Design of Ultra-Low Noise Amplifier for Quantum Applications (QLNA)
- URL: http://arxiv.org/abs/2111.15358v4
- Date: Sat, 17 Feb 2024 19:59:59 GMT
- Title: Design of Ultra-Low Noise Amplifier for Quantum Applications (QLNA)
- Authors: Ahmad Salmanogli and Vahid Sharif Sirat
- Abstract summary: This article focuses on the design of an ultra-low-noise amplifier specifically tailored for quantum applications.
The circuit design places a significant emphasis on improving the noise figure, as quantum-associated applications require the circuit's noise temperature to be around 0.4 K.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The present article primarily focuses on the design of an ultra-low-noise
amplifier specifically tailored for quantum applications. The circuit design
places a significant emphasis on improving the noise figure, as
quantum-associated applications require the circuit's noise temperature to be
around 0.4 K. This requirement aims to achieve performance comparable to the
Josephson Junction amplifier. Although this task presents considerable
challenges, the work concentrates on engineering the circuit to minimize
mismatch and reflection coefficients, while simultaneously enhancing circuit
transconductance. These efforts aim to improve the noise figure as efficiently
as possible. The results of this study indicate the possibility of achieving a
noise figure of approximately 0.009 dB for a unique circuit design operating at
10 K. In a departure from traditional approaches, this study employs quantum
mechanical theory to analyze the circuit comprehensively. By employing quantum
theory, the researchers derive relationships that highlight the crucial
quantities upon which the circuit design should focus to optimize the noise
figure. For example, the circuit's gain power, which depends on the circuit's
photonic modes, is theoretically derived and found to affect the noise figure
directly. Ultimately, by merging quantum theory with engineering approaches,
this study successfully designs a highly efficient circuit that significantly
minimizes the noise figure in a quantum application setting.
Related papers
- Design of a Specialized Low Noise Amplifier for Enhancing Non-Classicality in Quantum Applications [0.0]
We present the design and analysis of a Low Noise Amplifier tailored specifically for quantum applications.
The main goal is to minimize the noise figure within the C-band frequency range (4-8 GHz) to induce nonclassicality in quantum signals.
Quantum analysis of the circuit, employing a simplified model of HEMT due to its complexity, revealed insights into its nonlinear properties.
arXiv Detail & Related papers (2024-08-15T08:58:56Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - 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) - FragQC: An Efficient Quantum Error Reduction Technique using Quantum
Circuit Fragmentation [4.2754140179767415]
We present it FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold.
We achieve an increase of fidelity by 14.83% compared to direct execution without cutting the circuit, and 8.45% over the state-of-the-art ILP-based method.
arXiv Detail & Related papers (2023-09-30T17:38:31Z) - A single $T$-gate makes distribution learning hard [56.045224655472865]
This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits.
We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
arXiv Detail & Related papers (2022-07-07T08:04:15Z) - 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) - Mitigating depolarizing noise on quantum computers with noise-estimation
circuits [1.3375143521862154]
We present a method to mitigate the depolarizing noise by first estimating its rate with a noise-estimation circuit.
We find that our approach in combination with readout-error correction, compiling, randomized, and zero-noise extrapolation produces results close to exact results even for circuits containing hundreds of CNOT gates.
arXiv Detail & Related papers (2021-03-15T17:59:06Z) - 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) - Machine learning of noise-resilient quantum circuits [0.8258451067861933]
Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers.
We present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits.
Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state.
arXiv Detail & Related papers (2020-07-02T15:43:32Z) - A deep learning model for noise prediction on near-term quantum devices [137.6408511310322]
We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model.
A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise.
arXiv Detail & Related papers (2020-05-21T17:47:29Z)
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.