Browse studies and tools in visualization for quantum computing, organized by purpose, subject, and visualization approach.
D Huber, S J Glaser
Uses colored spheres to represent individual qubit states and correlations, with circuit overlays showing how gates transform these quantities.
Grant Sanderson
Explains Grover's search algorithm through state vectors and their geometric relationships, using a rotation view to illustrate the algorithm's mechanism.
Combined representations
Manusha Karunathilaka, Shaolun Ruan, Lin-Ping Yuan, Jiannan Li, Zhiding Liang, Kavinda Athapaththu, Qiang Guan, Yong Wang
Uses augmented reality and animated everyday objects to illustrate superposition, measurement, entanglement, and gates through interactive visual analogies.
Michael J. McGuffin, Jean-Marc Robert
Highlights gate-specific amplitude changes between circuit layers and displays pairwise qubit correlations and entanglement in a triangular matrix.
Szabolcs Jóczik, Bence Kecskés, Ádám Kovács, Orsolya Kálmán, Zoltán Zimborás
Combines drag-and-drop circuit design, explanatory annotations, generated code, and simulation plots in a browser canvas for several quantum computing models.
Combined representations
Anja Heim, Thomas Lang, Alexander Gall, Eduard Gröller, Christoph Heinzl
Traces quantum images through circuit gates using image summaries, difference views, and selected-pixel probability distributions to inspect processing behavior.
Zhen Wen, Jieyi Chen, Yao Lu, Siwei Tan, Jianwei Yin, Minfeng Zhu, Wei Chen
Links editable mathematical expressions with circuit components and measurement plots so users can formulate, assemble, and compare quantum algorithm implementations.
Hrishitva Patel
Presents Mathematica notebook examples with adjustable Bloch-sphere angles and illustrative probability plots for discussing qubit states and quantum algorithm concepts.
Fritz Schinkel
Represents quantum states with stacked bars whose heights encode measurement probabilities and horizontal positions encode phase angles for each basis state.
Hyeok Kim, Mingyoung J. Jeng, Kaitlin N. Smith
Links problem-level inputs, circuit diagrams, hardware information, and result charts in notebook interfaces for circuit authoring, optimization comparison, and interpretation.
Horizon Quantum
Triple Alpha is Horizon Quantum's environment for developing, compiling, and deploying programs across quantum processors and simulators. Its public product material presents a hardware-mapping view of processor topology, relating a quantum program's placement to the connectivity constraints of a selected device. The product also provides backend selection and describes resource analysis for programs and subroutines. Access is offered through a staged early-access program. Public visualization documentation remains limited to product material; an open visualization API or detailed visualization manual is not available among the linked resources. The hardware-mapping example provides the clearest public entry point to its visual support for device-aware development.
Tom Scruby
Uses Tanner-graph layouts and geometric diagrams to relate quantum product-code constructions to qubits, stabilizer checks, logical operators, and code parameters.
Priyabrata Senapati, Qiang Guan, David Pugmire, Cheng Chang Lu, Tushar M. Athawale
Combines distribution comparisons, dimensionality reduction, clustering, and violin plots to inspect variability in basis-state probabilities across quantum machine learning executions.
Shaolun Ruan, Feng Liang, Rohan Ramakrishna, Chao Ren, Rudai Yan, Qiang Guan, Jiannan Li, Yong Wang
Compares original data patterns with encoded quantum states through expectation maps, projected state distributions, and training plots to support encoder selection.
QuEra Computing
Bloqade is QuEra's open-source software for developing neutral-atom quantum programs. Its analog Python tools include a visual results report that combines measured bitstring counts with views of the corresponding atom states and spatial Rydberg densities. Selecting a measurement outcome connects its count to the configuration across the atom array, helping users inspect simulated or hardware results. The documented digital workflow also displays conventional circuit diagrams when comparing sequential and parallel programs. Those diagrams are rendered through Cirq after conversion from Bloqade programs. Together, these capabilities support routine inspection of measurement results and circuit structure during program development.
Yao-Hsin Chou, Yu-Chi Jiang, Shu-Yu Kuo, Sun-Yuan Kung
Connects signed-amplitude bars, circuit calculations, and geometric rotations so learners can step through Grover search and compare iteration choices.
