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qiskit runtime sessions and primitives a production guide skip to main content quantumcomputing courses com courses all courses course platforms coursera edx udemy brilliant hardware providers google quantum ai ibm quantum ionq quantinuum amazon braket azure quantum quera rigetti d wave tutorials all tutorials hello world qiskit hello world cirq hello world pennylane hello world braket quantum gates grover s algorithm shor s algorithm reference all frameworks qiskit cirq pennylane amazon braket pyquil tket d wave ocean q explore learn learning paths prerequisites programming guide case studies glossary books quantum news podcasts tools bloch sphere quantum pinball guides algorithm guide hardware guide qubit types framework comparison migration guide language timeline cheat sheets career events 2026 jobs careers certifications salary guide universities interview prep faq troubleshooting about about team search browse courses home tutorials qiskit runtime sessions and primitives a production guide qiskit intermediate free 59 61 in series 45 minutes 5 apr 2026 by dr donovan quantum zeitgeist editorial policy qiskit runtime sessions and primitives a production guide a production oriented guide to qiskit runtime s session and batch modes the estimator and sampler primitives primitive unified blocs pubs isa circuits and cost optimization strategies for ibm quantum hardware qiskit runtime estimator sampler sessions ibm quantum cost optimization prerequisites python proficiency beginner quantum computing concepts superposition entanglement linear algebra basics in this guide 7 sections 01 sessions vs batch mode 02 the estimator primitive 03 isa circuits 04 estimator options and resilience levels 05 the sampler primitive 06 complete vqe with runtimeestimator 07 cost optimization strategies ibm quantum s qiskit runtime is the execution layer that sits between your python code and real quantum hardware rather than submitting isolated jobs runtime introduces structured execution patterns sessions batches and primitives that dramatically simplify how you build algorithms and control qpu costs sessions vs batch mode session mode reserves a continuous block of qpu access once a session starts your jobs run without being interleaved with other users work the qpu stays warm no qubit re calibration between jobs which reduces job turnaround time for iterative algorithms like vqe or qaoa from qiskit_ibm_runtime import qiskitruntimeservice session service qiskitruntimeservice channel ibm_quantum backend service least_busy operational true simulator false with session backend backend as session all jobs submitted here share qpu access session closes automatically when the block exits pass sessions have a maximum duration typically 8 hours if your algorithm finishes sooner close the session explicitly with session close to stop qpu time charges batch mode is designed for embarrassingly parallel workloads where jobs do not depend on each other s results ibm schedules batch jobs more flexibly interleaving them with other users if needed so latency per job is higher but throughput for large job sets can be better from qiskit_ibm_runtime import batch with batch backend backend as batch submit many independent jobs pass choose session for iterative algorithms and batch for parameter sweeps benchmarking or running multiple independent circuits the estimator primitive the estimator primitive computes expectation values of observables given a circuit that prepares a quantum state it is the workhorse for variational algorithms there are two variants statevectorestimator for noiseless simulation useful for prototyping and estimatorv2 from qiskit_ibm_runtime for real hardware execution primitive unified blocs pubs both estimator and sampler accept inputs in pub format tuples of circuit observables parameter_values precision for estimator or circuit parameter_values shots for sampler pubs allow you to batch multiple circuit observable pairs into a single job reducing per job overhead significantly requires qiskit_ibm_runtime from qiskit circuit import quantumcircuit parametervector from qiskit quantum_info import sparsepauliop from qiskit_ibm_runtime import estimatorv2 session build a parameterized ansatz theta parametervector theta 4 ansatz quantumcircuit 2 ansatz ry theta 0 0 ansatz ry theta 1 1 ansatz cx 0 1 ansatz ry theta 2 0 ansatz ry theta 3 1 define the observable hamiltonian h sparsepauliop from_list zz 1 0 xx 0 5 yy 0 5 parameter sets to evaluate e g multiple optimizer steps batched import numpy as np param_values np random uniform np pi np pi size 5 4 each row of param_values becomes one pub entry with session backend backend as session estimator estimatorv2 mode session build pubs list of circuit observables param_values pubs ansatz h param_values job estimator run pubs result job result result 0 data evs contains a 5 array of expectation values evs result 0 data evs print f expectation values evs isa circuits before submitting to hardware circuits must be transpiled to instruction set architecture isa circuits circuits expressed only in the native gates of the target device with qubits mapped to physical qubits requires qiskit_ibm_runtime from qiskit transpiler preset_passmanagers import generate_preset_pass_manager generate isa circuit optimization_level 1 3 pm generate_preset_pass_manager backend backend optimization_level 2 isa_ansatz pm run ansatz isa_h h apply_layout isa_ansatz layout now use the isa circuit in your pubs pubs isa_ansatz isa_h param_values always use isa circuits with estimatorv2 on real hardware the primitive will reject non isa circuits to prevent silent transpilation errors estimator options and resilience levels estimatorv2 supports a range of error mitigation options through its options interface from qiskit_ibm_runtime options import estimatoroptions options estimatoroptions resilience_level controls the mitigation stack 0 no mitigation fastest cheapest 1 dynamical