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LTM Becomes First Global Systems Integrator to Launch Function in IBM Quantum Functions Catalog with AI-QEM

Global technology services firm LTM (a Larsen & Toubro Group company) has deployed its proprietary AI-QEM (AI-powered Quantum Error Mitigation) software in the IBM Quantum Functions Catalog, becoming the first global systems…

Quantum Computing Report

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Nov 13, 2026 at 4:37 AM UTC · Updated há uma hora · 2 min de leitura

LTM Becomes First Global Systems Integrator to Launch Function in IBM Quantum Functions Catalog with AI-QEM
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Global technology services firm LTM (a Larsen & Toubro Group company) has deployed its proprietary AI-QEM (AI-powered Quantum Error Mitigation) software in the IBM Quantum Functions Catalog, becoming the first global systems integrator to publish a circuit function on the platform. Available to IBM Quantum Premium, Flex, and On-Prem Plan users via the ltm/ai-qem interface, the function integrates into Qiskit Estimator workflows to suppress hardware noise through classical deep-learning inference.

LTM AI-QEM Circuit Function Specifications & Benchmarks
System DimensionTechnical SpecificationMeasured Benchmark / Platform Impact
Model ArchitectureGraph Convolutional Network (GCN)-TransformerPredicts per-Pauli correction factors (ζi) from circuit structure, backend topology, and qubit calibrations.
Hardware OverheadZero additional QPU runtime, circuit repetitions, or ancilla qubitsMatches native IBM EstimatorV2 QPU times (12 s commuting / 23 s non-commuting on ibm_kingston).
Hardware BenchmarksUp to 100 qubits on IBM Heron r2 processorsAchieved 95.06% accuracy on 100-qubit Clifford circuits (depth 45) and 90.29% on 96-qubit 2D Ising runs.
Current Scope & LimitsIBM Heron processor family; applied when |⟨O⟩noisy| ≥ 0.1Targets non-trivial expectation values while avoiding noise amplification near zero.

Physical quantum processors remain constrained by gate errors and environmental decoherence that bias expectation values as circuit depth and qubit counts increase. Conventional error-mitigation protocols such as zero-noise extrapolation (ZNE) and probabilistic error cancellation (PEC) compensate for noise by executing ensembles of modified circuits, introducing exponential sampling overheads that inflate quantum runtime costs.

AI-QEM workflow.

LTM’s circuit-agnostic approach bypasses additional quantum sampling by decomposing observables into Pauli operators and applying trained classical neural networks to rescale each noisy expectation value (⟨Pi⟩mitigated = ⟨Pi⟩noisy / ζi). Across 80- to 150-qubit validation suites, the GCN-Transformer architecture recorded a mean absolute error (MAE) of 0.343, outperforming random-forest mitigation baselines. The launch is accompanied by an IBM Quantum tutorial covering Trotterized time evolution of the 2D tilted-field Ising model.