Picking the Right Compute Backend for Optimization in 2025

Optimization rarely starts on a quantum chip. It starts with a solid classical baseline, then graduates to GPUs, hybrid jobs, or quantum processors when the use case and economics justify it. This article maps the compute backends available today — classical, quantum cloud, and hybrid or quantum-inspired — and explains where each one fits.

1) Classical backends: the reliable foundation

Gurobi A market leader for linear and mixed-integer optimization. It underpins routing, scheduling, supply chains, and portfolio construction, with mature features for multi-objective models, scenario analysis, and distributed solving.

IBM ILOG CPLEX Optimization Studio An industry standard alongside Gurobi. CPLEX covers LP, MIP, MINLP and constraint programming, ships with the OPL modeling language/IDE, and supports advanced parallel MIP.

SCIP Optimization Suite Open source and academically rigorous. SCIP 9 documents improvements across heuristics, cutting planes, symmetry handling, branching, and interfaces, with peer-reviewed performance notes.

Google OR-Tools A developer favorite for speed of iteration. CP-SAT delivers state-of-the-art performance on many discrete problems, with ready-to-run examples for VRP, flows, and scheduling.

GPU-accelerated optimization

When data volumes grow or latency targets tighten, parts of the stack move to GPUs:

NVIDIA cuOpt A GPU-accelerated engine for LP/MILP/VRP. Supports PDLP for very large LPs and integrates with modeling stacks like AMPL and GAMS. Used in near-real-time logistics and dynamic routing.

2) Quantum backends in the cloud

Production access to quantum is largely through IBM, AWS, and Microsoft. These platforms expose real QPUs and high-performance simulators, plus managed orchestration for hybrid jobs.

IBM Quantum with Qiskit Runtime

IBM exposes Sampler and Estimator “primitives” that package hardware-aware error suppression and efficient sessions. Updates through 2025 bring better support for dynamic circuits and tighter “near-time” loops that matter for variational optimization.

Amazon Braket

Braket is hardware-agnostic and aggregates multiple architectures:

• IonQ (trapped ions)
• Rigetti and IQM (superconducting)
• QuEra (neutral atoms)

It also offers managed Hybrid Jobs so you can run QAOA or VQE with classical compute colocated next to a QPU or simulator. QuEra Aquila is the neutral-atom device in the catalog with documented analog capabilities.

Microsoft Azure Quantum

Azure is both an ecosystem hub and a marketplace for quantum and quantum-inspired solvers. The platform positions a “quantum-ready” path, documents providers and services, and highlights research on error correction and scalable architectures.

Hardware to know

• Quantinuum H-series (trapped ions). All-to-all connectivity, mid-circuit measurement, conditional logic; H2 targets higher fidelities and stability.
• IonQ. Systems like Forte and Tempo push higher algorithmic qubit (#AQ) milestones.
• Rigetti. Superconducting platforms in the cloud and on-prem via Novera QPU.
• D-Wave. Quantum annealing with Advantage2 now generally available, plus hybrid solvers in the Leap service — a natural fit for QUBO/Ising formulations.
• OQC (Oxford Quantum Circuits). Superconducting systems on AWS Braket in the EU region.

3) Hybrids and quantum-inspired: where most traction is today

Hybrid quantum-classical

• QAOA is the textbook hybrid for discrete optimization such as Max-Cut. IBM publishes end-to-end tutorials (including “utility-scale” walk-throughs), and AWS shows QAOA in Braket Hybrid Jobs.
• PennyLane provides a unifying Python layer for quantum ML and hybrid optimization, with integrations to Braket Hybrid Jobs and GPU-backed simulators.
• Qiskit Runtime sessions keep the classical–quantum loop tight and cost-aware, which is crucial for variational runs.

Quantum-inspired optimization

• Toshiba SQBM+ (Simulated Bifurcation Machine). A quantum-inspired Ising solver running on standard hardware (CPU/GPU/FPGA). Available via Azure and AWS Marketplace, and proven to scale to very large instances.
• NVIDIA cuOpt. Not quantum-inspired per se, but competes in the same decision-optimization niche with aggressive GPU acceleration for LP/MILP/VRP and PDLP on GPU.

What this means for a modern optimization workflow

1. Start classically. Establish a trustworthy baseline with Gurobi, CPLEX, or SCIP — or OR-Tools CP-SAT for fast iteration. That gives you objective comparisons on cost, time, and solution quality.
2. Accelerate where it pays. Move large LPs and time-critical VRP to GPUs with cuOpt when latency or scale justify it.
3. Explore hybrid on scoped subproblems. Use QAOA/VQE via IBM Qiskit Runtime or Braket Hybrid Jobs. Run small “canary” experiments alongside your baseline and track cost and quality.
4. Use quantum-inspired as a bridge. Try SQBM+ when your formulation maps neatly to Ising/QUBO and you need scale today — without waiting for fully error-corrected QPUs.
5. Track vendor roadmaps. IBM continues platform upgrades; IonQ is pushing #AQ milestones; D-Wave rolled out Advantage2 GA; AWS keeps expanding device access. This shifts quarter by quarter.

Quick picker: which backend for what?

• VRP, scheduling, network flows. Prototype in OR-Tools. Ship with Gurobi or CPLEX. If you need near-real-time decisions, evaluate cuOpt.
• QUBO/Ising formulations. Use D-Wave annealing or SQBM+ for large, fast approximate solutions.
• Max-Cut pilots and small discrete hybrids. QAOA on IBM or via Braket Hybrid Jobs with cost tracking.
• Quantum ML and experimentation. PennyLane as the framework; target IBM or Braket under the hood.

Sources

Classical solvers

• Gurobi Optimization — https://www.gurobi.com/
• IBM ILOG CPLEX — https://www.ibm.com/products/ilog-cplex-optimization-studio
• SCIP Optimization Suite (Optimization Online / docs) — https://scipopt.org/
• Google OR-Tools — https://developers.google.com/optimization

GPU acceleration

• NVIDIA cuOpt product/docs — https://developer.nvidia.com/cuopt
• GAMS integration note — https://www.gams.com/latest/docs/solver/cuopt/index.html

Quantum platforms and devices

• IBM Quantum / Qiskit Runtime — https://quantum.ibm.com/ and https://qiskit.org/documentation/partners/qiskit_runtime/
• Amazon Braket (devices, Hybrid Jobs) — https://aws.amazon.com/braket/ and https://docs.aws.amazon.com/braket/latest/developerguide/braket-hybrid-jobs.html
• QuEra Aquila on Braket — https://aws.amazon.com/braket/devices/quera/
• Microsoft Azure Quantum — https://azure.microsoft.com/solutions/quantum/
• Quantinuum H-series — https://www.quantinuum.com/
• IonQ systems and roadmap — https://ionq.com/
• Rigetti (Novera and cloud) — https://www.rigetti.com/
• D-Wave Advantage2 and Leap — https://ir.dwavesys.com/ and https://www.dwavesys.com/
• OQC on AWS Braket (EU) — https://aws.amazon.com/braket/partners/oqc/

Hybrid and quantum-inspired

• IBM QAOA guides — https://quantum-computing.ibm.com/lab/docs/iql/optimization/qaoa
• Braket Hybrid Jobs tutorials — https://docs.aws.amazon.com/braket/latest/developerguide/braket-hybrid-jobs.html
• PennyLane framework — https://pennylane.ai/
• Toshiba SQBM+ — https://www.global.toshiba/ww/products-solutions/ai-ml/sqbm.html and Azure Marketplace listing — https://azuremarketplace.microsoft.com/