Meta tags:
description=Solutions to the most common problems in quantum computing: circuit errors, noisy results, transpilation failures, hardware limitations, and algorithm…;
author=QuantumComputingCourses.com;

Headings (most frequently used words):

quantum, and, computing, reference, troubleshooting, guide, 01, circuit, gate, errors, 02, noisy, results, accuracy, 03, simulation, performance, 04, hardware, access, jobs, 05, pennylane, specific, 06, quick, more, resources, get, one, email, week, learn, careers, about,

Text of the page (most frequently used words):
the (80), and (50), qiskit (45), hardware (39), #circuit (39), for (36), qubits (36), import (32), use (31), gates (29), from (27), with (26), qml (25), backend (25), qubit (24), print (23), pennylane (22), cause (22), params (22), gate (21), depth (21), not (21), circuits (20), quantum (19), are (19), guide (17), results (17), statevector (17), aersimulator (17), simulation (16), this (15), method (15), shots (15), job (14), numpy (14), that (14), transpiled (14), your (13), algorithm (13), all (13), transpile (13), wrong (12), too (12), simulator (12), check (12), run (12), native (11), ibm (11), noise (11), fix (11), result (11), before (11), see (10), jobs (10), wires (10), gradient (10), only (10), most (10), courses (9), random (9), gradients (9), quantinuum (9), backends (9), gpu (9), vqe (9), count (9), optimization_level (9), than (9), you (9), have (9), specific (8), accuracy (8), barren (8), ionq (8), many (8), error (8), ansatz (8), measurement (8), fixes (8), qiskitruntimeservice (8), service (8), free (7), tutorials (7), local (7), deep (7), braket (7), transpilation (7), problem (7), parameters (7), layers (7), has (7), framework (6), reference (6), computing (6), hello (6), world (6), noisy (6), problems (6), reduce (6), into (6), mps (6), sim (6), after (6), grad (6), which (6), 100 (6), can (6), quantumcircuit (6), handles (6), state (6), shot (6), 000 (6), common (6), plt (6), swap (6), faq (5), about (5), training (5), case (5), get (5), one (5), quick (5), errors (5), more (5), torch (5), qnode (5), cost (5), plateau (5), zero (5), pickle (5), aer (5), decomposition (5), matrix (5), hamiltonian (5), spsa (5), first (5), look (5), ccx (5), device (5), measurements (5), dev (5), range (5), diagnose (5), variational (5), two (5), but (5), counts (5), queues (5), qiskit_aer (5), performance (4), amazon (4), types (4), what (4), cirq (4), tutorial (4), test (4), superconducting (4), tensor (4), tier (4), qasm3 (4), parametervector (4), exceeds (4), during (4), any (4), exact (4), verify (4), pauli (4), optimizer (4), qaoa (4), noiseless (4), basis_gates (4), coupling (4), map (4), def (4), true (4), cnot (4), array (4), correct (4), grad_fn (4), exponentially (4), time (4), measure (4), variance (4), ops (4), count_ops (4), submitting (4), plan (4), when (4), qiskit_ibm_runtime (4), status (4), always (4), reset (4), requires (4), where (4), target (4), using (4), energy (4), layout (4), troubleshooting (4), 2026 (3), com (3), team (3), careers (3), programming (3), glossary (3), studies (3), learning (3), working (3), platforms (3), access (3), browse (3), search (3), consistently (3), graph (3), layerwise (3), pytket (3), sdk (3), transpiler (3), set (3), split (3), per (3), pending_jobs (3), dumps (3), aws (3), sv1 (3), ram (3), memory (3), sign (3), value (3), off (3), bad (3), few (3), measure_all (3), initial_layout (3), fails (3), connectivity (3), garbage (3), return (3), expval (3), pauliz (3), same (3), params_torch (3), pnp (3), default (3), autograd (3), will (3), uses (3), also (3), identity (3), plateaus (3), fewer (3), higher (3), better (3), observable (3), over (3), parameter (3), variances (3), applies (3), produces (3), values (3), ecr (3), open (3), debugging (3), systems (3), serialise (3), qasm_str (3), python (3), mid (3), supports (3), entanglement (3), matrix_product_state (3), runs (3), stabilizer (3), low (3), sparsepauliop (3), h_matrix (3), h_pauli (3), version (3), initial (3), real (3), costs (3), callback (3), 4096 (3), get_counts (3), add (3), noise_model (3), toffoli (3), draw (3), classical (3), barrier (3), sabre (3), connected (3), cookies (2), affiliate (2), our (2), policy (2), terms (2), books (2), podcasts (2), events (2), site (2), interview (2), certifications (2), salary (2), bloch (2), sphere (2), timeline (2), news (2), frameworks (2), paths (2), learn (2), references (2), tools (2), quantumcomputing (2), email (2), new (2), week (2), library (2), against (2), foundational (2), concepts (2), these (2), failure (2), modes (2), specs (2), trapped (2), ion (2), photonic (2), covers (2), start (2), match (2), tensors (2), computation (2), incompatible (2), function (2), wide (2), init (2), chunks (2), limit (2), failed (2), sort (2), high (2), stuck (2), queue (2), assign_parameters (2), cleanly (2), serialisation (2), accelerated (2), scales (2), slow (2), 8192 (2), expectation (2), constant (2), uccsd (2), interp (2), converging (2), model (2), non (2), supported (2), adjacent (2), decoherence (2), likely (2), interface (2), circuit_torch (2), requires_grad (2), plain (2), arrays (2), often (2), mitigation (2), initialise (2), expressibility (2), layer (2), instead (2), measuring (2), sum (2), single (2), vanish (2), functions (2), randomly (2), sets (2), uniform (2), append (2), mean (2), descent (2), makes (2), code (2), highly (2), entangled (2), parameterised (2), fidelity (2), system (2), typical (2), azure (2), natively (2), zzmax (2), provider (2), different (2), rotations (2), applying (2), incorrect (2), total (2), total_gates (2), batch (2), submission (2), limits (2), lower (2), plans (2), hours (2), while (2), operational (2), pending (2), ranked (2), name (2), num_qubits (2), least (2), used (2), 127 (2), option (2), assign (2), pickling (2), bound_qc (2), openqasm (2), custom (2), them (2), between (2), cloud (2), algorithms (2), reusing (2), wider (2), switch (2), limited (2), needs (2), laptops (2), fine (2), machine (2), best (2), cuda (2), need (2), works (2), clifford (2), interact (2), degrades (2), choose (2), product (2), efficient (2), sim_mps (2), complex (2), point (2), simply (2), 1024 (2), prototyping (2), quality (2), outcomes (2), even (2), lowest (2), converge (2), matches (2), original (2), h_reconstructed (2), max (2), those (2), estimates (2), optimizers (2), statevectorestimator (2), qiskit_algorithms (2), plot (2), iteration (2), several (2), poor (2), produce (2), design (2), small (2), step (2), confirm (2), noisemodel (2), noisy_sim (2), noisy_job (2), clear (2), decomposes (2), placement (2), must (2), due (2), routing (2), directly (2), phase (2), each (2), opt (2), transpiled_opt (2), wave (2), accept, decline, improve, experience, track, cookie, privacy, disclosure, hadamard, llc, quantumcomputingcourses, associate, earn, qualifying, purchases, contact, editorial, talent, pool, post, questions, cheatsheets, history, docs, compare, written, maintained, who, writes, research, industry, zeitgeist, donovan, curated, anyone, 241, 220, 112, subscribe, address, worth, taking, changed, spam, unsubscribe, anytime, every, tested, setup, behind, read, next, page, detailed, hands, practice, full, resources, mixed, compilation, exceeded, demand, doesn, insufficient, normalisation, missing, throughout, mix, backward, pytorch, jax, users, work, mark, requiring, its, own, differentiable, mixing, standard, inside, breaks, tracking, raises, silently, returns, typeerror, related, techniques, close, larger, near, initialisation, worsen, shallow, global, polynomially, train, freezing, previously, trained, avoids, initialising, var, below, across, initialisations, initialised, vanishing, landscape, essentially, flat, everywhere, progress, bug, fundamental, property, published, targeting, assuming, achieves, aria, closer, handle, molmer, sorensen, gpi2, gpi, optimisation, passes, compiles, recommended, interaction, optimised, basis, inefficient, phasedx, inspect, shows, type, smaller, impose, vary, premium, very, concatenate, experiments, trigger, region, available, regions, separate, submit, peak, late, night, utc, shorter, purposes, sufficient, other, strategies, false, sorted, key, lambda, find, busy, heavily, popular, eagle, heron, 156, priority, paid, json, theta, data, now, safe, text, format, portable, qasm, write, load, back, qc_loaded, loads, serialising, objects, classes, surfaces, caching, disk, passing, processes, restructure, segments, via, 256, server, workstation, 512, feasibility, complex128, crash, vram, arbitrary, polynomial, nearby, ring, graphs, states, sim_sv, fast, sim_clifford, package, pip, install, sim_gpu, right, stores, amplitudes, 