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Text of the page (random words):
built in full state simulator a toffoli simulator a sparse simulator a resource estimator for projecting hardware requirements and tight integration with visual studio and vs code in early 2024 microsoft released a modernized q compiler and runtime as part of the new azure quantum development kit this overhaul which accompanied the q 1 0 release dropped the net dependency for the python integration path simplified the language in several areas and added a browser based compiler accessible through vs code for the web the updated qdk includes a resource estimator that can project the physical qubit and runtime requirements of algorithms targeting fault tolerant hardware the most practically useful planning tool available for any quantum framework in 2025 microsoft announced majorana 1 its first topological qubit chip marking a milestone in the hardware strategy q was always designed to support q integrates with azure quantum microsoft s cloud platform for quantum computing which provides access to hardware from ionq quantinuum rigetti and others as of 2025 q has a smaller user community than qiskit or pennylane partly because its standalone language approach has a steeper learning curve than python based frameworks however it remains actively developed and is particularly valued for its resource estimation capabilities and its formal approach to quantum program correctness if you are coming from python first frameworks the q vs qiskit vs cirq comparison on quantumzeitgeist is a useful orientation for hands on learning start with the q hello world tutorial or work through the full getting started with q guide on this site installation q runs in three modes vs code extension recommended for larger projects jupyter notebooks via python best for exploration or the standalone qsharp cli python integration recommended for most users pip install qsharp vs code install the microsoft quantum development kit qdk extension jupyter qsharp kernel is included with the pip package verify installation python c import qsharp print qsharp __version__ the python package bundles the q compiler and simulator no separate net installation is required as of q 1 x q program structure q uses operations with side effects like quantum gates and functions pure classical computation all quantum code lives inside operations namespaces group related operations and functions namespace myprogram in qdk 1 x the standard library lives under std std intrinsic h x y z cnot m is opened automatically and the old microsoft quantum names still resolve as aliases open std canon applytoeach applyqft etc open std measurement mresetz measureeachz operation bellstate result result use q0 q1 qubit qubit h q0 cnot q0 q1 return mresetz q0 mresetz q1 key design rules qubits cannot be copied no cloning theorem enforced by the type system qubits must be returned to 0 before the use block ends operations can have side effects functions cannot the compiler auto generates adjoint and controlled variants when declared types type description qubit a quantum bit not copyable must be released in result measurement outcome zero or one bool classical boolean true or false int 64 bit signed integer bigint arbitrary precision integer double 64 bit floating point string immutable string for message output pauli paulix pauliy pauliz paulii qubit qubit array result measurement result array t generic type parameter unit void equivalent return type when nothing is returned int bool tuple type qubit allocation use q qubit single qubit starts in 0 use qs qubit 4 array of 4 qubits use a b qubit qubit tuple allocation use ctrl targets qubit qubit 3 mixed qubits must be reset before release reset q reset to 0 resetall qs reset entire array mresetz q measure then reset returns result qubits start in state 0 and must be returned to 0 before the use block ends the compiler will warn if qubits are potentially released in a non 0 state this enforces clean resource management and is one of q s strongest safety guarantees the bloch sphere simulator on this site is a useful visual aid for understanding qubit states before working with them in q gates intrinsic operations single qubit gates h q hadamard 0 0 1 2 x q pauli x not gate y q pauli y z q pauli z phase flip s q s gate z ½ t q t gate z ¼ building block for fault tolerant circuits i q identity rotation gates angles in radians rx theta q rotation around x axis ry theta q rotation around y axis rz theta q rotation around z axis r1 theta q phase rotation 1 e iθ 1 two qubit gates cnot control target controlled not cz q1 q2 controlled z swap q1 q2 swap multi qubit gates ccnot q1 q2 q3 toffoli doubly controlled not cswap control q1 q2 fredkin controlled swap general controlled versions controlled h control target controlled x c1 c2 target multi control x controlled rz theta c target see the quantum gates explained tutorial for a