Release Notes

17-OCTOBER-2025 – v1.3.0

Platform Features

  • FPGA Backend: Simulate Quantum Algorithms on FPGA
  • Algorithms: Deterministic Quantum Search (DQS),  Time-Evolution of Quantum System using Trotterization, QEC – Steane Seven-Qubit Code, Portfolio Optimization using QAOA
  • Photonic Quantum Simulator: Simulate Quantum Algorithms using Indigenous Quantum Photonic Simulator developed by C-DAC and realize the photonic component circuit.

Bug Fixes

  • For Quantum Machine Learning algorithms, the data-size support increased to 600
  • Compute Resources information updated for superstaq, quantum inspire, simulators
  • Creating custom gates with name in capital letters issue is solved.
  • Issue submitting job on superstaq solved.
  • Simon algorithm extended to support for decimal value 0
  • In QEC – Shor’s Nine-Qubit Code algorithm random error has been introducted along with bit-flip and phase-flip error.
  • CX-gate edit window the control and target qubit options are placed in order for better UI.
  • UI toggle button updated for dark & light theme.

04-AUGUST-2025 – v1.2.1

Platform Updates & Fixes

  • Cirq Backend: Resolved the issue where multiple measurement gates were incorrectly allowed on the same classical register. The interface now prompts users to assign separate classical registers for each measurement, enforcing this constraint at the client side itself!

  • Compute Resources: The “Credits” label has been renamed to “Balance Credits” for clearer resource tracking.

  • Debugging & Probability Distribution: Fixed the issue where the probability distribution was not displaying during the debug process. Resolved the issue where the “Ideal Probability Distribution” was not shown after clicking “Debug.”

  • QPU Job Results: Corrected the calculation, so the total probability of all states is now 100% instead of incorrectly summing.

  • Grover’s Algorithm: Added a measurement gate by default to Grover’s Algorithm circuits.

  • QPU Simulations (Jobs Page): Queue time and running time are now clearly displayed for simulations executed on QPU.

  • IBM QPU Integration: Due to a recent policy change, API key verification for IBM QPUs has been temporarily disabled until further notice.
  • Superstaq Hardware: Resolved the “Internal Server Error” that occurred during interactions with Superstaq hardware.

04-JULY-2025 – v1.2.0

Features

  • Registration Page: A user registration page has been created for seamless onboarding of the users.
  • Credit System: A new credit-based system has been implemented to manage simulation resource usage:
    • Welcome Bonus: Users receive 150 free credits upon successful registration.
    • Credits Deduction per Simulation:
      • CPU Simulation: 5 credits
      • GPU Simulation: 10 credits
      • QPU Simulation: 2 credits
  • Added new Simulators:
    • Qulacs – A high-performance quantum circuit simulator optimized for large-scale quantum circuit simulations.
  • Added new Algorithms:
    • QEC Algorithm – Implements 3-bit repetition code for detecting and correcting single-qubit bit-flip and phase-flip errors.
    • QEC Shor’s Nine-Qubit Code – Encodes one logical qubit into nine physical qubits to correct arbitrary single-qubit errors using nested bit-flip and phase-flip codes.
    • QHC Algorithm (Quantum Hierarchical Clustering) – Utilizes quantum similarity measures like the swap test to perform hierarchical clustering with enhanced efficiency via quantum parallelism.
    • QPCA Algorithm (Quantum Principal Component Analysis) – Quantum-enhanced technique for dimensionality reduction using principles of quantum mechanics to speed up classical PCA.
    • QPerceptron Algorithm – Quantum-inspired version of the classical perceptron algorithm, leveraging quantum mechanics to tackle machine learning tasks more efficiently.
    • QNN Algorithm (Quantum Neural Network) – A hybrid framework combining quantum feature maps and variational quantum layers with classical neural networks for predictive tasks.

Bug Fixes

  • Fixed Issues in job submission to QPU hardware through Superstaq
  • Addressed a user query for installing seaborn library in Python 3 Kernel.
  • Updated the compute resources page showing all the QPUs.