Mushahid Khan, Prashant J. Nair, Olivia Di Matteo
Links breakpoint debugging with circuit diagrams, function-call trees, and program outputs so developers can inspect executed subroutines and code changes.
Jieyi Chen, Zhen Wen, Li Zheng, Jiaying Lu, Hui Lu, Yiwen Ren, Wei Chen
Organizes measured bitstrings by their Hamming-distance relationships, linking probability charts, contribution matrices, and graphs to investigate noisy output distributions.
Hyeok Kim, Kaitlin N. Smith
Links logical and compiled circuits with hardware topology, calibration properties, and estimated reliability to inspect how circuit operations map onto devices.
Pasqal
Qadence is a Python framework for building differentiable digital-analog quantum programs through composable blocks. Its drawing package displays blocks, circuits, and quantum models as conventional wire-and-gate diagrams, including named groups and Hamiltonian-evolution blocks. Developers can inspect how operations are sequenced or combined, print block composition as an ASCII tree, and export diagrams as SVG or PNG files. The examples cover individual gates, parameterized circuits, feature maps, and variational ansätze. These views help developers inspect program structure and prepare circuit illustrations. The official repository currently states that Qadence is not actively maintained.
Samantha Norrie, Anthony Estey, Hausi Müller, Ulrike Stege
Lets learners select Grover circuit stages and inspect signed-amplitude bars while changing search targets, register size, and iteration count.
IBM Quantum
Qiskit's Python SDK supplies complementary views of quantum states, circuits, and processors. State plots include measurement histograms, Bloch spheres, density-matrix city and Hinton plots, Pauli expectation bars, and the Q-sphere representation of basis-state probabilities and phases. Circuit drawers produce text, Matplotlib, or LaTeX diagrams, while dependency graphs and scheduling timelines expose operation relationships and idle intervals. Device maps show connectivity, errors, and the placement of compiled circuits on physical qubits. These views support learning, debugging, and development. Individual-qubit Bloch spheres omit correlations between qubits, and the core scheduling drawer has limitations for dynamic circuits.
Combined representations
Pascal Debus, Sebastian Issel, Kilian Tscharke
Shows how training data move through quantum classifiers using Bloch spheres or probability tetrahedra, alongside decision boundaries and training curves.
Samantha Norrie, Anthony Estey, Hausi Müller, Ulrike Stege
Links circuit gates with Dirac notation, matrix expressions, and intermediate states so learners can compare equivalent representations of a quantum computation.
Eclipse Qrisp
Qrisp combines high-level quantum programming with visual inspection of algorithm states and compiled circuits. Its quantum-backtracking visualizer places complex state amplitudes on the nodes of a branching search tree: hue represents phase, while brightness varies with amplitude magnitude. This connects the simulated state to decoded search paths and branch structure. Measurement histograms display probabilities for decoded variable values, and optimization plots track QAOA cost during a run. Conventional circuit diagrams are available through Qiskit's drawing machinery, including LaTeX export. Together, these facilities help developers inspect quantum variables, investigate backtracking behavior, and examine the circuits generated from their programs.
Google Quantum AI
Qualtran represents quantum programs as composable operations called bloqs and provides several ways to inspect their structure and resource costs. Register-aware graphs show operation blocks, their input and output ports, and the connections between them. Musical-score diagrams place operations along qubit or register wires. Hierarchical call graphs reveal how larger operations decompose into subroutines and can carry T, Clifford, and rotation counts; flame graphs expose the distribution of T costs across that hierarchy. A specialized graph drawer can also annotate classical simulation values. These views support program construction, debugging, and logical resource analysis, with Graphviz and Matplotlib providing rendering backends.
Raphael Seidel, René Zander, Matic Petrič, Niklas Steinmann, David Q. Liu, Nikolay Tcholtchev, Manfred Hauswirth
Draws quantum backtracking trees with signed node amplitudes, showing how search states evolve through successive operations and stop expanding below rejected nodes.
Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang
Combines state-evolution diagrams with amplitude and probability charts to trace how circuit gates change quantum states and contribute to measurement outcomes.
Devanshu Brahmbhatt, Yilun Xu, Neel Vora, Larry Chen, Neelay Fruitwala, Gang Huang, Qing Ji, Phuc Nguyen
Plots qubit characterization measurements over time and calibration settings across saved revisions, supporting comparisons among qubits, gates, and configurations.
