Symbolic Quantum Simulation with Quasimodo
- URL: http://arxiv.org/abs/2302.04349v2
- Date: Mon, 29 May 2023 20:20:00 GMT
- Title: Symbolic Quantum Simulation with Quasimodo
- Abstract summary: We present Quasimodo, an open-source Python library for symbolic simulation of quantum circuits.
Quasimodo allows simulations of quantum circuits, checking properties of the outputs of quantum circuits.
It can be easily extended to support other symbolic data-structures.
- Score: 7.638042073679073
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The simulation of quantum circuits on classical computers is an important
problem in quantum computing. Such simulation requires representations of
distributions over very large sets of basis vectors, and recent work has used
symbolic data-structures such as Binary Decision Diagrams (BDDs) for this
purpose. In this tool paper, we present Quasimodo, an extensible, open-source
Python library for symbolic simulation of quantum circuits. Quasimodo is
specifically designed for easy extensibility to other backends. Quasimodo
allows simulations of quantum circuits, checking properties of the outputs of
quantum circuits, and debugging quantum circuits. It also allows the user to
choose from among several symbolic data-structures -- both unweighted and
weighted BDDs, and a recent structure called Context-Free-Language Ordered
Binary Decision Diagrams (CFLOBDDs) -- and can be easily extended to support
other symbolic data-structures.
Related papers
- Verifiable quantum advantage in extremely low depth [52.51019642214249]
We give a problem that is solvable by shallow quantum circuits, hard for computation in time under lattice-based assumptions.<n>We prove that shallow quantum circuits have sufficient structure to solve certain classically hard tasks whose solutions can be verified efficiently.
arXiv Detail & Related papers (2026-09-01T15:54:34Z) - QSeqSim: A Symbolic Simulator for Qiskit While Loops Using Sequential Quantum Circuits [11.52320320397442]
QSeqSim takes Qiskit QuantumCircuit objects, translates them into OpenQASM 3 code, and organises the resulting program.<n>Building on this semantics, QSeqSim adopts a Binary Decision Diagram (BDD)-based symbolic representation.<n>Our experiments demonstrate that QSeqSim scales to substantial while-induced sequential circuits.
arXiv Detail & Related papers (2026-05-14T14:25:39Z) - Qompiler: A Traceable Quantum Circuit Synthesizer for Arbitrary Hamiltonians [0.0]
We present a quantum compiler framework that bridges the gap between physics modeling and quantum software development.<n>At the core of this framework is a versatile quantum circuit synthesizer capable of decomposing arbitrary Hamiltonians into quantum circuits.<n>The intermediate representation serves as a universal, hardware-agnostic carrier of compiled code, allowing it to be readily rendered on most quantum hardware backends.
arXiv Detail & Related papers (2025-09-18T14:27:33Z) - An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.
We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.
We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
arXiv Detail & Related papers (2025-04-13T11:49:53Z) - QuForge: A Library for Qudits Simulation [0.0]
QuForge is a Python-based library designed to simulate quantum circuits with qudits.
It supports execution on accelerating devices such as GPUs and TPUs, significantly speeding up simulations.
It also supports sparse operations, leading to a reduction in memory consumption compared to other libraries.
arXiv Detail & Related papers (2024-09-26T10:38:35Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Simulating Quantum Circuits by Model Counting [0.0]
We show for the first time that a strong simulation of universal quantum circuits can be efficiently tackled through weighted model counting.
Our work paves the way to apply the existing array of powerful classical reasoning tools to realize efficient quantum circuit compilation.
arXiv Detail & Related papers (2024-03-11T22:40:15Z) - Tensor Networks or Decision Diagrams? Guidelines for Classical Quantum
Circuit Simulation [65.93830818469833]
tensor networks and decision diagrams have independently been developed with differing perspectives, terminologies, and backgrounds in mind.
We consider how these techniques approach classical quantum circuit simulation, and examine their (dis)similarities with regard to their most applicable abstraction level.
We provide guidelines for when to better use tensor networks and when to better use decision diagrams in classical quantum circuit simulation.
arXiv Detail & Related papers (2023-02-13T19:00:00Z) - The Basis of Design Tools for Quantum Computing: Arrays, Decision
Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
Quantum computers promise to efficiently solve important problems classical computers never will.
A fully automated quantum software stack needs to be developed.
This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
arXiv Detail & Related papers (2023-01-10T19:00:00Z) - QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum
Systems [7.416447177941264]
textbfQuDiet is a python-based higher-dimensional quantum computing simulator.
textbfQuDiet offers multi-valued logic operations by utilizing generalized quantum gates.
textbfQuDiet provides a full qubit-qudit hybrid quantum simulator package.
arXiv Detail & Related papers (2022-11-15T06:07:04Z) - DisCoPy for the quantum computer scientist [0.0]
DisCoPy is an open source toolbox for computing with string diagrams and functors.
In particular, the diagram data structure allows to encode various kinds of quantum processes, with functors for classical simulation and optimisation.
This includes the ZX calculus and its many variants, the parameterised circuits used in quantum machine learning, but also linear optical quantum computing.
arXiv Detail & Related papers (2022-05-10T22:13:11Z) - An Algebraic Quantum Circuit Compression Algorithm for Hamiltonian
Simulation [55.41644538483948]
Current generation noisy intermediate-scale quantum (NISQ) computers are severely limited in chip size and error rates.
We derive localized circuit transformations to efficiently compress quantum circuits for simulation of certain spin Hamiltonians known as free fermions.
The proposed numerical circuit compression algorithm behaves backward stable and scales cubically in the number of spins enabling circuit synthesis beyond $mathcalO(103)$ spins.
arXiv Detail & Related papers (2021-08-06T19:38:03Z) - Fast quantum circuit simulation using hardware accelerated general
purpose libraries [69.43216268165402]
CuPy is a general purpose library (linear algebra) developed specifically for GPU-based quantum circuits.
For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators.
arXiv Detail & Related papers (2021-06-26T10:41:43Z) - Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits
at Exascale [57.84751206630535]
We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework.
The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators.
By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
arXiv Detail & Related papers (2021-04-21T13:26:42Z) - Bit-Slicing the Hilbert Space: Scaling Up Accurate Quantum Circuit
Simulation to a New Level [10.765480856320018]
We enhance quantum circuit simulation in two dimensions: accuracy and scalability.
Experimental results demonstrate that our method can be superior to the state-of-the-art for various quantum circuits.
arXiv Detail & Related papers (2020-07-18T01:26:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.