Scalable quantum circuit design for QFT-based arithmetic
- URL: http://arxiv.org/abs/2411.00260v1
- Date: Thu, 31 Oct 2024 23:34:06 GMT
- Title: Scalable quantum circuit design for QFT-based arithmetic
- Authors: Murat Kurt, Ayda Kaltehei, Azmi Gençten, Selçuk Çakmak,
- Abstract summary: We create a scalable version of the quantum Fourier transform-based arithmetic circuit to perform addition and subtraction operations on N n-bit unsigned integers encoded in quantum registers.
We present qubit- and ququart-based multi-input QFT adders, and we compare and discuss potential benefits such as circuit simplicity and noise sensitivity.
- Score: 0.0
- License:
- Abstract: In this research, we create a scalable version of the quantum Fourier transform-based arithmetic circuit to perform addition and subtraction operations on N n-bit unsigned integers encoded in quantum registers, and it is compatible with d-level quantum sources, called qudits. We present qubit- and ququart-based multi-input QFT adders, and we compare and discuss potential benefits such as circuit simplicity and noise sensitivity. The results show that a ququart-based system significantly reduces gate count and improves computational efficiency compared to qubit-based systems. Overall, the findings presented in this study represent a promising step forward in the development of efficient quantum arithmetic circuits, particularly for multi-input operations, with clear advantages for ququart-based systems in reducing gate count, decoherence, and circuit complexity.
Related papers
- 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) - 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) - Learning the expressibility of quantum circuit ansatz using transformer [5.368973814856243]
We propose using a transformer model to predict the expressibility of quantum circuit ansatze.
This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.
arXiv Detail & Related papers (2024-05-29T07:34:07Z) - Characterization, synthesis, and optimization of quantum circuits over
multiple-control $\textit{Z}$-rotation gates: A systematic study [4.385466953937176]
We study quantum circuits composed of multiple-control $Z$-rotation (MCZR) gates as primitives.
We present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any MCZR circuit.
arXiv Detail & Related papers (2023-04-18T06:34:18Z) - Robust Quantum Arithmetic Operations with Intermediate Qutrits in the
NISQ-era [9.769081901589614]
NISQ-era (Noisy Intermediate Scale Quantum) developments have raised the importance for quantum algorithms.
In this paper, we introduce an intermediate qutrit method for efficient implementation of gate count and circuit-depth without T gate and ancilla.
We demonstrate that the percentage decrease in the probability of error is significant due to the fact that we achieve circuit efficiency by reducing circuit-depth in comparison to qubit-only works.
arXiv Detail & Related papers (2022-12-21T19:00:53Z) - 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) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - Performance Evaluations of Noisy Approximate Quantum Fourier Arithmetic [1.1140384738063092]
We implement QFT-based integer addition and multiplications on quantum computers.
These operations are fundamental to various quantum applications.
We evaluate these implementations based on IBM's superconducting qubit architecture.
arXiv Detail & Related papers (2021-12-17T06:51:18Z) - Efficient realization of quantum algorithms with qudits [0.70224924046445]
We propose a technique for an efficient implementation of quantum algorithms with multilevel quantum systems (qudits)
Our method uses a transpilation of a circuit in the standard qubit form, which depends on the parameters of a qudit-based processor.
We provide an explicit scheme of transpiling qubit circuits into sequences of single-qudit and two-qudit gates taken from a particular universal set.
arXiv Detail & Related papers (2021-11-08T11:09:37Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
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.