268, million, numbers, billion, runtime, grows, some, stalls, report, studying, gives, probabilities, faster, large, practical, advice, publication, validation, 024, sanity, minimum, statistical, doubling, quadrupling, reasons, how, actually, convention, minimises, expects, defined, such, ground, eigenvalue, quantum_info, built, manually, form, dtype, from_operator, should, give, coeffs, reconstructing, to_matrix, abs, correctly, normalised, coefficients, signs, was, computed, incorrectly, scaled, shifted, factor, try, strategy, solve, simpler, optimal, starting, harder, evaluations, regardless, tolerates, finite, difference, methods, chemistry, unitary, coupled, cluster, singles, doubles, rather, freeze, earlier, ones, matplotlib, pyplot, primitives, nfev, stddev, estimator, maxiter, 200, initial_point, num_parameters, compute_minimum_eigenvalue, xlabel, ylabel, title, convergence, show, independent, choice, cannot, express, based, corrupts, increase, concluding, both, flaw, apply, packages, zne, without, impact, ibm_nairobi, from_backend, either, being, overwhelmed, signal, itself, producing, peaked, distribution, issue, answer, automatically, explicit, implement, typically, support, level, like, unitaries, enough, reuse, significantly, attempting, fail, qubitcount, maximum, visual, scheduling, hint, does, early, then, added, register, effect, measured, order, come, adds, forgetting, comes, second, zeros, repeated, beginner, pass, tries, multiple, seeds, picks, manual, unless, knowledge, alternative, let, pick, place, logical, physically, devices, maps, etc, specify, respects, ibm_sherbrooke, pairs, coupling_map, insert, route, positions, choices, excessively, long, subcircuits, feed, forward, allows, replace, relative, variant, required, rewrite, multi, depending, multiplying, cancellation, reductions, raw, meaningless, chip, much, deeper, insertion, coherence, decohere, roughly, microseconds, hundreds, takes, longer, execute, completes, sections, paper, simulators, out, refuse, actionable, categories, exist, home, prep, universities, career, cheat, sheets, language, migration, comparison, guides, pinball, prerequisites, explore, ocean, tket, pyquil, shor, grover, rigetti, quera, google, providers, brilliant, udemy, edx, coursera, course, skip, main, content,

Text of the page (random words):
onfirm order print qc draw 04 qubitcount exceeds device maximum qiskit hardware simulation cause your circuit uses more qubits than the target backend supports ibm s free tier systems range from 5 to 127 qubits attempting to run a 40 qubit circuit on a 27 qubit system will fail at submission fixes for simulation aersimulator handles 30 qubits with the statevector method and significantly more with the matrix product state method see problem 10 for hardware choose a backend with enough qubits or reduce your algorithm s qubit count by reusing qubits reset and reuse mid circuit with qc reset from qiskit_aer import aersimulator statevector handles 30 qubits mps handles wider circuits sim aersimulator method statevector up to 30 qubits sim_mps aersimulator method matrix_product_state handles more check a backend s qubit count before submitting from qiskit_ibm_runtime import qiskitruntimeservice service qiskitruntimeservice for b in service backends print b name b num_qubits 05 gate not supported on target backend qiskit hardware cause hardware backends only implement a small native gate set ibm systems typically support cx or ecr id rz sx and x using higher level gates like ccx toffoli swap or custom unitaries requires decomposition fix transpile with explicit basis_gates from qiskit import transpile quantumcircuit qc quantumcircuit 3 qc ccx 0 1 2 toffoli not native on most superconducting hardware transpile decomposes ccx into cx u gates automatically transpiled transpile qc basis_gates cx u optimization_level 2 print transpiled draw print gate counts transpiled count_ops to see what gates a specific backend supports natively print backend basis_gates e g cx id rz sx x 02 noisy results and accuracy 06 results look random no clear answer from hardware qiskit hardware noise cause either the algorithm is being overwhelmed by gate errors and decoherence the shot count is too low to see a clear signal or the algorithm itself