# Haiqu

> Haiqu is a quantum computing software company building a full-stack quantum intelligence platform for enterprise, research, and developer teams.

Haiqu helps quantum R&D teams move from application ideas to executable prototypes and run frontier quantum workloads on real quantum hardware. The platform combines agentic R&D workflows, Haiqu SDK optimization, and runtime orchestration to improve quantum application development, circuit performance, and execution cost. AI agents: The agent orientation is installed to ``haiqu/sdk/AGENTS.md``, and can also be found at https://github.com/haiqu-ai/haiqu-sdk/blob/main/haiqu/sdk/AGENTS.md

- [Haiqu](https://docs.haiqu.ai/index.md)
- [Haiqu AgenticOS](https://docs.haiqu.ai/agentic-os.md)
- [Get Started](https://docs.haiqu.ai/quickstart/qs_intro.md): Get up and running with Haiqu SDK
- [Local Installation](https://docs.haiqu.ai/quickstart/qs_local.md): Get up and running with Haiqu SDK in under 5 minutes
- [Update the SDK](https://docs.haiqu.ai/quickstart/update-the-sdk.md): Get the latest Haiqu SDK with the most powerful features
- [AI Assisted Development](https://docs.haiqu.ai/quickstart/ai-assisted-development.md)
- [SDK Overview](https://docs.haiqu.ai/core_features/overview.md)
- [Dashboard Overview](https://docs.haiqu.ai/dashboard-overview.md)
- [Distribution Loading](https://docs.haiqu.ai/core_features/data_loading_dist.md): Load scipy.stats distributions into compact circuits at record scales; returns an opaque Haiqu gate to embed in your circuits
- [Data Loading](https://docs.haiqu.ai/core_features/data_loading_vect.md): Load custom vectors into compact quantum circuits; returns an opaque Haiqu gate to embed in your circuits
- [Feature embedding](https://docs.haiqu.ai/core_features/data_loading_isom.md): Encodes classical (machine learning) features with compact quantum circuits
- [Compression](https://docs.haiqu.ai/core_features/compression.md): Reduce circuit depth up to 100x with state_compression: usage, quality metric, parameters, and how to measure the compressed result
- [Working with Haiqu Circuits](https://docs.haiqu.ai/core_features/haiqu_circuits.md): Compose, transpile, execute, and measure opaque Haiqu-generated circuits — and why local Qiskit tooling fails on them
- [Transpilation](https://docs.haiqu.ai/core_features/transpilation.md): Cloud-side transpilation to any device, comparing transpilations, and the only transpilation path for circuits with Haiqu-generated gates
- [Measuring in different bases](https://docs.haiqu.ai/core_features/readout_bases.md): Measuring your circuits in different Pauli bases and getting counts back
- [Error Mitigation](https://docs.haiqu.ai/core_features/error_shield.md): Mitigating the effects of the noise in your workloads
- [Variational algorithms](https://docs.haiqu.ai/core_features/qml.md): Execute variational quantum algorithms
- [SU(2)-equivariant gates](https://docs.haiqu.ai/core_features/equivariant_gates.md): Build spin-symmetry-preserving gates and ansatze with Haiqu SDK
- [Optimization & Postprocessing](https://docs.haiqu.ai/core_features/postprocessing.md): Classical enhancement of your quantum optimization for the best performance.
- [Experiment Tracking](https://docs.haiqu.ai/core_features/exp_tracking.md): Organize, track, and manage your quantum experiments, circuits, and jobs in Haiqu SDK
- [Experiment Sharing](https://docs.haiqu.ai/core_features/experiment-sharing.md): Fetch circuits and jobs from existing Haiqu experiments
- [Circuit Analytics](https://docs.haiqu.ai/core_features/circ_analytics.md): Depth, gate counts, and advanced metrics for any circuit with the Haiqu SDK — including compressed and data-loaded circuits that local Qiskit tooling cannot measure
- [Job Performance Metrics](https://docs.haiqu.ai/core_features/quality.md)
- [Overview](https://docs.haiqu.ai/hybrid/overview.md): Build hybrid quantum-classical programs from composable layers and run them with haiqu.flow
- [Building and running flows](https://docs.haiqu.ai/hybrid/flows.md): Compose a hybrid program from layers and execute it with haiqu.flow
- [IBM Quantum](https://docs.haiqu.ai/integrations/ibm.md): Seamless Access to Quantum Processing Units by IBM.