deeper walkthrough of what each gate does to qubit state measurement let result m q measure in z basis result zero or one let result mresetz q measure reset to 0 preferred let result mresetx q measure in x basis reset let result mresety q measure in y basis reset multi qubit let results measureeachz qs result measures each qubit independently let parity measure pauliz pauliz q0 q1 joint measurement conditional on result if result one x q classical feedforward if result zero message measured zero measurement collapses the qubit to a definite state in q measurement always returns a classical result value there is no way to inspect the quantum state directly in a real execution only in simulation classical control flow conditionals if condition elif other else for loops bounded required for adjoint generation for i in 0 n 1 h qs i for q in qs x q repeat until for probabilistic algorithms repeat h q let result m q until result zero fixup x q runs if condition not met then tries again while for classical only functions mutable i 0 while i 10 set i 1 variables let x 5 immutable binding mutable y 0 mutable variable set y 10 update mutable arrays let arr 1 2 3 let first arr 0 let len length arr let slice arr 1 2 2 3 strings for debugging output only message iteration i result result adjoint and controlled every operation can declare adj ctl or adj ctl variants the compiler generates them automatically when the body allows declaring an adjointable controllable operation operation myop theta double q qubit unit is adj ctl rx theta q h q t q using the variants adjoint myop theta q inverse uncomputation controlled myop ctrl theta q controlled version adjoint controlled myop ctrl theta q this is one of q s most powerful features writing an operation once gives you the forward pass the inverse essential for uncomputation in quantum algorithms and the controlled version automatically the grover s algorithm in q tutorial makes heavy use of this for oracle construction canon library the std canon namespace microsoft quantum canon in older code provides higher level building blocks open std canon open std arithmetic apply to each qubit applytoeach h qs h to every qubit applytoeacha h qs adj variant applytoeachca h qs adj ctl variant quantum fourier transform little endian register applyqft qs adjoint applyqft qs inverse qft used in qpe arithmetic std arithmetic little endian register adders addle a b c c a b incbyle a b b a note that the classic qdk s qft operation and microsoft quantum arithmetic namespace were retired with the classic qdk the modern equivalents are applyqft and std arithmetic the quantum phase estimation in q tutorial demonstrates applyqft and adjoint applyqft in a full working implementation running from python the recommended workflow for exploration and testing import qsharp evaluate a q expression directly result qsharp eval use q qubit h q let r mresetz q r print result qsharp result zero or qsharp result one run with multiple shots results qsharp run use q0 q1 qubit qubit h q0 cnot q0 q1 let r mresetz q0 mresetz q1 r shots 1000 zeros sum 1 for r in results if r qsharp result zero qsharp result zero ones sum 1 for r in results if r qsharp result one qsharp result one print f 00 zeros 10 1f 11 ones 10 1f expect 00 50 11 50 loading a qs file import qsharp load and compile a q source file qsharp eval open myalgorithm qs read run an operation from it results qsharp run mynamespace myoperation shots 500 see the hello world in q tutorial for a complete working example from installation to first run resource estimation one of q s flagship features estimate how much fault tolerant hardware an algorithm would require without running it on a real device import qsharp estimate resources for a q operation result qsharp estimate use qs qubit 10 your algorithm resetall qs print result physicalcounts physicalqubits print result physicalcounts runtime the resource estimator accounts for error correction overhead magic state distillation t gate cost and target hardware parameters it is the most rigorous planning tool available for projecting real world fault tolerant requirements azure quantum to run on real hardware through azure import qsharp import azure quantum compile the q program to qir with a hardware compatible profile qsharp init target_profile qsharp targetprofile base program qsharp compile myprogram runbell workspace azure quantum workspace subscription_id your subscription id resource_group your resource group name your workspace location eastus list available targets hardware providers for target in workspace get_targets print target name ionq simulator ionq qpu aria 1 quantinuum qpu h2 1 rigetti sim qvm submit the compiled program to a target target workspace get_targets ionq simulator job target submit program bell job shots 100 result job get_results jobs are submitted as compiled qir qsharp compile output not raw q source the older submit_job input_data_format qsharp v2 flow