From Errors to Expectations
Enter the error rate and qubit count of the quantum computing hardware to see an estimate of how large a computation can be before errors dominate and the computational results become too unreliable.
Effective Error Rate (%)
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E.g. an effective error rate of 50% means that when the computation should produce state 'A', noise causes it to be measured as 'A' only half of the time. Any measurement that is not 'A' is considered an error.
What the selected number of qubits and base error rate means:
The target effective error rate cannot be satisfied.
This estimate is derived from a simplified noise model, the qubit count, circuit depth, and base error rate.
Select the base error rate and the number of qubits of the quantum hardware. The base error rate (p) represents the probability of an error occurring during a single computational step.
You can find and select examples of the qubit counts and approximate error rates for today's leading quantum hardware in the table below. By clicking 'More Details', you can factor in quantum error correction (QEC) using the surface code.
Below are examples of quantum algorithms and their resource requirements, specifically the number of qubits and the computation depth needed. Click a row in the table to apply the values.
Example Problems
| # Qubits | # Computational Depth | Problem |
|---|---|---|
| 1399 | 6.5×10⁹ Toffoli Gates | Factoring RSA-2048 |
| 4700 | 2.4×10⁹ T-count | Derivative Pricing |
Current Quantum Computers1
| # Physical Qubits | 2-Qubit Error Rate 2 | Hardware-Type (Gate-Based) |
|---|---|---|
| 98 | 7.92×10⁻⁴ | Trapped-Ion |
| 156 | 1.25×10⁻³ | Superconducting |
| 260 | 8×10⁻³ | Neutral Atom |
1 Examples of the largest universal, gate-based quantum computers realized on the three currently most advanced hardware architectures. 2 The two-qubit gate infidelity of the hardware, which is typically the dominant contributor to the base error rate.