- [Amazon Braket](https://docs.haiqu.ai/integrations/amazon.md): Explore different hardware modalities and providers by running on AWS Braket devices
- [IonQ Cloud](https://docs.haiqu.ai/integrations/ionq.md): Access to the trapped ion devices via IonQ Cloud.
- [IQM Resonance](https://docs.haiqu.ai/integrations/iqm.md): Access IQM superconducting QPUs directly via the IQM Resonance platform.
- [QML building blocks](https://docs.haiqu.ai/catalog/qml_building_blocks.md): Gates, ansatze, and variational solvers provided by Haiqu SDK
- [Benchmarks data sheet](https://docs.haiqu.ai/catalog/datasheet.md): Specifications and benchmarks of core functions
- [Characteristic Functions](https://docs.haiqu.ai/catalog/characteristic_functions.md): Load probability density functions of financial, physical, and heavy-tail distributions into quantum states using characteristic functions.
- [Multivariate Probability Distributions](https://docs.haiqu.ai/catalog/multivariate_distributions.md): Load multidimensional probability densities and copula-based joint probability density functions into quantum states.
- [Dashboard Overview](https://docs.haiqu.ai/dashboard/overview.md): The web companion to the Haiqu SDK: see, organize and share your quantum experiments
- [Dashboard Home](https://docs.haiqu.ai/dashboard/home.md): Your API key, compute credits and recent activity in one place
- [Experiments](https://docs.haiqu.ai/dashboard/experiments.md): Find, describe and compare your experiments, with their circuits, jobs and metrics
- [Circuits](https://docs.haiqu.ai/dashboard/circuits.md): Inspect a circuit's structure, cost and the jobs that ran it
- [Jobs](https://docs.haiqu.ai/dashboard/jobs.md): Track job status, read results and metrics, and fix jobs that fail or stall
- [Sharing & Collaboration](https://docs.haiqu.ai/dashboard/sharing.md): Share experiments with people, domains or teams, publish them, and discuss them in comments
- [Devices](https://docs.haiqu.ai/dashboard/devices.md): Compare QPUs and simulators by queue, quality and price, and get a ready-to-run snippet
- [Account & Credits](https://docs.haiqu.ai/dashboard/account.md): Credits, Haiqu Lab, getting help and troubleshooting
- [Login](https://docs.haiqu.ai/reference/core/login.md)
- [Init or Update Experiment](https://docs.haiqu.ai/reference/core/init.md)
- [Log Objects](https://docs.haiqu.ai/reference/core/log.md)
- [List Objects](https://docs.haiqu.ai/reference/core/list.md)
- [Get Objects](https://docs.haiqu.ai/reference/core/get.md)
- [Draw](https://docs.haiqu.ai/reference/core/draw.md)
- [Credentials Helpers](https://docs.haiqu.ai/reference/core/credentials_helpers.md)
- [Distribution Loading](https://docs.haiqu.ai/reference/data/distribution.md)
- [Multivariate Distribution Loading](https://docs.haiqu.ai/reference/data/multivariate_distribution.md)
- [Vector Loading](https://docs.haiqu.ai/reference/data/vector.md)
- [Block-wise Vector Loading](https://docs.haiqu.ai/reference/data/block_vector.md)
- [Entangled Manifold Embedding](https://docs.haiqu.ai/reference/data/entangled_manifold_embedding.md)
- [MPS Loading](https://docs.haiqu.ai/reference/data/mps_loading.md)
- [Function Loading](https://docs.haiqu.ai/reference/data/function.md)
- [Fourier Loading](https://docs.haiqu.ai/reference/data/fourier.md)
- [State Compression](https://docs.haiqu.ai/reference/middleware/compression.md)
- [Observable Backpropagation](https://docs.haiqu.ai/reference/middleware/observable-backpropagation.md)
- [Flow](https://docs.haiqu.ai/reference/run/flow.md)
- [Hybrid Program](https://docs.haiqu.ai/reference/run/hybrid_program.md)
- [Run](https://docs.haiqu.ai/reference/run/run.md)
- [Statevector Run](https://docs.haiqu.ai/reference/run/statevector_run.md)
- [Transpile](https://docs.haiqu.ai/reference/run/transpile.md)
- [Problem Formulations](https://docs.haiqu.ai/reference/optimization/problem.md): Optimization problem intake for Haiqu QUBO solvers.