belongs to the retired classic qdk azure quantum currently provides access to ionq trapped ion quantinuum trapped ion rigetti superconducting and pasqal neutral atom hardware note that quantinuum retired its h1 1 machine from azure quantum in october 2025 h2 series targets are current see getting started with microsoft azure quantum on quantumzeitgeist for a practical walkthrough of the platform common patterns bell state operation bellpair q0 qubit q1 qubit unit is adj ctl h q0 cnot q0 q1 operation runbell result result use q0 q1 qubit qubit bellpair q0 q1 return mresetz q0 mresetz q1 phase kickback used in algorithms like grover s algorithm and bernstein vazirani to transfer phase information from a target qubit to a control operation phasekickback oracle qubit unit is adj ctl control qubit unit is adj use target qubit x target put target in 1 h control control into superposition controlled oracle control target phase kickback h control reset target repeat until success rus for probabilistic state preparation or non deterministic algorithms operation preparearbitrarystate q qubit unit mutable done false repeat h q let r m q set done r zero until done fixup reset q reset and try again quantum phase estimation qpe skeleton see the full quantum phase estimation in q tutorial for a complete implementation the skeleton operation quantumphaseestimation oracle int qubit unit is adj ctl eigenstate qubit precision int double use countingregister qubit precision applytoeach h countingregister for idx ctrl in enumerated countingregister controlled oracle ctrl 1 idx eigenstate adjoint applyqft countingregister let results measureeachz countingregister resetall countingregister convert results to phase estimate return 0 0 debugging and output print to console simulator only message hello from q message qubit count length qs message result result dump state simulator only not available on hardware dumpmachine prints full state vector dumpregister qs prints state of a specific register assertions fail loudly in simulation stripped on hardware fact length qs 4 expected 4 qubits equalityfacti count 3 count should be 3 error correction in q q is well suited to expressing quantum error correction circuits directly note that the classic qdk s microsoft quantum errorcorrection library with types like logicalregister was retired along with the classic qdk and is not part of the modern qdk encoding and syndrome measurement circuits are written as plain operations see the quantum error correction in q tutorial for a hands on introduction to stabilizer codes in q encode a logical qubit using the 3 qubit bit flip code operation encodeinbitflipcode physical qubit unit is adj cnot physical 0 physical 1 cnot physical 0 physical 2 q vs other frameworks feature q qiskit pennylane language standalone dsl python library python library autodiff gradients no partial yes core feature resource estimation excellent limited no adjoint auto generation yes manual manual hardware access azure quantum ibm quantum multiple backends learning curve steep moderate moderate best for fault tolerant algorithms resource planning gate level control ibm hardware variational algorithms qml if you are primarily interested in variational algorithms and machine learning pennylane is a better fit if you want the deepest integration with ibm s hardware ecosystem qiskit is the standard q is the right choice if you care about long term algorithm correctness formal resource estimation and eventual fault tolerant hardware learning resources on this site hello world in q installation to first circuit getting started with q operations types measurement and running on hardware grover s algorithm in q the canonical quantum search speedup quantum phase estimation in q qpe with qft quantum chemistry simulation with q molecular energy estimation quantum error correction in q stabilizer codes courses quantum katas microsoft s self paced interactive exercises free q training on microsoft learn structured learning paths from microsoft azure quantum learning path microsoft s official learning path external top 20 q terms to know quantumzeitgeist glossary companion introduction to q 5 minute primer on quantumzeitgeist q and machine learning applying q to ml problems official q documentation microsoft learn q github repository source issues and samples q tutorials this page is the syntax these are the 7 tutorials where you run it quantum teleportation in q intermediate 30 min quantum error correction in q intermediate 25 min read implementing grover s algorithm in q intermediate 40 minutes quantum phase estimation in q intermediate 25 min read all 7 q tutorials where to go next the reference index puts q 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 q program structure 04 types 05 qubit allocation 06 gates intrinsic operations 07 measurement 08 classical control flow 09 variables 10 adjoint and controlled 11 canon library 12 running from pytho...