Visualizing Quantum Circuit Probability -- estimating computational
action for quantum program synthesis
- URL: http://arxiv.org/abs/2304.02358v1
- Date: Wed, 5 Apr 2023 10:49:36 GMT
- Title: Visualizing Quantum Circuit Probability -- estimating computational
action for quantum program synthesis
- Authors: Bao Gia Bach, Akash Kundu, Tamal Acharya, Aritra Sarkar
- Abstract summary: The probability of states in the circuit model of computation is defined.
The reachability and expressibility in a space-time-bounded setting for classical and quantum gate sets are enumerated and visualized.
The article suggests how applications like geometric quantum machine learning, novel quantum algorithm and quantum artificial general intelligence can benefit from studying circuit probabilities.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This research applies concepts from algorithmic probability to Boolean and
quantum combinatorial logic circuits. A tutorial-style introduction to states
and various notions of the complexity of states are presented. Thereafter, the
probability of states in the circuit model of computation is defined. Classical
and quantum gate sets are compared to select some characteristic sets. The
reachability and expressibility in a space-time-bounded setting for these gate
sets are enumerated and visualized. These results are studied in terms of
computational resources, universality and quantum behavior. The article
suggests how applications like geometric quantum machine learning, novel
quantum algorithm synthesis and quantum artificial general intelligence can
benefit by studying circuit probabilities.
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) - Character Complexity: A Novel Measure for Quantum Circuit Analysis [0.0]
This paper introduces Character Complexity, a novel measure that bridges Group-theoretic concepts with practical quantum computing concerns.
I prove several key properties of character complexity and establish a surprising connection to the classical simulability of quantum circuits.
I present innovative visualization methods for character complexity, providing intuitive insights into the structure of quantum circuits.
arXiv Detail & Related papers (2024-08-19T01:58:54Z) - Quantum Circuit Ansatz: Patterns of Abstraction and Reuse of Quantum Algorithm Design [3.8425905067219492]
The paper presents a categorized catalog of quantum circuit ansatzes.
Each ansatz is described with details such as intent, motivation, applicability, circuit diagram, implementation, example, and see also.
Practical examples are provided to illustrate their application in quantum algorithm design.
arXiv Detail & Related papers (2024-05-08T12:44:37Z) - Quantum simulation of excited states from parallel contracted quantum
eigensolvers [5.915403570478968]
We show that a ground-state contracted quantum eigensolver can be generalized to compute any number of quantum eigenstates simultaneously.
We introduce two excited-state CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm.
arXiv Detail & Related papers (2023-11-08T23:52:31Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - A Quantum Algorithm for Computing All Diagnoses of a Switching Circuit [73.70667578066775]
Faults are by nature while most man-made systems, and especially computers, work deterministically.
This paper provides such a connecting via quantum information theory which is an intuitive approach as quantum physics obeys probability laws.
arXiv Detail & Related papers (2022-09-08T17:55:30Z) - Quantum variational learning for entanglement witnessing [0.0]
This work focuses on the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits.
We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled.
We made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness.
arXiv Detail & Related papers (2022-05-20T20:14:28Z) - An Introduction to Quantum Machine Learning for Engineers [36.18344598412261]
Quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers.
This book provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra.
arXiv Detail & Related papers (2022-05-11T12:10:52Z) - 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.