is not producing a peaked distribution a design issue diagnose run on a noiseless simulator first from qiskit_aer import aersimulator step 1 confirm the algorithm works without noise sim aersimulator method statevector job sim run qc shots 4096 counts job result get_counts print noiseless result counts step 2 add a noise model from real hardware to test noise impact from qiskit_aer noise import noisemodel from qiskit_ibm_runtime import qiskitruntimeservice service qiskitruntimeservice backend service backend ibm_nairobi noise_model noisemodel from_backend backend noisy_sim aersimulator noise_model noise_model noisy_job noisy_sim run qc shots 4096 print noisy result noisy_job result get_counts fixes if the noiseless simulator is correct and hardware is not reduce circuit depth apply error mitigation see qiskit s zne or m3 packages or use a higher fidelity backend if both are wrong the algorithm has a design flaw verify with a small test case on the statevector simulator increase shots to at least 4096 before concluding results are random low shot counts produce high variance on even correct circuits 07 vqe qaoa not converging qiskit pennylane variational cause several independent problems can cause this the most common are barren plateaus gradients vanish in deep circuits a poor initial parameter choice an ansatz that cannot express the target state too few layers or using a gradient based optimizer on hardware where shot noise corrupts gradient estimates diagnose plot cost vs iteration import numpy as np import matplotlib pyplot as plt from qiskit primitives import statevectorestimator from qiskit_algorithms import vqe from qiskit_algorithms optimizers import spsa costs def callback nfev params energy stddev costs append energy vqe vqe estimator statevectorestimator ansatz ansatz optimizer spsa maxiter 200 callback callback initial_point np random uniform np pi np pi ansatz num_parameters result vqe compute_minimum_eigenvalue hamiltonian plt plot costs plt xlabel iteration plt ylabel energy plt title vqe convergence plt show fixes barren plateaus reduce circuit depth switch to local cost functions use layerwise training freeze earlier layers while training new ones bad ansatz for chemistry problems use uccsd unitary coupled cluster singles and doubles rather than a hardware efficient ansatz the expressibility needs to match the problem hardware optimizers use spsa instead of gradient descent on real hardware spsa estimates gradients with only two circuit evaluations regardless of parameter count and tolerates shot noise better than finite difference methods initial parameters try the interp strategy solve a simpler version of the problem first e g fewer qaoa layers and use those optimal parameters as the starting point for the harder version 08 expectation values are off by a constant factor qiskit pennylane variational cause the hamiltonian is not correctly normalised observable coefficients have wrong signs or the pauli decomposition was computed incorrectly this produces results that are consistently scaled or shifted from the true value fix verify the pauli decomposition import numpy as np from qiskit quantum_info import sparsepauliop if you built the hamiltonian manually verify it matches the matrix form h_matrix np array 1 0 0 1 dtype complex h_pauli sparsepauliop from_operator h_matrix print pauli decomposition h_pauli should give 1 0 i 1 0 z sparsepauliop i z coeffs 1 0 j 1 0 j check that reconstructing the matrix matches the original h_reconstructed h_pauli to_matrix print max error np max np abs h_matrix h_reconstructed also check the sign convention for the hamiltonian in the algorithm vqe minimises energy so it expects h to be defined such that the ground state has the lowest eigenvalue if your h has the wrong sign vqe will converge to the wrong state 09 how many shots do i actually need simulation hardware cause statistical error in measurement outcomes scales as 1 shots doubling accuracy requires quadrupling shots using too few shots is one of the most common reasons results look noisy even on a simulator target accuracy minimum shots typical use case 10 100 quick sanity check 3 1 024 prototyping debugging 1 10 000 algorithm validation 0 3 100 000 publication quality on simulator 0 1 1 000 000 statevector simulator only free practical advice use 1024 shots