Quantum computing platform offers transparent and verifiable circuit processing
Qyantram: A Transparent and Verifiable Quantum Computing Platform
Emerging Technologies
Summary
Quantum computers require complicated steps to turn ideas into instructions that hardware can run, but many existing tools hide these steps behind black boxes. The authors developed QYANTRAM, a platform that makes every step visible and checkable, so users can understand and verify how the quantum circuits are prepared and run. It speeds up verification greatly and identifies errors precisely by focusing only on the active parts of the quantum system. While it is less efficient at making some routing decisions compared to other methods, its openness helps build trust in the results.
What this means in practice
- •For quantum hardware engineers: Use a fully transparent and verifiable platform to compile and verify quantum circuits on large qubit layouts, improving trust in device operations.
- •For quantum algorithm developers: Train quantum variational models faster and more accurately with QYANTRAM's machine-learning engine that aligns closely with industry tools.
Authors
Sharyu Kekane, Sadique Khatib, Abhushan Bokade, Uday Wad, S. M. Kamalapur, Y. D. Bhise
Abstract
Most quantum circuit platforms focus on idealized circuit assembly and simulation, hiding hardware compilation, error identification, and variational model training behind black-box operations. QYANTRAM addresses this gap through an open platform comprising four subsystems, built around evidence-backed results and independent verification. Its staged compilation pipeline makes every transformation traceable, while the router publishes rejected SWAP candidates and their scores for transparent decision-making. Verification exploits inactive qubits remaining in the |0> state, reducing complexity from 2^N to 2^k for k active qubits. This mathematically exact reduction enables full pre-compilation verification on 127- and 156-qubit layouts, achieving approximately 10,000x speed-ups with execution times below one millisecond. The error identification engine exhaustively evaluates all Pauli error patterns without sampling, matching closed-form predictions within 2.8e-17. Its machine-learning engine achieves numerical agreement within 2.0e-15 of qiskit.quantum_info while training 25-50 times faster. The router introduces approximately 1.27x more SWAP gates than Qiskit's SABRE due to its omission of optimization, trading routing efficiency for complete decision transparency and traceability.