The race for quantum supremacy has captured the imagination of scientists, technologists, and investors worldwide. At the heart of this race lies a single metric that has dominated headlines and guided development: the number of qubits in a quantum computer.
While qubit count has been a useful benchmark in the early stages of quantum computing, it no longer tells the full story of progress. As the industry matures, experts are increasingly calling for a more nuanced, multidimensional approach to evaluating quantum computing advancements.
In this article, we’ll explore why it’s time to move beyond qubits as the primary yardstick for quantum computing progress and what alternative metrics offer a clearer picture of real-world capability.
What Are Qubits and Why Do They Matter?
A qubit, or quantum bit, is the fundamental unit of quantum information. Unlike classical bits that exist as either 0 or 1, qubits can exist in superpositions—being both 0 and 1 simultaneously. This property allows quantum computers to process complex computations far faster than classical systems for certain problems.
For years, the number of qubits in a quantum system has served as a shorthand for its potential power. Tech giants like IBM, Google, and startups like Rigetti and IonQ have released successive generations of quantum processors, each boasting higher qubit counts.
But the problem? More qubits doesn’t automatically mean better performance.
The Limitations of Qubit Count as a Metric
1. Qubit Quality Varies
Not all qubits are created equal. Factors like coherence time, gate fidelity, and error rates dramatically affect a qubit’s usefulness in computation. A 1000-qubit system with high error rates might perform worse than a 100-qubit system with high fidelity and stability.
2. Quantum Error Correction Needs Overhead
To build a fault-tolerant quantum computer, many physical qubits are required to create a single logical qubit. Estimates suggest it could take thousands of physical qubits to make one error-corrected logical qubit. Therefore, focusing on raw qubit count can be misleading when it comes to true computational capacity.
3. Scalability Challenges
Adding more qubits also introduces engineering challenges, such as qubit connectivity, control precision, and cryogenic cooling. Many systems struggle to maintain qubit coherence and fidelity as scale increases.
4. Software and Algorithm Maturity
Even with sufficient hardware, software and quantum algorithms must catch up to realize practical benefits. Without real-world applications or optimized code, a large quantum system may not deliver any tangible advantage.
The Shift Toward Holistic Quantum Metrics
Recognizing the limitations of using qubit count as the primary indicator of progress, the industry is beginning to embrace alternative and more holistic performance metrics.
Let’s look at some of the most promising:
1. Quantum Volume (QV)
First introduced by IBM, quantum volume attempts to quantify the overall performance of a quantum system. It considers:
- Number of qubits
- Gate and measurement errors
- Connectivity
- Circuit depth
A higher quantum volume suggests a more powerful and reliable quantum system. It emphasizes usable qubits, not just total qubits.
IBM has steadily increased its quantum volume across generations, showcasing improvements not only in scale but also in fidelity and control.
2. CLOPS (Circuit Layer Operations Per Second)
CLOPS is a benchmark focused on speed and efficiency. It measures how many circuit layers a quantum system can process per second, factoring in latency, control hardware, and software stack.
CLOPS is important for evaluating quantum computers in hybrid systems, where they work alongside classical systems in real-time.
3. Algorithmic Qubits
Companies like Quantinuum have introduced the concept of algorithmic qubits, which refers to the number of qubits that can be used in real-world quantum algorithms with acceptable fidelity.
This metric blends hardware capabilities with software optimization, offering a more application-focused view of system performance.
4. Logical Qubits
The holy grail of quantum computing is a fault-tolerant system based on logical qubits—error-corrected units that can run long, reliable computations.
Focusing on logical qubits shifts the narrative from raw hardware to true quantum advantage, as only error-corrected systems can run complex quantum algorithms at scale.
5. Application-Specific Benchmarks
Emerging benchmarks now test quantum systems on domain-specific problems in chemistry, finance, and logistics.
Examples include:
- VQE (Variational Quantum Eigensolver) performance for molecular simulations
- QAOA (Quantum Approximate Optimization Algorithm) efficiency in combinatorial problems
- Portfolio optimization in finance
These real-world benchmarks provide insight into how quantum computers may deliver value in practical scenarios, rather than synthetic metrics.
Quantum Advantage: The True Milestone
The ultimate goal is to achieve quantum advantage—a point at which quantum systems outperform classical counterparts in a meaningful task.