- [Postprocessing](https://docs.haiqu.ai/reference/optimization/postprocessing.md): Techniques for postprocessing optimization results.
- [Variational Quantum Optimization](https://docs.haiqu.ai/reference/qml/variational.md): Variational quantum optimization with the NFT or scipy optimizers.
- [SU(2)-equivariant gates](https://docs.haiqu.ai/reference/qml/equivariant.md): SU(2)-equivariant gates, ansatze, verification, spin helpers, and compilation.
- [Pretrain](https://docs.haiqu.ai/reference/qml/pretrain.md)
- [Postprocessing](https://docs.haiqu.ai/reference/skqd/postprocess_skqd.md)
- [Configuration recovery](https://docs.haiqu.ai/reference/skqd/configuration_recovery.md): How SKQD repairs noisy measurement bitstrings using orbital occupancies.
- [Hubbard Model](https://docs.haiqu.ai/reference/skqd/hubbard.md)
- [SIAM Model](https://docs.haiqu.ai/reference/skqd/siam.md)
- [SKQD Helpers](https://docs.haiqu.ai/reference/skqd/helpers.md)
- [Submit Circuit](https://docs.haiqu.ai/api-reference/submit-circuit.md): Create circuit endpoint.
- [Transpile Circuit](https://docs.haiqu.ai/api-reference/transpile-circuit.md): Transpile a circuit to a given device.
- [Build Lr Qaoa Circuit](https://docs.haiqu.ai/api-reference/build-lr-qaoa-circuit.md): Build an LR-QAOA circuit for an unconstrained binary polynomial (synchronous).
- [Backpropagate Observables](https://docs.haiqu.ai/api-reference/backpropagate-observables.md): Submit a job to backpropagate observables through the circuit.
- [Compute Analytics](https://docs.haiqu.ai/api-reference/compute-analytics.md): Explicitly fire the job to compute analytics on the backend.
- [Get Circuit](https://docs.haiqu.ai/api-reference/get-circuit.md): Get circuit by ID.
- [Delete Circuit](https://docs.haiqu.ai/api-reference/delete-circuit.md): Delete circuit.
- [Get Circuit By Hash](https://docs.haiqu.ai/api-reference/get-circuit-by-hash.md): Get circuit by HASH.
- [Post Metrics](https://docs.haiqu.ai/api-reference/post-metrics.md): Post user-defined metrics data for the circuit.
- [Get Analytics](https://docs.haiqu.ai/api-reference/get-analytics.md): Get circuit analytics.
- [Get Metrics Evolution](https://docs.haiqu.ai/api-reference/get-metrics-evolution.md): Get data for metrics evolution.
- [Collect Csp Report](https://docs.haiqu.ai/api-reference/collect-csp-report.md): Collect a Content-Security-Policy violation report.
- [List Devices Endpoint](https://docs.haiqu.ai/api-reference/list-devices-endpoint.md): List devices endpoint.
- [Get Device Endpoint](https://docs.haiqu.ai/api-reference/get-device-endpoint.md): Get device endpoint.
- [Get Device Gate Map](https://docs.haiqu.ai/api-reference/get-device-gate-map.md): Get data for device visualization.
- [List Experiments](https://docs.haiqu.ai/api-reference/list-experiments.md): List user's experiments endpoint.
- [Create Experiment](https://docs.haiqu.ai/api-reference/create-experiment.md): Create experiment endpoint.
- [Init Experiment](https://docs.haiqu.ai/api-reference/init-experiment.md): Initialize experiment endpoint.
- [Get Experiment](https://docs.haiqu.ai/api-reference/get-experiment.md): Get experiment endpoint.
- [Update Experiment](https://docs.haiqu.ai/api-reference/update-experiment.md): Update an existing experiment endpoint.
- [Delete Experiment](https://docs.haiqu.ai/api-reference/delete-experiment.md): Delete experiment action endpoint.
- [Post Metrics](https://docs.haiqu.ai/api-reference/post-metrics-1.md): Post metrics data for experiment.
- [List Circuits](https://docs.haiqu.ai/api-reference/list-circuits.md): List experiment's circuits endpoint.
- [List Jobs](https://docs.haiqu.ai/api-reference/list-jobs.md): List the jobs for the experiment or the circuit.
- [List Artifacts](https://docs.haiqu.ai/api-reference/list-artifacts.md): List the artifacts logged to an experiment.
- [Get Artifact](https://docs.haiqu.ai/api-reference/get-artifact.md): Get a single artifact by name, including its data.