for prototyping use 8192 for results you plan to report when shot noise is not what you are studying use aersimulator method statevector with no shot limit it gives exact probabilities and runs faster than 1 000 000 shots on large circuits 03 simulation performance 10 simulation is too slow qiskit simulation cause statevector simulation stores 2 n complex amplitudes at 28 qubits that is 268 million numbers at 30 qubits it is over a billion runtime grows exponentially and at some point simulation simply stalls fix use the right simulator method for your circuit from qiskit_aer import aersimulator default statevector exact but limited to 30 qubits sim_sv aersimulator method statevector matrix product state efficient for low entanglement circuits handles 100 qubits when entanglement is local sim_mps aersimulator method matrix_product_state stabilizer exponentially fast for clifford only circuits h s cnot measure sim_clifford aersimulator method stabilizer gpu accelerated statevector requires aer gpu package and cuda gpu pip install qiskit aer gpu sim_gpu aersimulator method statevector device gpu which method to choose mps best for circuits where qubits interact only with nearby qubits e g 1d variational circuits qaoa on ring graphs accuracy degrades for highly entangled states stabilizer only works for clifford circuits no t gates no arbitrary rotations but is exact and runs in polynomial time gpu best if you have a cuda gpu and need statevector accuracy on 25 32 qubits a gpu with 16 gb vram handles about 30 qubits 11 memory error during simulation qiskit simulation cause statevector simulation of n qubits requires 2 n complex128 values at 30 qubits that is 16 gb ram at 32 qubits it is 64 gb most laptops crash at 26 28 qubits qubits statevector ram feasibility 20 16 mb fine on any machine 25 512 mb fine on any machine 28 4 gb ok on most laptops 30 16 gb needs a workstation 32 64 gb server only 34 256 gb aws sv1 cloud simulator fixes switch to method matrix_product_state if your circuit has limited entanglement see problem 10 use aws braket sv1 supports up to 34 qubits or the local mps simulator for wider circuits restructure the algorithm to use fewer qubits many algorithms can be run in segments reusing qubits via mid circuit reset qc reset qubit 12 circuit serialisation pickling errors qiskit simulation cause parametervector objects and custom gate classes do not always serialise cleanly with python s pickle this surfaces when caching circuits to disk passing them between processes or submitting to cloud job queues fix assign parameters before serialising or use openqasm from qiskit import quantumcircuit from qiskit circuit import parametervector from qiskit import qasm3 import json parameterised circuit params parametervector theta 3 qc quantumcircuit 3 qc ry params 0 0 qc ry params 1 1 qc ry params 2 2 option 1 assign parameters before pickling bound_qc qc assign_parameters params 0 1 2 params 1 0 5 params 2 0 9 import pickle data pickle dumps bound_qc now safe to serialise option 2 serialise as openqasm 3 text format always portable qasm_str qasm3 dumps qc with open circuit qasm w as f f write qasm_str load it back qc_loaded qasm3 loads qasm_str 04 hardware access and jobs 13 ibm job stuck in queue for hours qiskit hardware cause ibm quantum s free open tier is heavily used the most popular systems eagle 127 qubit heron r2 156 qubit can have queues of 50 100 jobs free tier jobs also have lower priority than paid plans fix find the least busy backend from qiskit_ibm_runtime import qiskitruntimeservice service qiskitruntimeservice get all operational backends and sort by pending jobs backends service backends operational true simulator false ranked sorted backends key lambda b b status pending_jobs for b in ranked 5 status b status print f b name status pending_jobs pending jobs b num_qubits qubits other strategies run on the local aersimulator while your hardware job queues for most debugging purposes the simulator result is sufficient submit jobs during off peak hours late night utc when queues are shorter use a different ibm quantum region if available on your plan us eu ap regions have separate queues 14 job failed with too many gates on hardware qiskit hardware cause hardware backends impose per job gate count limits that vary by provider and plan ibm s open plan