QRTlib: A Library for Fast Quantum Real Transforms
- URL: http://arxiv.org/abs/2510.16625v1
- Date: Sat, 18 Oct 2025 19:45:31 GMT
- Title: QRTlib: A Library for Fast Quantum Real Transforms
- Authors: Armin Ahmadkhaniha, Lu Chen, Jake Doliskani, Zhifu Sun,
- Abstract summary: Real-valued transforms such as the discrete cosine, sine, and Hartley transforms play a central role in classical computing.<n>This article introduces QRTlib, a library for fast and practical implementations of quantum real transforms.<n>We develop new algorithms and circuit optimizations that make these transforms efficient and suitable for near-term devices.
- Score: 5.23032256592014
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Real-valued transforms such as the discrete cosine, sine, and Hartley transforms play a central role in classical computing, complementing the Fourier transform in applications from signal and image processing to data compression. However, their quantum counterparts have not evolved in parallel, and no unified framework exists for implementing them efficiently on quantum hardware. This article addresses this gap by introducing QRTlib, a library for fast and practical implementations of quantum real transforms, including the quantum Hartley, cosine, and sine transforms of various types. We develop new algorithms and circuit optimizations that make these transforms efficient and suitable for near-term devices. In particular, we present a quantum Hartley transform based on the linear combination of unitaries (LCU) technique, achieving a fourfold reduction in circuit size compared to prior methods, and an improved quantum sine transform of Type I that removes large multi-controlled operations. We also introduce circuit-level optimizations, including two's-complement and or-tree constructions. QRTlib provides the first complete implementations of these quantum real transforms in Qiskit.
Related papers
- Quantum-Inspired Algorithms beyond Unitary Circuits: the Laplace Transform [0.0]
Quantum-inspired algorithms can deliver substantial speedups over classical state-of-the-art methods.<n>We introduce a tensor-network approach to compute the discrete Laplace transform, a non-unitary, aperiodic transform.<n>We demonstrate simulations up to $N=230$ input data points, with up to $260$ output data points, and quantify how bond dimension controls runtime and accuracy.
arXiv Detail & Related papers (2026-01-25T07:19:56Z) - Neural Guided Sampling for Quantum Circuit Optimization [26.90377134346014]
Translating a quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit.<n>One method to address efficient transpilation is based on approaches known from optimization, e.g. by using random sampling and token replacement strategies.<n>Here, we propose in this work 2D neural guided sampling. Thus, given a 2D representation of a quantum circuit, a neural network predicts groups of gates in the quantum circuit, which are likely reducible.
arXiv Detail & Related papers (2025-10-14T12:09:05Z) - Quantum Approximate Optimization Algorithm for MIMO with Quantized b-bit Beamforming [47.98440449939344]
Multiple-input multiple-output (MIMO) is critical for 6G communication, offering improved spectral efficiency and reliability.<n>This paper explores the use of the Quantum Approximate Optimization Algorithm (QAOA) and alternating optimization to address the problem of b-bit quantized phase shifters both at the transmitter and the receiver.<n>We demonstrate that the structure of this quantized beamforming problem aligns naturally with hybrid-classical methods like QAOA, as the phase shifts used in beamforming can be directly mapped to rotation gates in a quantum circuit.
arXiv Detail & Related papers (2025-10-07T17:53:02Z) - Accelerating Transpilation in Quantum Machine Learning with Haiqu's Rivet-transpiler [45.88028371034407]
We develop the Rivet transpiler, which accelerates transpilation by reusing previously transpiled circuits.<n>We demonstrate up to 600% improvement in transpilation time for quantum layerwise learning.
arXiv Detail & Related papers (2025-08-29T06:00:29Z) - Quantum-Efficient Convolution through Sparse Matrix Encoding and Low-Depth Inner Product Circuits [0.0]
We present a resource-efficient quantum algorithm that reformulates the convolution product as a structured matrix multiplication.<n>We construct a quantum framework wherein sparse input patches are prepared using optimized key-value QRAM state encoding.<n>Our architecture supports batched convolution across multiple filters using a generalized SWAP circuit.
arXiv Detail & Related papers (2025-07-25T20:08:12Z) - A novel quantum circuit for the quantum Fourier transform [0.0]
We introduce a new quantum circuit for implementing the Quantum Fourier Transform.<n>We show that this circuit is more efficient than the conventional design.<n>We also develop alternative versions of the HHL algorithm and Shor's algorithm.
arXiv Detail & Related papers (2025-07-11T15:51:27Z) - Efficient Quantum Circuit Compilation for Near-Term Quantum Advantage [17.38734393793605]
We propose an approximate method for compiling target quantum circuits into brick-wall layouts.<n>This new circuit design consists of two-qubit CNOT gates that can be directly implemented on real quantum computers.
arXiv Detail & Related papers (2025-01-13T15:04:39Z) - Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
We present a model for parallelizing simulation of quantum circuit executions.
The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
arXiv Detail & Related papers (2024-06-05T17:16:07Z) - Learning with SASQuaTCh: a Novel Variational Quantum Transformer Architecture with Kernel-Based Self-Attention [0.464982780843177]
We present a variational quantum circuit architecture named Self-Attention Sequential Quantum Transformer Channel (SASQuaT)<n>Our approach leverages recent insights from kernel-based operator learning in the context of predicting vision transformer network using simple gate operations and a set of multi-dimensional quantum Fourier transforms.<n>To validate our approach, we consider image classification tasks in simulation and with hardware, where with only 9 qubits and a handful of parameters we are able to simultaneously embed and classify a grayscale image of handwritten digits with high accuracy.
arXiv Detail & Related papers (2024-03-21T18:00:04Z) - Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding [29.69773024077467]
We show that neural compression can be highly sub-optimal on synthetic sources whose intrinsic dimensionality is greater than one.<n>With integer rounding in the latent space, the quantization regions induced by neural transformations, remain square-like and fail to match those of optimal vector quantization.<n>We propose lattice quantization instead, we show that it approximately recovers optimal vector quantization at reasonable complexity.
arXiv Detail & Related papers (2024-03-12T05:09:25Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Gravitational-wave matched filtering on a quantum computer [0.0]
We show the first experimental demonstration of qubit-based matched filtering for a detection of the gravitational-wave signal from a binary black hole merger.
With our implementation on superconducting qubits, we obtained a similar signal-to-noise ratio for the binary black hole merger as achievable with classical computation.
arXiv Detail & Related papers (2022-04-08T16:08:40Z) - Protocols for Trainable and Differentiable Quantum Generative Modelling [21.24186888129542]
We propose an approach for learning probability distributions as differentiable quantum circuits (DQC)
We perform training of a DQC-based model, where data is encoded in a latent space with a phase feature map, followed by a variational quantum circuit.
This allows fast sampling from parametrized distributions using a single-shot readout.
arXiv Detail & Related papers (2022-02-16T18:55:48Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Verifying Results of the IBM Qiskit Quantum Circuit Compilation Flow [7.619626059034881]
We propose an efficient scheme for quantum circuit equivalence checking.
The proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less.
arXiv Detail & Related papers (2020-09-04T19:58:53Z)
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.