- [Submit Local Job](https://docs.haiqu.ai/api-reference/submit-local-job.md): Submit user-run job (user executed on it's own) endpoint.
- [Post Metrics](https://docs.haiqu.ai/api-reference/post-metrics-2.md): Post metrics data for circuit.
- [Get Job](https://docs.haiqu.ai/api-reference/get-job.md): Unified endpoint to get different types of the jobs.
- [Get Local Job By Hash](https://docs.haiqu.ai/api-reference/get-local-job-by-hash.md): Get local job by HASH endpoint.
- [Cancel Job](https://docs.haiqu.ai/api-reference/cancel-job.md): Unified endpoint to cancel different types of the jobs.
- [Restart Job](https://docs.haiqu.ai/api-reference/restart-job.md): Unified endpoint to restart different types of the jobs.
- [Get User](https://docs.haiqu.ai/api-reference/get-user.md): Get user profile endpoint.
- [Data Loading](https://docs.haiqu.ai/api-reference/data-loading.md): Data Loading endpoint.
- [Data Loading Estimates](https://docs.haiqu.ai/api-reference/data-loading-estimates.md): Data Loading estimates endpoint.
- [Flow](https://docs.haiqu.ai/api-reference/flow.md): Flow (hybrid program execution) endpoint. Creates a job.
- [Run](https://docs.haiqu.ai/api-reference/run.md): Run endpoint. Creates a job.
- [Compression](https://docs.haiqu.ai/api-reference/compression.md): Haiqu's state compression endpoint.
- [Compression Estimates](https://docs.haiqu.ai/api-reference/compression-estimates.md): Haiqu's state compression estimates endpoint.
- [Postprocess Counts](https://docs.haiqu.ai/api-reference/postprocess-counts.md): Apply post-processing to quantum measurement counts.
- [Variational](https://docs.haiqu.ai/api-reference/variational.md): Variational optimization endpoint. Creates a variational job.
- [Pretraining](https://docs.haiqu.ai/api-reference/pretraining.md): Pretraining endpoint. Creates a pretraining job.
- [Postprocess Skqd](https://docs.haiqu.ai/api-reference/postprocess-skqd.md): Submit an SKQD postprocessing job (async worker).
- [Create New Experiment](https://docs.haiqu.ai/api-reference/create-new-experiment.md): Create an experiment or return the existing experiment with the same name.
- [List Experiments Mcp](https://docs.haiqu.ai/api-reference/list-experiments-mcp.md): List all experiments owned by the authenticated user.
- [Get Experiment By Name](https://docs.haiqu.ai/api-reference/get-experiment-by-name.md): Return one caller-owned experiment by exact name.
- [Get Experiment](https://docs.haiqu.ai/api-reference/get-experiment-1.md): Return one experiment by opaque identifier.
- [Update Experiment](https://docs.haiqu.ai/api-reference/update-experiment-1.md): Rename the current experiment and/or set its description.
- [Get Experiment Url On Dashboard By Name](https://docs.haiqu.ai/api-reference/get-experiment-url-on-dashboard-by-name.md): Return the Haiqu dashboard URL for one caller-owned experiment.
- [List Circuits In Experiment](https://docs.haiqu.ai/api-reference/list-circuits-in-experiment.md): List circuits that belong to an experiment.
- [List Jobs](https://docs.haiqu.ai/api-reference/list-jobs-1.md): List jobs associated with an experiment.
- [Log Artifact In Experiment](https://docs.haiqu.ai/api-reference/log-artifact-in-experiment.md): Log or append an artifact value under an experiment metrics key.
- [List Artifacts In Experiment](https://docs.haiqu.ai/api-reference/list-artifacts-in-experiment.md): List normalized artifact descriptors derived from experiment metrics.
- [Get Artifact In Experiment](https://docs.haiqu.ai/api-reference/get-artifact-in-experiment.md): Get one normalized artifact descriptor by name from an experiment.
- [Get Artifact Content In Experiment](https://docs.haiqu.ai/api-reference/get-artifact-content-in-experiment.md): Return raw bytes for a base64-backed artifact entry.
- [Get Artifact Download Url In Experiment](https://docs.haiqu.ai/api-reference/get-artifact-download-url-in-experiment.md): Get a fetchable URL for the bytes of a binary artifact.