Chad A. Steed, Junghoon Chae, Samudra Dasgupta, Travis S. Humble
Links qubit performance histories, similarity groups, and hardware topology, while comparing transpiled circuits through circuit diagrams, depth, and gate counts.
Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Griffin, Xiaolin Wen, Yanna Lin, Yong Wang
Connects quantum neural network inputs, circuit steps, and predictions through probability charts and decision maps, with comparisons across training epochs.
Jonas Bley, Eva Rexigel, Alda Arias, Nikolas Longen, Lars Krupp, Maximilian Kiefer-Emmanouilidis, Paul Lukowicz, Anna Donhauser, Stefan Küchemann, Jochen Kuhn, Artur Widera
Arranges amplitude circles along qubit axes, using their sizes and phase directions to expose separability patterns and illustrate quantum operations.
Priyabrata Senapati, Tushar M. Athawale, David Pugmire, Qiang Guan
Uses functional box plots and divergence heatmaps to compare variation and differences among measured basis-state output distributions from quantum applications.
NVIDIA
CUDA-Q is NVIDIA’s open-source platform for developing quantum applications in Python and C++, with execution across CPUs, GPUs, and quantum processors. Its core visualization tools support inspection of quantum programs and single-qubit states. The cudaq.draw interface represents a kernel’s execution path as a conventional circuit diagram, producing ASCII or LaTeX output. For state visualization, cudaq.add_to_bloch_sphere and cudaq.show use QuTiP to display Bloch spheres, including several state vectors on one sphere or multiple spheres in a grid. Developers can use these views to inspect gate sequences and compare single-qubit states. The Bloch-sphere interface accepts a single-qubit state vector or density matrix.
Microsoft
Q# is Microsoft’s quantum programming language, accompanied by visualization tools in the Quantum Development Kit. In VS Code and Jupyter notebooks, developers can inspect qubit wires, gates, and measurements, view circuits for individual operations, and follow circuit changes while debugging. The graphical circuit editor links gate editing to a state panel showing basis-state probabilities and phases. For machine-level inspection, the QDK offers a neutral-atom device animation of qubit movement through storage, interaction, and measurement zones, plus resource-estimation plots comparing physical qubit requirements with runtime. These first-party tools support program development and analysis; the device animation represents execution without qubit loss or other noise.
Combined representations
Adrien Suau, Gabriel Staffelbach, Aida Todri-Sanial
Turns quantum program profiling reports into call graphs, showing routine dependencies, call counts, and estimated costs to locate expensive subroutines.
Zhen Wen, Yihan Liu, Siwei Tan, Jieyi Chen, Minfeng Zhu, Dongming Han, Jianwei Yin, Mingliang Xu, Wei Chen
Organizes large quantum circuits into expandable components and repeated patterns, linking circuit structure with qubit operation histories, parallelism, and connectivity.
PanQEC team
Quantum Code Visualizer and PanQEC support the inspection and evaluation of quantum error-correcting codes. PanQEC's browser interface displays two- and three-dimensional code lattices, using shapes and colors to distinguish qubits, stabilizers, Pauli errors, and violated checks. Users can insert errors, display logical operators, and apply a selected decoder's correction while examining the same code geometry. Custom codes can supply their own visual representations. The Python package also provides threshold and finite-size-scaling plots for comparing simulation results. These views support checking code implementations and studying error-correction behavior.
Addie Jordon, Austin Hawkins-Seagram, Samantha Norrie, José Ossorio, Ulrike Stege
Colors positions by quantum-walk probability on a line, grid, or cube, with a slider for comparing distributions across steps.
Shaolun Ruan, Yong Wang, Weiwen Jiang, Ying Mao, Qiang Guan
Coordinates hardware noise histories, compiled-circuit filtering, and detailed comparisons to help users inspect device conditions and candidate circuit executions.
Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang
Uses linked triangles and semicircles to show complex amplitudes, measurement probabilities, and normalization for pure states of one or two qubits.
Addie Jordon, Austin Hawkins-Seagram, Ulrike Stege
Plots quantum-walk output distributions as spatial heatmaps, showing where the walker concentrates after repeated steps on a grid.
