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A Benchmark of Cryo-CMOS 40-nm Embedded SRAM/DRAMs for quantum computing

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A Benchmark of Cryo-CMOS 40-nm Embedded SRAM/DRAMs for Quant...

European Conference on Solid-State Circuits (ESSCIRC)

作者： Rob A. Damsteegt Ramon W.J. Overwater Masoud Babaie Fabio Sebastiano Department of Quantum & Computing Engineering & QuTech TU Delft The Netherlands Department of Microelectronics & QuTech TU Delft The Netherlands

The cryogenic electronic interface for quantum pro-cessors requires cryo-CMOS embedded memories that cover a wide range of specifications. The temperature dependence of device parameters, such as the threshold voltage, the gate/subthreshold leakage, and the variability, severely alters the memories’ performance between room temperature (RT) and cryogenic temperatures (4.2K). To assess the best memory design for a given application, this paper benchmarks three custom DRAMs and a custom SRAM in 40-nm CMOS at 4.2 K and RT, e.g., identifying that, while the SRAM is more power efficient for moderate-to-high speeds at RT, the 2T DRAM performs better than SRAM and 3T DRAMs at 4.2 K.

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EO-GRAPE and EO-DRLPE: Open and Closed Loop Approaches for Energy Efficient quantum Optimal Control

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arXiv 2024年

作者： Fauquenot, Sebastiaan Sarkar, Aritra Feld, Sebastian Quantum Machine Learning Research Group Quantum Computing Division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands

This research investigates the possibility of using quantum optimal control techniques to co-optimize the energetic cost and the process fidelity of a quantum unitary gate. The energetic cost is theoretically defined, and thereby, the gradient of the energetic cost for pulse engineering is derived. We empirically demonstrate the Pareto optimality in the trade-off between process fidelity and energetic cost. Thereafter, two novel numerical quantum optimal control approaches are proposed: (i) energy-optimized gradient ascent pulse engineering (EO-GRAPE) as an open-loop gradient-based method, and (ii) energy-optimized deep reinforcement learning for pulse engineering (EO-DRLPE) as a closed-loop method. The performance of both methods is probed in the presence of increasing noise. We find that the EO-GRAPE method performs better than the EO-DRLPE methods with and without a warm start for most experimental settings. Additionally, for one qubit unitary gate, we illustrate the correlation between the Bloch sphere path length and the energetic cost. © 2024, CC BY-NC-ND.

关键词： Deep reinforcement learning

DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search

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arXiv 2025年

作者： Xie, Shubing Sarkar, Aritra Feld, Sebastian Instituut-Lorentz Leiden University Netherlands Quantum Machine Learning Research Group Quantum Computing Division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands

Demonstrating quantum advantage using conventional quantum algorithms remains challenging on current noisy gate-based quantum computers. Automated quantum circuit synthesis via quantum machine learning has emerged as a promising solution, employing trainable parametric quantum circuits to alleviate this. The circuit ansatz in these solutions is often designed through reinforcement learning-based quantum architecture search when the domain knowledge of the problem and hardware are not effective. However, the interpretability of these synthesized circuits remains a significant bottleneck, limiting their scalability and applicability across diverse problem domains. This work addresses the challenge of explainability in quantum architecture search (QAS) by introducing a novel genetic programming-based decompiler framework for reverse-engineering high-level quantum algorithms from low-level circuit representations. The proposed approach, implemented in the open-source tool DeQompile, employs program synthesis techniques, including symbolic regression and abstract syntax tree manipulation, to distill interpretable Qiskit algorithms from quantum assembly language. Validation of benchmark algorithms demonstrates the efficacy of our tool. By integrating the decompiler with online learning frameworks, this research potentiates explainable QAS by fostering the development of generalizable and provable quantum algorithms. © 2025, CC BY-NC-ND.

关键词： Qubits

Lightcone Bounds for quantum Circuit Mapping via Uncomplexity

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arXiv 2024年

作者： Steinberg, Matthew Bandić, Medina Szkudlarek, Sacha Almudever, Carmen G. Sarkar, Aritra Feld, Sebastian Quantum Computing Division QuTech Delft University of Technology Delft Netherlands Department of Quantum & Computer Engineering Delft University of Technology Delft Netherlands Computer Engineering Department Technical University of Valencia Valencia Spain