- [Submit Circuit For Analytics](https://docs.haiqu.ai/api-reference/submit-circuit-for-analytics.md): Submit a QASM circuit and persist core analytics for MCP workflows.
- [Backpropagate Observables Mcp](https://docs.haiqu.ai/api-reference/backpropagate-observables-mcp.md): Backpropagate observables through a circuit to shorten what must be run.
- [Get Circuit By Id](https://docs.haiqu.ai/api-reference/get-circuit-by-id.md): Get one stored circuit by ID, including its analytics snapshot.
- [Get Circuit Metrics Evolution](https://docs.haiqu.ai/api-reference/get-circuit-metrics-evolution.md): Get the ordered analytics snapshots recorded for a circuit.
- [Compute Circuit Analytics](https://docs.haiqu.ai/api-reference/compute-circuit-analytics.md): Start an analytics job for a stored circuit.
- [List Children Circuits](https://docs.haiqu.ai/api-reference/list-children-circuits.md): List derived transpiled and compressed child circuits for a parent ID.
- [List Jobs For Circuit](https://docs.haiqu.ai/api-reference/list-jobs-for-circuit.md): List jobs that reference a circuit directly or through derivatives.
- [Transpile Circuits](https://docs.haiqu.ai/api-reference/transpile-circuits.md): Submit one or more circuits for transpilation on a target backend.
- [Get Job Results And Status](https://docs.haiqu.ai/api-reference/get-job-results-and-status.md): Get current status and result payload for a submitted job.
- [Cancel Job](https://docs.haiqu.ai/api-reference/cancel-job-1.md): Cancel a submitted or running job.
- [Restart Job](https://docs.haiqu.ai/api-reference/restart-job-1.md): Resubmit a finished job so a worker runs it again.
- [List Qpus And Simulators](https://docs.haiqu.ai/api-reference/list-qpus-and-simulators.md): List available QPUs and simulators for execution planning.
- [Get Device Details](https://docs.haiqu.ai/api-reference/get-device-details.md): Get one backend with its basis gates and qubit connectivity.
- [Run Circuits On Qpu Or Simulator](https://docs.haiqu.ai/api-reference/run-circuits-on-qpu-or-simulator.md): Run one or more circuits on a selected QPU or simulator backend.
- [Compress Circuit With State Compression](https://docs.haiqu.ai/api-reference/compress-circuit-with-state-compression.md): Submit state-compression jobs for existing or inline circuits.
- [Estimate State Compression](https://docs.haiqu.ai/api-reference/estimate-state-compression.md): Estimate the time and cost of a state-compression job before submitting it.
- [Load Classical Data With Data Loading](https://docs.haiqu.ai/api-reference/load-classical-data-with-data-loading.md): Submit a classical-to-quantum data-loading job and return polling context.
- [Estimate Data Loading](https://docs.haiqu.ai/api-reference/estimate-data-loading.md): Estimate the time and cost of a data-loading job before submitting it.
- [List Haiqu Algorithms](https://docs.haiqu.ai/api-reference/list-haiqu-algorithms.md): List the available quantum algorithms in the catalog.
- [Get Haiqu Algorithm Recipe](https://docs.haiqu.ai/api-reference/get-haiqu-algorithm-recipe.md): Return a complete, runnable Python script for one algorithm.
- [Build Lr Qaoa Circuit Mcp](https://docs.haiqu.ai/api-reference/build-lr-qaoa-circuit-mcp.md): Build and store a linear-ramp QAOA circuit for a QUBO problem.
- [Postprocess Measurement Counts](https://docs.haiqu.ai/api-reference/postprocess-measurement-counts.md): Improve sampled QUBO solutions with Haiqu's classical post-processing.
- [Run Variational Workload](https://docs.haiqu.ai/api-reference/run-variational-workload.md): Train the parameters of a variational circuit, on a device or classically.
- [Postprocess Skqd Samples](https://docs.haiqu.ai/api-reference/postprocess-skqd-samples.md): Diagonalize a molecular Hamiltonian in the subspace spanned by measured samples.
- [Run Hybrid Flow](https://docs.haiqu.ai/api-reference/run-hybrid-flow.md): Run a multi-stage hybrid flow as a single job.
- [Notify Certificate Code](https://docs.haiqu.ai/api-reference/notify-certificate-code.md): Report a certificate code as sent to a recipient or activated by them.
- [Root](https://docs.haiqu.ai/api-reference/root.md): Root endpoint of the API.