Ian Arawjo, Anthony J. DeArmas, Michael Roberts, Shrutarshi Basu, Tapan Parikh
Connects handwritten quantum circuit diagrams with notebook code, using wire bundles and recursive subcircuits to express and execute circuit abstractions.
Xanadu
PennyLane combines quantum programming with circuit inspection and analysis. Its text and Matplotlib drawers show operations, wires, measurements, and classical dependencies, while Fourier visualizations reveal the frequency content of parametrized circuit families. Available coefficient views include bar and violin plots, radial box plots, and panels in the complex plane, helping readers examine circuit expressivity. Through its Catalyst integration, PennyLane can also display compiled quantum operations as data-flow graphs with loops, conditionals, and compilation-pass changes for a single compiled QNode. These views support understanding circuit representations, inspecting program transformations, and exploring the behavior of parametrized circuit models.
qBraid Development Team
qBraid brings circuit and result visualization into a Python workflow that spans quantum programming frameworks and execution providers. Its visualization module draws conventional circuit diagrams and displays measurement counts or probabilities as bar charts, including comparisons between multiple runs. The circuit drawer delegates rendering to supported frameworks or PyQASM, so available output formats depend on the selected backend. Measurement plots associate computational-basis bitstrings with observed counts or probabilities, and plotting options distinguish several datasets within one figure. These views help developers inspect circuit structure and compare the output of quantum experiments.
Adrien Suau, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin
Projects single-qubit tomography onto a flat map of the Bloch sphere, using arrows for directional errors and color for reconstructed purity.
Matthias Miller, Daniel Miller
Links editable graph states with adjacency matrices, stabilizer summaries, and noise thresholds to explore their structure and entanglement properties.
Qibo team
Qibo is an open-source quantum-computing framework with built-in visual aids for inspecting small quantum programs and their simulated states. Its circuit tools produce Unicode diagrams or Matplotlib figures showing qubit wires, gates, controls, and measurements. Graphical drawing supports preset styles, gate clustering, and layout adjustments. For state inspection, plot_density_hist displays the real and imaginary density-matrix entries as paired three-dimensional bar charts. Users can adjust labels, colors, transparency, and figure size for these plots. These conventional views support circuit inspection and analysis of simulated states within Python workflows.
Craig Gidney
Stim is a stabilizer-circuit simulator with visualization tools aimed at quantum error correction. Its diagrams range from ordinary operation timelines to spatial circuit layers, detector slices, and matching graphs derived from detector error models. Detector-slice overlays show how circuit operations relate to the stabilizers associated with detectors, while matching graphs expose connections between detectors and possible error mechanisms. SVG and three-dimensional outputs support inspection of larger structures. The bundled Crumble editor provides interactive circuit editing and Pauli-flow tracing. These views support the design and debugging of error-correction circuits, with each diagram exposing a different part of the circuit or error model.
Amazon Web Services
Amazon Braket combines a cloud quantum-computing service with a Python SDK for building and running quantum programs. The SDK renders circuits as text diagrams that show qubit wires and operations, supporting inspection during program construction. Official notebooks also demonstrate how to turn measurement counts into bar charts using Matplotlib. This separates the circuit drawer supplied by the SDK from plotting code supplied in examples. The linked resources offer a small starting workflow: construct a circuit, inspect its diagram, run a simulator, and visualize the resulting measurement distribution.
qecsim developers
qecsim is a Python package for simulating quantum error correction with stabilizer codes. Its lattice renderers show Pauli operators and syndrome checks in the spatial structure of planar, toric, rotated, and color codes. Developers can inspect an error, a proposed recovery, and their combined action to understand a decoding result. Official demonstrations add colored notebook output and use Matplotlib to compare logical failure rates across simulation settings. The visual focus is error-correction structure and behavior, with lightweight text drawings for individual runs and example plotting code for aggregate statistics.
Quantinuum
TKET is a quantum compilation toolkit with a Python interface, pytket. Its visualization facilities range from interactive wire-and-gate circuits to directed dependency graphs and graphs of qubit interactions. The circuit renderer supports notebook and browser display, comparison of multiple circuits, and inspection of nested circuit structure. Graphviz-based views expose dependencies that can be harder to follow in a long gate sequence. The ZX module provides another representation for composing and transforming quantum processes, with diagrams showing successive rewrite steps. These resources support inspection of program structure and the transformations used in quantum compilation.
