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How to measure the performance of a quantum computer<br>Three key hardware metrics reveal the scale, quality, and speed of any quantum computer—factors that determine computational capability and cost efficiency.

Date<br>16 Jul 2026

Authors<br>David McKay<br>Scott Crowder<br>Jerry Chow<br>Oliver Dial<br>Robert Davis<br>Catherine Dundon

Topics<br>Systems<br>Enablement

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Key takeaways

The performance of any quantum computer can be evaluated using three fundamental metrics: programmable qubits, qubit operations, and maximum circuits per second.

Programmable qubits measure scale by counting the qubits users can directly control and incorporate into quantum algorithms.

Qubit operations measure quality by indicating how many complex operations a quantum computer can reliably execute.

Maximum circuits per second measures speed by capturing circuit throughput, or how much useful computation a quantum system can perform over time.

Circuit throughput is a key indicator of quantum computing price-performance, helping quantify computational cost efficiency.

These metrics apply across quantum-computing modalities, including superconducting, trapped-ion, quantum-dot, and other hardware platforms. They give us a simple way of comparing quantum computers across different hardware modalities and platforms.

IBM has long tracked the progression of quantum computing hardware performance across three fundamental dimensions: scale, quality, and speed . Together, they tell us not only what a quantum computer can do, but also how efficiently and cost-effectively it can do it. So, what are the specific metrics we use to quantify these dimensions?

Scale: Programmable qubits. How many qubits can you program directly?

Quality: Qubit operations. How many of the most complex operations can a system reliably execute?

Speed: Maximum circuits per second (circuit throughput). How much useful work can a system perform per second, and at what cost?

These metrics provide a clear picture of the performance, computational capability, scalability, and cost efficiency of today’s quantum computers, regardless of the underlying hardware technology. They capture essential aspects of performance that apply across modalities—from IBM’s superconducting hardware to trapped-ion, quantum-dot, and other quantum computing approaches.

Even as quantum computers grow more powerful and less error-prone, the underlying framework will remain largely the same, though some of the details may change. Let’s take a closer look at what these metrics mean and how we define them for the current generation of quantum hardware.

What are programmable qubits?

Today’s chips use hundreds of controllable quantum elements virtually indistinguishable from qubits to function properly. However, many of those components are there only to support the quantum circuit; they aren’t intended for direct use on a computation. Therefore, the number of programmable qubits in a quantum processor provides a more useful measure of a processor’s scale . It tells us how many qubits users can directly control and integrate into quantum algorithms .

A programmable qubit is any qubit that can be:

Prepared in an arbitrary quantum state

Manipulated using high-fidelity universal operations

Measured and reset as part of a computation

These are the qubits developers interact with when building quantum applications. We use the term programmable qubits to distinguish the qubits available directly to users from other quantum elements on a processor that support computation but are not themselves programmed as part of an algorithm. Collectively, we refer to both programmable qubits and the supporting quantum elements that surround them as the physical qubits on a quantum chip.

One example of a supporting quantum element is the coupler qubit in IBM quantum hardware. Both programmable qubits and coupler qubits are made from very similar Josephson junction-based transmon circuits that act as artificial atoms. Future systems will include more and more qubit resources supporting computation, communication, error correction, and device control that can’t be directly programmed.

Take the recently announced IBM Quantum Nighthawk r2, for example. Users interact with its 120 programmable qubits, but those qubits are supported by hundreds of additional physical-qubit resources, including coupler qubits and qubit reset gadgets. These supporting quantum elements help improve both the quality and speed of computation without increasing the number of programmable qubits available to users.

Over time, the difference between the number of programmable qubits and the number of physical qubits on a chip may grow significantly. Similar patterns appear in other quantum-computing modalities. For example, some quantum-dot architectures use multiple quantum dots to realize a single...