has lower limits than premium plans submitting very deep circuits or batch circuits that concatenate many experiments into one job can trigger this error fixes reduce circuit depth with optimization_level 3 during transpilation see problem 1 split a batch of circuits into smaller submission chunks check the gate count before submitting transpiled count_ops shows gate counts by type from qiskit import transpile transpiled transpile qc backend backend optimization_level 3 inspect total gate count ops transpiled count_ops total_gates sum ops values print f total gates total_gates print f two qubit gates ops get cx 0 ops get ecr 0 check circuit depth print f circuit depth transpiled depth 15 ionq quantinuum results look wrong qiskit hardware cause ionq and quantinuum use different native gate sets from ibm ionq s native gates are single qubit rotations and the two qubit xx interaction quantinuum uses the zzmax and phasedx gates applying a transpiler optimised for ibm s cx u basis to these backends produces incorrect or inefficient circuits fix use provider recommended transpilation quantinuum use pytket quantinuum which applies quantinuum specific optimisation passes and compiles to native zzmax gates ionq use the amazon braket sdk or azure quantum sdk which handle ionq transpilation to gpi gpi2 and ms molmer sorensen gates natively check published two qubit gate fidelity for the specific system you are targeting before assuming results are wrong quantinuum h2 achieves 99 9 but ionq s aria is closer to 97 99 for typical circuits 05 pennylane specific 16 barren plateau gradients are all zero pennylane variational cause deep wide circuits with randomly initialised parameters have exponentially vanishing gradients the cost function landscape is essentially flat everywhere so gradient descent makes no progress this is the barren plateau problem and it is not a bug in your code it is a fundamental property of highly entangled parameterised circuits diagnose check gradient variance across random initialisations import pennylane as qml import numpy as np dev qml device default qubit wires 6 qml qnode dev def circuit params for i in range 6 qml ry params i wires i for i in range 5 qml cnot wires i i 1 for i in range 6 qml ry params i 6 wires i return qml expval qml pauliz 0 measure gradient variance over many random parameter sets grad_fn qml grad circuit variances for _ in range 100 params np random uniform 0 2 np pi 12 grad grad_fn params variances append np var grad print f mean gradient variance np mean variances 2e if this is below 1e 4 you likely have a barren plateau fixes layerwise training train one layer at a time freezing previously trained layers this avoids initialising all parameters randomly at the same time local cost functions instead of measuring a global observable sum over all qubits use a local observable single qubit which has gradients that vanish only polynomially not exponentially reduce depth barren plateaus worsen with circuit depth fewer layers with higher expressibility per layer is better than many shallow layers identity initialisation initialise parameters so the circuit is close to the identity at the start gradients are larger near the identity see also pennylane noise mitigation tutorial for related techniques 17 qnode incompatible with numpy arrays pennylane autograd cause pennylane uses its own differentiable tensor types mixing standard numpy arrays with pennylane s autograd inside the same computation graph breaks gradient tracking and often raises a typeerror or silently returns zero gradients fix use qml numpy or a framework tensor consistently import pennylane as qml import pennylane numpy as pnp use this not plain numpy import numpy as np dev qml device default qubit wires 2 qml qnode dev interface autograd def circuit params qml ry params 0 wires 0 qml ry params 1 wires 1 qml cnot wires 0 1 return qml expval qml pauliz 0 wrong plain numpy array gradients will not work params np array 0 5 1 2 correct use pnp pennylane numpy and mark as requiring grad params pnp array 0 5 1 2 requires_grad true grad_fn qml grad circuit print gradient grad_fn params for pytorch or jax users import torch qml qnode dev interface torch def circuit_torch params qml ry params 0 wires 0 qml ry params 1 wires 1 return qml expval qml pauliz 0 use torch tensors thro...