Effects of quantum resources on the statistical complexity of quantum
circuits
- URL: http://arxiv.org/abs/2102.03282v1
- Date: Fri, 5 Feb 2021 16:42:35 GMT
- Title: Effects of quantum resources on the statistical complexity of quantum
circuits
- Authors: Kaifeng Bu, Dax Enshan Koh, Lu Li, Qingxian Luo, Yaobo Zhang
- Abstract summary: We investigate how the addition of quantum resources changes the statistical complexity of quantum circuits.
We show that the increase in the statistical complexity of a quantum circuit when an additional quantum channel is added is upper bounded by the free robustness of the added channel.
- Score: 4.318152590967423
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We investigate how the addition of quantum resources changes the statistical
complexity of quantum circuits by utilizing the framework of quantum resource
theories. Measures of statistical complexity that we consider include the
Rademacher complexity and the Gaussian complexity, which are well-known
measures in computational learning theory that quantify the richness of classes
of real-valued functions. We derive bounds for the statistical complexities of
quantum circuits that have limited access to certain resources and apply our
results to two special cases: (1) stabilizer circuits that are supplemented
with a limited number of T gates and (2) instantaneous quantum polynomial-time
Clifford circuits that are supplemented with a limited number of CCZ gates. We
show that the increase in the statistical complexity of a quantum circuit when
an additional quantum channel is added to it is upper bounded by the free
robustness of the added channel. Finally, we derive bounds for the
generalization error associated with learning from training data arising from
quantum circuits.
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) - Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness [0.0]
We show the natural robustness of quantum statistical queries for learning quantum processes.
We adapt a learning algorithm for constant-depth quantum circuits to the quantum statistical query setting.
We prove that pseudorandom unitaries (PRUs) cannot be constructed using circuits of constant depth.
arXiv Detail & Related papers (2024-05-20T14:55:20Z) - Comparative Study of Quantum-Circuit Scalability in a Financial Problem [0.0]
This study examines the number of two-qubit gates in the superconducting circuit and ion-trap quantum system.
The ion-trap system exhibits a two to three factor reduction in the number of required two-qubit gates when compared to the superconducting circuit system.
arXiv Detail & Related papers (2024-04-07T10:39:33Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Wasserstein Complexity of Quantum Circuits [10.79258896719392]
Given a unitary transformation, what is the size of the smallest quantum circuit that implements it?
This quantity, known as the quantum circuit complexity, is a fundamental property of quantum evolutions.
We show that our new measure also provides a lower bound for the experimental cost of implementing quantum circuits.
arXiv Detail & Related papers (2022-08-12T14:44:13Z) - Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state.
In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
arXiv Detail & Related papers (2021-12-27T21:15:35Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z) - On the statistical complexity of quantum circuits [4.318152590967423]
We study how the statistical complexity depends on various quantum circuit parameters.
We introduce a measure of magic based on the $(p,q)$ group norm, which quantifies the amount of magic in the quantum channels associated with the circuit.
The bounds we obtain can be used to constrain the capacity of quantum neural networks in terms of their depths and widths.
arXiv Detail & Related papers (2021-01-15T14:55:55Z) - Boundaries of quantum supremacy via random circuit sampling [69.16452769334367]
Google's recent quantum supremacy experiment heralded a transition point where quantum computing performed a computational task, random circuit sampling.
We examine the constraints of the observed quantum runtime advantage in a larger number of qubits and gates.
arXiv Detail & Related papers (2020-05-05T20:11:53Z)
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.