06-MAY-2025 – v1.1.0

Features

  • Shortcut Keys:Added convenient shortcut keys for commonly used operations such as deleting gates, undoing and redoing actions. These shortcuts can be found listed under the ‘Help’ menu.
  • Qubit Initialization: Enhanced Qubit Initialization
  • QuEst Simulator: Added Quantum Exact Simulation Toolkit (QuEst). QuEst is a high performance simulator of quantum circuits, state-vectors and density matrices. QuEST uses multithreading, GPU acceleration and distribution to run lightning first on laptops, desktops and networked supercomputers.
  • Added new Simulators:
    • Quest_simulator – a high performance simulator of quantum circuits, state-vectors and density matrices.
    • qsimcirq_simulator – qsimcirq is a full wave function simulator written in C++. It uses gate fusion, AVX/FMA vectorized instructions and multi-threading using OpenMP to achieve state of the art simulations of quantum circuits. qsim is integrated with Cirq for CPU & GPU.
    • qsimcirq_cuquantum_gpu_simulator – GPU based Quantum Simulation using Qsim Cirq Simulator.
  • Added new Algorithms:
    • Veto Algorithm – The Veto Algorithm is a filtering technique that rejects events or data points based on predefined exclusion criteria to enhance the quality of the remaining dataset.
    • QSVM Algorithm – The QSVM (Quantum Support Vector Machine) algorithm leverages quantum computing to perform classification tasks by mapping data into a high-dimensional Hilbert space using quantum kernels for enhanced pattern recognition.
    • QKMeans Algorithm – The QKMeans (Quantum K-Means) algorithm is a quantum-enhanced clustering method that uses quantum distance estimation to group data into clusters more efficiently than classical K-Means.
    • QPC for the socialist millionaire problem – Quantum Private Comparison (QPC) for the Socialist Millionaire Problem enables two parties to compare whether their private inputs are equal—without revealing the actual values—using quantum properties like entanglement and superposition to ensure security and privacy.
    • QKNN – QKNN (Quantum k-Nearest Neighbors) is a quantum-enhanced classification algorithm that uses quantum parallelism and distance estimation to efficiently identify the k closest training samples to a query point for decision making.

Bug Fixes

  • Added horizontal auto scrolling of circuit composer while drag and drop the gates
  • Proper error messages are now implemented for all mandatory fields in the Profile section.
  • Proper error messages are now displayed for all required fields in the Algorithm Input section.
  • The issue where angles were not updating correctly while creating custom gates (RX, RY, RZ, P, U, and CP) has been resolved.
  • The issue with incorrect display of state angles (θ, λ, φ) during initialization has been fixed.

 

03-APR-2025 – v1.0.0

First Public Release

Features

  • Powerful Circuit Builder: The intuitive, drag-and-drop circuit builder enables users to visually design quantum circuits by selecting and placing quantum gates. 
  • Rich Gate Palette: Qniverse offers a Rich Gate Palette, enabling the construction of complex quantum circuits and custom gates by leveraging fundamental quantum gates.
  • Realtime debug: Qniverse facilitates real-time debugging of quantum circuits up to 6 qubits.
  • Quantum Transpiler: An integrated transpiler allows users to “code once and execute on multiple platforms“. It efficiently converts quantum circuits into formats supported by various simulation frameworks(Qiskit, CirQ, CudaQ etc.,) and transforms them into optimized instruction sequences compatible with different quantum hardware architectures(IBMs superconducting).
  • Modular Libraries: The in-built libraries enable users to work with pre-built components and easily integrate them into their workflows, enhancing development efficiency. Whether creating optimization algorithms, machine learning models, or cryptographic protocols, the modular libraries offer the flexibility needed to address a broad spectrum of quantum computing challenges.
  • Ideal visualization:Qniverse provides an intuitive visualization of probability distributions for quantum circuits up to 6 qubits, facilitating clear inference of quantum state outcomes.
  • Algorithm Implementations: Qniverse platform provides a powerful platform for implementing quantum algorithms. They don’t offer a one-stop shop for pre-built solutions, but rather equip you with the tools and knowledge to bridge the gap between theoretical and practical applications of quantum computing. This capability brings the power of quantum algorithms closer to real-world applications, advancing industries such as cryptography, materials science, and more.
  • Connect to multiple Quantum Hardware: Qniverse enables the execution of quantum circuits on multiple quantum hardware through seamless connectivity.
  • Accelerated Quantum Computing: Qniverse offers a diverse range of compute resources, including CPUs and classical accelerators like GPUs, FPGAs, and vector processors, to support quantum computing workflows.
  • Persistent Quantum Circuit Access and SLURM Integration: Qniverse integrates with HPC’s SLURM scheduler for efficient resource management and provides persistent access to previously executed quantum circuits.
  • Documentation: Qniverse offers comprehensive and user-friendly documentation, detailing its SDK, tools, and algorithms to facilitate seamless learning and implementation.
  • Advanced Quantum Programming with JupyterLab: Qniverse integrates JupyterLab, empowering advanced users to program, execute, and iterate on complex quantum circuits and algorithms within a familiar environment