While Google claimed quantum supremacy in 2019, that demonstration was a narrow proof of concept with limited practical application. Moving forward, the quantum community is focused on useful quantum advantage, where real-world applications show superiority over classical systems.
To reach this point, qubit count alone is not enough. Hardware stability, software maturity, error correction, and algorithm performance must align.
Case Study: IBM’s Roadmap Beyond Qubits
IBM’s quantum roadmap provides a concrete example of the industry’s pivot beyond raw qubit numbers.
Highlights:
- Focus on quantum volume and circuit layer execution
- Introduction of modular quantum systems with scalable architectures
- Emphasis on error mitigation and software stack integration
- Target to deliver over 1000 logical qubits in the next few years
Rather than hyping big qubit jumps, IBM now emphasizes system-wide performance, open-source tools (like Qiskit), and user-ready applications.
The Role of Software in Quantum Progress
Quantum computing is not just a hardware challenge. Progress is deeply tied to advances in software, compilers, and hybrid algorithms.
Key developments include:
- Error mitigation libraries that improve results from noisy hardware
- Quantum-classical hybrid algorithms that utilize both types of processors efficiently
- Cross-platform tools that abstract hardware differences, enabling easier development
Companies like Xanadu, Zapata, and Classiq are focusing heavily on the quantum software stack, helping developers make meaningful use of current systems, regardless of hardware limitations.
Investor and Market Implications
For investors, focusing solely on qubit count can be a dangerous oversimplification. True value lies in:
- Scalable architectures
- Hardware-software co-design
- Industry-specific applications
- Partnerships with end-users
Evaluating a quantum company’s progress should include its ecosystem maturity, developer tools, customer traction, and roadmap realism.
Educational and Public Perception Shifts
Media narratives have often centered around qubit counts, fueling misconceptions that quantum computing is a straightforward numbers game. This oversimplification can hurt public understanding and set unrealistic expectations.
Educational institutions, media outlets, and public figures need to promote literacy in quantum performance metrics, helping shift the focus to meaningful progress indicators.
Frequently Asked Question
Why is qubit count no longer the best way to measure quantum progress?
While qubit count shows how many quantum bits a computer has, it doesn’t reflect their quality, error rates, or usability. A quantum system with fewer, more stable and accurate qubits may outperform a larger but noisier one. Thus, modern metrics now focus on performance, not just scale.
What are the limitations of using qubit count alone?
Qubit count doesn’t account for:
- Gate fidelity (how accurate quantum operations are)
- Coherence time (how long qubits maintain their quantum state)
- Error rates and crosstalk
- Connectivity between qubits
As a result, it can give a misleading view of a system’s true capabilities.
What are better alternatives to measuring quantum computing performance?
Newer metrics include:
- Quantum Volume (QV) – a holistic measure of circuit depth and quality
- rQOPS (Reliable Quantum Operations Per Second)
- CLOPS (Circuit Layer Operations Per Second)
- Gate fidelity and coherence time
These benchmarks offer a more complete view of system performance.
What is Quantum Volume and why does it matter?
Quantum Volume is a widely accepted metric that combines several factors — number of qubits, gate errors, circuit depth, connectivity, and more — to evaluate how complex a quantum circuit a computer can execute. A higher QV means a system can solve more advanced problems reliably.
How does gate fidelity impact quantum computing progress?
Gate fidelity measures how accurately a quantum operation performs. Low fidelity means high error rates, making results unreliable. High gate fidelity is essential for running meaningful quantum algorithms, especially as systems scale.
Why is coherence time important in quantum computing?
Coherence time refers to how long a qubit retains its quantum state before decoherence (loss of information). Longer coherence times allow for more complex computations without errors, making it a key factor in evaluating real-world quantum performance.
What does the future of quantum benchmarking look like?
The future lies in application-specific metrics, logical qubit performance, and real-world usability benchmarks like speed-to-solution and energy efficiency. These will give a better picture of how quantum systems can solve problems that matter — not just how many qubits they have.
Conclusion
Counting qubits was a useful heuristic early on — it represented progress in scaling. But the quantum field has matured to the point where what matters most is how well those qubits work, not just how many there are. By embracing metrics like quantum volume, rQOPS, coherence times, gate fidelity, connectivity, and speed, the community gains a more nuanced, actionable, and realistic view of how quantum hardware is progressing. These new benchmarks help us understand not just “how many qubits can we build?” but rather “what can we do with them — reliably, usefully, and efficiently.