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d wave ocean reference dictionary 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 reference d wave ocean framework reference d wave ocean d wave s sdk for quantum annealing and hybrid solvers python version 7 x 10 sections quantum annealers are cryogenic superconducting machines with a different model of computing quick reference for d wave ocean classes bqm qubo formulation samplers and solving combinatorial optimisation problems in this guide 10 sections 01 background and history 02 installation 03 key imports 04 core concepts 05 binary quadratic model bqm 06 samplers 07 reading results 08 constrained quadratic model cqm 09 common patterns 10 gotchas at a glance language python version 7 x install pip install dwave ocean sdk copy docs docs ocean dwavesys com source dwavesystems dwave ocean sdk background and history d wave ocean is the software development kit for d wave systems a canadian company that has been building quantum annealing processors since its founding in 1999 d wave is the longest operating commercial quantum computing company and the first to sell quantum computing systems with its initial d wave one system 128 qubits shipped to lockheed martin in 2011 the company was founded by geordie rose and haig farris in vancouver british columbia d wave s software tools evolved over several iterations before coalescing into the ocean sdk early access to d wave hardware used lower level interfaces including a c library and the sapi solver api python client as the user base grew d wave developed higher level tools dimod for defining binary quadratic models dwave system for hardware access and minorminer for embedding problem graphs onto the processor s chimera or pegasus topology around 2018 these components were bundled into the dwave ocean sdk meta package which provided a unified installation and a coherent programming model d wave s hardware has gone through several generations the chimera topology used in d wave 2000q and earlier systems up to 2 048 qubits the pegasus topology introduced with the advantage system in 2020 offering over 5 000 qubits and the zephyr topology introduced with the advantage2 prototype in 2023 with improved connectivity the ocean sdk has tracked these hardware changes adding support for new topologies and solvers with each generation the leap cloud platform launched in 2018 provides remote access to d wave processors and hybrid solvers ocean occupies a unique position in the quantum computing ecosystem because d wave s quantum annealers solve optimization problems natively not gate model circuits users formulate problems as qubos quadratic unconstrained binary optimization or ising models rather than sequences of quantum gates the leaphybridsampler and leaphybridcqmsampler are particularly notable combining classical and quantum resources to handle problems with thousands of variables that exceed the native qubit count as of 2025 ocean is actively maintained with d wave continuing to release regular updates the sdk has a dedicated user community particularly among operations research and combinatorial optimization practitioners installation pip install dwave ocean sdk configure access to d wave s cloud dwave setup interactive setup saves token to config dwave dwave conf key imports import dimod from dwave system import dwavesampler embeddingcomposite leaphybridsampler from dwave samplers import simulatedannealingsampler steepestdescentsampler core concepts d wave solves quadratic unconstrained binary optimisation qubo problems not gate model quantum circuits the workflow is express your problem as a binary quadratic model bqm or qubo submit to a sampler annealer hybrid solver or classical simulator read back lowest energy samples as solutions binary quadratic model bqm import dimod create from scratch bqm dimod binaryquadraticmodel binary variables in 0 1 bqm dimod binaryquadraticmodel spin variables in 1 1 add linear single variable terms bqm add_variable x 1 0 bias for variable x bqm add_variable y 1 0 add quadratic interaction terms bqm add_interaction x y 2 0 from a dict qubo format q x x 1 y y 1 x y 2 bqm dimod binaryquadraticmodel from_qubo q energy formula e σ bias_i x_i σ j_ij x_i x_j the sampler finds the assignment of variables that minimises e samplers simulatedannealingsampler local no account needed from dwave samplers import simulatedannealingsampler sampler simulatedannealingsampler sampleset sampler sample bqm num_reads 1000 print sampleset first sample best solution print sampleset first energy lowest energy found dwavesampler real qpu