rather than generic figures of merit.
Scalability: From Prototype to Factory
Building a few dozen coherent qubits is hard; manufacturing millions is exponentially harder.
Key questions include wafer-level yield (for superconductors), photonic integration density, ion-trap modularity, cryogenic infrastructure, and control electronics.
A “good” quantum computer must have a credible roadmap to scale without performance collapse.
The Road Ahead
No single parameter crowns a champion. Instead, engineers juggle coherence, fidelity, connectivity, and scalability while theorists design algorithms that tolerate today’s imperfections.
As these fronts advance in concert, the abstract notions of supremacy and fault tolerance will gradually translate into real-world quantum advantage—solving problems that genuinely outstrip classical machines.
