Intent-Level Quantum Programming with Assertion-Guided Execution and Inspectable Intermediate Representation

2026-07-31

AI Summary

This work addresses the challenges in verifying and porting quantum programs, which stem from imperative gate sequencing, probabilistic outputs, and backend dependencies. To overcome these limitations, the authors propose a novel quantum domain-specific language (QDSL) featuring a separation-of-concerns architecture that decouples intent from execution, an introspectable intent-level intermediate representation (IR), and an assertion-guided mechanism for automatic execution-mode inference. This approach enables pre-execution circuit-structure validation, automatically selects the optimal execution modality without user intervention, and incorporates structured logging with endianness standardization. Experimental results demonstrate that IR generation incurs sub-millisecond overhead, the total variation distance-based fault detector achieves a true positive rate of 1.0 under fault injection, and compiled outputs exhibit numerical consistency across both PennyLane and Qiskit backends.

Abstract

Quantum programs are difficult to validate: circuits are typically expressed as imperative gate sequences with limited inspectability, execution modalities must be selected manually, and outputs are inherently probabilistic. These challenges are compounded when programs must be portable across backend frameworks with differing internal conventions. We present a quantum domain-specific language (QDSL) that addresses these problems through three mechanisms: (i) intent-execution separation, where algorithmic constructs such as preparation, superposition, entanglement, and measurement are represented as inspectable objects compiled into backend-specific circuits only after pre-execution Intermediate Representation (IR) validation; (ii) first-class IR introspection, exposing circuit width, wire mapping, operation order, and measurement intent for developer inspection prior to execution; and (iii) assertion-guided modality inference, where the engine examines declared verification properties to automatically select sampling, statevector, or dual execution without user intervention. Results are logged in structured form to support reproducible regression testing. We evaluate our prototype on benchmark circuits spanning entanglement, oracle-based, structured-transform, and variational examples. In fault-injection experiments on Bell and 3-qubit GHZ circuits, a total-variation-distance detector identifies the injected faults across all evaluated shot budgets (True Positive Rate 1.0), with observed false positives (False Positive Rate 2.7-3.8%) only at 128 shots. Differential testing demonstrates numerical agreement across PennyLane and Qiskit compilation targets for the evaluated constructs after endianness canonicalization. IR generation costs are sub-millisecond.

Problem

• quantum programming
• program validation
• backend portability
• probabilistic output
• circuit inspectability

Innovation

• intent-execution separation
• inspectable intermediate representation
• assertion-guided execution
• quantum domain-specific language
• modality inference