from dwave system import dwavesampler embeddingcomposite embeddingcomposite handles the minor embedding step automatically sampler embeddingcomposite dwavesampler sampleset sampler sample bqm num_reads 1000 leaphybridsampler recommended for large problems from dwave system import leaphybridsampler sampler leaphybridsampler sampleset sampler sample bqm time_limit 5 seconds reading results sampleset sampler sample bqm num_reads 100 sampleset first best lowest energy sample sampleset first sample dict variable value sampleset first energy float all samples as dataframe df sampleset to_pandas_dataframe iterate for sample energy in sampleset data sample energy print sample energy constrained quadratic model cqm for problems with constraints e g exactly one item selected import dimod cqm dimod constrainedquadraticmodel x dimod binary f x_ i for i in range 5 objective minimise sum of costs costs 3 1 4 1 5 cqm set_objective sum c x_i for c x_i in zip costs x constraint pick exactly 2 items cqm add_constraint sum x 2 label pick_two from dwave system import leaphybridcqmsampler sampler leaphybridcqmsampler sampleset sampler sample_cqm cqm time_limit 5 feasible sampleset filter lambda s s is_feasible print feasible first sample common patterns maximum cut graph partitioning import dimod networkx as nx from dwave samplers import simulatedannealingsampler g nx karate_club_graph bqm dimod generators maximum_weight_matching g or build manually build maxcut bqm bqm dimod binaryquadraticmodel binary for u v in g edges bqm add_interaction u v 1 bqm add_variable u 0 5 bqm add_variable v 0 5 sampler simulatedannealingsampler result sampler sample bqm num_reads 100 print result first sample quadratic assignment ising form ising form variables in 1 1 h 0 1 1 1 linear biases j 0 1 1 quadratic couplings sampler simulatedannealingsampler sampleset sampler sample_ising h j num_reads 100 gotchas minor embedding a problem graph onto the qpu s sparse hardware topology is not guaranteed to succeed dense problems with many pairwise interactions can fail to embed cleanly or produce broken chains where qubits meant to represent one logical variable disagree check the chain break fraction in your sampleset when using embeddingcomposite on dwavesampler for problems that do not embed cleanly or that exceed the qpu s native size leaphybridsampler is usually the more reliable starting point it manages the classical quantum split internally instead of requiring you to hand tune an embedding d wave ocean tutorials this page is the syntax these are the 12 tutorials where you run it solving network problems with d wave ocean tsp and graph coloring intermediate 50 minutes constrained optimization with d wave s cqm sampler intermediate 13 min read graph coloring with d wave ocean intermediate 30 min hello world in d wave ocean beginner 20 min read all 12 d wave ocean tutorials other python frameworks amazon braket aws s fully managed quantum computing service and python sdk amazon braket sdk unified sdk for quantum computing on amazon braket bloqade quera s framework for neutral atom quantum computing and analog hamiltonian simulation cirq google s python framework for quantum circuits and algorithms where to go next the reference index puts d wave ocean next to the other 25 frameworks documented here and the comparison page runs the same circuit through each of the major sdks reference index compare frameworks on this page 01 background and history 02 installation 03 key imports 04 core concepts 05 binary quadratic model bqm 06 samplers 07 reading results 08 constrained quadratic model cqm 09 common patterns 10 gotchas read next all 26 framework dictionaries the full index filterable by language the same circuit in every framework side by side syntax across the major sdks framework tutorials step by step hello world guides for every major quantum sdk get one quantum email a week new tutorials courses worth taking and what changed in qiskit cirq pennylane this week no spam unsubscribe anytime email address subscribe 112 courses 220 tutorials 241 glossary terms 26 framework references 34 case studies quantumcomputing courses com free tutorials curated courses framework references and tools for anyone learning quantum computing written and maintained by dr donovan who writes on quantum computing research hardware and industry at quantum zeitgeist learn all courses free tutorials learning paths compare frameworks algorithm guide case studies quantum news reference glossary framework docs hardware guide qubit types history timeline cheatsheets 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