Tools index
Build addons-powered workflows faster by using this collection of functions for creating Hamiltonians, generating Trotter time-evolution circuits, and applying the latest error mitigation capabilities.
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Mitigate Estimator expectation values with an AI model, with no additional QPU time.
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Construct high-fidelity circuits with reduced depth using approximate quantum compilation with tensor networks.
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Work with bosonic systems using data structures for bosonic operators and circuits, as well as frameworks for boson-to-qubit encodings and circuit synthesis through the Qiskit transpiler integration.
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Solve constrained binary optimization problems on IBM Quantum hardware by using the iterative warm-starting QAOA with XY-mixers.
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Simulate the time evolution of quantum many-body models and estimate time traces of expectation values, with automated error suppression.
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Work with fermionic systems using data structures for fermionic operators and circuits, as well as frameworks for fermion-to-qubit encodings and circuit synthesis through the Qiskit transpiler integration.
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Simulate fermionic quantum circuits which conserve particle number and the z component of spin.
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A set of tools for enabling the solution to general large-scale Hamiltonian subspace eigenproblems.
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Solve for the ground state and ground state energy of chemical systems with Qunova Computing's HI-VQE Quantum Function.
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Run workloads on IBM quantum hardware using Qiskit primitives and execution modes.
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Efficiently solve optimization problems with Kipu Quantum's Iskay Quantum Optimizer.
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Deploy your own quantum-classical workflows to IBM Quantum Functions and run them on managed classical compute.
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Mitigate measurement errors by processing in a reduced subspace defined by noisy bitstrings.
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Build custom error mitigation pipelines with techniques such as Probabilistic Error Cancellation (PEC), Zero-Noise Extrapolation (ZNE), Twirled Readout Error eXtinction (TREX), and Probabilistic Error Amplification (PEA) directly with Samplomatic.
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Reduce the Trotter error of Hamiltonian dynamics through a weighted combination of several circuit executions.
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Build randomization-based quantum noise characterization experiments and model-fitting analysis pipelines
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Reduce circuit depth in expectation value estimations by backpropagating observables through the circuit.
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Model optimization problems and map them into representations that can be understood by a quantum computer.
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Start solving utility-scale optimization problems with the Fire Opal Optimization Solver by Q-CTRL.
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Apply low-overhead dynamical decoupling to improve circuit performance on IBM quantum hardware.
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Solve optimization problems on quantum computers using Parity Twine, which minimizes gate counts and circuit depths.
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A framework to approximate operator evolution, which can be used to simulate expectation values and implement error mitigation techniques, such as propagated noise absorption (PNA) and shaded lightcones.
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Boost the fidelity of sampled distributions using hardware-efficient Pauli checks based on spacetime codes.
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Apply automated error suppression using Fire Opal Performance Management by Q-CTRL.
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Technique for mitigating errors in observable expectation values by 'absorbing' the inverses of learned noise channels into an observable using Pauli propagation.
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Produce unbiased expectation values with lower variance and resource overhead for circuits with fractional or parameterized gates and high-weight observables.
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Build, optimize, and execute quantum programs using the open-source software for working with quantum circuits and running them on quantum hardware.
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Use quantum computation for multi-physics PDEs, preparing simulation workflows for quantum hardware while keeping full control over quantum and physical modeling parameters.
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Solve financial optimization problems, seeking optimal portfolio strategies over time while minimizing risk and maximizing returns.
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Extract quantum-transformed features for classical machine learning pipelines using digitized counterdiabatic evolution of a spin-glass Hamiltonian.
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Estimate the spectrum of quantum Hamiltonians by processing noisy samples and diagonalizing in a reduced subspace.
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Support customized sampling randomizations for the directed execution model, using annotated circuit boxes to capture design intents such as twirling and noise injection on the client side.
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SBD (Selected Basis Diagonalization) is a high-performance library for quantum chemistry calculations.
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Reduce probabilistic error cancellation (PEC) sampling overhead by removing noise model terms that have low impact on the observable estimation.
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Improve the accuracy and computational efficiency of classical machine learning classification workflows by using quantum optimization executed on IBM hardware.
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An HPC-ready implementation of the SQD addon, written in modern C++17 standards and designed to enable HPC workflows and applications.
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Solve large combinatorial optimization problems formulated as QUBOs by combining quantum random access optimization with sample-based quantum diagonalization and classical postprocessing.
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Reduce measurement overhead and variance for workloads with low-weight observables and loop-free circuits, outperforming standard error mitigation baselines such as ZNE and PEC.
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