decoupling readout error mitigation 2 level 1 zero noise extrapolation zne 3 level 2 probabilistic error cancellation pec options resilience_level 1 twirling randomizes noise for better zne behavior options twirling enable_gates true options twirling num_randomizations 32 readout mitigation options resilience measure_mitigation true estimator estimatorv2 mode session options options resilience level 1 is a good default for most production runs it provides meaningful noise reduction at modest cost level 2 zne can cut errors in half for shallow circuits but increases qpu time by 3 5x level 3 adds further mitigation at the cost of substantially more shots the sampler primitive samplerv2 returns quasi probability distributions bitstring counts rather than expectation values it is the right choice when you need measurement outcome distributions requires qiskit_ibm_runtime from qiskit_ibm_runtime import samplerv2 qc quantumcircuit 3 3 qc h 0 qc cx 0 1 qc cx 0 2 qc measure_all isa_qc pm run qc with session backend backend as session sampler samplerv2 mode session pub for sampler circuit param_values shots pub isa_qc 4096 job sampler run pub result job result counts result 0 data meas get_counts print counts complete vqe with runtimeestimator here is a full vqe implementation using estimatorv2 inside a session including isa transpilation and optimizer integration import numpy as np from scipy optimize import minimize from qiskit circuit library import twolocal from qiskit quantum_info import sparsepauliop from qiskit transpiler preset_passmanagers import generate_preset_pass_manager from qiskit_ibm_runtime import qiskitruntimeservice session estimatorv2 from qiskit_ibm_runtime options import estimatoroptions service qiskitruntimeservice channel ibm_quantum backend service least_busy operational true simulator false min_num_qubits 2 h2 hamiltonian simplified 2 qubit form hamiltonian sparsepauliop from_list ii 1 0523732 iz 0 3979374 zi 0 3979374 zz 0 0112801 xx 0 1809312 ansatz twolocal with ry and cnot ansatz twolocal 2 ry cx reps 2 num_params ansatz num_parameters transpile to isa pm generate_preset_pass_manager backend backend optimization_level 2 isa_ansatz pm run ansatz isa_h hamiltonian apply_layout isa_ansatz layout options resilience level 1 for production run options estimatoroptions options resilience_level 1 options default_shots 8192 with session backend backend as session estimator estimatorv2 mode session options options eval_count 0 def cost_function params global eval_count pub isa_ansatz isa_h params result estimator run pub result energy float result 0 data evs 0 eval_count 1 print f step eval_count energy energy 6f ha return energy x0 np zeros num_params opt_result minimize cost_function x0 method slsqp options maxiter 50 ftol 1e 5 print f n vqe converged opt_result success print f ground state energy opt_result fun 6f ha print f total evaluations eval_count print f session id session session_id cost optimization strategies qpu time on ibm quantum is billed in seconds of usage several practices keep costs manageable use the right resilience level levels 2 and 3 multiply qpu time by large factors start with level 1 and only increase if the noise is unacceptable for your application batch parameter evaluations within a session each estimator run call has overhead passing multiple pubs in one call or multiple parameter sets per pub amortizes that overhead prefer least_busy with min_num_qubits larger backends have longer queues use the minimum number of qubits you need and pick the least busy machine set default_shots appropriately more shots reduce statistical variance but cost more for variational optimization steps 2000 4000 shots are often sufficient reserve 8192 for final result verification close sessions promptly a session that sits idle still consumes qpu time allocation use the context manager with session to ensure automatic cleanup qiskit runtime s session model combined with pubs and isa circuits represents a mature interface for running real quantum algorithms at production scale understanding the cost levers resilience level shots session lifetime and job batching lets you get the most out of limited qpu access written by dr donovan who writes on quantum computing research hardware and industry at quantum zeitgeist dr donovan ran his first quantum circuit on ibm s 5 qubit quantum experience in 2018 and has not put the subject down since he built quantumcomputingcourses com because the material out there forced a choice nobody should have to make pop science hand waving at one end phd level physics with no on ramp at the other and tutorial code that had usually stopped working by the time you found it he also writes on quantum computing at quantum zeitgeist quantumzeitgeist com was this tutorial helpful yes no share ready to go deeper browse structured courses from coursera edx udemy brilliant and more browse courses related tutorials continue learning with these guides build your first quantum circuit in qiskit complete beginner guide beginner 25 min read read the deutsch jozsa algorithm quantum s first speedup explained beginner 35 minutes read fault tolerant quantum gates why t gates need magic states advanced 22 min read read previous dynamic quantum circuits mid circuit measurement and classical feedforward in qiskit next qiskit basics quantum gates circuits and measurements on this page 01 sessions vs batch mode 02 the estimator primitive 03 isa circuits 04 estimator options and resilience levels 05 the sampler primitive 06 complete vqe with runtimeestimator 07 cost optimization strategies at a glance level intermediate read time 45 minutes language python updated aug 2026 related tutorials build your first quantum circuit in qiskit complete beginner guide 25 min read the deutsch jozsa algorithm quantum s first speedup explained 35 minutes fault tolerant quantum gates why t gates need magic states 22 min read courses on this quantum computing in 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