- [Version](https://docs.haiqu.ai/api-reference/version.md): Version endpoint of the API.
- [Get Benchmarks](https://docs.haiqu.ai/api-reference/get-benchmarks.md): Get data for benchmarks for key circuit metrics against references.
- [Health Live](https://docs.haiqu.ai/api-reference/health-live.md): Used by the AI client to get health status of the MCP service.
- [Health Ready](https://docs.haiqu.ai/api-reference/health-ready.md): Used by the AI client to know that MCP server is ready to accept connection.
- [Mcp Meta](https://docs.haiqu.ai/api-reference/mcp-meta.md): Used by the AI client to understand if process is changed (happens after reload/restart of the server). So the API chat will keep context and reestablish connection.
- [Publications](https://docs.haiqu.ai/publications.md)
- [Changelog](https://docs.haiqu.ai/changelog.md): Product updates and announcements
- [FAQ](https://docs.haiqu.ai/faq.md): Answers to common questions about Haiqu SDK—from first setup to running circuits at scale.
- [Optimisation at scale](https://docs.haiqu.ai/cookbook/lr-qaoa.md): Get up and running with Haiqu SDK in under 5 minutes
- [Utility-scale quantum dynamics](https://docs.haiqu.ai/cookbook/q-dynamics.md): Get up and running with Haiqu SDK in under 5 minutes
- [Circuit Analysis](https://docs.haiqu.ai/examples/circuit-analysis.md): Quantum circuit analysis is important for understanding the structure, complexity, and feasibility of executing a circuit on quantum hardware or simulators. It helps to identify key properties such as gate composition, depth, connectivity, and noise susceptibility, which impact performance and accur…
- [Circuit Execution](https://docs.haiqu.ai/examples/circuit-execution.md): In this example, you'll learn how to execute quantum circuits using the Haiqu SDK on a variety of backends — from ideal simulators to real quantum hardware (e.g. via IBM Quantum). We will cover different scenarios and walk you through the steps required to execute the circuits on a quantum device or…
- [Experiment tracking with Haiqu SDK](https://docs.haiqu.ai/examples/experiment-tracking.md): Description of your new file.
- [Introduction to Data Loading](https://docs.haiqu.ai/examples/introduction-to-data-loading.md): A key challenge in quantum computing applications — such as quantum machine learning, finance, and optimization — is efficiently encoding classical data into quantum states. This process, dubbed as Quantum Data Loading, involves preparing quantum circuits that convert classical information into a su…
- [Option Pricing](https://docs.haiqu.ai/examples/option-pricing.md): In this example, we'll follow the Qiskit Finance tutorial on Pricing European Call Options. We recommend reviewing that tutorial to better understand the problem and its quantum solution.
- [Welcome](https://docs.haiqu.ai/index copy.md): Boost your quantum R&D with deep analytics, optimized application subroutines, and a collaborative environment that delivers breakthrough performance on today’s noisy hardware.
- [Quickstart](https://docs.haiqu.ai/quickstart_old.md): A simple end-to-end workflow example with Haiqu
- [Explore the Features](https://docs.haiqu.ai/quickstart/qs_advanced.md)
- [Revolutionizing AI Development: How Haiqu's Optimized Quantum Execution and Error Mitigation Pave the Way](https://docs.haiqu.ai/use-cases/ai.md): Quantum computing has long been heralded as the next transformative leap in technology. While classical computing thrives on binary operations limited to 0s and 1s, quantum computing promises exponential growth in computational power by harnessing the principles of superposition, entanglement, and i…
- [Cybersecurity](https://docs.haiqu.ai/use-cases/cybersecurity.md): Description of your new file.
- [Revolutionizing Risk Management in Finance with Quantum Data Encoding](https://docs.haiqu.ai/use-cases/finance.md): In the ever-evolving world of finance, precision and efficiency are paramount. For financial institutions, making informed decisions—whether assessing credit risk, managing portfolios, or identifying fraudulent transactions—depends on the ability to process and analyze vast sets of data with minimal…
- [Unlocking New Frontiers: How Quantum Computers Are Revolutionizing Drug Discovery](https://docs.haiqu.ai/use-cases/healthcare.md): Optimized Execution and Error Mitigation for Faster Solutions to Global Health Challenges
- [Logistics](https://docs.haiqu.ai/use-cases/logistics.md): Description of your new file.

## OpenAPI Specs

- [openapi](https://api.haiqu.ai/openapi.json)


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