Quantum Development Suite

Multi-tier quantum platform for NISQ expectation primitives, fault-tolerant hardware scaling, mathematical transparency, and OpenQASM extensibility.

Circuit Primitives & Options

Active Qubits ($N$)

Error Mitigation Scheme

Expectation Value & Noise Results

Expectation ($\langle H \rangle$)

-0.7842

Calculated via Estimator Primitive

Circuit Fidelity

74.2%

Estimated mitigated outcome

This module acts as a runtime primitive evaluator for near-term intermediate-scale quantum (NISQ) devices, calculating expectation values and deploying local noise mitigation wrappers.

Algorithm & Hardware Parameters

Algorithm Benchmark Preset

Logical Qubits ($N_{\text{logical}}$)

Circuit Gate Depth ($D$)

Magic State Distillation Overhead

Hardware Architecture

Error Correction Scheme

Target Success Probability

Scalability & Coherence Projections

Physical Qubits

-- physical per logical

Code Distance (d)

Surface Code

Execution Time

-- avg gate speed

Coherence Limit (T1)

Hardware decoherence boundary

Physical Qubit Allocation Breakdown --

Data: --

Syndrome: --

MSD Factories: --

Coherence Time Ratio ($t_{\text{exec}} / T_1$) 0%

Hardware Architecture Baseline Specs

2-Qubit Gate Speed	--
Physical Error Rate (p)	--
Coherence Time (T1)	--
Fault-Tolerance Threshold (p_th)	1.0%

Cross-Hardware Architecture Comparison

Real-time resource estimation and coherence feasibility metrics evaluated across all built-in hardware backends for the current benchmark parameters.

Hardware Architecture	Physical Qubits	Code Dist. (d)	Execution Time	Coherence Ratio	Status

Quantum Phase Estimation (QPE) Explorer

Precision Bits ($m$)

Unitary Control Complexity

Subroutine Scaling Output

Ancilla Qubits

Phase register size ($m$)

Controlled-U Operations

65,535

Total sub-circuit depth

Quantum Phase Estimation (QPE) serves as a foundational subroutine for eigenvalue determination, quantum chemistry energy evaluations, and Shor's factoring loop.

Mathematical Transparency & Formula Inspector

Quantum professionals require rigorous verification. Below are the exact algebraic formulas, scaling derivations, and method disclosures embedded in the estimator engine.

1. Fault-Tolerant Logical Error Rate ($p_{\text{logical}}$)

$$p_{\text{logical}} \approx 0.03 \times \left(\frac{p}{p_{\text{th}}}\right)^{\frac{d+1}{2}}$$

Where $p$ is the physical gate error rate, $p_{\text{th}}$ is the hardware fault-tolerance threshold, and $d$ is the odd code distance. The code distance $d$ is dynamically solved to satisfy the target success probability across total circuit gate operations.

2. Physical Qubit Overhead per Logical Qubit

$$N_{\text{physical/logical}} = 2 \cdot d^2 \cdot \eta_{\text{qLDPC}} \cdot \mu_{\text{MSD}}$$

For standard 2D rotated surface codes, $\eta_{\text{qLDPC}} = 1.0$, yielding $2d^2$ qubits per data/syndrome block. High-rate qLDPC bivariate bicycle codes achieve $\eta_{\text{qLDPC}} \approx 0.15$. $\mu_{\text{MSD}}$ represents the magic state distillation factory multiplier ($1.0\times$ to $5.0\times$).

3. Coherence Feasibility Boundary

$$t_{\text{exec}} = D \cdot \left( t_{\text{gate}} \cdot N_{\text{cycle}} + t_{\text{interconnect}} \right) < T_1$$

The total circuit execution time $t_{\text{exec}}$ must remain strictly bounded below the hardware relaxation time $T_1$ for uncorrected circuits, or maintained via syndrome extraction cycles in fault-tolerant architectures.

4. Estimation Methodology: Analytical Scaling vs. Monte Carlo

This tool relies on deterministic analytical scaling models rather than stochastic Monte Carlo sampling. This architecture enables instantaneous, real-time performance feedback as sliders are adjusted. For microscopic validation, statistical error sampling, and decoding failure rate profiling, users can export configurations to specialized simulation frameworks like stim and pymatching via Tab 5.

OpenQASM 3.0 Parser & Editor

Paste OpenQASM 3.0 Code or Circuit File

Parsed Circuit & Custom Decoder Diagnostics

Parsed Qubits

From `qubit[N]` declaration

Estimated Depth ($D$)

1,024

Parsed gate operations

Custom Decoder Algorithm

Parsing OpenQASM automatically feeds gate counts and logical register sizing directly into the Fault-Tolerant Estimator engine.