Jean-Baptiste Lamy
Arranges boxes across qubit columns to show state factors, encoding basis-state probabilities and relative phases as a quantum program executes.
Cirq developers
Cirq is a Python framework for constructing, transforming, and executing quantum circuits. Its circuit diagrams expose gates, qubit wires, parallel moments, and nested circuit operations, while state histograms summarize measured outcomes. Hardware-oriented views include heatmaps that place single-qubit and two-qubit metrics in a device's spatial layout. The cirq-google package also provides calibration plots and comparison histograms for processor metrics. These visualizations support circuit inspection, result analysis, and examination of hardware characteristics. The linked guides show the relevant APIs and distinguish locally supplied example data from workflows requiring access to Google's quantum service.
Combined representations
Siyuan Lin, Jiang Hao, Lingyun Sun
Links a circuit editor to amplitude-flow diagrams, tracing how each gate combines, exchanges, or changes the phases of basis-state amplitudes.
Abhishek Parakh, Mahadevan Subramaniam, Elliott Ostler
Embeds polarization, superposition, and measurement displays in photon-shooting puzzles that let learners manipulate angles, basis components, and filters.
Zewei Tao, Yun Pan, Anying Chen, Licheng Wang
Combines circuit diagrams, Bloch-sphere animations, and probability distributions to walk through the registers and operations of Shor's factoring algorithm.
Combined representations
V. N. Chernega, O. V. Man’ko, V. I. Man’ko
Encodes a qubit's three measurement probabilities as triangle vertices, using attached squares to explore state constraints and transformations geometrically.
Rigetti Computing
pyQuil is Rigetti's Python library for constructing Quil programs and working with quantum processors and simulators. Its visualization support includes conventional circuit diagrams and probability bar charts. The LaTeX module translates a program into a wire-and-gate diagram and can display the rendered result in a notebook when the required external tools are installed. Wavefunction.plot displays computational-basis outcome probabilities by bitstring. Together, these features let developers compare the structure of a program with its simulated output. The resources below focus on pyQuil's own interfaces for these tasks.
Charles Tahan
Presents quantum-state matching puzzles in which players arrange gate blocks and compare visualizations of their current and target states.
Mate Galambos, Sandor Imre
Builds recursive bar patterns that encode multiqubit probabilities and phases, then shows how measurements and gates transform their structure.
QuTiP developers and contributors
QuTiP is a Python toolbox for simulating quantum systems and inspecting their states and dynamics. Its core visualization functions include Bloch spheres, probability distributions, density-matrix plots, and phase-space views. Qubism and Schmidt plots offer complementary ways to inspect many-particle amplitudes, partitions, and entanglement through spatial arrangements and color. Animation functions can display sequences of simulated states. These views are useful for exploring simulation results and building explanations of quantum behavior. Its documentation provides both introductory examples and detailed references for choosing and customizing a representation.
Combined representations
H Mäkelä, A Messina
Maps pure multiqubit states to constellations of points on a sphere, comparing geometric encodings and the point patterns associated with separable states.
J. B. Altepeter, E. R. Jeffrey, M. Medic, P. Kumar
Uses colored surfaces inside two Poincaré spheres to show which state of one qubit is prepared by measuring the other.
Bob Coecke, Ross Duncan
Represents quantum states and operations as connected diagrams, with graphical rewrite rules for reasoning about gates, entanglement, and equivalent computations.
George F. Viamontes, Manoj Rajagopalan, Igor L. Markov, John P. Hayes
Uses branching diagrams to represent quantum state vectors and gate matrices, sharing repeated substructures to illustrate compact simulation.
Ioannis G. Karafyllidis
Shows simulated quantum Fourier transform steps as a grayscale grid, tracking how measurement probabilities move across basis states.
Scott N Walck, Nathan C Hansell
Combines two spheres with a shared-information disc to depict pure two-qubit states, separating local orientations from entanglement and joint phase information.
F. Bloch
Represents a single qubit as a point on or inside a sphere, showing state orientation, purity, and rotations caused by gates.