Efficiently mapping quantum circuits onto hardware is an integral part of the quantum compilation process, wherein a circuit is modified in accordance with the stringent architectural demands of a quantum processor. Many techniques exist for solving the quantum circuit mapping problem, in addition to several theoretical perspectives that relate quantum circuit mapping to problems in classical computer science. This work considers a novel perspective on quantum circuit mapping, in which the routing process of a simplified circuit is viewed as a composition of quantum operations acting on density matrices representing the quantum circuit and processor. Drawing on insight from recent advances in quantum circuit complexity and information geometry, we show that a minimal SWAP-gate count for executing a quantum circuit on a device emerges via the minimization of the distance between quantum states using the quantum Jensen-Shannon divergence, which we dub the lightcone bound. Additionally, we develop a novel initial placement algorithm based on a graph similarity search that selects the partition nearest to a graph isomorphism between interaction and coupling graphs. From these two ingredients, we construct an algorithm for calculating the lightcone bound, which is directly compared alongside the IBM Qiskit compiler for over 600 realistic benchmark experiments, as well as against a brute-force method for smaller benchmarks. In our simulations, we unambiguously find that neither the brute-force method nor the Qiskit compiler surpasses our bound, signaling utility for estimating minimal overhead when realizing quantum algorithms on constrained quantum hardware. This work also constitutes the first use of quantum circuit uncomplexity to practically-relevant quantum computing. We anticipate that this method may have diverse applicability outside of the scope of quantum information science. Copyright © 2024, The Authors. All rights reserved.

关键词： quantum computers

Automated quantum Software engineering : why? what? how?

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arXiv 2022年

作者： Sarkar, Aritra Quantum Machine Learning research group Quantum Computing division QuTech Department of Quantum & Computer Engineering Delft University of Technology Netherlands

This article provides a personal perspective on research in Automated quantum Software engineering (AQSE). It elucidates the motivation to research AQSE (why?), a precise description of such a framework (what?), and r... 详细信息

关键词： Software engineering

Impedance Standard Substrate Characterization and EM model definition for Cryogenic and quantum-computing Applications

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Impedance Standard Substrate Characterization and EM model d...

Microwave, MTT-S International Symposium

作者： Ehsan Shokrolahzade Fabio Sebastiano Faisal Mubarak Masoud Babaie Marco Spirito Electronic Circuits and Architectures (ELCA) TU Delft The Netherlands Department of Quantum & Computing Engineering & QuTech TU Delft The Netherlands VSL Dutch Metrology Institute The Netherlands

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YAQQ: Yet Another quantum Quantizer - Design Space Exploration of quantum Gate Sets using Novelty Search

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arXiv 2024年

作者： Sarkar, Aritra Kundu, Akash Steinberg, Matthew Mishra, Sibasish Fauquenot, Sebastiaan Acharya, Tamal Miszczak, Jaroslaw A. Feld, Sebastian Quantum Intelligence research team Quantum Machine Learning research group Quantum Computing division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland QTF Centre of Excellence Department of Physics University of Helsinki Finland

The standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms, represented as unitary matrices, to bounded depth quantum circuits depends strongly on the set of gates available for the decomposition routine. To investigate this dependence, we explore the design space of discrete quantum gate sets and present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The evaluation is conditioned on a set of unitary transformations representing target use cases on the quantum processors. The cost function considers three key factors: (i) the statistical distribution of the decomposed circuits’ depth, (ii) the statistical distribution of process fidelities for the approximate decomposition, and (iii) the relative novelty of a gate set compared to other gate sets in terms of the aforementioned properties. The developed software, called YAQQ (Yet Another quantum Quantizer), enables the discovery of an optimized set of quantum gates through this tunable joint cost function. To identify these gate sets, we use the novelty search algorithm, circuit decomposition techniques (like Solovay-Kitaev, Cartan, and quantum Shannon decomposition), and stochastic optimization to implement YAQQ within the Qiskit quantum simulator environment. YAQQ exploits reachability tradeoffs conceptually derived from quantum algorithmic information theory. Our results demonstrate the pragmatic application of identifying gate sets that are advantageous to popularly used quantum gate sets in representing quantum algorithms. Consequently, we demonstrate pragmatic use cases of YAQQ in comparing transversal logical gate sets in quantum error correction codes, designing optimal quantum instruction sets, and compiling to specific quantum processors. © 202

关键词： Cost functions

Visualizing quantum Circuit Probability estimating computational action for quantum program synthesis

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arXiv 2023年

作者： Bach, Bao Gia Kundu, Akash Acharya, Tamal Sarkar, Aritra Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Viet Nam Viet Nam Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Independent Researcher Bengaluru India Quantum Machine Learning Research group Quantum Computing Division QuTech Netherlands Quantum Intelligence Research team Department of Quantum & Computer Engineering Delft University of Technology Netherlands

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. © 2023, CC BY-NC-SA.

关键词： Machine learning

A scalable quantum gate-based implementation for causal hypothesis testing

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arXiv 2022年

作者： Kundu, Akash Acharya, Tamal Sarkar, Aritra Institute of Theoretical and Applied Informatics Polish Academy of Sciences Joint Doctoral School Silesian University of Technology Gliwice Poland Quantum Intelligence Research Team Department of Quantum & Computer Engineering Delft University of Technology Delft2628 CD Netherlands Quantum Machine Learning Group Quantum Computing Division QuTech Delft2628 CJ Netherlands

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence. © 2022, CC BY.

关键词： Inference engines